Speeding up dynamic spiral chemical shift imaging with incoherent sampling and low-rank matrix completion.

PubMed

DeVience, Stephen J; Mayer, Dirk

2017-03-01

To improve the temporal and spatial resolution of dynamic 13 C spiral chemical shift imaging via incoherent sampling and low-rank matrix completion (LRMC). Spiral CSI data were both simulated and acquired in rats, and undersampling was implemented retrospectively and prospectively by pseudorandomly omitting a fraction of the spiral interleaves. Undersampled data were reconstructed with both LRMC and a conventional inverse nonuniform fast Fourier transform (iNUFFT) and compared with fully sampled data. Two-fold undersampling with LRMC reconstruction enabled a two-fold improvement in temporal or spatial resolution without significant artifacts or spatiotemporal distortion. Conversely, undersampling with iNUFFT reconstruction created strong artifacts that obscured the image. LRMC performed better at time points with strong metabolite signal. Incoherent undersampling and LRMC provides a way to increase the spatiotemporal resolution of spiral CSI without degrading data integrity. Magn Reson Med 77:951-960, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints.

PubMed

Chiew, Mark; Graedel, Nadine N; Miller, Karla L

2018-07-01

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

General phase regularized reconstruction using phase cycling.

PubMed

Ong, Frank; Cheng, Joseph Y; Lustig, Michael

2018-07-01

To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. The problem of enforcing phase constraints in reconstruction was studied under a regularized inverse problem framework. A general phase regularized reconstruction algorithm was proposed to enable various joint reconstruction of partial Fourier imaging, water-fat imaging and flow imaging, along with parallel imaging (PI) and compressed sensing (CS). Since phase regularized reconstruction is inherently non-convex and sensitive to phase wraps in the initial solution, a reconstruction technique, named phase cycling, was proposed to render the overall algorithm invariant to phase wraps. The proposed method was applied to retrospectively under-sampled in vivo datasets and compared with state of the art reconstruction methods. Phase cycling reconstructions showed reduction of artifacts compared to reconstructions without phase cycling and achieved similar performances as state of the art results in partial Fourier, water-fat and divergence-free regularized flow reconstruction. Joint reconstruction of partial Fourier + water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized flow imaging + PI + CS were demonstrated. The proposed phase cycling reconstruction provides an alternative way to perform phase regularized reconstruction, without the need to perform phase unwrapping. It is robust to the choice of initial solutions and encourages the joint reconstruction of phase imaging applications. Magn Reson Med 80:112-125, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Accelerating Dynamic Magnetic Resonance Imaging (MRI) for Lung Tumor Tracking Based on Low-Rank Decomposition in the Spatial–Temporal Domain: A Feasibility Study Based on Simulation and Preliminary Prospective Undersampled MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarma, Manoj; Department of Radiation Oncology, University of California, Los Angeles, California; Hu, Peng

Purpose: To evaluate a low-rank decomposition method to reconstruct down-sampled k-space data for the purpose of tumor tracking. Methods and Materials: Seven retrospective lung cancer patients were included in the simulation study. The fully-sampled k-space data were first generated from existing 2-dimensional dynamic MR images and then down-sampled by 5 × -20 × before reconstruction using a Cartesian undersampling mask. Two methods, a low-rank decomposition method using combined dynamic MR images (k-t SLR based on sparsity and low-rank penalties) and a total variation (TV) method using individual dynamic MR frames, were used to reconstruct images. The tumor trajectories were derived on the basis ofmore » autosegmentation of the resultant images. To further test its feasibility, k-t SLR was used to reconstruct prospective data of a healthy subject. An undersampled balanced steady-state free precession sequence with the same undersampling mask was used to acquire the imaging data. Results: In the simulation study, higher imaging fidelity and low noise levels were achieved with the k-t SLR compared with TV. At 10 × undersampling, the k-t SLR method resulted in an average normalized mean square error <0.05, as opposed to 0.23 by using the TV reconstruction on individual frames. Less than 6% showed tracking errors >1 mm with 10 × down-sampling using k-t SLR, as opposed to 17% using TV. In the prospective study, k-t SLR substantially reduced reconstruction artifacts and retained anatomic details. Conclusions: Magnetic resonance reconstruction using k-t SLR on highly undersampled dynamic MR imaging data results in high image quality useful for tumor tracking. The k-t SLR was superior to TV by better exploiting the intrinsic anatomic coherence of the same patient. The feasibility of k-t SLR was demonstrated by prospective imaging acquisition and reconstruction.« less

Color and Vector Flow Imaging in Parallel Ultrasound With Sub-Nyquist Sampling.

PubMed

Madiena, Craig; Faurie, Julia; Poree, Jonathan; Garcia, Damien; Garcia, Damien; Madiena, Craig; Faurie, Julia; Poree, Jonathan

2018-05-01

RF acquisition with a high-performance multichannel ultrasound system generates massive data sets in short periods of time, especially in "ultrafast" ultrasound when digital receive beamforming is required. Sampling at a rate four times the carrier frequency is the standard procedure since this rule complies with the Nyquist-Shannon sampling theorem and simplifies quadrature sampling. Bandpass sampling (or undersampling) outputs a bandpass signal at a rate lower than the maximal frequency without harmful aliasing. Advantages over Nyquist sampling are reduced storage volumes and data workflow, and simplified digital signal processing tasks. We used RF undersampling in color flow imaging (CFI) and vector flow imaging (VFI) to decrease data volume significantly (factor of 3 to 13 in our configurations). CFI and VFI with Nyquist and sub-Nyquist samplings were compared in vitro and in vivo. The estimate errors due to undersampling were small or marginal, which illustrates that Doppler and vector Doppler images can be correctly computed with a drastically reduced amount of RF samples. Undersampling can be a method of choice in CFI and VFI to avoid information overload and reduce data transfer and storage.

Accelerating cine-MR Imaging in Mouse Hearts Using Compressed Sensing

PubMed Central

Wech, Tobias; Lemke, Angela; Medway, Debra; Stork, Lee-Anne; Lygate, Craig A; Neubauer, Stefan; Köstler, Herbert; Schneider, Jürgen E

2011-01-01

Purpose To combine global cardiac function imaging with compressed sensing (CS) in order to reduce scan time and to validate this technique in normal mouse hearts and in a murine model of chronic myocardial infarction. Materials and Methods To determine the maximally achievable acceleration factor, fully acquired cine data, obtained in sham and chronically infarcted (MI) mouse hearts were 2–4-fold undersampled retrospectively, followed by CS reconstruction and blinded image segmentation. Subsequently, dedicated CS sampling schemes were implemented at a preclinical 9.4 T magnetic resonance imaging (MRI) system, and 2- and 3-fold undersampled cine data were acquired in normal mouse hearts with high temporal and spatial resolution. Results The retrospective analysis demonstrated that an undersampling factor of three is feasible without impairing accuracy of cardiac functional parameters. Dedicated CS sampling schemes applied prospectively to normal mouse hearts yielded comparable left-ventricular functional parameters, and intra- and interobserver variability between fully and 3-fold undersampled data. Conclusion This study introduces and validates an alternative means to speed up experimental cine-MRI without the need for expensive hardware. J. Magn. Reson. Imaging 2011. © 2011 Wiley Periodicals, Inc. PMID:21932360

SPECT reconstruction with nonuniform attenuation from highly under-sampled projection data

NASA Astrophysics Data System (ADS)

Li, Cuifen; Wen, Junhai; Zhang, Kangping; Shi, Donghao; Dong, Haixiang; Li, Wenxiao; Liang, Zhengrong

2012-03-01

Single photon emission computed tomography (SPECT) is an important nuclear medicine imaging technique and has been using in clinical diagnoses. The SPECT image can reflect not only organizational structure but also functional activities of human body, therefore diseases can be found much earlier. In SPECT, the reconstruction is based on the measurement of gamma photons emitted by the radiotracer. The number of gamma photons detected is proportional to the dose of radiopharmaceutical, but the dose is limited because of patient safety. There is an upper limit in the number of gamma photons that can be detected per unit time, so it takes a long time to acquire SPECT projection data. Sometimes we just can obtain highly under-sampled projection data because of the limit of the scanning time or imaging hardware. How to reconstruct an image using highly under-sampled projection data is an interesting problem. One method is to minimize the total variation (TV) of the reconstructed image during the iterative reconstruction. In this work, we developed an OSEM-TV SPECT reconstruction algorithm, which could reconstruct the image from highly under-sampled projection data with non-uniform attenuation. Simulation results demonstrate that the OSEM-TV algorithm performs well in SPECT reconstruction with non-uniform attenuation.

Denoising Sparse Images from GRAPPA using the Nullspace Method (DESIGN)

PubMed Central

Weller, Daniel S.; Polimeni, Jonathan R.; Grady, Leo; Wald, Lawrence L.; Adalsteinsson, Elfar; Goyal, Vivek K

2011-01-01

To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with GRAPPA alone, the Denoising of Sparse Images from GRAPPA using the Nullspace method (DESIGN) is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DESIGN are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), PSNR, and noise amplification (g-factors) as measured using the pseudo-multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g-factors suggest that DESIGN mitigates noise amplification better than both GRAPPA and L1 SPIR-iT (the latter limited here by uniform undersampling). PMID:22213069

Accelerating acquisition strategies for low-frequency conductivity imaging using MREIT

NASA Astrophysics Data System (ADS)

Song, Yizhuang; Seo, Jin Keun; Chauhan, Munish; Indahlastari, Aprinda; Ashok Kumar, Neeta; Sadleir, Rosalind

2018-02-01

We sought to improve efficiency of magnetic resonance electrical impedance tomography data acquisition so that fast conductivity changes or electric field variations could be monitored. Undersampling of k-space was used to decrease acquisition times in spin-echo-based sequences by a factor of two. Full MREIT data were reconstructed using continuity assumptions and preliminary scans gathered without current. We found that phase data were reconstructed faithfully from undersampled data. Conductivity reconstructions of phantom data were also possible. Therefore, undersampled k-space methods can potentially be used to accelerate MREIT acquisition. This method could be an advantage in imaging real-time conductivity changes with MREIT.

Edge-oriented dual-dictionary guided enrichment (EDGE) for MRI-CT image reconstruction.

PubMed

Li, Liang; Wang, Bigong; Wang, Ge

2016-01-01

In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled k-space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone.

High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling

PubMed Central

Sung, Kyunghyun; Hargreaves, Brian A

2013-01-01

Purpose To presents and validates a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. Theory and Methods High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing (CS) problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, CS can be used for high-spatial-frequency regions while parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for CS and regular undersampling for parallel imaging. Results Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution 3D breast imaging with a net acceleration of 11 to 12. Conclusion The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. PMID:23280540

Phase Retrieval on Undersampled Data from the Thermal Infrared Sensor (TIRS)

NASA Technical Reports Server (NTRS)

Bolcar, Matthew R.; Mentzell, Eric

2011-01-01

Phase retrieval was applied to under-sampled data from a thermal infrared imaging system to estimate defocus across the field of view (FOV). We compare phase retrieval estimated values to those obtained using an independent technique.

WE-G-17A-01: Improving Tracking Image Spatial Resolution for Onboard MR Image Guided Radiation Therapy Using the WHISKEE Technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Y; Mutic, S; Du, D

Purpose: To evaluate the feasibility of using the weighted hybrid iterative spiral k-space encoded estimation (WHISKEE) technique to improve spatial resolution of tracking images for onboard MR image guided radiation therapy (MR-IGRT). Methods: MR tracking images of abdomen and pelvis had been acquired from healthy volunteers using the ViewRay onboard MRIGRT system (ViewRay Inc. Oakwood Village, OH) at a spatial resolution of 2.0mm*2.0mm*5.0mm. The tracking MR images were acquired using the TrueFISP sequence. The temporal resolution had to be traded off to 2 frames per second (FPS) to achieve the 2.0mm in-plane spatial resolution. All MR images were imported intomore » the MATLAB software. K-space data were synthesized through the Fourier Transform of the MR images. A mask was created to selected k-space points that corresponded to the under-sampled spiral k-space trajectory with an acceleration (or undersampling) factor of 3. The mask was applied to the fully sampled k-space data to synthesize the undersampled k-space data. The WHISKEE method was applied to the synthesized undersampled k-space data to reconstructed tracking MR images at 6 FPS. As a comparison, the undersampled k-space data were also reconstructed using the zero-padding technique. The reconstructed images were compared to the original image. The relatively reconstruction error was evaluated using the percentage of the norm of the differential image over the norm of the original image. Results: Compared to the zero-padding technique, the WHISKEE method was able to reconstruct MR images with better image quality. It significantly reduced the relative reconstruction error from 39.5% to 3.1% for the pelvis image and from 41.5% to 4.6% for the abdomen image at an acceleration factor of 3. Conclusion: We demonstrated that it was possible to use the WHISKEE method to expedite MR image acquisition for onboard MR-IGRT systems to achieve good spatial and temporal resolutions simultaneously. Y. Hu and O. green receive travel reimbursement from ViewRay. S. Mutic has consulting and research agreements with ViewRay. Q. Zeng, R. Nana, J.L. Patrick, S. Shvartsman and J.F. Dempsey are ViewRay employees.« less

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

Accelerated radial Fourier-velocity encoding using compressed sensing.

PubMed

Hilbert, Fabian; Wech, Tobias; Hahn, Dietbert; Köstler, Herbert

2014-09-01

Phase Contrast Magnetic Resonance Imaging (MRI) is a tool for non-invasive determination of flow velocities inside blood vessels. Because Phase Contrast MRI only measures a single mean velocity per voxel, it is only applicable to vessels significantly larger than the voxel size. In contrast, Fourier Velocity Encoding measures the entire velocity distribution inside a voxel, but requires a much longer acquisition time. For accurate diagnosis of stenosis in vessels on the scale of spatial resolution, it is important to know the velocity distribution of a voxel. Our aim was to determine velocity distributions with accelerated Fourier Velocity Encoding in an acquisition time required for a conventional Phase Contrast image. We imaged the femoral artery of healthy volunteers with ECG-triggered, radial CINE acquisition. Data acquisition was accelerated by undersampling, while missing data were reconstructed by Compressed Sensing. Velocity spectra of the vessel were evaluated by high resolution Phase Contrast images and compared to spectra from fully sampled and undersampled Fourier Velocity Encoding. By means of undersampling, it was possible to reduce the scan time for Fourier Velocity Encoding to the duration required for a conventional Phase Contrast image. Acquisition time for a fully sampled data set with 12 different Velocity Encodings was 40 min. By applying a 12.6-fold retrospective undersampling, a data set was generated equal to 3:10 min acquisition time, which is similar to a conventional Phase Contrast measurement. Velocity spectra from fully sampled and undersampled Fourier Velocity Encoded images are in good agreement and show the same maximum velocities as compared to velocity maps from Phase Contrast measurements. Compressed Sensing proved to reliably reconstruct Fourier Velocity Encoded data. Our results indicate that Fourier Velocity Encoding allows an accurate determination of the velocity distribution in vessels in the order of the voxel size. Thus, compared to normal Phase Contrast measurements delivering only mean velocities, no additional scan time is necessary to retrieve meaningful velocity spectra in small vessels. Copyright © 2013. Published by Elsevier GmbH.

Self-calibrated multiple-echo acquisition with radial trajectories using the conjugate gradient method (SMART-CG).

PubMed

Jung, Youngkyoo; Samsonov, Alexey A; Bydder, Mark; Block, Walter F

2011-04-01

To remove phase inconsistencies between multiple echoes, an algorithm using a radial acquisition to provide inherent phase and magnitude information for self correction was developed. The information also allows simultaneous support for parallel imaging for multiple coil acquisitions. Without a separate field map acquisition, a phase estimate from each echo in multiple echo train was generated. When using a multiple channel coil, magnitude and phase estimates from each echo provide in vivo coil sensitivities. An algorithm based on the conjugate gradient method uses these estimates to simultaneously remove phase inconsistencies between echoes, and in the case of multiple coil acquisition, simultaneously provides parallel imaging benefits. The algorithm is demonstrated on single channel, multiple channel, and undersampled data. Substantial image quality improvements were demonstrated. Signal dropouts were completely removed and undersampling artifacts were well suppressed. The suggested algorithm is able to remove phase cancellation and undersampling artifacts simultaneously and to improve image quality of multiecho radial imaging, the important technique for fast three-dimensional MRI data acquisition. Copyright © 2011 Wiley-Liss, Inc.

Self-calibrated Multiple-echo Acquisition with Radial Trajectories using the Conjugate Gradient Method (SMART-CG)

PubMed Central

Jung, Youngkyoo; Samsonov, Alexey A; Bydder, Mark; Block, Walter F.

2011-01-01

Purpose To remove phase inconsistencies between multiple echoes, an algorithm using a radial acquisition to provide inherent phase and magnitude information for self correction was developed. The information also allows simultaneous support for parallel imaging for multiple coil acquisitions. Materials and Methods Without a separate field map acquisition, a phase estimate from each echo in multiple echo train was generated. When using a multiple channel coil, magnitude and phase estimates from each echo provide in-vivo coil sensitivities. An algorithm based on the conjugate gradient method uses these estimates to simultaneously remove phase inconsistencies between echoes, and in the case of multiple coil acquisition, simultaneously provides parallel imaging benefits. The algorithm is demonstrated on single channel, multiple channel, and undersampled data. Results Substantial image quality improvements were demonstrated. Signal dropouts were completely removed and undersampling artifacts were well suppressed. Conclusion The suggested algorithm is able to remove phase cancellation and undersampling artifacts simultaneously and to improve image quality of multiecho radial imaging, the important technique for fast 3D MRI data acquisition. PMID:21448967

Feasibility of through-time spiral generalized autocalibrating partial parallel acquisition for low latency accelerated real-time MRI of speech.

PubMed

Lingala, Sajan Goud; Zhu, Yinghua; Lim, Yongwan; Toutios, Asterios; Ji, Yunhua; Lo, Wei-Ching; Seiberlich, Nicole; Narayanan, Shrikanth; Nayak, Krishna S

2017-12-01

To evaluate the feasibility of through-time spiral generalized autocalibrating partial parallel acquisition (GRAPPA) for low-latency accelerated real-time MRI of speech. Through-time spiral GRAPPA (spiral GRAPPA), a fast linear reconstruction method, is applied to spiral (k-t) data acquired from an eight-channel custom upper-airway coil. Fully sampled data were retrospectively down-sampled to evaluate spiral GRAPPA at undersampling factors R = 2 to 6. Pseudo-golden-angle spiral acquisitions were used for prospective studies. Three subjects were imaged while performing a range of speech tasks that involved rapid articulator movements, including fluent speech and beat-boxing. Spiral GRAPPA was compared with view sharing, and a parallel imaging and compressed sensing (PI-CS) method. Spiral GRAPPA captured spatiotemporal dynamics of vocal tract articulators at undersampling factors ≤4. Spiral GRAPPA at 18 ms/frame and 2.4 mm 2 /pixel outperformed view sharing in depicting rapidly moving articulators. Spiral GRAPPA and PI-CS provided equivalent temporal fidelity. Reconstruction latency per frame was 14 ms for view sharing and 116 ms for spiral GRAPPA, using a single processor. Spiral GRAPPA kept up with the MRI data rate of 18ms/frame with eight processors. PI-CS required 17 minutes to reconstruct 5 seconds of dynamic data. Spiral GRAPPA enabled 4-fold accelerated real-time MRI of speech with a low reconstruction latency. This approach is applicable to wide range of speech RT-MRI experiments that benefit from real-time feedback while visualizing rapid articulator movement. Magn Reson Med 78:2275-2282, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study.

PubMed

Wang, Chunhao; Yin, Fang-Fang; Kirkpatrick, John P; Chang, Zheng

2017-08-01

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K trans in the extended Tofts model and blood flow F B and blood volume V B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

PubMed

Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

2015-07-21

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

DLA based compressed sensing for high resolution MR microscopy of neuronal tissue

NASA Astrophysics Data System (ADS)

Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa

2015-10-01

In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm.

Fast carotid artery MR angiography with compressed sensing based three-dimensional time-of-flight sequence.

PubMed

Li, Bo; Li, Hao; Dong, Li; Huang, Guofu

2017-11-01

In this study, we sought to investigate the feasibility of fast carotid artery MR angiography (MRA) by combining three-dimensional time-of-flight (3D TOF) with compressed sensing method (CS-3D TOF). A pseudo-sequential phase encoding order was developed for CS-3D TOF to generate hyper-intense vessel and suppress background tissues in under-sampled 3D k-space. Seven healthy volunteers and one patient with carotid artery stenosis were recruited for this study. Five sequential CS-3D TOF scans were implemented at 1, 2, 3, 4 and 5-fold acceleration factors for carotid artery MRA. Blood signal-to-tissue ratio (BTR) values for fully-sampled and under-sampled acquisitions were calculated and compared in seven subjects. Blood area (BA) was measured and compared between fully sampled acquisition and each under-sampled one. There were no significant differences between the fully-sampled dataset and each under-sampled in BTR comparisons (P>0.05 for all comparisons). The carotid vessel BAs measured from the images of CS-3D TOF sequences with 2, 3, 4 and 5-fold acceleration scans were all highly correlated with that of the fully-sampled acquisition. The contrast between blood vessels and background tissues of the images at 2 to 5-fold acceleration is comparable to that of fully sampled images. The images at 2× to 5× exhibit the comparable lumen definition to the corresponding images at 1×. By combining the pseudo-sequential phase encoding order, CS reconstruction, and 3D TOF sequence, this technique provides excellent visualizations for carotid vessel and calcifications in a short scan time. It has the potential to be integrated into current multiple blood contrast imaging protocol. Copyright © 2017. Published by Elsevier Inc.

MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance

PubMed Central

2014-01-01

Background Computer simulations are important for validating novel image acquisition and reconstruction strategies. In cardiovascular magnetic resonance (CMR), numerical simulations need to combine anatomical information and the effects of cardiac and/or respiratory motion. To this end, a framework for realistic CMR simulations is proposed and its use for image reconstruction from undersampled data is demonstrated. Methods The extended Cardiac-Torso (XCAT) anatomical phantom framework with various motion options was used as a basis for the numerical phantoms. Different tissue, dynamic contrast and signal models, multiple receiver coils and noise are simulated. Arbitrary trajectories and undersampled acquisition can be selected. The utility of the framework is demonstrated for accelerated cine and first-pass myocardial perfusion imaging using k-t PCA and k-t SPARSE. Results MRXCAT phantoms allow for realistic simulation of CMR including optional cardiac and respiratory motion. Example reconstructions from simulated undersampled k-t parallel imaging demonstrate the feasibility of simulated acquisition and reconstruction using the presented framework. Myocardial blood flow assessment from simulated myocardial perfusion images highlights the suitability of MRXCAT for quantitative post-processing simulation. Conclusion The proposed MRXCAT phantom framework enables versatile and realistic simulations of CMR including breathhold and free-breathing acquisitions. PMID:25204441

Towards tracer dose reduction in PET studies: Simulation of dose reduction by retrospective randomized undersampling of list-mode data.

PubMed

Gatidis, Sergios; Würslin, Christian; Seith, Ferdinand; Schäfer, Jürgen F; la Fougère, Christian; Nikolaou, Konstantin; Schwenzer, Nina F; Schmidt, Holger

2016-01-01

Optimization of tracer dose regimes in positron emission tomography (PET) imaging is a trade-off between diagnostic image quality and radiation exposure. The challenge lies in defining minimal tracer doses that still result in sufficient diagnostic image quality. In order to find such minimal doses, it would be useful to simulate tracer dose reduction as this would enable to study the effects of tracer dose reduction on image quality in single patients without repeated injections of different amounts of tracer. The aim of our study was to introduce and validate a method for simulation of low-dose PET images enabling direct comparison of different tracer doses in single patients and under constant influencing factors. (18)F-fluoride PET data were acquired on a combined PET/magnetic resonance imaging (MRI) scanner. PET data were stored together with the temporal information of the occurrence of single events (list-mode format). A predefined proportion of PET events were then randomly deleted resulting in undersampled PET data. These data sets were subsequently reconstructed resulting in simulated low-dose PET images (retrospective undersampling of list-mode data). This approach was validated in phantom experiments by visual inspection and by comparison of PET quality metrics contrast recovery coefficient (CRC), background-variability (BV) and signal-to-noise ratio (SNR) of measured and simulated PET images for different activity concentrations. In addition, reduced-dose PET images of a clinical (18)F-FDG PET dataset were simulated using the proposed approach. (18)F-PET image quality degraded with decreasing activity concentrations with comparable visual image characteristics in measured and in corresponding simulated PET images. This result was confirmed by quantification of image quality metrics. CRC, SNR and BV showed concordant behavior with decreasing activity concentrations for measured and for corresponding simulated PET images. Simulation of dose-reduced datasets based on clinical (18)F-FDG PET data demonstrated the clinical applicability of the proposed data. Simulation of PET tracer dose reduction is possible with retrospective undersampling of list-mode data. Resulting simulated low-dose images have equivalent characteristics with PET images actually measured at lower doses and can be used to derive optimal tracer dose regimes.

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

PubMed

Guo, Yi; Lingala, Sajan Goud; Zhu, Yinghua; Lebel, R Marc; Nayak, Krishna S

2017-10-01

The purpose of this work was to develop and evaluate a T 1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Quantitative DLA-based compressed sensing for T1-weighted acquisitions

NASA Astrophysics Data System (ADS)

Svehla, Pavel; Nguyen, Khieu-Van; Li, Jing-Rebecca; Ciobanu, Luisa

2017-08-01

High resolution Manganese Enhanced Magnetic Resonance Imaging (MEMRI), which uses manganese as a T1 contrast agent, has great potential for functional imaging of live neuronal tissue at single neuron scale. However, reaching high resolutions often requires long acquisition times which can lead to reduced image quality due to sample deterioration and hardware instability. Compressed Sensing (CS) techniques offer the opportunity to significantly reduce the imaging time. The purpose of this work is to test the feasibility of CS acquisitions based on Diffusion Limited Aggregation (DLA) sampling patterns for high resolution quantitative T1-weighted imaging. Fully encoded and DLA-CS T1-weighted images of Aplysia californica neural tissue were acquired on a 17.2T MRI system. The MR signal corresponding to single, identified neurons was quantified for both versions of the T1 weighted images. For a 50% undersampling, DLA-CS can accurately quantify signal intensities in T1-weighted acquisitions leading to only 1.37% differences when compared to the fully encoded data, with minimal impact on image spatial resolution. In addition, we compared the conventional polynomial undersampling scheme with the DLA and showed that, for the data at hand, the latter performs better. Depending on the image signal to noise ratio, higher undersampling ratios can be used to further reduce the acquisition time in MEMRI based functional studies of living tissues.

Accelerated Fast Spin-Echo Magnetic Resonance Imaging of the Heart Using a Self-Calibrated Split-Echo Approach

PubMed Central

Klix, Sabrina; Hezel, Fabian; Fuchs, Katharina; Ruff, Jan; Dieringer, Matthias A.; Niendorf, Thoralf

2014-01-01

Purpose Design, validation and application of an accelerated fast spin-echo (FSE) variant that uses a split-echo approach for self-calibrated parallel imaging. Methods For self-calibrated, split-echo FSE (SCSE-FSE), extra displacement gradients were incorporated into FSE to decompose odd and even echo groups which were independently phase encoded to derive coil sensitivity maps, and to generate undersampled data (reduction factor up to R = 3). Reference and undersampled data were acquired simultaneously. SENSE reconstruction was employed. Results The feasibility of SCSE-FSE was demonstrated in phantom studies. Point spread function performance of SCSE-FSE was found to be competitive with traditional FSE variants. The immunity of SCSE-FSE for motion induced mis-registration between reference and undersampled data was shown using a dynamic left ventricular model and cardiac imaging. The applicability of black blood prepared SCSE-FSE for cardiac imaging was demonstrated in healthy volunteers including accelerated multi-slice per breath-hold imaging and accelerated high spatial resolution imaging. Conclusion SCSE-FSE obviates the need of external reference scans for SENSE reconstructed parallel imaging with FSE. SCSE-FSE reduces the risk for mis-registration between reference scans and accelerated acquisitions. SCSE-FSE is feasible for imaging of the heart and of large cardiac vessels but also meets the needs of brain, abdominal and liver imaging. PMID:24728341

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

PubMed

Hu, Changwei; Qu, Xiaobo; Guo, Di; Bao, Lijun; Chen, Zhong

2011-09-01

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression. Copyright © 2011 Elsevier Inc. All rights reserved.

DLA based compressed sensing for high resolution MR microscopy of neuronal tissue.

PubMed

Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa

2015-10-01

In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm. Copyright © 2015 Elsevier Inc. All rights reserved.

A self-calibrated angularly continuous 2D GRAPPA kernel for propeller trajectories

PubMed Central

Skare, Stefan; Newbould, Rexford D; Nordell, Anders; Holdsworth, Samantha J; Bammer, Roland

2008-01-01

The k-space readout of propeller-type sequences may be accelerated by the use of parallel imaging (PI). For PROPELLER, the main benefits are reduced blurring due to T2 decay and SAR reduction, while for EPI-based propeller acquisitions such as Turbo-PROP and SAP-EPI, the faster k-space traversal alleviates geometric distortions. In this work, the feasibility of calculating a 2D GRAPPA kernel on only the undersampled propeller blades themselves is explored, using the matching orthogonal undersampled blade. It is shown that the GRAPPA kernel varies slowly across blades, therefore an angularly continuous 2D GRAPPA kernel is proposed, in which the angular variation of the weights is parameterized. This new angularly continuous kernel formulation greatly increases the numerical stability of the GRAPPA weight estimation, allowing the generation of fully sampled diagnostic quality images using only the undersampled propeller data. PMID:19025911

Implementation of time-efficient adaptive sampling function design for improved undersampled MRI reconstruction

NASA Astrophysics Data System (ADS)

Choi, Jinhyeok; Kim, Hyeonjin

2016-12-01

To improve the efficacy of undersampled MRI, a method of designing adaptive sampling functions is proposed that is simple to implement on an MR scanner and yet effectively improves the performance of the sampling functions. An approximation of the energy distribution of an image (E-map) is estimated from highly undersampled k-space data acquired in a prescan and efficiently recycled in the main scan. An adaptive probability density function (PDF) is generated by combining the E-map with a modeled PDF. A set of candidate sampling functions are then prepared from the adaptive PDF, among which the one with maximum energy is selected as the final sampling function. To validate its computational efficiency, the proposed method was implemented on an MR scanner, and its robust performance in Fourier-transform (FT) MRI and compressed sensing (CS) MRI was tested by simulations and in a cherry tomato. The proposed method consistently outperforms the conventional modeled PDF approach for undersampling ratios of 0.2 or higher in both FT-MRI and CS-MRI. To fully benefit from undersampled MRI, it is preferable that the design of adaptive sampling functions be performed online immediately before the main scan. In this way, the proposed method may further improve the efficacy of the undersampled MRI.

Optoelectronic image scanning with high spatial resolution and reconstruction fidelity

NASA Astrophysics Data System (ADS)

Craubner, Siegfried I.

2002-02-01

In imaging systems the detector arrays deliver at the output time-discrete signals, where the spatial frequencies of the object scene are mapped into the electrical signal frequencies. Since the spatial frequency spectrum cannot be bandlimited by the front optics, the usual detector arrays perform a spatial undersampling and as a consequence aliasing occurs. A means to partially suppress the backfolded alias band is bandwidth limitation in the reconstruction low-pass, at the price of resolution loss. By utilizing a bilinear detector array in a pushbroom-type scanner, undersampling and aliasing can be overcome. For modeling the perception, the theory of discrete systems and multirate digital filter banks is applied, where aliasing cancellation and perfect reconstruction play an important role. The discrete transfer function of a bilinear array can be imbedded into the scheme of a second-order filter bank. The detector arrays already build the analysis bank and the overall filter bank is completed with the synthesis bank, for which stabilized inverse filters are proposed, to compensate for the low-pass characteristics and to approximate perfect reconstruction. The synthesis filter branch can be realized in a so-called `direct form,' or the `polyphase form,' where the latter is an expenditure-optimal solution, which gives advantages when implemented in a signal processor. This paper attempts to introduce well-established concepts of the theory of multirate filter banks into the analysis of scanning imagers, which is applicable in a much broader sense than for the problems addressed here. To the author's knowledge this is also a novelty.

Accelerated Fractional Ventilation Imaging with Hyperpolarized Gas MRI

PubMed Central

Emami, Kiarash; Xu, Yinan; Hamedani, Hooman; Profka, Harrilla; Kadlecek, Stephen; Xin, Yi; Ishii, Masaru; Rizi, Rahim R.

2013-01-01

PURPOSE To investigate the utility of accelerated imaging to enhance multi-breath fractional ventilation (r) measurement accuracy using HP gas MRI. Undersampling shortens the breath-hold time, thereby reducing the O2-induced signal decay and allows subjects to maintain a more physiologically relevant breathing pattern. Additionally it may improve r estimation accuracy by reducing RF destruction of HP gas. METHODS Image acceleration was achieved by using an 8-channel phased array coil. Undersampled image acquisition was simulated in a series of ventilation images and images were reconstructed for various matrix sizes (48–128) using GRAPPA. Parallel accelerated r imaging was also performed on five mechanically ventilated pigs. RESULTS Optimal acceleration factor was fairly invariable (2.0–2.2×) over the range of simulated resolutions. Estimation accuracy progressively improved with higher resolutions (39–51% error reduction). In vivo r values were not significantly different between the two methods: 0.27±0.09, 0.35±0.06, 0.40±0.04 (standard) versus 0.23±0.05, 0.34±0.03, 0.37±0.02 (accelerated); for anterior, medial and posterior slices, respectively, whereas the corresponding vertical r gradients were significant (P < 0.001): 0.021±0.007 (standard) versus 0.019±0.005 (accelerated) [cm−1]. CONCLUSION Quadruple phased array coil simulations resulted in an optimal acceleration factor of ~2× independent of imaging resolution. Results advocate undersampled image acceleration to improve accuracy of fractional ventilation measurement with HP gas MRI. PMID:23400938

SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

NASA Astrophysics Data System (ADS)

Koesters, Thomas; Knoll, Florian; Sodickson, Aaron; Sodickson, Daniel K.; Otazo, Ricardo

2017-03-01

State-of-the-art low-dose CT methods reduce the x-ray tube current and use iterative reconstruction methods to denoise the resulting images. However, due to compromises between denoising and image quality, only moderate dose reductions up to 30-40% are accepted in clinical practice. An alternative approach is to reduce the number of x-ray projections and use compressed sensing to reconstruct the full-tube-current undersampled data. This idea was recognized in the early days of compressed sensing and proposals for CT dose reduction appeared soon afterwards. However, no practical means of undersampling has yet been demonstrated in the challenging environment of a rapidly rotating CT gantry. In this work, we propose a moving multislit collimator as a practical incoherent undersampling scheme for compressed sensing CT and evaluate its application for radiation dose reduction. The proposed collimator is composed of narrow slits and moves linearly along the slice dimension (z), to interrupt the incident beam in different slices for each x-ray tube angle (θ). The reduced projection dataset is then reconstructed using a sparse approach, where 3D image gradients are employed to enforce sparsity. The effects of the collimator slits on the beam profile were measured and represented as a continuous slice profile. SparseCT was tested using retrospective undersampling and compared against commercial current-reduction techniques on phantoms and in vivo studies. Initial results suggest that SparseCT may enable higher performance than current-reduction, particularly for high dose reduction factors.

Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging

NASA Astrophysics Data System (ADS)

Vidya Shankar, Rohini; Hu, Houchun Harry; Bikkamane Jayadev, Nutandev; Chang, John C.; Kodibagkar, Vikram D.

2017-03-01

Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

PubMed

Eo, Taejoon; Jun, Yohan; Kim, Taeseong; Jang, Jinseong; Lee, Ho-Joon; Hwang, Dosik

2018-04-06

To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a deep CNN operating on an image domain (ICNN), and (3) an interleaved data consistency operations. These components are alternately applied, and each CNN is trained to minimize the loss between the reconstructed and corresponding fully sampled k-spaces. The final reconstructed image is obtained by forward-propagating the undersampled k-space data through the entire network. Performances of K-net (KCNN with inverse Fourier transform), I-net (ICNN with interleaved data consistency), and various combinations of the 2 different networks were tested. The test results indicated that K-net and I-net have different advantages/disadvantages in terms of tissue-structure restoration. Consequently, the combination of K-net and I-net is superior to single-domain CNNs. Three MR data sets, the T 2 fluid-attenuated inversion recovery (T 2 FLAIR) set from the Alzheimer's Disease Neuroimaging Initiative and 2 data sets acquired at our local institute (T 2 FLAIR and T 1 weighted), were used to evaluate the performance of 7 conventional reconstruction algorithms and the proposed cross-domain CNNs, which hereafter is referred to as KIKI-net. KIKI-net outperforms conventional algorithms with mean improvements of 2.29 dB in peak SNR and 0.031 in structure similarity. KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling. © 2018 International Society for Magnetic Resonance in Medicine.

Compressed sensing for rapid late gadolinium enhanced imaging of the left atrium: A preliminary study.

PubMed

Kamesh Iyer, Srikant; Tasdizen, Tolga; Burgon, Nathan; Kholmovski, Eugene; Marrouche, Nassir; Adluru, Ganesh; DiBella, Edward

2016-09-01

Current late gadolinium enhancement (LGE) imaging of left atrial (LA) scar or fibrosis is relatively slow and requires 5-15min to acquire an undersampled (R=1.7) 3D navigated dataset. The GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) based parallel imaging method is the current clinical standard for accelerating 3D LGE imaging of the LA and permits an acceleration factor ~R=1.7. Two compressed sensing (CS) methods have been developed to achieve higher acceleration factors: a patch based collaborative filtering technique tested with acceleration factor R~3, and a technique that uses a 3D radial stack-of-stars acquisition pattern (R~1.8) with a 3D total variation constraint. The long reconstruction time of these CS methods makes them unwieldy to use, especially the patch based collaborative filtering technique. In addition, the effect of CS techniques on the quantification of percentage of scar/fibrosis is not known. We sought to develop a practical compressed sensing method for imaging the LA at high acceleration factors. In order to develop a clinically viable method with short reconstruction time, a Split Bregman (SB) reconstruction method with 3D total variation (TV) constraints was developed and implemented. The method was tested on 8 atrial fibrillation patients (4 pre-ablation and 4 post-ablation datasets). Blur metric, normalized mean squared error and peak signal to noise ratio were used as metrics to analyze the quality of the reconstructed images, Quantification of the extent of LGE was performed on the undersampled images and compared with the fully sampled images. Quantification of scar from post-ablation datasets and quantification of fibrosis from pre-ablation datasets showed that acceleration factors up to R~3.5 gave good 3D LGE images of the LA wall, using a 3D TV constraint and constrained SB methods. This corresponds to reducing the scan time by half, compared to currently used GRAPPA methods. Reconstruction of 3D LGE images using the SB method was over 20 times faster than standard gradient descent methods. Copyright © 2016 Elsevier Inc. All rights reserved.

Accelerating 4D flow MRI by exploiting vector field divergence regularization.

PubMed

Santelli, Claudio; Loecher, Michael; Busch, Julia; Wieben, Oliver; Schaeffter, Tobias; Kozerke, Sebastian

2016-01-01

To improve velocity vector field reconstruction from undersampled four-dimensional (4D) flow MRI by penalizing divergence of the measured flow field. Iterative image reconstruction in which magnitude and phase are regularized separately in alternating iterations was implemented. The approach allows incorporating prior knowledge of the flow field being imaged. In the present work, velocity data were regularized to reduce divergence, using either divergence-free wavelets (DFW) or a finite difference (FD) method using the ℓ1-norm of divergence and curl. The reconstruction methods were tested on a numerical phantom and in vivo data. Results of the DFW and FD approaches were compared with data obtained with standard compressed sensing (CS) reconstruction. Relative to standard CS, directional errors of vector fields and divergence were reduced by 55-60% and 38-48% for three- and six-fold undersampled data with the DFW and FD methods. Velocity vector displays of the numerical phantom and in vivo data were found to be improved upon DFW or FD reconstruction. Regularization of vector field divergence in image reconstruction from undersampled 4D flow data is a valuable approach to improve reconstruction accuracy of velocity vector fields. © 2014 Wiley Periodicals, Inc.

Image Reconstruction from Under sampled Fourier Data Using the Polynomial Annihilation Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Archibald, Richard K.; Gelb, Anne; Platte, Rodrigo

Fourier samples are collected in a variety of applications including magnetic resonance imaging and synthetic aperture radar. The data are typically under-sampled and noisy. In recent years, l 1 regularization has received considerable attention in designing image reconstruction algorithms from under-sampled and noisy Fourier data. The underlying image is assumed to have some sparsity features, that is, some measurable features of the image have sparse representation. The reconstruction algorithm is typically designed to solve a convex optimization problem, which consists of a fidelity term penalized by one or more l 1 regularization terms. The Split Bregman Algorithm provides a fastmore » explicit solution for the case when TV is used for the l1l1 regularization terms. Due to its numerical efficiency, it has been widely adopted for a variety of applications. A well known drawback in using TV as an l 1 regularization term is that the reconstructed image will tend to default to a piecewise constant image. This issue has been addressed in several ways. Recently, the polynomial annihilation edge detection method was used to generate a higher order sparsifying transform, and was coined the “polynomial annihilation (PA) transform.” This paper adapts the Split Bregman Algorithm for the case when the PA transform is used as the l 1 regularization term. In so doing, we achieve a more accurate image reconstruction method from under-sampled and noisy Fourier data. Our new method compares favorably to the TV Split Bregman Algorithm, as well as to the popular TGV combined with shearlet approach.« less

Highly undersampled contrast-enhanced MRA with iterative reconstruction: Integration in a clinical setting.

PubMed

Stalder, Aurelien F; Schmidt, Michaela; Quick, Harald H; Schlamann, Marc; Maderwald, Stefan; Schmitt, Peter; Wang, Qiu; Nadar, Mariappan S; Zenge, Michael O

2015-12-01

To integrate, optimize, and evaluate a three-dimensional (3D) contrast-enhanced sparse MRA technique with iterative reconstruction on a standard clinical MR system. Data were acquired using a highly undersampled Cartesian spiral phyllotaxis sampling pattern and reconstructed directly on the MR system with an iterative SENSE technique. Undersampling, regularization, and number of iterations of the reconstruction were optimized and validated based on phantom experiments and patient data. Sparse MRA of the whole head (field of view: 265 × 232 × 179 mm(3) ) was investigated in 10 patient examinations. High-quality images with 30-fold undersampling, resulting in 0.7 mm isotropic resolution within 10 s acquisition, were obtained. After optimization of the regularization factor and of the number of iterations of the reconstruction, it was possible to reconstruct images with excellent quality within six minutes per 3D volume. Initial results of sparse contrast-enhanced MRA (CEMRA) in 10 patients demonstrated high-quality whole-head first-pass MRA for both the arterial and venous contrast phases. While sparse MRI techniques have not yet reached clinical routine, this study demonstrates the technical feasibility of high-quality sparse CEMRA of the whole head in a clinical setting. Sparse CEMRA has the potential to become a viable alternative where conventional CEMRA is too slow or does not provide sufficient spatial resolution. © 2014 Wiley Periodicals, Inc.

Accelerated echo-planar J-resolved spectroscopic imaging in the human brain using compressed sensing: a pilot validation in obstructive sleep apnea.

PubMed

Sarma, M K; Nagarajan, R; Macey, P M; Kumar, R; Villablanca, J P; Furuyama, J; Thomas, M A

2014-06-01

Echo-planar J-resolved spectroscopic imaging is a fast spectroscopic technique to record the biochemical information in multiple regions of the brain, but for clinical applications, time is still a constraint. Investigations of neural injury in obstructive sleep apnea have revealed structural changes in the brain, but determining the neurochemical changes requires more detailed measurements across multiple brain regions, demonstrating a need for faster echo-planar J-resolved spectroscopic imaging. Hence, we have extended the compressed sensing reconstruction of prospectively undersampled 4D echo-planar J-resolved spectroscopic imaging to investigate metabolic changes in multiple brain locations of patients with obstructive sleep apnea and healthy controls. Nonuniform undersampling was imposed along 1 spatial and 1 spectral dimension of 4D echo-planar J-resolved spectroscopic imaging, and test-retest reliability of the compressed sensing reconstruction of the nonuniform undersampling data was tested by using a brain phantom. In addition, 9 patients with obstructive sleep apnea and 11 healthy controls were investigated by using a 3T MR imaging/MR spectroscopy scanner. Significantly reduced metabolite differences were observed between patients with obstructive sleep apnea and healthy controls in multiple brain regions: NAA/Cr in the left hippocampus; total Cho/Cr and Glx/Cr in the right hippocampus; total NAA/Cr, taurine/Cr, scyllo-Inositol/Cr, phosphocholine/Cr, and total Cho/Cr in the occipital gray matter; total NAA/Cr and NAA/Cr in the medial frontal white matter; and taurine/Cr and total Cho/Cr in the left frontal white matter regions. The 4D echo-planar J-resolved spectroscopic imaging technique using the nonuniform undersampling-based acquisition and compressed sensing reconstruction in patients with obstructive sleep apnea and healthy brain is feasible in a clinically suitable time. In addition to brain metabolite changes previously reported by 1D MR spectroscopy, our results show changes of additional metabolites in patients with obstructive sleep apnea compared with healthy controls. © 2014 by American Journal of Neuroradiology.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.

Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation

NASA Astrophysics Data System (ADS)

Han, Hao; Gao, Hao; Xing, Lei

2017-08-01

Excessive radiation exposure is still a major concern in 4D cone-beam computed tomography (4D-CBCT) due to its prolonged scanning duration. Radiation dose can be effectively reduced by either under-sampling the x-ray projections or reducing the x-ray flux. However, 4D-CBCT reconstruction under such low-dose protocols is prone to image artifacts and noise. In this work, we propose a novel joint regularization-based iterative reconstruction method for low-dose 4D-CBCT. To tackle the under-sampling problem, we employ spatiotemporal tensor framelet (STF) regularization to take advantage of the spatiotemporal coherence of the patient anatomy in 4D images. To simultaneously suppress the image noise caused by photon starvation, we also incorporate spatiotemporal nonlocal total variation (SNTV) regularization to make use of the nonlocal self-recursiveness of anatomical structures in the spatial and temporal domains. Under the joint STF-SNTV regularization, the proposed iterative reconstruction approach is evaluated first using two digital phantoms and then using physical experiment data in the low-dose context of both under-sampled and noisy projections. Compared with existing approaches via either STF or SNTV regularization alone, the presented hybrid approach achieves improved image quality, and is particularly effective for the reconstruction of low-dose 4D-CBCT data that are not only sparse but noisy.

Non-Cartesian Parallel Imaging Reconstruction

PubMed Central

Wright, Katherine L.; Hamilton, Jesse I.; Griswold, Mark A.; Gulani, Vikas; Seiberlich, Nicole

2014-01-01

Non-Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non-Cartesian trajectories can enable more efficient coverage of k-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be employed to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the non-homogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian GRAPPA, and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered. PMID:24408499

Using learned under-sampling pattern for increasing speed of cardiac cine MRI based on compressive sensing principles

NASA Astrophysics Data System (ADS)

Zamani, Pooria; Kayvanrad, Mohammad; Soltanian-Zadeh, Hamid

2012-12-01

This article presents a compressive sensing approach for reducing data acquisition time in cardiac cine magnetic resonance imaging (MRI). In cardiac cine MRI, several images are acquired throughout the cardiac cycle, each of which is reconstructed from the raw data acquired in the Fourier transform domain, traditionally called k-space. In the proposed approach, a majority, e.g., 62.5%, of the k-space lines (trajectories) are acquired at the odd time points and a minority, e.g., 37.5%, of the k-space lines are acquired at the even time points of the cardiac cycle. Optimal data acquisition at the even time points is learned from the data acquired at the odd time points. To this end, statistical features of the k-space data at the odd time points are clustered by fuzzy c-means and the results are considered as the states of Markov chains. The resulting data is used to train hidden Markov models and find their transition matrices. Then, the trajectories corresponding to transition matrices far from an identity matrix are selected for data acquisition. At the end, an iterative thresholding algorithm is used to reconstruct the images from the under-sampled k-space datasets. The proposed approaches for selecting the k-space trajectories and reconstructing the images generate more accurate images compared to alternative methods. The proposed under-sampling approach achieves an acceleration factor of 2 for cardiac cine MRI.

A model-based reconstruction for undersampled radial spin echo DTI with variational penalties on the diffusion tensor

PubMed Central

Knoll, Florian; Raya, José G; Halloran, Rafael O; Baete, Steven; Sigmund, Eric; Bammer, Roland; Block, Tobias; Otazo, Ricardo; Sodickson, Daniel K

2015-01-01

Radial spin echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled DTI. A model-based reconstruction implicitly exploits redundancies in the diffusion weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a Total Variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (3 and 2 volunteers, respectively). Evaluation of the new approach was conducted by comparing the results to reconstructions performed with gridding, combined parallel imaging and compressed sensing, and a recently proposed model-based approach. The experiments demonstrated improvement in terms of reduction of noise and streaking artifacts in the quantitative parameter maps as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin echo diffusion tensor imaging without degrading parameter quantification and/or SNR. PMID:25594167

U.S. Army Research Laboratory Image Enhancement Test Bed User’s Manual

DTIC Science & Technology

2013-07-01

Web Services; ARL-TR- 6393; U.S. Army Research Laboratory: Adelphi, MD, March 2013. 2. Young, S. Susan; Driggers, Ronald G. Superresolution Image...Young, S. Susan; Theilke, Matthew.; Schuler, Jonathan M. Superresolution Performance for Undersampled Imagers. Optical Engineering 2005, 44 (01). 4

Android REST Client Application to View, Collect, and Exploit Video and Image Data

DTIC Science & Technology

2013-09-01

Superresolution Image Reconstruction From a Sequence of Aliased Imagery. Applied Optics 2006, 45 (21), 5073–5085. 3, Driggers, R. G.; Krapels, K. A...Murrill, S.; Young, S. S.; Theilke, M.; Schuler, J. M. Superresolution Performance for Undersampled Imagers. Optical Engineering 2005, 44 (01). 4. Young

Decomposed direct matrix inversion for fast non-cartesian SENSE reconstructions.

PubMed

Qian, Yongxian; Zhang, Zhenghui; Wang, Yi; Boada, Fernando E

2006-08-01

A new k-space direct matrix inversion (DMI) method is proposed here to accelerate non-Cartesian SENSE reconstructions. In this method a global k-space matrix equation is established on basic MRI principles, and the inverse of the global encoding matrix is found from a set of local matrix equations by taking advantage of the small extension of k-space coil maps. The DMI algorithm's efficiency is achieved by reloading the precalculated global inverse when the coil maps and trajectories remain unchanged, such as in dynamic studies. Phantom and human subject experiments were performed on a 1.5T scanner with a standard four-channel phased-array cardiac coil. Interleaved spiral trajectories were used to collect fully sampled and undersampled 3D raw data. The equivalence of the global k-space matrix equation to its image-space version, was verified via conjugate gradient (CG) iterative algorithms on a 2x undersampled phantom and numerical-model data sets. When applied to the 2x undersampled phantom and human-subject raw data, the decomposed DMI method produced images with small errors (< or = 3.9%) relative to the reference images obtained from the fully-sampled data, at a rate of 2 s per slice (excluding 4 min for precalculating the global inverse at an image size of 256 x 256). The DMI method may be useful for noise evaluations in parallel coil designs, dynamic MRI, and 3D sodium MRI with fixed coils and trajectories. Copyright 2006 Wiley-Liss, Inc.

k-space sampling optimization for ultrashort TE imaging of cortical bone: Applications in radiation therapy planning and MR-based PET attenuation correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Lingzhi, E-mail: hlingzhi@gmail.com, E-mail: raymond.muzic@case.edu; Traughber, Melanie; Su, Kuan-Hao

Purpose: The ultrashort echo-time (UTE) sequence is a promising MR pulse sequence for imaging cortical bone which is otherwise difficult to image using conventional MR sequences and also poses strong attenuation for photons in radiation therapy and PET imaging. The authors report here a systematic characterization of cortical bone signal decay and a scanning time optimization strategy for the UTE sequence through k-space undersampling, which can result in up to a 75% reduction in acquisition time. Using the undersampled UTE imaging sequence, the authors also attempted to quantitatively investigate the MR properties of cortical bone in healthy volunteers, thus demonstratingmore » the feasibility of using such a technique for generating bone-enhanced images which can be used for radiation therapy planning and attenuation correction with PET/MR. Methods: An angularly undersampled, radially encoded UTE sequence was used for scanning the brains of healthy volunteers. Quantitative MR characterization of tissue properties, including water fraction and R2{sup ∗} = 1/T2{sup ∗}, was performed by analyzing the UTE images acquired at multiple echo times. The impact of different sampling rates was evaluated through systematic comparison of the MR image quality, bone-enhanced image quality, image noise, water fraction, and R2{sup ∗} of cortical bone. Results: A reduced angular sampling rate of the UTE trajectory achieves acquisition durations in proportion to the sampling rate and in as short as 25% of the time required for full sampling using a standard Cartesian acquisition, while preserving unique MR contrast within the skull at the cost of a minimal increase in noise level. The R2{sup ∗} of human skull was measured as 0.2–0.3 ms{sup −1} depending on the specific region, which is more than ten times greater than the R2{sup ∗} of soft tissue. The water fraction in human skull was measured to be 60%–80%, which is significantly less than the >90% water fraction in brain. High-quality, bone-enhanced images can be generated using a reduced sampled UTE sequence with no visible compromise in image quality and they preserved bone-to-air contrast with as low as a 25% sampling rate. Conclusions: This UTE strategy with angular undersampling preserves the image quality and contrast of cortical bone, while reducing the total scanning time by as much as 75%. The quantitative results of R2{sup ∗} and the water fraction of skull based on Dixon analysis of UTE images acquired at multiple echo times provide guidance for the clinical adoption and further parameter optimization of the UTE sequence when used for radiation therapy and MR-based PET attenuation correction.« less

An Efficient Framework for Compressed Sensing Reconstruction of Highly Accelerated Dynamic Cardiac MRI

NASA Astrophysics Data System (ADS)

Ting, Samuel T.

The research presented in this work seeks to develop, validate, and deploy practical techniques for improving diagnosis of cardiovascular disease. In the philosophy of biomedical engineering, we seek to identify an existing medical problem having significant societal and economic effects and address this problem using engineering approaches. Cardiovascular disease is the leading cause of mortality in the United States, accounting for more deaths than any other major cause of death in every year since 1900 with the exception of the year 1918. Cardiovascular disease is estimated to account for almost one-third of all deaths in the United States, with more than 2150 deaths each day, or roughly 1 death every 40 seconds. In the past several decades, a growing array of imaging modalities have proven useful in aiding the diagnosis and evaluation of cardiovascular disease, including computed tomography, single photon emission computed tomography, and echocardiography. In particular, cardiac magnetic resonance imaging is an excellent diagnostic tool that can provide within a single exam a high quality evaluation of cardiac function, blood flow, perfusion, viability, and edema without the use of ionizing radiation. The scope of this work focuses on the application of engineering techniques for improving imaging using cardiac magnetic resonance with the goal of improving the utility of this powerful imaging modality. Dynamic cine imaging, or the capturing of movies of a single slice or volume within the heart or great vessel region, is used in nearly every cardiac magnetic resonance imaging exam, and adequate evaluation of cardiac function and morphology for diagnosis and evaluation of cardiovascular disease depends heavily on both the spatial and temporal resolution as well as the image quality of the reconstruction cine images. This work focuses primarily on image reconstruction techniques utilized in cine imaging; however, the techniques discussed are also relevant to other dynamic and static imaging techniques based on cardiac magnetic resonance. Conventional segmented techniques for cardiac cine imaging require breath-holding as well as regular cardiac rhythm, and can be time-consuming to acquire. Inadequate breath-holding or irregular cardiac rhythm can result in completely non-diagnostic images, limiting the utility of these techniques in a significant patient population. Real-time single-shot cardiac cine imaging enables free-breathing acquisition with significantly shortened imaging time and promises to significantly improve the utility of cine imaging for diagnosis and evaluation of cardiovascular disease. However, utility of real-time cine images depends heavily on the successful reconstruction of final cine images from undersampled data. Successful reconstruction of images from more highly undersampled data results directly in images exhibiting finer spatial and temporal resolution provided that image quality is sufficient. This work focuses primarily on the development, validation, and deployment of practical techniques for enabling the reconstruction of real-time cardiac cine images at the spatial and temporal resolutions and image quality needed for diagnostic utility. Particular emphasis is placed on the development of reconstruction approaches resulting in with short computation times that can be used in the clinical environment. Specifically, the use of compressed sensing signal recovery techniques is considered; such techniques show great promise in allowing successful reconstruction of highly undersampled data. The scope of this work concerns two primary topics related to signal recovery using compressed sensing: (1) long reconstruction times of these techniques, and (2) improved sparsity models for signal recovery from more highly undersampled data. Both of these aspects are relevant to the practical application of compressed sensing techniques in the context of improving image reconstruction of real-time cardiac cine images. First, algorithmic and implementational approaches are proposed for reducing the computational time for a compressed sensing reconstruction framework. Specific optimization algorithms based on the fast iterative/shrinkage algorithm (FISTA) are applied in the context of real-time cine image reconstruction to achieve efficient per-iteration computation time. Implementation within a code framework utilizing commercially available graphics processing units (GPUs) allows for practical and efficient implementation directly within the clinical environment. Second, patch-based sparsity models are proposed to enable compressed sensing signal recovery from highly undersampled data. Numerical studies demonstrate that this approach can help improve image quality at higher undersampling ratios, enabling real-time cine imaging at higher acceleration rates. In this work, it is shown that these techniques yield a holistic framework for achieving efficient reconstruction of real-time cine images with spatial and temporal resolution sufficient for use in the clinical environment. A thorough description of these techniques from both a theoretical and practical view is provided - both of which may be of interest to the reader in terms of future work.

Iterative feature refinement for accurate undersampled MR image reconstruction

NASA Astrophysics Data System (ADS)

Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

2016-05-01

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

Under-sampling trajectory design for compressed sensing based DCE-MRI.

PubMed

Liu, Duan-duan; Liang, Dong; Zhang, Na; Liu, Xin; Zhang, Yuan-ting

2013-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) needs high temporal and spatial resolution to accurately estimate quantitative parameters and characterize tumor vasculature. Compressed Sensing (CS) has the potential to accomplish this mutual importance. However, the randomness in CS under-sampling trajectory designed using the traditional variable density (VD) scheme may translate to uncertainty in kinetic parameter estimation when high reduction factors are used. Therefore, accurate parameter estimation using VD scheme usually needs multiple adjustments on parameters of Probability Density Function (PDF), and multiple reconstructions even with fixed PDF, which is inapplicable for DCE-MRI. In this paper, an under-sampling trajectory design which is robust to the change on PDF parameters and randomness with fixed PDF is studied. The strategy is to adaptively segment k-space into low-and high frequency domain, and only apply VD scheme in high-frequency domain. Simulation results demonstrate high accuracy and robustness comparing to VD design.

Accelerated self-gated UTE MRI of the murine heart

NASA Astrophysics Data System (ADS)

Motaal, Abdallah G.; Noorman, Nils; De Graaf, Wolter L.; Florack, Luc J.; Nicolay, Klaas; Strijkers, Gustav J.

2014-03-01

We introduce a new protocol to obtain radial Ultra-Short TE (UTE) MRI Cine of the beating mouse heart within reasonable measurement time. The method is based on a self-gated UTE with golden angle radial acquisition and compressed sensing reconstruction. The stochastic nature of the retrospective triggering acquisition scheme produces an under-sampled and random kt-space filling that allows for compressed sensing reconstruction, hence reducing scan time. As a standard, an intragate multislice FLASH sequence with an acquisition time of 4.5 min per slice was used to produce standard Cine movies of 4 mice hearts with 15 frames per cardiac cycle. The proposed self-gated sequence is used to produce Cine movies with short echo time. The total scan time was 11 min per slice. 6 slices were planned to cover the heart from the base to the apex. 2X, 4X and 6X under-sampled k-spaces cine movies were produced from 2, 1 and 0.7 min data acquisitions for each slice. The accelerated cine movies of the mouse hearts were successfully reconstructed with a compressed sensing algorithm. Compared to the FLASH cine images, the UTE images showed much less flow artifacts due to the short echo time. Besides, the accelerated movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters derived from the standard and the accelerated cine movies were nearly identical.

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

NASA Astrophysics Data System (ADS)

Taubmann, O.; Haase, V.; Lauritsch, G.; Zheng, Y.; Krings, G.; Hornegger, J.; Maier, A.

2017-04-01

Time-resolved tomographic cardiac imaging using an angiographic C-arm device may support clinicians during minimally invasive therapy by enabling a thorough analysis of the heart function directly in the catheter laboratory. However, clinically feasible acquisition protocols entail a highly challenging reconstruction problem which suffers from sparse angular sampling of the trajectory. Compressed sensing theory promises that useful images can be recovered despite massive undersampling by means of sparsity-based regularization. For a multitude of reasons—most notably the desired reduction of scan time, dose and contrast agent required—it is of great interest to know just how little data is actually sufficient for a certain task. In this work, we apply a convex optimization approach based on primal-dual splitting to 4D cardiac C-arm computed tomography. We examine how the quality of spatially and temporally total-variation-regularized reconstruction degrades when using as few as 6.9+/- 1.2 projection views per heart phase. First, feasible regularization weights are determined in a numerical phantom study, demonstrating the individual benefits of both regularizers. Secondly, a task-based evaluation is performed in eight clinical patients. Semi-automatic segmentation-based volume measurements of the left ventricular blood pool performed on strongly undersampled images show a correlation of close to 99% with measurements obtained from less sparsely sampled data.

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.

PubMed

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-02-15

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. © 2013 Elsevier Inc. All rights reserved.

Sparse Reconstruction Techniques in MRI: Methods, Applications, and Challenges to Clinical Adoption

PubMed Central

Yang, Alice Chieh-Yu; Kretzler, Madison; Sudarski, Sonja; Gulani, Vikas; Seiberlich, Nicole

2016-01-01

The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed. PMID:27003227

Three-dimensional through-time radial GRAPPA for renal MR angiography.

PubMed

Wright, Katherine L; Lee, Gregory R; Ehses, Philipp; Griswold, Mark A; Gulani, Vikas; Seiberlich, Nicole

2014-10-01

To achieve high temporal and spatial resolution for contrast-enhanced time-resolved MR angiography exams (trMRAs), fast imaging techniques such as non-Cartesian parallel imaging must be used. In this study, the three-dimensional (3D) through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) method is used to reconstruct highly accelerated stack-of-stars data for time-resolved renal MRAs. Through-time radial GRAPPA has been recently introduced as a method for non-Cartesian GRAPPA weight calibration, and a similar concept can also be used in 3D acquisitions. By combining different sources of calibration information, acquisition time can be reduced. Here, different GRAPPA weight calibration schemes are explored in simulation, and the results are applied to reconstruct undersampled stack-of-stars data. Simulations demonstrate that an accurate and efficient approach to 3D calibration is to combine a small number of central partitions with as many temporal repetitions as exam time permits. These findings were used to reconstruct renal trMRA data with an in-plane acceleration factor as high as 12.6 with respect to the Nyquist sampling criterion, where the lowest root mean squared error value of 16.4% was achieved when using a calibration scheme with 8 partitions, 16 repetitions, and a 4 projection × 8 read point segment size. 3D through-time radial GRAPPA can be used to successfully reconstruct highly accelerated non-Cartesian data. By using in-plane radial undersampling, a trMRA can be acquired with a temporal footprint less than 4s/frame with a spatial resolution of approximately 1.5 mm × 1.5 mm × 3 mm. © 2014 Wiley Periodicals, Inc.

Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy.

PubMed

Ahmad, Moiz; Balter, Peter; Pan, Tinsu

2011-10-01

Data sufficiency are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) on linear accelerator-integrated scanners for image-guided radiotherapy. Scan times must be in the range of 4-6 min to avoid undersampling artifacts. Various image reconstruction algorithms have been proposed to accommodate undersampled data acquisitions, but these algorithms are computationally expensive, may require long reconstruction times, and may require algorithm parameters to be optimized. The authors present a novel reconstruction method, 4D volume-of-interest (4D-VOI) reconstruction which suppresses undersampling artifacts and resolves lung tumor motion for undersampled 1-min scans. The 4D-VOI reconstruction is much less computationally expensive than other 4D-CBCT algorithms. The 4D-VOI method uses respiration-correlated projection data to reconstruct a four-dimensional (4D) image inside a VOI containing the moving tumor, and uncorrelated projection data to reconstruct a three-dimensional (3D) image outside the VOI. Anatomical motion is resolved inside the VOI and blurred outside the VOI. The authors acquired a 1-min. scan of an anthropomorphic chest phantom containing a moving water-filled sphere. The authors also used previously acquired 1-min scans for two lung cancer patients who had received CBCT-guided radiation therapy. The same raw data were used to test and compare the 4D-VOI reconstruction with the standard 4D reconstruction and the McKinnon-Bates (MB) reconstruction algorithms. Both the 4D-VOI and the MB reconstructions suppress nearly all the streak artifacts compared with the standard 4D reconstruction, but the 4D-VOI has 3-8 times greater contrast-to-noise ratio than the MB reconstruction. In the dynamic chest phantom study, the 4D-VOI and the standard 4D reconstructions both resolved a moving sphere with an 18 mm displacement. The 4D-VOI reconstruction shows a motion blur of only 3 mm, whereas the MB reconstruction shows a motion blur of 13 mm. With graphics processing unit hardware used to accelerate computations, the 4D-VOI reconstruction required a 40-s reconstruction time. 4D-VOI reconstruction effectively reduces undersampling artifacts and resolves lung tumor motion in 4D-CBCT. The 4D-VOI reconstruction is computationally inexpensive compared with more sophisticated iterative algorithms. Compared with these algorithms, our 4D-VOI reconstruction is an attractive alternative in 4D-CBCT for reconstructing target motion without generating numerous streak artifacts.

Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy

PubMed Central

Ahmad, Moiz; Balter, Peter; Pan, Tinsu

2011-01-01

Purpose: Data sufficiency are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) on linear accelerator-integrated scanners for image-guided radiotherapy. Scan times must be in the range of 4–6 min to avoid undersampling artifacts. Various image reconstruction algorithms have been proposed to accommodate undersampled data acquisitions, but these algorithms are computationally expensive, may require long reconstruction times, and may require algorithm parameters to be optimized. The authors present a novel reconstruction method, 4D volume-of-interest (4D-VOI) reconstruction which suppresses undersampling artifacts and resolves lung tumor motion for undersampled 1-min scans. The 4D-VOI reconstruction is much less computationally expensive than other 4D-CBCT algorithms. Methods: The 4D-VOI method uses respiration-correlated projection data to reconstruct a four-dimensional (4D) image inside a VOI containing the moving tumor, and uncorrelated projection data to reconstruct a three-dimensional (3D) image outside the VOI. Anatomical motion is resolved inside the VOI and blurred outside the VOI. The authors acquired a 1-min. scan of an anthropomorphic chest phantom containing a moving water-filled sphere. The authors also used previously acquired 1-min scans for two lung cancer patients who had received CBCT-guided radiation therapy. The same raw data were used to test and compare the 4D-VOI reconstruction with the standard 4D reconstruction and the McKinnon-Bates (MB) reconstruction algorithms. Results: Both the 4D-VOI and the MB reconstructions suppress nearly all the streak artifacts compared with the standard 4D reconstruction, but the 4D-VOI has 3–8 times greater contrast-to-noise ratio than the MB reconstruction. In the dynamic chest phantom study, the 4D-VOI and the standard 4D reconstructions both resolved a moving sphere with an 18 mm displacement. The 4D-VOI reconstruction shows a motion blur of only 3 mm, whereas the MB reconstruction shows a motion blur of 13 mm. With graphics processing unit hardware used to accelerate computations, the 4D-VOI reconstruction required a 40-s reconstruction time. Conclusions: 4D-VOI reconstruction effectively reduces undersampling artifacts and resolves lung tumor motion in 4D-CBCT. The 4D-VOI reconstruction is computationally inexpensive compared with more sophisticated iterative algorithms. Compared with these algorithms, our 4D-VOI reconstruction is an attractive alternative in 4D-CBCT for reconstructing target motion without generating numerous streak artifacts. PMID:21992381

Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI

PubMed Central

Pisharady, Pramod Kumar; Duarte-Carvajalino, Julio M; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-01-01

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations. PMID:28845484

Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Duarte-Carvajalino, Julio M; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2015-10-01

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations.

Max CAPR: high-resolution 3D contrast-enhanced MR angiography with acquisition times under 5 seconds.

PubMed

Haider, Clifton R; Borisch, Eric A; Glockner, James F; Mostardi, Petrice M; Rossman, Phillip J; Young, Phillip M; Riederer, Stephen J

2010-10-01

High temporal and spatial resolution is desired in imaging of vascular abnormalities having short arterial-to-venous transit times. Methods that exploit temporal correlation to reduce the observed frame time demonstrate temporal blurring, obfuscating bolus dynamics. Previously, a Cartesian acquisition with projection reconstruction-like (CAPR) sampling method has been demonstrated for three-dimensional contrast-enhanced angiographic imaging of the lower legs using two-dimensional sensitivity-encoding acceleration and partial Fourier acceleration, providing 1mm isotropic resolution of the calves, with 4.9-sec frame time and 17.6-sec temporal footprint. In this work, the CAPR acquisition is further undersampled to provide a net acceleration approaching 40 by eliminating all view sharing. The tradeoff of frame time and temporal footprint in view sharing is presented and characterized in phantom experiments. It is shown that the resultant 4.9-sec acquisition time, three-dimensional images sets have sufficient spatial and temporal resolution to clearly portray arterial and venous phases of contrast passage. It is further hypothesized that these short temporal footprint sequences provide diagnostic quality images. This is tested and shown in a series of nine contrast-enhanced MR angiography patient studies performed with the new method.

Distortion correction of echo planar images applying the concept of finite rate of innovation to point spread function mapping (FRIP).

PubMed

Nunes, Rita G; Hajnal, Joseph V

2018-06-01

Point spread function (PSF) mapping enables estimating the displacement fields required for distortion correction of echo planar images. Recently, a highly accelerated approach was introduced for estimating displacements from the phase slope of under-sampled PSF mapping data. Sampling schemes with varying spacing were proposed requiring stepwise phase unwrapping. To avoid unwrapping errors, an alternative approach applying the concept of finite rate of innovation to PSF mapping (FRIP) is introduced, using a pattern search strategy to locate the PSF peak, and the two methods are compared. Fully sampled PSF data was acquired in six subjects at 3.0 T, and distortion maps were estimated after retrospective under-sampling. The two methods were compared for both previously published and newly optimized sampling patterns. Prospectively under-sampled data were also acquired. Shift maps were estimated and deviations relative to the fully sampled reference map were calculated. The best performance was achieved when using FRIP with a previously proposed sampling scheme. The two methods were comparable for the remaining schemes. The displacement field errors tended to be lower as the number of samples or their spacing increased. A robust method for estimating the position of the PSF peak has been introduced.

Efficient radial tagging CMR exam: A coherent k-space reading and image reconstruction approach.

PubMed

Golshani, Shokoufeh; Nasiraei-Moghaddam, Abbas

2017-04-01

Cardiac MR tagging techniques, which facilitate the strain evaluation, have not yet been widely adopted in clinics due to inefficiencies in acquisition and postprocessing. This problem may be alleviated by exploiting the coherency in the three steps of tagging: preparation, acquisition, and reconstruction. Herein, we propose a fully polar-based tagging approach that may lead to real-time strain mapping. Radial readout trajectories were used to acquire radial tagging images and a Hankel-based algorithm, referred to as Polar Fourier Transform (PFT), has been adapted for reconstruction of the acquired raw data. In both phantom and human subjects, the overall performance of the method was investigated against radial undersampling and compared with the conventional reconstruction methods. Radially tagged images were reconstructed by the proposed PFT method from as few as 24 spokes with normalized root-mean-square-error of less than 3%. The reconstructed images showed a central focusing behavior, where the undersampling effects were pushed to the peripheral areas out of the central region of interest. Comparing the results with the re-gridding reconstruction technique, superior image quality and high robustness of the method were further established. In addition, a relative increase of 68 ± 2.5% in tagline sharpness was achieved for the PFT images and also higher tagging contrast (72 ± 5.6%), resulted from the well-tolerated undersampling artifacts, was observed in all reconstructions. The proposed approach led to the acceleration of the acquisition process, which was evaluated for up to eight-fold retrospectively from the fully sampled data. This is promising toward real-time imaging, and in contrast to iterative techniques, the method is consistent with online reconstruction. Magn Reson Med 77:1459-1472, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

T2 shuffling: Sharp, multicontrast, volumetric fast spin-echo imaging.

PubMed

Tamir, Jonathan I; Uecker, Martin; Chen, Weitian; Lai, Peng; Alley, Marcus T; Vasanawala, Shreyas S; Lustig, Michael

2017-01-01

A new acquisition and reconstruction method called T 2 Shuffling is presented for volumetric fast spin-echo (three-dimensional [3D] FSE) imaging. T 2 Shuffling reduces blurring and recovers many images at multiple T 2 contrasts from a single acquisition at clinically feasible scan times (6-7 min). The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in signal-to-noise ratio (SNR). By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and radiofrequency field inhomogeneity. Retrospective undersampling and in vivo scans confirm the increase in sharpness afforded by T 2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow. The proposed T 2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan. Magn Reson Med 77:180-195, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.

PubMed

Yin, X X; Ng, B W-H; Ramamohanarao, K; Baghai-Wadji, A; Abbott, D

2012-09-01

It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a model-based method for the restoration of MRIs. The reduced-order model, in which a full-system-response is projected onto a subspace of lower dimensionality, has been used to accelerate image reconstruction by reducing the size of the involved linear system. In this paper, the singular value threshold (SVT) technique is applied as a denoising scheme to reduce and select the model order of the inverse Fourier transform image, and to restore multi-slice breast MRIs that have been compressively sampled in k-space. The restored MRIs with SVT for denoising show reduced sampling errors compared to the direct MRI restoration methods via spatial FD, or DCT. Compressive sampling is a technique for finding sparse solutions to underdetermined linear systems. The sparsity that is implicit in MRIs is to explore the solution to MRI reconstruction after transformation from significantly undersampled k-space. The challenge, however, is that, since some incoherent artifacts result from the random undersampling, noise-like interference is added to the image with sparse representation. These recovery algorithms in the literature are not capable of fully removing the artifacts. It is necessary to introduce a denoising procedure to improve the quality of image recovery. This paper applies a singular value threshold algorithm to reduce the model order of image basis functions, which allows further improvement of the quality of image reconstruction with removal of noise artifacts. The principle of the denoising scheme is to reconstruct the sparse MRI matrices optimally with a lower rank via selecting smaller number of dominant singular values. The singular value threshold algorithm is performed by minimizing the nuclear norm of difference between the sampled image and the recovered image. It has been illustrated that this algorithm improves the ability of previous image reconstruction algorithms to remove noise artifacts while significantly improving the quality of MRI recovery.

Combined Dynamic Contrast Enhanced Liver MRI and MRA Using Interleaved Variable Density Sampling

PubMed Central

Rahimi, Mahdi Salmani; Korosec, Frank R.; Wang, Kang; Holmes, James H.; Motosugi, Utaroh; Bannas, Peter; Reeder, Scott B.

2014-01-01

Purpose To develop and evaluate a method for volumetric contrast-enhanced MR imaging of the liver, with high spatial and temporal resolutions, for combined dynamic imaging and MR angiography using a single injection of contrast. Methods An interleaved variable density (IVD) undersampling pattern was implemented in combination with a real-time-triggered, time-resolved, dual-echo 3D spoiled gradient echo sequence. Parallel imaging autocalibration lines were acquired only once during the first time-frame. Imaging was performed in ten subjects with focal nodular hyperplasia (FNH) and compared with their clinical MRI. The angiographic phase of the proposed method was compared to a dedicated MR angiogram acquired during a second injection of contrast. Results A total of 21 FNH, 3 cavernous hemangiomas, and 109 arterial segments were visualized in 10 subjects. The temporally-resolved images depicted the characteristic arterial enhancement pattern of the lesions with a 4 s update rate. Images were graded as having significantly higher quality compared to the clinical MRI. Angiograms produced from the IVD method provided non-inferior diagnostic assessment compared to the dedicated MRA. Conclusion Using an undersampled IVD imaging method, we have demonstrated the feasibility of obtaining high spatial and temporal resolution dynamic contrast-enhanced imaging and simultaneous MRA of the liver. PMID:24639130

3D single point imaging with compressed sensing provides high temporal resolution R 2* mapping for in vivo preclinical applications.

PubMed

Rioux, James A; Beyea, Steven D; Bowen, Chris V

2017-02-01

Purely phase-encoded techniques such as single point imaging (SPI) are generally unsuitable for in vivo imaging due to lengthy acquisition times. Reconstruction of highly undersampled data using compressed sensing allows SPI data to be quickly obtained from animal models, enabling applications in preclinical cellular and molecular imaging. TurboSPI is a multi-echo single point technique that acquires hundreds of images with microsecond spacing, enabling high temporal resolution relaxometry of large-R 2 * systems such as iron-loaded cells. TurboSPI acquisitions can be pseudo-randomly undersampled in all three dimensions to increase artifact incoherence, and can provide prior information to improve reconstruction. We evaluated the performance of CS-TurboSPI in phantoms, a rat ex vivo, and a mouse in vivo. An algorithm for iterative reconstruction of TurboSPI relaxometry time courses does not affect image quality or R 2 * mapping in vitro at acceleration factors up to 10. Imaging ex vivo is possible at similar acceleration factors, and in vivo imaging is demonstrated at an acceleration factor of 8, such that acquisition time is under 1 h. Accelerated TurboSPI enables preclinical R 2 * mapping without loss of data quality, and may show increased specificity to iron oxide compared to other sequences.

Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy

NASA Astrophysics Data System (ADS)

Mickevicius, Nikolai J.; Paulson, Eric S.

2017-04-01

The purpose of this work is to investigate the effects of undersampling and reconstruction algorithm on the total processing time and image quality of respiratory phase-resolved 4D MRI data. Specifically, the goal is to obtain quality 4D-MRI data with a combined acquisition and reconstruction time of five minutes or less, which we reasoned would be satisfactory for pre-treatment 4D-MRI in online MRI-gRT. A 3D stack-of-stars, self-navigated, 4D-MRI acquisition was used to scan three healthy volunteers at three image resolutions and two scan durations. The NUFFT, CG-SENSE, SPIRiT, and XD-GRASP reconstruction algorithms were used to reconstruct each dataset on a high performance reconstruction computer. The overall image quality, reconstruction time, artifact prevalence, and motion estimates were compared. The CG-SENSE and XD-GRASP reconstructions provided superior image quality over the other algorithms. The combination of a 3D SoS sequence and parallelized reconstruction algorithms using computing hardware more advanced than those typically seen on product MRI scanners, can result in acquisition and reconstruction of high quality respiratory correlated 4D-MRI images in less than five minutes.

Accelerating non-contrast-enhanced MR angiography with inflow inversion recovery imaging by skipped phase encoding and edge deghosting (SPEED).

PubMed

Chang, Zheng; Xiang, Qing-San; Shen, Hao; Yin, Fang-Fang

2010-03-01

To accelerate non-contrast-enhanced MR angiography (MRA) with inflow inversion recovery (IFIR) with a fast imaging method, Skipped Phase Encoding and Edge Deghosting (SPEED). IFIR imaging uses a preparatory inversion pulse to reduce signals from static tissue, while leaving inflow arterial blood unaffected, resulting in sparse arterial vasculature on modest tissue background. By taking advantage of vascular sparsity, SPEED can be simplified with a single-layer model to achieve higher efficiency in both scan time reduction and image reconstruction. SPEED can also make use of information available in multiple coils for further acceleration. The techniques are demonstrated with a three-dimensional renal non-contrast-enhanced IFIR MRA study. Images are reconstructed by SPEED based on a single-layer model to achieve an undersampling factor of up to 2.5 using one skipped phase encoding direction. By making use of information available in multiple coils, SPEED can achieve an undersampling factor of up to 8.3 with four receiver coils. The reconstructed images generally have comparable quality as that of the reference images reconstructed from full k-space data. As demonstrated with a three-dimensional renal IFIR scan, SPEED based on a single-layer model is able to reduce scan time further and achieve higher computational efficiency than the original SPEED.

3D undersampled golden-radial phase encoding for DCE-MRA using inherently regularized iterative SENSE.

PubMed

Prieto, Claudia; Uribe, Sergio; Razavi, Reza; Atkinson, David; Schaeffter, Tobias

2010-08-01

One of the current limitations of dynamic contrast-enhanced MR angiography is the requirement of both high spatial and high temporal resolution. Several undersampling techniques have been proposed to overcome this problem. However, in most of these methods the tradeoff between spatial and temporal resolution is constant for all the time frames and needs to be specified prior to data collection. This is not optimal for dynamic contrast-enhanced MR angiography where the dynamics of the process are difficult to predict and the image quality requirements are changing during the bolus passage. Here, we propose a new highly undersampled approach that allows the retrospective adaptation of the spatial and temporal resolution. The method combines a three-dimensional radial phase encoding trajectory with the golden angle profile order and non-Cartesian Sensitivity Encoding (SENSE) reconstruction. Different regularization images, obtained from the same acquired data, are used to stabilize the non-Cartesian SENSE reconstruction for the different phases of the bolus passage. The feasibility of the proposed method was demonstrated on a numerical phantom and in three-dimensional intracranial dynamic contrast-enhanced MR angiography of healthy volunteers. The acquired data were reconstructed retrospectively with temporal resolutions from 1.2 sec to 8.1 sec, providing a good depiction of small vessels, as well as distinction of different temporal phases.

Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum.

PubMed

Yasuma, Fumihito; Mitsunaga, Tomoo; Iso, Daisuke; Nayar, Shree K

2010-09-01

We propose the concept of a generalized assorted pixel (GAP) camera, which enables the user to capture a single image of a scene and, after the fact, control the tradeoff between spatial resolution, dynamic range and spectral detail. The GAP camera uses a complex array (or mosaic) of color filters. A major problem with using such an array is that the captured image is severely under-sampled for at least some of the filter types. This leads to reconstructed images with strong aliasing. We make four contributions in this paper: 1) we present a comprehensive optimization method to arrive at the spatial and spectral layout of the color filter array of a GAP camera. 2) We develop a novel algorithm for reconstructing the under-sampled channels of the image while minimizing aliasing artifacts. 3) We demonstrate how the user can capture a single image and then control the tradeoff of spatial resolution to generate a variety of images, including monochrome, high dynamic range (HDR) monochrome, RGB, HDR RGB, and multispectral images. 4) Finally, the performance of our GAP camera has been verified using extensive simulations that use multispectral images of real world scenes. A large database of these multispectral images has been made available at http://www1.cs.columbia.edu/CAVE/projects/gap_camera/ for use by the research community.

Experimental Investigation of the Performance of Image Registration and De-aliasing Algorithms

DTIC Science & Technology

2009-09-01

spread function In the literature these types of algorithms are sometimes hcluded under the broad umbrella of superresolution . However, in the current...We use one of these patterns to visually demonstrate successful de-aliasing 15. SUBJECT TERMS Image de-aliasing Superresolution Microscanning Image...undersampled point spread function. In the literature these types of algorithms are sometimes included under the broad umbrella of superresolution . However, in

Combining Imaging and Non-Imaging Observations for Improved Space-Object Identification

DTIC Science & Technology

2011-09-27

Optical and Digital Superresolution Early in the project, we expolited Fisher information (FI) to characterize the extent of spatial-frequency...extrapolation beyond the diffraction-limited optical bandwidth when the support of the object is known a priori. This support-assisted optical superresolution ...both digital (DSR) and optical superresolution (OSR). Indeed, by analyzing a se- quence of sub-pixel-shifted undersampled images one can show the

High-quality compressive ghost imaging

NASA Astrophysics Data System (ADS)

Huang, Heyan; Zhou, Cheng; Tian, Tian; Liu, Dongqi; Song, Lijun

2018-04-01

We propose a high-quality compressive ghost imaging method based on projected Landweber regularization and guided filter, which effectively reduce the undersampling noise and improve the resolution. In our scheme, the original object is reconstructed by decomposing of regularization and denoising steps instead of solving a minimization problem in compressive reconstruction process. The simulation and experimental results show that our method can obtain high ghost imaging quality in terms of PSNR and visual observation.

Chemical Shift Encoded Water–Fat Separation Using Parallel Imaging and Compressed Sensing

PubMed Central

Sharma, Samir D.; Hu, Houchun H.; Nayak, Krishna S.

2013-01-01

Chemical shift encoded techniques have received considerable attention recently because they can reliably separate water and fat in the presence of off-resonance. The insensitivity to off-resonance requires that data be acquired at multiple echo times, which increases the scan time as compared to a single echo acquisition. The increased scan time often requires that a compromise be made between the spatial resolution, the volume coverage, and the tolerance to artifacts from subject motion. This work describes a combined parallel imaging and compressed sensing approach for accelerated water–fat separation. In addition, the use of multiscale cubic B-splines for B0 field map estimation is introduced. The water and fat images and the B0 field map are estimated via an alternating minimization. Coil sensitivity information is derived from a calculated k-space convolution kernel and l1-regularization is imposed on the coil-combined water and fat image estimates. Uniform water–fat separation is demonstrated from retrospectively undersampled data in the liver, brachial plexus, ankle, and knee as well as from a prospectively undersampled acquisition of the knee at 8.6x acceleration. PMID:22505285

Phase retrieval on broadband and under-sampled images for the JWST testbed telescope

NASA Astrophysics Data System (ADS)

Smith, J. Scott; Aronstein, David L.; Dean, Bruce H.; Acton, D. Scott

2009-08-01

The James Webb Space Telescope (JWST) consists of an optical telescope element (OTE) that sends light to five science instruments. The initial steps for commissioning the telescope are performed with the Near-Infrared Camera (NIRCam) instrument, but low-order optical aberrations in the remaining science instruments must be determined (using phase retrieval) in order to ensure good performance across the entire field of view. These remaining instruments were designed to collect science data, and not to serve as wavefront sensors. Thus, the science cameras are not ideal phase-retrieval imagers for several reasons: they record under-sampled data and have a limited range of diversity defocus, and only one instrument has an internal, narrowband filter. To address these issues, we developed the capability of sensing these aberrations using an extension of image-based iterative-transform phase retrieval called Variable Sampling Mapping (VSM). The results show that VSM-based phase retrieval is capable of sensing low-order aberrations to a few nm RMS from images that are consistent with the non-ideal conditions expected during JWST multi-field commissioning. The algorithm is validated using data collected from the JWST Testbed Telescope (TBT).

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

PubMed

Cai, Ailong; Wang, Linyuan; Zhang, Hanming; Yan, Bin; Li, Lei; Xi, Xiaoqi; Li, Jianxin

2014-01-01

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

Blind Compressed Sensing Enables 3-Dimensional Dynamic Free Breathing Magnetic Resonance Imaging of Lung Volumes and Diaphragm Motion.

PubMed

Bhave, Sampada; Lingala, Sajan Goud; Newell, John D; Nagle, Scott K; Jacob, Mathews

2016-06-01

The objective of this study was to increase the spatial and temporal resolution of dynamic 3-dimensional (3D) magnetic resonance imaging (MRI) of lung volumes and diaphragm motion. To achieve this goal, we evaluate the utility of the proposed blind compressed sensing (BCS) algorithm to recover data from highly undersampled measurements. We evaluated the performance of the BCS scheme to recover dynamic data sets from retrospectively and prospectively undersampled measurements. We also compared its performance against that of view-sharing, the nuclear norm minimization scheme, and the l1 Fourier sparsity regularization scheme. Quantitative experiments were performed on a healthy subject using a fully sampled 2D data set with uniform radial sampling, which was retrospectively undersampled with 16 radial spokes per frame to correspond to an undersampling factor of 8. The images obtained from the 4 reconstruction schemes were compared with the fully sampled data using mean square error and normalized high-frequency error metrics. The schemes were also compared using prospective 3D data acquired on a Siemens 3 T TIM TRIO MRI scanner on 8 healthy subjects during free breathing. Two expert cardiothoracic radiologists (R1 and R2) qualitatively evaluated the reconstructed 3D data sets using a 5-point scale (0-4) on the basis of spatial resolution, temporal resolution, and presence of aliasing artifacts. The BCS scheme gives better reconstructions (mean square error = 0.0232 and normalized high frequency = 0.133) than the other schemes in the 2D retrospective undersampling experiments, producing minimally distorted reconstructions up to an acceleration factor of 8 (16 radial spokes per frame). The prospective 3D experiments show that the BCS scheme provides visually improved reconstructions than the other schemes do. The BCS scheme provides improved qualitative scores over nuclear norm and l1 Fourier sparsity regularization schemes in the temporal blurring and spatial blurring categories. The qualitative scores for aliasing artifacts in the images reconstructed by nuclear norm scheme and BCS scheme are comparable.The comparisons of the tidal volume changes also show that the BCS scheme has less temporal blurring as compared with the nuclear norm minimization scheme and the l1 Fourier sparsity regularization scheme. The minute ventilation estimated by BCS for tidal breathing in supine position (4 L/min) and the measured supine inspiratory capacity (1.5 L) is in good correlation with the literature. The improved performance of BCS can be explained by its ability to efficiently adapt to the data, thus providing a richer representation of the signal. The feasibility of the BCS scheme was demonstrated for dynamic 3D free breathing MRI of lung volumes and diaphragm motion. A temporal resolution of ∼500 milliseconds, spatial resolution of 2.7 × 2.7 × 10 mm, with whole lung coverage (16 slices) was achieved using the BCS scheme.

Metrics for Diagnosing Undersampling in Monte Carlo Tally Estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perfetti, Christopher M.; Rearden, Bradley T.

This study explored the potential of using Markov chain convergence diagnostics to predict the prevalence and magnitude of biases due to undersampling in Monte Carlo eigenvalue and flux tally estimates. Five metrics were applied to two models of pressurized water reactor fuel assemblies and their potential for identifying undersampling biases was evaluated by comparing the calculated test metrics with known biases in the tallies. Three of the five undersampling metrics showed the potential to accurately predict the behavior of undersampling biases in the responses examined in this study.

Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging

PubMed Central

Ravishankar, Saiprasad; Moore, Brian E.; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamic magnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method. PMID:28092528

A Bayesian Model for Highly Accelerated Phase-Contrast MRI

PubMed Central

Rich, Adam; Potter, Lee C.; Jin, Ning; Ash, Joshua; Simonetti, Orlando P.; Ahmad, Rizwan

2015-01-01

Purpose Phase-contrast magnetic resonance imaging (PC-MRI) is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to 4D flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to PC-MRI. Theory and Methods ReVEAL models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers. Results ReVEAL is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R ≤ 10. For SV, Pearson r ≥ 0.996 for phantom imaging (n = 24) and r ≥ 0.956 for prospectively accelerated in vivo imaging (n = 10) for R ≤ 10. Conclusion ReVEAL enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to 4D flow imaging, where higher acceleration may be possible due to additional redundancy. PMID:26444911

Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning.

PubMed

Song, Ying; Zhu, Zhen; Lu, Yang; Liu, Qiegen; Zhao, Jun

2014-03-01

To improve the magnetic resonance imaging (MRI) data acquisition speed while maintaining the reconstruction quality, a novel method is proposed for multislice MRI reconstruction from undersampled k-space data based on compressed-sensing theory using dictionary learning. There are two aspects to improve the reconstruction quality. One is that spatial correlation among slices is used by extending the atoms in dictionary learning from patches to blocks. The other is that the dictionary-learning scheme is used at two resolution levels; i.e., a low-resolution dictionary is used for sparse coding and a high-resolution dictionary is used for image updating. Numerical experiments are carried out on in vivo 3D MR images of brains and abdomens with a variety of undersampling schemes and ratios. The proposed method (dual-DLMRI) achieves better reconstruction quality than conventional reconstruction methods, with the peak signal-to-noise ratio being 7 dB higher. The advantages of the dual dictionaries are obvious compared with the single dictionary. Parameter variations ranging from 50% to 200% only bias the image quality within 15% in terms of the peak signal-to-noise ratio. Dual-DLMRI effectively uses the a priori information in the dual-dictionary scheme and provides dramatically improved reconstruction quality. Copyright © 2013 Wiley Periodicals, Inc.

Image denoising for real-time MRI.

PubMed

Klosowski, Jakob; Frahm, Jens

2017-03-01

To develop an image noise filter suitable for MRI in real time (acquisition and display), which preserves small isolated details and efficiently removes background noise without introducing blur, smearing, or patch artifacts. The proposed method extends the nonlocal means algorithm to adapt the influence of the original pixel value according to a simple measure for patch regularity. Detail preservation is improved by a compactly supported weighting kernel that closely approximates the commonly used exponential weight, while an oracle step ensures efficient background noise removal. Denoising experiments were conducted on real-time images of healthy subjects reconstructed by regularized nonlinear inversion from radial acquisitions with pronounced undersampling. The filter leads to a signal-to-noise ratio (SNR) improvement of at least 60% without noticeable artifacts or loss of detail. The method visually compares to more complex state-of-the-art filters as the block-matching three-dimensional filter and in certain cases better matches the underlying noise model. Acceleration of the computation to more than 100 complex frames per second using graphics processing units is straightforward. The sensitivity of nonlocal means to small details can be significantly increased by the simple strategies presented here, which allows partial restoration of SNR in iteratively reconstructed images without introducing a noticeable time delay or image artifacts. Magn Reson Med 77:1340-1352, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Use of three-dimensional time-resolved phase-contrast magnetic resonance imaging with vastly undersampled isotropic projection reconstruction to assess renal blood flow in a renal cell carcinoma patient treated with sunitinib: a case report.

PubMed

Takayama, Tatsuya; Takehara, Yasuo; Sugiyama, Masataka; Sugiyama, Takayuki; Ishii, Yasuo; Johnson, Kevin E; Wieben, Oliver; Wakayama, Tetsuya; Sakahara, Harumi; Ozono, Seiichiro

2014-08-14

New imaging modalities to assess the efficacy of drugs that have molecular targets remain under development. Here, we describe for the first time the use of time-resolved three-dimensional phase-contrast magnetic resonance imaging to monitor changes in blood supply to a tumor during sunitinib treatment in a patient with localized renal cell carcinoma. A 43-year-old Japanese woman with a tumor-bearing but functional single kidney presented at our hospital in July 2012. Computed tomography and magnetic resonance imaging revealed a cT1aN0M0 renal cell carcinoma embedded in the upper central region of the left kidney. She was prescribed sunitinib as neoadjuvant therapy for 8 months, and then underwent partial nephrectomy. Tumor monitoring during this time was done using time-resolved three-dimensional phase-contrast magnetic resonance imaging, a recent technique which specifically measures blood flow in the various vessels of the kidney. This imaging allowed visualization of the redistribution of renal blood flow during treatment, and showed that flow to the tumor was decreased and flows to other areas increased. Of note, this change occurred in the absence of any change in tumor size. The ability of time-resolved three-dimensional phase-contrast magnetic resonance imaging to provide quantitative information on blood supply to tumors may be useful in monitoring the efficacy of sunitinib treatment.

Golden-ratio rotated stack-of-stars acquisition for improved volumetric MRI.

PubMed

Zhou, Ziwu; Han, Fei; Yan, Lirong; Wang, Danny J J; Hu, Peng

2017-12-01

To develop and evaluate an improved stack-of-stars radial sampling strategy for reducing streaking artifacts. The conventional stack-of-stars sampling strategy collects the same radial angle for every partition (slice) encoding. In an undersampled acquisition, such an aligned acquisition generates coherent aliasing patterns and introduces strong streaking artifacts. We show that by rotating the radial spokes in a golden-angle manner along the partition-encoding direction, the aliasing pattern is modified, resulting in improved image quality for gridding and more advanced reconstruction methods. Computer simulations were performed and phantom as well as in vivo images for three different applications were acquired. Simulation, phantom, and in vivo experiments confirmed that the proposed method was able to generate images with less streaking artifact and sharper structures based on undersampled acquisitions in comparison with the conventional aligned approach at the same acceleration factors. By combining parallel imaging and compressed sensing in the reconstruction, streaking artifacts were mostly removed with improved delineation of fine structures using the proposed strategy. We present a simple method to reduce streaking artifacts and improve image quality in 3D stack-of-stars acquisitions by re-arranging the radial spoke angles in the 3D partition direction, which can be used for rapid volumetric imaging. Magn Reson Med 78:2290-2298, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

A Bayesian model for highly accelerated phase-contrast MRI.

PubMed

Rich, Adam; Potter, Lee C; Jin, Ning; Ash, Joshua; Simonetti, Orlando P; Ahmad, Rizwan

2016-08-01

Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging. The proposed approach models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers. The proposed approach is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R≤10. For SV, Pearson r≥0.99 for phantom imaging (n = 24) and r≥0.96 for prospectively accelerated in vivo imaging (n = 10) for R≤10. The proposed approach enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to four-dimensional flow imaging, where higher acceleration may be possible due to additional redundancy. Magn Reson Med 76:689-701, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Diagnosing Undersampling Biases in Monte Carlo Eigenvalue and Flux Tally Estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perfetti, Christopher M.; Rearden, Bradley T.; Marshall, William J.

2017-02-08

Here, this study focuses on understanding the phenomena in Monte Carlo simulations known as undersampling, in which Monte Carlo tally estimates may not encounter a sufficient number of particles during each generation to obtain unbiased tally estimates. Steady-state Monte Carlo simulations were performed using the KENO Monte Carlo tools within the SCALE code system for models of several burnup credit applications with varying degrees of spatial and isotopic complexities, and the incidence and impact of undersampling on eigenvalue and flux estimates were examined. Using an inadequate number of particle histories in each generation was found to produce a maximum bias of ~100 pcm in eigenvalue estimates and biases that exceeded 10% in fuel pin flux tally estimates. Having quantified the potential magnitude of undersampling biases in eigenvalue and flux tally estimates in these systems, this study then investigated whether Markov Chain Monte Carlo convergence metrics could be integrated into Monte Carlo simulations to predict the onset and magnitude of undersampling biases. Five potential metrics for identifying undersampling biases were implemented in the SCALE code system and evaluated for their ability to predict undersampling biases by comparing the test metric scores with the observed undersampling biases. Finally, of the five convergence metrics that were investigated, three (the Heidelberger-Welch relative half-width, the Gelman-Rubin more » $$\\hat{R}_c$$ diagnostic, and tally entropy) showed the potential to accurately predict the behavior of undersampling biases in the responses examined.« less

Whole lung morphometry with 3D multiple b-value hyperpolarized gas MRI and compressed sensing.

PubMed

Chan, Ho-Fung; Stewart, Neil J; Parra-Robles, Juan; Collier, Guilhem J; Wild, Jim M

2017-05-01

To demonstrate three-dimensional (3D) multiple b-value diffusion-weighted (DW) MRI of hyperpolarized 3 He gas for whole lung morphometry with compressed sensing (CS). A fully-sampled, two b-value, 3D hyperpolarized 3 He DW-MRI dataset was acquired from the lungs of a healthy volunteer and retrospectively undersampled in the k y and k z phase-encoding directions for CS simulations. Optimal k-space undersampling patterns were determined by minimizing the mean absolute error between reconstructed and fully-sampled 3 He apparent diffusion coefficient (ADC) maps. Prospective three-fold, undersampled, 3D multiple b-value 3 He DW-MRI datasets were acquired from five healthy volunteers and one chronic obstructive pulmonary disease (COPD) patient, and the mean values of maps of ADC and mean alveolar dimension (Lm D ) were validated against two-dimensional (2D) and 3D fully-sampled 3 He DW-MRI experiments. Reconstructed undersampled datasets showed no visual artifacts and good preservation of the main image features and quantitative information. A good agreement between fully-sampled and prospective undersampled datasets was found, with a mean difference of +3.4% and +5.1% observed in mean global ADC and Lm D values, respectively. These differences were within the standard deviation range and consistent with values reported from healthy and COPD lungs. Accelerated CS acquisition has facilitated 3D multiple b-value 3 He DW-MRI scans in a single breath-hold, enabling whole lung morphometry mapping. Magn Reson Med 77:1916-1925, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Improving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L+S) reconstruction.

PubMed

Petrov, Andrii Y; Herbst, Michael; Andrew Stenger, V

2017-08-15

Rapid whole-brain dynamic Magnetic Resonance Imaging (MRI) is of particular interest in Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI). Faster acquisitions with higher temporal sampling of the BOLD time-course provide several advantages including increased sensitivity in detecting functional activation, the possibility of filtering out physiological noise for improving temporal SNR, and freezing out head motion. Generally, faster acquisitions require undersampling of the data which results in aliasing artifacts in the object domain. A recently developed low-rank (L) plus sparse (S) matrix decomposition model (L+S) is one of the methods that has been introduced to reconstruct images from undersampled dynamic MRI data. The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain. This suggests that L+S might be suited for undersampled task or slow event-related fMRI acquisitions because the periodic nature of the BOLD signal is sparse in the temporal Fourier transform domain and slowly varying low-rank brain background signals, such as physiological noise and drift, will be predominantly low-rank. In this work, as a proof of concept, we exploit the L+S method for accelerating block-design fMRI using a 3D stack of spirals (SoS) acquisition where undersampling is performed in the k z -t domain. We examined the feasibility of the L+S method to accurately separate temporally correlated brain background information in the L component while capturing periodic BOLD signals in the S component. We present results acquired in control human volunteers at 3T for both retrospective and prospectively acquired fMRI data for a visual activation block-design task. We show that a SoS fMRI acquisition with an acceleration of four and L+S reconstruction can achieve a brain coverage of 40 slices at 2mm isotropic resolution and 64 x 64 matrix size every 500ms. Copyright © 2017 Elsevier Inc. All rights reserved.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

General solution of undersampling frequency conversion and its optimization for parallel photodisplacement imaging.

PubMed

Nakata, Toshihiko; Ninomiya, Takanori

2006-10-10

A general solution of undersampling frequency conversion and its optimization for parallel photodisplacement imaging is presented. Phase-modulated heterodyne interference light generated by a linear region of periodic displacement is captured by a charge-coupled device image sensor, in which the interference light is sampled at a sampling rate lower than the Nyquist frequency. The frequencies of the components of the light, such as the sideband and carrier (which include photodisplacement and topography information, respectively), are downconverted and sampled simultaneously based on the integration and sampling effects of the sensor. A general solution of frequency and amplitude in this downconversion is derived by Fourier analysis of the sampling procedure. The optimal frequency condition for the heterodyne beat signal, modulation signal, and sensor gate pulse is derived such that undesirable components are eliminated and each information component is converted into an orthogonal function, allowing each to be discretely reproduced from the Fourier coefficients. The optimal frequency parameters that maximize the sideband-to-carrier amplitude ratio are determined, theoretically demonstrating its high selectivity over 80 dB. Preliminary experiments demonstrate that this technique is capable of simultaneous imaging of reflectivity, topography, and photodisplacement for the detection of subsurface lattice defects at a speed corresponding to an acquisition time of only 0.26 s per 256 x 256 pixel area.

Comparison of centric and reverse-centric trajectories for highly accelerated three-dimensional saturation recovery cardiac perfusion imaging.

PubMed

Wang, Haonan; Bangerter, Neal K; Park, Daniel J; Adluru, Ganesh; Kholmovski, Eugene G; Xu, Jian; DiBella, Edward

2015-10-01

Highly undersampled three-dimensional (3D) saturation-recovery sequences are affected by k-space trajectory since the magnetization does not reach steady state during the acquisition and the slab excitation profile yields different flip angles in different slices. This study compares centric and reverse-centric 3D cardiac perfusion imaging. An undersampled (98 phase encodes) 3D ECG-gated saturation-recovery sequence that alternates centric and reverse-centric acquisitions each time frame was used to image phantoms and in vivo subjects. Flip angle variation across the slices was measured, and contrast with each trajectory was analyzed via Bloch simulation. Significant variations in flip angle were observed across slices, leading to larger signal variation across slices for the centric acquisition. In simulation, severe transient artifacts were observed when using the centric trajectory with higher flip angles, placing practical limits on the maximum flip angle used. The reverse-centric trajectory provided less contrast, but was more robust to flip angle variations. Both of the k-space trajectories can provide reasonable image quality. The centric trajectory can have higher CNR, but is more sensitive to flip angle variation. The reverse-centric trajectory is more robust to flip angle variation. © 2014 Wiley Periodicals, Inc.

A variational reconstruction method for undersampled dynamic x-ray tomography based on physical motion models

NASA Astrophysics Data System (ADS)

Burger, Martin; Dirks, Hendrik; Frerking, Lena; Hauptmann, Andreas; Helin, Tapio; Siltanen, Samuli

2017-12-01

In this paper we study the reconstruction of moving object densities from undersampled dynamic x-ray tomography in two dimensions. A particular motivation of this study is to use realistic measurement protocols for practical applications, i.e. we do not assume to have a full Radon transform in each time step, but only projections in few angular directions. This restriction enforces a space-time reconstruction, which we perform by incorporating physical motion models and regularization of motion vectors in a variational framework. The methodology of optical flow, which is one of the most common methods to estimate motion between two images, is utilized to formulate a joint variational model for reconstruction and motion estimation. We provide a basic mathematical analysis of the forward model and the variational model for the image reconstruction. Moreover, we discuss the efficient numerical minimization based on alternating minimizations between images and motion vectors. A variety of results are presented for simulated and real measurement data with different sampling strategy. A key observation is that random sampling combined with our model allows reconstructions of similar amount of measurements and quality as a single static reconstruction.

3D Compressed Sensing for Highly Accelerated Hyperpolarized 13C MRSI With In Vivo Applications to Transgenic Mouse Models of Cancer

PubMed Central

Hu, Simon; Lustig, Michael; Balakrishnan, Asha; Larson, Peder E. Z.; Bok, Robert; Kurhanewicz, John; Nelson, Sarah J.; Goga, Andrei; Pauly, John M.; Vigneron, Daniel B.

2010-01-01

High polarization of nuclear spins in liquid state through hyperpolarized technology utilizing dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at a high signal-to-noise ratio. Acquisition time limitations due to T1 decay of the hyperpolarized signal require accelerated imaging methods, such as compressed sensing, for optimal speed and spatial coverage. In this paper, the design and testing of a new echo-planar 13C three-dimensional magnetic resonance spectroscopic imaging (MRSI) compressed sensing sequence is presented. The sequence provides up to a factor of 7.53 in acceleration with minimal reconstruction artifacts. The key to the design is employing x and y gradient blips during a fly-back readout to pseudorandomly undersample kf-kx-ky space. The design was validated in simulations and phantom experiments where the limits of undersampling and the effects of noise on the compressed sensing nonlinear reconstruction were tested. Finally, this new pulse sequence was applied in vivo in preclinical studies involving transgenic prostate cancer and transgenic liver cancer murine models to obtain much higher spatial and temporal resolution than possible with conventional echo-planar spectroscopic imaging methods. PMID:20017160

ACCELERATING MR PARAMETER MAPPING USING SPARSITY-PROMOTING REGULARIZATION IN PARAMETRIC DIMENSION

PubMed Central

Velikina, Julia V.; Alexander, Andrew L.; Samsonov, Alexey

2013-01-01

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist. PMID:23213053

SU-G-IeP1-13: Sub-Nyquist Dynamic MRI Via Prior Rank, Intensity and Sparsity Model (PRISM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, B; Gao, H

Purpose: Accelerated dynamic MRI is important for MRI guided radiotherapy. Inspired by compressive sensing (CS), sub-Nyquist dynamic MRI has been an active research area, i.e., sparse sampling in k-t space for accelerated dynamic MRI. This work is to investigate sub-Nyquist dynamic MRI via a previously developed CS model, namely Prior Rank, Intensity and Sparsity Model (PRISM). Methods: The proposed method utilizes PRISM with rank minimization and incoherent sampling patterns for sub-Nyquist reconstruction. In PRISM, the low-rank background image, which is automatically calculated by rank minimization, is excluded from the L1 minimization step of the CS reconstruction to further sparsify themore » residual image, thus allowing for higher acceleration rates. Furthermore, the sampling pattern in k-t space is made more incoherent by sampling a different set of k-space points at different temporal frames. Results: Reconstruction results from L1-sparsity method and PRISM method with 30% undersampled data and 15% undersampled data are compared to demonstrate the power of PRISM for dynamic MRI. Conclusion: A sub- Nyquist MRI reconstruction method based on PRISM is developed with improved image quality from the L1-sparsity method.« less

Volumetric spiral chemical shift imaging of hyperpolarized [2-(13) c]pyruvate in a rat c6 glioma model.

PubMed

Park, Jae Mo; Josan, Sonal; Jang, Taichang; Merchant, Milton; Watkins, Ron; Hurd, Ralph E; Recht, Lawrence D; Mayer, Dirk; Spielman, Daniel M

2016-03-01

MRS of hyperpolarized [2-(13)C]pyruvate can be used to assess multiple metabolic pathways within mitochondria as the (13)C label is not lost with the conversion of pyruvate to acetyl-CoA. This study presents the first MR spectroscopic imaging of hyperpolarized [2-(13)C]pyruvate in glioma-bearing brain. Spiral chemical shift imaging with spectrally undersampling scheme (1042 Hz) and a hard-pulse excitation was exploited to simultaneously image [2-(13)C]pyruvate, [2-(13)C]lactate, and [5-(13)C]glutamate, the metabolites known to be produced in brain after an injection of hyperpolarized [2-(13)C]pyruvate, without chemical shift displacement artifacts. A separate undersampling scheme (890 Hz) was also used to image [1-(13)C]acetyl-carnitine. Healthy and C6 glioma-implanted rat brains were imaged at baseline and after dichloroacetate administration, a drug that modulates pyruvate dehydrogenase kinase activity. The baseline metabolite maps showed higher lactate and lower glutamate in tumor as compared to normal-appearing brain. Dichloroacetate led to an increase in glutamate in both tumor and normal-appearing brain. Dichloroacetate-induced %-decrease of lactate/glutamate was comparable to the lactate/bicarbonate decrease from hyperpolarized [1-(13)C]pyruvate studies. Acetyl-carnitine was observed in the muscle/fat tissue surrounding the brain. Robust volumetric imaging with hyperpolarized [2-(13)C]pyruvate and downstream products was performed in glioma-bearing rat brains, demonstrating changes in mitochondrial metabolism with dichloroacetate. © 2015 Wiley Periodicals, Inc.

Accelerated 1 H MRSI using randomly undersampled spiral-based k-space trajectories.

PubMed

Chatnuntawech, Itthi; Gagoski, Borjan; Bilgic, Berkin; Cauley, Stephen F; Setsompop, Kawin; Adalsteinsson, Elfar

2014-07-30

To develop and evaluate the performance of an acquisition and reconstruction method for accelerated MR spectroscopic imaging (MRSI) through undersampling of spiral trajectories. A randomly undersampled spiral acquisition and sensitivity encoding (SENSE) with total variation (TV) regularization, random SENSE+TV, is developed and evaluated on single-slice numerical phantom, in vivo single-slice MRSI, and in vivo three-dimensional (3D)-MRSI at 3 Tesla. Random SENSE+TV was compared with five alternative methods for accelerated MRSI. For the in vivo single-slice MRSI, random SENSE+TV yields up to 2.7 and 2 times reduction in root-mean-square error (RMSE) of reconstructed N-acetyl aspartate (NAA), creatine, and choline maps, compared with the denoised fully sampled and uniformly undersampled SENSE+TV methods with the same acquisition time, respectively. For the in vivo 3D-MRSI, random SENSE+TV yields up to 1.6 times reduction in RMSE, compared with uniform SENSE+TV. Furthermore, by using random SENSE+TV, we have demonstrated on the in vivo single-slice and 3D-MRSI that acceleration factors of 4.5 and 4 are achievable with the same quality as the fully sampled data, as measured by RMSE of reconstructed NAA map, respectively. With the same scan time, random SENSE+TV yields lower RMSEs of metabolite maps than other methods evaluated. Random SENSE+TV achieves up to 4.5-fold acceleration with comparable data quality as the fully sampled acquisition. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Stationary wavelet transform for under-sampled MRI reconstruction.

PubMed

Kayvanrad, Mohammad H; McLeod, A Jonathan; Baxter, John S H; McKenzie, Charles A; Peters, Terry M

2014-12-01

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions. Copyright © 2014 Elsevier Inc. All rights reserved.

Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).

PubMed

Velikina, Julia V; Samsonov, Alexey A

2015-11-01

To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. © 2014 Wiley Periodicals, Inc.

RECONSTRUCTION OF DYNAMIC IMAGE SERIES FROM UNDERSAMPLED MRI DATA USING DATA-DRIVEN MODEL CONSISTENCY CONDITION (MOCCO)

PubMed Central

Velikina, Julia V.; Samsonov, Alexey A.

2014-01-01

Purpose To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models pre-estimated from training data. Theory We introduce the MOdel Consistency COndition (MOCCO) technique that utilizes temporal models to regularize the reconstruction without constraining the solution to be low-rank as performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Methods Our method was compared to standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE MRA) and cardiac CINE imaging. We studied sensitivity of all methods to rank-reduction and temporal subspace modeling errors. Results MOCCO demonstrated reduced sensitivity to modeling errors compared to the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. Conclusions MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. PMID:25399724

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

PubMed Central

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. PMID:24176869

K-space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging.

PubMed

Miao, Jun; Wong, Wilbur C K; Narayan, Sreenath; Wilson, David L

2011-11-01

Partially parallel imaging (PPI) greatly accelerates MR imaging by using surface coil arrays and under-sampling k-space. However, the reduction factor (R) in PPI is theoretically constrained by the number of coils (N(C)). A symmetrically shaped kernel is typically used, but this often prevents even the theoretically possible R from being achieved. Here, the authors propose a kernel design method to accelerate PPI faster than R = N(C). K-space data demonstrates an anisotropic pattern that is correlated with the object itself and to the asymmetry of the coil sensitivity profile, which is caused by coil placement and B(1) inhomogeneity. From spatial analysis theory, reconstruction of such pattern is best achieved by a signal-dependent anisotropic shape kernel. As a result, the authors propose the use of asymmetric kernels to improve k-space reconstruction. The authors fit a bivariate Gaussian function to the local signal magnitude of each coil, then threshold this function to extract the kernel elements. A perceptual difference model (Case-PDM) was employed to quantitatively evaluate image quality. A MR phantom experiment showed that k-space anisotropy increased as a function of magnetic field strength. The authors tested a K-spAce Reconstruction with AnisOtropic KErnel support ("KARAOKE") algorithm with both MR phantom and in vivo data sets, and compared the reconstructions to those produced by GRAPPA, a popular PPI reconstruction method. By exploiting k-space anisotropy, KARAOKE was able to better preserve edges, which is particularly useful for cardiac imaging and motion correction, while GRAPPA failed at a high R near or exceeding N(C). KARAOKE performed comparably to GRAPPA at low Rs. As a rule of thumb, KARAOKE reconstruction should always be used for higher quality k-space reconstruction, particularly when PPI data is acquired at high Rs and/or high field strength.

K-space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging

PubMed Central

Miao, Jun; Wong, Wilbur C. K.; Narayan, Sreenath; Wilson, David L.

2011-01-01

Purpose: Partially parallel imaging (PPI) greatly accelerates MR imaging by using surface coil arrays and under-sampling k-space. However, the reduction factor (R) in PPI is theoretically constrained by the number of coils (NC). A symmetrically shaped kernel is typically used, but this often prevents even the theoretically possible R from being achieved. Here, the authors propose a kernel design method to accelerate PPI faster than R = NC. Methods: K-space data demonstrates an anisotropic pattern that is correlated with the object itself and to the asymmetry of the coil sensitivity profile, which is caused by coil placement and B1 inhomogeneity. From spatial analysis theory, reconstruction of such pattern is best achieved by a signal-dependent anisotropic shape kernel. As a result, the authors propose the use of asymmetric kernels to improve k-space reconstruction. The authors fit a bivariate Gaussian function to the local signal magnitude of each coil, then threshold this function to extract the kernel elements. A perceptual difference model (Case-PDM) was employed to quantitatively evaluate image quality. Results: A MR phantom experiment showed that k-space anisotropy increased as a function of magnetic field strength. The authors tested a K-spAce Reconstruction with AnisOtropic KErnel support (“KARAOKE”) algorithm with both MR phantom and in vivo data sets, and compared the reconstructions to those produced by GRAPPA, a popular PPI reconstruction method. By exploiting k-space anisotropy, KARAOKE was able to better preserve edges, which is particularly useful for cardiac imaging and motion correction, while GRAPPA failed at a high R near or exceeding NC. KARAOKE performed comparably to GRAPPA at low Rs. Conclusions: As a rule of thumb, KARAOKE reconstruction should always be used for higher quality k-space reconstruction, particularly when PPI data is acquired at high Rs and∕or high field strength. PMID:22047378

High Spatial and Temporal Resolution Dynamic Contrast-Enhanced Magnetic Resonance Angiography (CE-MRA) using Compressed Sensing with Magnitude Image Subtraction

PubMed Central

Rapacchi, Stanislas; Han, Fei; Natsuaki, Yutaka; Kroeker, Randall; Plotnik, Adam; Lehman, Evan; Sayre, James; Laub, Gerhard; Finn, J Paul; Hu, Peng

2014-01-01

Purpose We propose a compressed-sensing (CS) technique based on magnitude image subtraction for high spatial and temporal resolution dynamic contrast-enhanced MR angiography (CE-MRA). Methods Our technique integrates the magnitude difference image into the CS reconstruction to promote subtraction sparsity. Fully sampled Cartesian 3D CE-MRA datasets from 6 volunteers were retrospectively under-sampled and three reconstruction strategies were evaluated: k-space subtraction CS, independent CS, and magnitude subtraction CS. The techniques were compared in image quality (vessel delineation, image artifacts, and noise) and image reconstruction error. Our CS technique was further tested on 7 volunteers using a prospectively under-sampled CE-MRA sequence. Results Compared with k-space subtraction and independent CS, our magnitude subtraction CS provides significantly better vessel delineation and less noise at 4X acceleration, and significantly less reconstruction error at 4X and 8X (p<0.05 for all). On a 1–4 point image quality scale in vessel delineation, our technique scored 3.8±0.4 at 4X, 2.8±0.4 at 8X and 2.3±0.6 at 12X acceleration. Using our CS sequence at 12X acceleration, we were able to acquire dynamic CE-MRA with higher spatial and temporal resolution than current clinical TWIST protocol while maintaining comparable image quality (2.8±0.5 vs. 3.0±0.4, p=NS). Conclusion Our technique is promising for dynamic CE-MRA. PMID:23801456

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI

PubMed Central

Kamesh Iyer, Srikant; Tasdizen, Tolga; Likhite, Devavrat; DiBella, Edward

2016-01-01

Purpose: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. Methods: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Results: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. Conclusions: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly. PMID:27036592

Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

PubMed Central

Correia, Teresa; Yin, Jun; Ramel, Marie-Christine; Andrews, Natalie; Katan, Matilda; Bugeon, Laurence; Dallman, Margaret J.; McGinty, James; Frankel, Paul; French, Paul M. W.; Arridge, Simon

2015-01-01

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds. PMID:26308086

Non-Cartesian MRI Reconstruction With Automatic Regularization Via Monte-Carlo SURE

PubMed Central

Weller, Daniel S.; Nielsen, Jon-Fredrik; Fessler, Jeffrey A.

2013-01-01

Magnetic resonance image (MRI) reconstruction from undersampled k-space data requires regularization to reduce noise and aliasing artifacts. Proper application of regularization however requires appropriate selection of associated regularization parameters. In this work, we develop a data-driven regularization parameter adjustment scheme that minimizes an estimate (based on the principle of Stein’s unbiased risk estimate—SURE) of a suitable weighted squared-error measure in k-space. To compute this SURE-type estimate, we propose a Monte-Carlo scheme that extends our previous approach to inverse problems (e.g., MRI reconstruction) involving complex-valued images. Our approach depends only on the output of a given reconstruction algorithm and does not require knowledge of its internal workings, so it is capable of tackling a wide variety of reconstruction algorithms and nonquadratic regularizers including total variation and those based on the ℓ1-norm. Experiments with simulated and real MR data indicate that the proposed approach is capable of providing near mean squared-error (MSE) optimal regularization parameters for single-coil undersampled non-Cartesian MRI reconstruction. PMID:23591478

Reproducibility of cerebrospinal venous blood flow and vessel anatomy with the use of phase contrast-vastly undersampled isotropic projection reconstruction and contrast-enhanced MRA.

PubMed

Schrauben, E M; Johnson, K M; Huston, J; Del Rio, A M; Reeder, S B; Field, A; Wieben, O

2014-05-01

The chronic cerebrospinal venous insufficiency hypothesis raises interest in cerebrospinal venous blood flow imaging, which is more complex and less established than in arteries. For accurate assessment of venous flow in chronic cerebrospinal venous insufficiency diagnosis and research, we must account for physiologic changes in flow patterns. This study examines day-to-day flow variability in cerebrospinal veins by use of 4D MR flow and contrast-enhanced MRA under typical, uncontrolled conditions in healthy individuals. Ten healthy volunteers were scanned in a test-retest fashion by use of a 4D flow MR imaging technique and contrast-enhanced MRA. Flow parameters obtained from phase contrast-vastly undersampled isotropic projection reconstruction and contrast-enhanced MRA scoring measurements in the head, neck, and chest veins were analyzed for internal consistency and interscan reproducibility. Internal consistency was satisfied at the torcular herophili, with an input-output difference of 2.2%. Percentages of variations in flow were 20.3%, internal jugular vein; 20.4%, azygos vein; 6.8%, transverse sinus; and 5.1%, common carotid artery. Retrograde flow was found in the lower internal jugular vein (4.8%) and azygos vein (7.2%). Contrast-enhanced MRA interscan κ values for the internal jugular vein (left: 0.474, right: 0.366) and azygos vein (-0.053) showed poor interscan agreement. Phase contrast-vastly undersampled isotropic projection reconstruction blood flow measurements are reliable and highly reproducible in intracranial veins and in the common carotid artery but not in veins of the neck (internal jugular vein) and chest (azygos vein) because of normal physiologic variation. Retrograde flow normally may be observed in the lower internal jugular vein and azygos vein. Low interrater agreement in contrast-enhanced MRA scans was observed. These findings have important implications for imaging diagnosis and experimental research of chronic cerebrospinal venous insufficiency. © 2014 by American Journal of Neuroradiology.

Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint.

PubMed

Liao, Congyu; Chen, Ying; Cao, Xiaozhi; Chen, Song; He, Hongjian; Mani, Merry; Jacob, Mathews; Magnotta, Vincent; Zhong, Jianhui

2017-03-01

To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies

DOE PAGES

Maddali, S.; Calvo-Almazan, I.; Almer, J.; ...

2018-03-21

Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this datamore » set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. Lastly, we use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.« less

Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddali, S.; Calvo-Almazan, I.; Almer, J.

Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this datamore » set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. Lastly, we use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.« less

Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies.

PubMed

Maddali, S; Calvo-Almazan, I; Almer, J; Kenesei, P; Park, J-S; Harder, R; Nashed, Y; Hruszkewycz, S O

2018-03-21

Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this data set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. We use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.

Real-time cardiovascular magnetic resonance at 1.5 T using balanced SSFP and 40 ms resolution

PubMed Central

2013-01-01

Background While cardiovascular magnetic resonance (CMR) commonly employs ECG-synchronized cine acquisitions with balanced steady-state free precession (SSFP) contrast at 1.5 T, recent developments at 3 T demonstrate significant potential for T1-weighted real-time imaging at high spatiotemporal resolution using undersampled radial FLASH. The purpose of this work was to combine both ideas and to evaluate a corresponding real-time CMR method at 1.5 T with SSFP contrast. Methods Radial gradient-echo sequences with fully balanced gradients and at least 15-fold undersampling were implemented on two CMR systems with different gradient performance. Image reconstruction by regularized nonlinear inversion (NLINV) was performed offline and resulted in real-time SSFP CMR images at a nominal resolution of 1.8 mm and with acquisition times of 40 ms. Results Studies of healthy subjects demonstrated technical feasibility in terms of robustness and general image quality. Clinical applicability with access to quantitative evaluations (e.g., ejection fraction) was confirmed by preliminary applications to 27 patients with typical indications for CMR including arrhythmias and abnormal wall motion. Real-time image quality was slightly lower than for cine SSFP recordings, but considered diagnostic in all cases. Conclusions Extending conventional cine approaches, real-time radial SSFP CMR with NLINV reconstruction provides access to individual cardiac cycles and allows for studies of patients with irregular heartbeat. PMID:24028285

Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI Data.

PubMed

Abascal, Juan F P J; Desco, Manuel; Parra-Robles, Juan

2018-02-01

Diffusion MRI data are generally acquired using hyperpolarized gases during patient breath-hold, which yields a compromise between achievable image resolution, lung coverage, and number of -values. In this paper, we propose a novel method that accelerates the acquisition of diffusion MRI data by undersampling in both the spatial and -value dimensions and incorporating knowledge about signal decay into the reconstruction (SIDER). SIDER is compared with total variation (TV) reconstruction by assessing its effect on both the recovery of ventilation images and the estimated mean alveolar dimensions (MADs). Both methods are assessed by retrospectively undersampling diffusion data sets ( =8) of healthy volunteers and patients with Chronic Obstructive Pulmonary Disease (COPD) for acceleration factors between x2 and x10. TV led to large errors and artifacts for acceleration factors equal to or larger than x5. SIDER improved TV, with a lower solution error and MAD histograms closer to those obtained from fully sampled data for acceleration factors up to x10. SIDER preserved image quality at all acceleration factors, although images were slightly smoothed and some details were lost at x10. In conclusion, we developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and provide more options in the study of lung diseases with MRI.

Direct measurement and calibration of the Kepler CCD Pixel Response Function for improved photometry and astrometry

NASA Astrophysics Data System (ADS)

Ninkov, Zoran

Stellar images taken with telescopes and detectors in space are usually undersampled, and to correct for this, an accurate pixel response function is required. The standard approach for HST and KEPLER has been to measure the telescope PSF combined ("convolved") with the actual pixel response function, super-sampled by taking into account dithered or offset observed images of many stars (Lauer [1999]). This combined response function has been called the "PRF" (Bryson et al. [2011]). However, using such results has not allowed astrometry from KEPLER to reach its full potential (Monet et al. [2010], [2014]). Given the precision of KEPLER photometry, it should be feasible to use a pre-determined detector pixel response function (PRF) and an optical point spread function (PSF) as separable quantities to more accurately correct photometry and astrometry for undersampling. Wavelength (i.e. stellar color) and instrumental temperature should be affecting each of these differently. Discussion of the PRF in the "KEPLER Instrument Handbook" is limited to an ad-hoc extension of earlier measurements on a quite different CCD. It is known that the KEPLER PSF typically has a sharp spike in the middle, and the main bulk of the PSF is still small enough to be undersampled, so that any substructure in the pixel may interact significantly with the optical PSF. Both the PSF and PRF are probably asymmetric. We propose to measure the PRF for an example of the CCD sensors used on KEPLER at sufficient sampling resolution to allow significant improvement of KEPLER photometry and astrometry, in particular allowing PSF fitting techniques to be used on the data archive.

Resolution Versus Error for Computational Electron Microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luzi, Lorenzo; Stevens, Andrew; Yang, Hao

Images that are collected via scanning transmission electron microscopy (STEM) can be undersampled to avoid damage to the specimen while maintaining resolution [1, 2]. We have used BPFA to impute missing data and reduce noise [3]. The reconstruction is typically evaluated using the peak signal-to-noise ratio (PSNR). This measure is too conservative for STEM images and we propose that the Fourier ring correlation (FRC) is used instead to evaluate the reconstruction. We are not concerned with exact reconstruction of the truth image, and therefore PSNR is a conservative estimation of the quality of the reconstruction. Instead, we are concerned withmore » the visual resolution of the image and whether atoms can be distinguished. We have evaluated the reconstruction of a simulated STEM image using the FRC and compared the results with the PSNR measurements. The FRC captures the resolution of the image and is not affected by a large MSE if the atom peaks are still distinguishable. The noisy and reconstructed images are shown in Figure 1. The simulated STEM image was sampled at 100%, 80%, 40%, and 20% of the original pixels to simulate an undersampled scan. The reconstruction was done using BPFA with a patch size of 10 x 10 and no overlapping patches. Not having overlapping patches produces inferior results but they are still acceptable. The dictionary size is 64 and 30 iterations were completed during each reconstruction. The 100% image was denoised instead of reconstructed. Poisson noise was applied to the simulated image with λ values of 500, 50, and 5 to simulate lower imaging dose. The original simulated STEM image was also included in our calculations and was generated using a dose of 1000. The simulated STEM image is 100 by 100 pixels and has essentially no high frequency components. The image reconstruction tends to smooth the data, also resulting in no high frequency components. This causes the FRC of the two images to be large at higher resolutions and may be misleading. For this reason, the BPFA has no overlap to avoid excessive smoothing. Moreover, the resolution of the simulated image is approximately 9.2 (1/nm), so we only look that far in the frequency domain when performing FRC. If the FRC curve does not crossover the threshold, a resolution value of 9.2 is used. We emphasize that our reported results are conservative. The FRC and PSNR values using the ground truth and the reconstructed images are shown in Tables 1 and 2. The left side show the metrics without using BPFA (missing pixels) and the right side show the metrics after using BPFA. When we did not use BPFA, the Fourier transform was estimated [4]. Some threshold curves have been studied [5], but they are derived for additive noise models. Since we have a Poisson noise model, we have used the more conservative threshold of 0.5 for our calculations. Ten images were used to construct each cell of tables in the form of the mean of the metric plus or minus its standard deviation. As expected, the PSNR dies off much quicker than the FRC values for the same image. For the 100% and 80% sampled versions of the truth image, the resolution only dies off when the dose is 5. However, the PSNR dies off rapidly as the dose is reduced. For the 1000, 500, and 50 dose images, the FRC is the maximum, or close, until we undersample at 20%. The PSNR for these values tapers down as we get into the bottom right hand corner of the table, even though the resolution remains high. Overall, we find that undersampled images can be reconstructed to acceptable resolution even when the dose per pixel is also reduced[6]. References: [1]A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2]A Stevens, L Kovarik, P Abellan et al. Advanced Structural and Chemical Imaging 1(1), (2015), pp. 1. [3]M Zhou, H Chen, J Paisley et al. Image Processing, IEEE Transactions on 21(1), (2012), pp. 130. [4]V. Y. Liepin’sh. Automatic control and computer sciences 30(3), (1996), pp. 20.« less

Network compensation for missing sensors

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Mulligan, Jeffrey B.

1991-01-01

A network learning translation invariance algorithm to compute interpolation functions is presented. This algorithm with one fixed receptive field can construct a linear transformation compensating for gain changes, sensor position jitter, and sensor loss when there are enough remaining sensors to adequately sample the input images. However, when the images are undersampled and complete compensation is not possible, the algorithm need to be modified. For moderate sensor losses, the algorithm works if the transformation weight adjustment is restricted to the weights to output units affected by the loss.

Aliasing Detection and Reduction Scheme on Angularly Undersampled Light Fields.

PubMed

Xiao, Zhaolin; Wang, Qing; Zhou, Guoqing; Yu, Jingyi

2017-05-01

When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, and so on. In this paper, we present a different solution that first detects and then removes angular aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the angular aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing versus non-aliasing regions and angular aliasing removal. Experiments on both synthetic scene and real light field data sets (camera array and Lytro camera) demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques.

Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, Kuan-Hao; Hu, Lingzhi; Traughber, Melanie

Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spreadmore » functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2{sup ∗} images (1/T2{sup ∗}) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2{sup ∗}, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation.« less

Diagnosing Undersampling in Monte Carlo Eigenvalue and Flux Tally Estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perfetti, Christopher M; Rearden, Bradley T

2015-01-01

This study explored the impact of undersampling on the accuracy of tally estimates in Monte Carlo (MC) calculations. Steady-state MC simulations were performed for models of several critical systems with varying degrees of spatial and isotopic complexity, and the impact of undersampling on eigenvalue and fuel pin flux/fission estimates was examined. This study observed biases in MC eigenvalue estimates as large as several percent and biases in fuel pin flux/fission tally estimates that exceeded tens, and in some cases hundreds, of percent. This study also investigated five statistical metrics for predicting the occurrence of undersampling biases in MC simulations. Threemore » of the metrics (the Heidelberger-Welch RHW, the Geweke Z-Score, and the Gelman-Rubin diagnostics) are commonly used for diagnosing the convergence of Markov chains, and two of the methods (the Contributing Particles per Generation and Tally Entropy) are new convergence metrics developed in the course of this study. These metrics were implemented in the KENO MC code within the SCALE code system and were evaluated for their reliability at predicting the onset and magnitude of undersampling biases in MC eigenvalue and flux tally estimates in two of the critical models. Of the five methods investigated, the Heidelberger-Welch RHW, the Gelman-Rubin diagnostics, and Tally Entropy produced test metrics that correlated strongly to the size of the observed undersampling biases, indicating their potential to effectively predict the size and prevalence of undersampling biases in MC simulations.« less

3D hyperpolarized C-13 EPI with calibrationless parallel imaging

NASA Astrophysics Data System (ADS)

Gordon, Jeremy W.; Hansen, Rie B.; Shin, Peter J.; Feng, Yesu; Vigneron, Daniel B.; Larson, Peder E. Z.

2018-04-01

With the translation of metabolic MRI with hyperpolarized 13C agents into the clinic, imaging approaches will require large volumetric FOVs to support clinical applications. Parallel imaging techniques will be crucial to increasing volumetric scan coverage while minimizing RF requirements and temporal resolution. Calibrationless parallel imaging approaches are well-suited for this application because they eliminate the need to acquire coil profile maps or auto-calibration data. In this work, we explored the utility of a calibrationless parallel imaging method (SAKE) and corresponding sampling strategies to accelerate and undersample hyperpolarized 13C data using 3D blipped EPI acquisitions and multichannel receive coils, and demonstrated its application in a human study of [1-13C]pyruvate metabolism.

SNR Degradation in Undersampled Phase Measurement Systems

PubMed Central

Salido-Monzú, David; Meca-Meca, Francisco J.; Martín-Gorostiza, Ernesto; Lázaro-Galilea, José L.

2016-01-01

A wide range of measuring applications rely on phase estimation on sinusoidal signals. These systems, where the estimation is mainly implemented in the digital domain, can generally benefit from the use of undersampling to reduce the digitizer and subsequent digital processing requirements. This may be crucial when the application characteristics necessarily imply a simple and inexpensive sensor. However, practical limitations related to the phase stability of the band-pass filter prior digitization establish restrictions to the reduction of noise bandwidth. Due to this, the undersampling intensity is practically defined by noise aliasing, taking into account the amount of signal-to-noise ratio (SNR) reduction caused by it considering the application accuracy requirements. This work analyzes the relationship between undersampling frequency and SNR reduction, conditioned by the stability requirements of the filter that defines the noise bandwidth before digitization. The effect of undersampling is quantified in a practical situation where phase differences are measured by in-phase and quadrature (I/Q) demodulation for an infrared ranging application. PMID:27783033

Learning toward practical head pose estimation

NASA Astrophysics Data System (ADS)

Sang, Gaoli; He, Feixiang; Zhu, Rong; Xuan, Shibin

2017-08-01

Head pose is useful information for many face-related tasks, such as face recognition, behavior analysis, human-computer interfaces, etc. Existing head pose estimation methods usually assume that the face images have been well aligned or that sufficient and precise training data are available. In practical applications, however, these assumptions are very likely to be invalid. This paper first investigates the impact of the failure of these assumptions, i.e., misalignment of face images, uncertainty and undersampling of training data, on head pose estimation accuracy of state-of-the-art methods. A learning-based approach is then designed to enhance the robustness of head pose estimation to these factors. To cope with misalignment, instead of using hand-crafted features, it seeks suitable features by learning from a set of training data with a deep convolutional neural network (DCNN), such that the training data can be best classified into the correct head pose categories. To handle uncertainty and undersampling, it employs multivariate labeling distributions (MLDs) with dense sampling intervals to represent the head pose attributes of face images. The correlation between the features and the dense MLD representations of face images is approximated by a maximum entropy model, whose parameters are optimized on the given training data. To estimate the head pose of a face image, its MLD representation is first computed according to the model based on the features extracted from the image by the trained DCNN, and its head pose is then assumed to be the one corresponding to the peak in its MLD. Evaluation experiments on the Pointing'04, FacePix, Multi-PIE, and CASIA-PEAL databases prove the effectiveness and efficiency of the proposed method.

Accelerating volumetric cine MRI (VC-MRI) using undersampling for real-time 3D target localization/tracking in radiation therapy: a feasibility study

NASA Astrophysics Data System (ADS)

Harris, Wendy; Yin, Fang-Fang; Wang, Chunhao; Zhang, You; Cai, Jing; Ren, Lei

2018-01-01

Purpose. To accelerate volumetric cine MRI (VC-MRI) using undersampled 2D-cine MRI to provide real-time 3D guidance for gating/target tracking in radiotherapy. Methods. 4D-MRI is acquired during patient simulation. One phase of the prior 4D-MRI is selected as the prior images, designated as MRIprior. The on-board VC-MRI at each time-step is considered a deformation of the MRIprior. The deformation field map is represented as a linear combination of the motion components extracted by principal component analysis from the prior 4D-MRI. The weighting coefficients of the motion components are solved by matching the corresponding 2D-slice of the VC-MRI with the on-board undersampled 2D-cine MRI acquired. Undersampled Cartesian and radial k-space acquisition strategies were investigated. The effects of k-space sampling percentage (SP) and distribution, tumor sizes and noise on the VC-MRI estimation were studied. The VC-MRI estimation was evaluated using XCAT simulation of lung cancer patients and data from liver cancer patients. Volume percent difference (VPD) and Center of Mass Shift (COMS) of the tumor volumes and tumor tracking errors were calculated. Results. For XCAT, VPD/COMS were 11.93  ±  2.37%/0.90  ±  0.27 mm and 11.53  ±  1.47%/0.85  ±  0.20 mm among all scenarios with Cartesian sampling (SP  =  10%) and radial sampling (21 spokes, SP  =  5.2%), respectively. When tumor size decreased, higher sampling rate achieved more accurate VC-MRI than lower sampling rate. VC-MRI was robust against noise levels up to SNR  =  20. For patient data, the tumor tracking errors in superior-inferior, anterior-posterior and lateral (LAT) directions were 0.46  ±  0.20 mm, 0.56  ±  0.17 mm and 0.23  ±  0.16 mm, respectively, for Cartesian-based sampling with SP  =  20% and 0.60  ±  0.19 mm, 0.56  ±  0.22 mm and 0.42  ±  0.15 mm, respectively, for radial-based sampling with SP  =  8% (32 spokes). Conclusions. It is feasible to estimate VC-MRI from a single undersampled on-board 2D cine MRI. Phantom and patient studies showed that the temporal resolution of VC-MRI can potentially be improved by 5-10 times using a 2D cine image acquired with 10-20% k-space sampling.

Low-dose 4D cardiac imaging in small animals using dual source micro-CT

NASA Astrophysics Data System (ADS)

Holbrook, M.; Clark, D. P.; Badea, C. T.

2018-01-01

Micro-CT is widely used in preclinical studies, generating substantial interest in extending its capabilities in functional imaging applications such as blood perfusion and cardiac function. However, imaging cardiac structure and function in mice is challenging due to their small size and rapid heart rate. To overcome these challenges, we propose and compare improvements on two strategies for cardiac gating in dual-source, preclinical micro-CT: fast prospective gating (PG) and uncorrelated retrospective gating (RG). These sampling strategies combined with a sophisticated iterative image reconstruction algorithm provide faster acquisitions and high image quality in low-dose 4D (i.e. 3D  +  Time) cardiac micro-CT. Fast PG is performed under continuous subject rotation which results in interleaved projection angles between cardiac phases. Thus, fast PG provides a well-sampled temporal average image for use as a prior in iterative reconstruction. Uncorrelated RG incorporates random delays during sampling to prevent correlations between heart rate and sampling rate. We have performed both simulations and animal studies to validate these new sampling protocols. Sampling times for 1000 projections using fast PG and RG were 2 and 3 min, respectively, and the total dose was 170 mGy each. Reconstructions were performed using a 4D iterative reconstruction technique based on the split Bregman method. To examine undersampling robustness, subsets of 500 and 250 projections were also used for reconstruction. Both sampling strategies in conjunction with our iterative reconstruction method are capable of resolving cardiac phases and provide high image quality. In general, for equal numbers of projections, fast PG shows fewer errors than RG and is more robust to undersampling. Our results indicate that only 1000-projection based reconstruction with fast PG satisfies a 5% error criterion in left ventricular volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.

Zero source insertion technique to account for undersampling in GPR imaging

DOEpatents

Chambers, David H; Mast, Jeffrey E; Paglieroni, David W

2014-02-25

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

High Temporospatial Resolution Dynamic Contrast Enhanced (DCE) Wrist MRI with Variable-Density Pseudo-Random CIRcular Cartesian UnderSampling (CIRCUS) Acquisition: Evaluation of Perfusion in Rheumatoid Arthritis Patients

PubMed Central

Liu, Jing; Pedoia, Valentina; Heilmeier, Ursula; Ku, Eric; Su, Favian; Khanna, Sameer; Imboden, John; Graf, Jonathan; Link, Thomas; Li, Xiaojuan

2016-01-01

This study is to evaluate highly accelerated 3D dynamic contrast-enhanced (DCE) wrist MRI for assessment of perfusion in rheumatoid arthritis (RA) patients. A pseudo-random variable-density undersampling strategy, CIRcular Cartesian UnderSampling (CIRCUS), was combined with k-t SPARSE-SENSE reconstruction to achieve a highly accelerated 3D DCE wrist MRI. Two healthy volunteers and ten RA patients were studied. Two patients were on methotrexate (MTX) only (Group I) and the other eight were treated with a combination therapy of MTX and Anti-Tumour Necrosis Factor (TNF) therapy (Group II). Patients were scanned at baseline and 3-month follow-up. DCE MR images were used to evaluate perfusion in synovitis and bone marrow edema pattern in the RA wrist joints. A series of perfusion parameters were derived and compared with clinical disease activity scores of 28 joints (DAS28). 3D DCE wrist MR images were obtained with a spatial resolution of 0.3×0.3×1.5mm3 and temporal resolution of 5 s (with an acceleration factor of 20). The derived perfusion parameters, most notably, transition time (dT) of synovitis, showed significant negative correlations with DAS28-ESR (r=-0.80, p<0.05) and DAS28-CRP (r=-0.87, p<0.05) at baseline and also correlated significantly with treatment responses evaluated by clinical score changes between baseline and 3-month follow-up (with DAS28-ESR: r=-0.79, p<0.05, and DAS28-CRP: r=-0.82, p<0.05). Highly accelerated 3D DCE wrist MRI with improved temporospatial resolution has been achieved in RA patients and provides accurate assessment of neovascularization and perfusion in RA joints, showing promise as a potential tool for evaluating treatment responses. PMID:26608949

Accelerated whole brain intracranial vessel wall imaging using black blood fast spin echo with compressed sensing (CS-SPACE).

PubMed

Zhu, Chengcheng; Tian, Bing; Chen, Luguang; Eisenmenger, Laura; Raithel, Esther; Forman, Christoph; Ahn, Sinyeob; Laub, Gerhard; Liu, Qi; Lu, Jianping; Liu, Jing; Hess, Christopher; Saloner, David

2018-06-01

Develop and optimize an accelerated, high-resolution (0.5 mm isotropic) 3D black blood MRI technique to reduce scan time for whole-brain intracranial vessel wall imaging. A 3D accelerated T 1 -weighted fast-spin-echo prototype sequence using compressed sensing (CS-SPACE) was developed at 3T. Both the acquisition [echo train length (ETL), under-sampling factor] and reconstruction parameters (regularization parameter, number of iterations) were first optimized in 5 healthy volunteers. Ten patients with a variety of intracranial vascular disease presentations (aneurysm, atherosclerosis, dissection, vasculitis) were imaged with SPACE and optimized CS-SPACE, pre and post Gd contrast. Lumen/wall area, wall-to-lumen contrast ratio (CR), enhancement ratio (ER), sharpness, and qualitative scores (1-4) by two radiologists were recorded. The optimized CS-SPACE protocol has ETL 60, 20% k-space under-sampling, 0.002 regularization factor with 20 iterations. In patient studies, CS-SPACE and conventional SPACE had comparable image scores both pre- (3.35 ± 0.85 vs. 3.54 ± 0.65, p = 0.13) and post-contrast (3.72 ± 0.58 vs. 3.53 ± 0.57, p = 0.15), but the CS-SPACE acquisition was 37% faster (6:48 vs. 10:50). CS-SPACE agreed with SPACE for lumen/wall area, ER measurements and sharpness, but marginally reduced the CR. In the evaluation of intracranial vascular disease, CS-SPACE provides a substantial reduction in scan time compared to conventional T 1 -weighted SPACE while maintaining good image quality.

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms.

PubMed

Tang, Jie; Nett, Brian E; Chen, Guang-Hong

2009-10-07

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.

PubMed

Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi

2017-05-28

In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.

Joint reconstruction of PET-MRI by exploiting structural similarity

NASA Astrophysics Data System (ADS)

Ehrhardt, Matthias J.; Thielemans, Kris; Pizarro, Luis; Atkinson, David; Ourselin, Sébastien; Hutton, Brian F.; Arridge, Simon R.

2015-01-01

Recent advances in technology have enabled the combination of positron emission tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners simultaneously acquire functional PET and anatomical or functional MRI data. As function and anatomy are not independent of one another the images to be reconstructed are likely to have shared structures. We aim to exploit this inherent structural similarity by reconstructing from both modalities in a joint reconstruction framework. The structural similarity between two modalities can be modelled in two different ways: edges are more likely to be at similar positions and/or to have similar orientations. We analyse the diffusion process generated by minimizing priors that encapsulate these different models. It turns out that the class of parallel level set priors always corresponds to anisotropic diffusion which is sometimes forward and sometimes backward diffusion. We perform numerical experiments where we jointly reconstruct from blurred Radon data with Poisson noise (PET) and under-sampled Fourier data with Gaussian noise (MRI). Our results show that both modalities benefit from each other in areas of shared edge information. The joint reconstructions have less artefacts and sharper edges compared to separate reconstructions and the ℓ2-error can be reduced in all of the considered cases of under-sampling.

SU-E-J-237: Real-Time 3D Anatomy Estimation From Undersampled MR Acquisitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glitzner, M; Lagendijk, J; Raaymakers, B

Recent developments made MRI guided radiotherapy feasible. Performing simultaneous imaging during fractions can provide information about changing anatomy by means of deformable image registration for either immediate plan adaptations or accurate dose accumulation on the changing anatomy. In 3D MRI, however, acquisition time is considerable and scales with resolution. Furthermore, intra-scan motion degrades image quality.In this work, we investigate the sensitivity of registration quality on imageresolution: potentially, by employing spatial undersampling, the acquisition timeof MR images for the purpose of deformable image registration can be reducedsignificantly.On a volunteer, 3D-MR imaging data was sampled in a navigator-gated manner, acquiring one axialmore » volume (360×260×100mm{sup 3}) per 3s during exhale phase. A T1-weighted FFE sequence was used with an acquired voxel size of (2.5mm{sup 3}) for a duration of 17min. Deformation vector fields were evaluated for 100 imaging cycles with respect to the initial anatomy using deformable image registration based on optical flow. Subsequently, the imaging data was downsampled by a factor of 2, simulating a fourfold acquisition speed. Displacements of the downsampled volumes were then calculated by the same process.In kidneyliver boundaries and the region around stomach/duodenum, prominent organ drifts could be observed in both the original and the downsampled imaging data. An increasing displacement of approximately 2mm was observed for the kidney, while an area around the stomach showed sudden displacements of 4mm. Comparison of the motile points over time showed high reproducibility between the displacements of high-resolution and downsampled volumes: over a 17min acquisition, the componentwise RMS error was not more than 0.38mm.Based on the synthetic experiments, 3D nonrigid image registration shows little sensitivity to image resolution and the displacement information is preserved even when halving the resolution. This can be employed to greatly reduce image acquisition times for interventional applications in real-time. This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution.« less

Multicontrast reconstruction using compressed sensing with low rank and spatially varying edge-preserving constraints for high-resolution MR characterization of myocardial infarction.

PubMed

Zhang, Li; Athavale, Prashant; Pop, Mihaela; Wright, Graham A

2017-08-01

To enable robust reconstruction for highly accelerated three-dimensional multicontrast late enhancement imaging to provide improved MR characterization of myocardial infarction with isotropic high spatial resolution. A new method using compressed sensing with low rank and spatially varying edge-preserving constraints (CS-LASER) is proposed to improve the reconstruction of fine image details from highly undersampled data. CS-LASER leverages the low rank feature of the multicontrast volume series in MR relaxation and integrates spatially varying edge preservation into the explicit low rank constrained compressed sensing framework using weighted total variation. With an orthogonal temporal basis pre-estimated, a multiscale iterative reconstruction framework is proposed to enable the practice of CS-LASER with spatially varying weights of appropriate accuracy. In in vivo pig studies with both retrospective and prospective undersamplings, CS-LASER preserved fine image details better and presented tissue characteristics with a higher degree of consistency with histopathology, particularly in the peri-infarct region, than an alternative technique for different acceleration rates. An isotropic resolution of 1.5 mm was achieved in vivo within a single breath-hold using the proposed techniques. Accelerated three-dimensional multicontrast late enhancement with CS-LASER can achieve improved MR characterization of myocardial infarction with high spatial resolution. Magn Reson Med 78:598-610, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Effect of probe diffusion on the SOFI imaging accuracy.

PubMed

Vandenberg, Wim; Dedecker, Peter

2017-03-23

Live-cell super-resolution fluorescence imaging is becoming commonplace for exploring biological systems, though sample dynamics can affect the imaging quality. In this work we evaluate the effect of probe diffusion on super-resolution optical fluctuation imaging (SOFI), using a theoretical model and numerical simulations based on the imaging of live cells labelled with photochromic fluorescent proteins. We find that, over a range of physiological conditions, fluorophore diffusion results in a change in the amplitude of the SOFI signal. The magnitude of this change is approximately proportional to the on-time ratio of the fluorophores. However, for photochromic fluorescent proteins this effect is unlikely to present a significant distortion in practical experiments in biological systems. Due to this lack of distortions, probe diffusion strongly enhances the SOFI imaging by avoiding spatial undersampling caused by the limited labeling density.

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation.

PubMed

Jia, Xun; Lou, Yifei; Li, Ruijiang; Song, William Y; Jiang, Steve B

2010-04-01

Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

Moving Beam-Blocker-Based Low-Dose Cone-Beam CT

NASA Astrophysics Data System (ADS)

Lee, Taewon; Lee, Changwoo; Baek, Jongduk; Cho, Seungryong

2016-10-01

This paper experimentally demonstrates a feasibility of moving beam-blocker-based low-dose cone-beam CT (CBCT) and exploits the beam-blocking configurations to reach an optimal one that leads to the highest contrast-to-noise ratio (CNR). Sparse-view CT takes projections at sparse view angles and provides a viable option to reducing dose. We have earlier proposed a many-view under-sampling (MVUS) technique as an alternative to sparse-view CT. Instead of switching the x-ray tube power, one can place a reciprocating multi-slit beam-blocker between the x-ray tube and the patient to partially block the x-ray beam. We used a bench-top circular cone-beam CT system with a lab-made moving beam-blocker. For image reconstruction, we used a modified total-variation minimization (TV) algorithm that masks the blocked data in the back-projection step leaving only the measured data through the slits to be used in the computation. The number of slits and the reciprocation frequency have been varied and the effects of them on the image quality were investigated. For image quality assessment, we used CNR and the detectability. We also analyzed the sampling efficiency in the context of compressive sensing: the sampling density and data incoherence in each case. We tested three sets of slits with their number of 6, 12 and 18, each at reciprocation frequencies of 10, 30, 50 and 70 Hz/rot. The optimum condition out of the tested sets was found to be using 12 slits at 30 Hz/rot.

Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

PubMed Central

Tian, Zhen; Jia, Xun; Yuan, Kehong; Pan, Tinsu; Jiang, Steve B.

2014-01-01

High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original total variation norm. During the reconstruction process, the pixels at edges would be gradually identified and given small penalty weight. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low contrast structures and therefore maintain acceptable spatial resolution. PMID:21860076

High throughput dual-wavelength temperature distribution imaging via compressive imaging

NASA Astrophysics Data System (ADS)

Yao, Xu-Ri; Lan, Ruo-Ming; Liu, Xue-Feng; Zhu, Ge; Zheng, Fu; Yu, Wen-Kai; Zhai, Guang-Jie

2018-03-01

Thermal imaging is an essential tool in a wide variety of research areas. In this work we demonstrate high-throughput double-wavelength temperature distribution imaging using a modified single-pixel camera without the requirement of a beam splitter (BS). A digital micro-mirror device (DMD) is utilized to display binary masks and split the incident radiation, which eliminates the necessity of a BS. Because the spatial resolution is dictated by the DMD, this thermal imaging system has the advantage of perfect spatial registration between the two images, which limits the need for the pixel registration and fine adjustments. Two bucket detectors, which measures the total light intensity reflected from the DMD, are employed in this system and yield an improvement in the detection efficiency of the narrow-band radiation. A compressive imaging algorithm is utilized to achieve under-sampling recovery. A proof-of-principle experiment was presented to demonstrate the feasibility of this structure.

WFPC2 Stellar Photometry with HSTphot

NASA Technical Reports Server (NTRS)

Dolphin, Andrew E.

2000-01-01

HSTphot, a photometry package designed to handle the undersampled PSFs found in WFPC2 images, is introduced and described, as well as some of the considerations that have to be made in order to obtain accurate PSF-fitting stellar photometry with WFPC2 data. Tests of HSTphot's internal reliability are made using multiple observations of the same field, and tests of external reliability are made by comparing with DoPHOT reductions of the same data. Subject headz'ngs: techniques: photometric

Radial k-t SPIRiT: autocalibrated parallel imaging for generalized phase-contrast MRI.

PubMed

Santelli, Claudio; Schaeffter, Tobias; Kozerke, Sebastian

2014-11-01

To extend SPIRiT to additionally exploit temporal correlations for highly accelerated generalized phase-contrast MRI and to compare the performance of the proposed radial k-t SPIRiT method relative to frame-by-frame SPIRiT and radial k-t GRAPPA reconstruction for velocity and turbulence mapping in the aortic arch. Free-breathing navigator-gated two-dimensional radial cine imaging with three-directional multi-point velocity encoding was implemented and fully sampled data were obtained in the aortic arch of healthy volunteers. Velocities were encoded with three different first gradient moments per axis to permit quantification of mean velocity and turbulent kinetic energy. Velocity and turbulent kinetic energy maps from up to 14-fold undersampled data were compared for k-t SPIRiT, frame-by-frame SPIRiT, and k-t GRAPPA relative to the fully sampled reference. Using k-t SPIRiT, improvements in magnitude and velocity reconstruction accuracy were found. Temporally resolved magnitude profiles revealed a reduction in spatial blurring with k-t SPIRiT compared with frame-by-frame SPIRiT and k-t GRAPPA for all velocity encodings, leading to improved estimates of turbulent kinetic energy. k-t SPIRiT offers improved reconstruction accuracy at high radial undersampling factors and hence facilitates the use of generalized phase-contrast MRI for routine use. Copyright © 2013 Wiley Periodicals, Inc.

Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data.

PubMed

Doneva, Mariya; Amthor, Thomas; Koken, Peter; Sommer, Karsten; Börnert, Peter

2017-09-01

An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data. A projection onto the low dimensional data subspace is performed as a matrix multiplication instead of a singular value thresholding typically used in low rank matrix completion, further reducing the computational complexity of the reconstruction. The method is theoretically described and validated in phantom and in-vivo experiments. The quality of the parameter maps can be significantly improved compared to direct matching on undersampled data. Copyright © 2017 Elsevier Inc. All rights reserved.

Sparse sampling and reconstruction for electron and scanning probe microscope imaging

DOEpatents

Anderson, Hyrum; Helms, Jovana; Wheeler, Jason W.; Larson, Kurt W.; Rohrer, Brandon R.

2015-07-28

Systems and methods for conducting electron or scanning probe microscopy are provided herein. In a general embodiment, the systems and methods for conducting electron or scanning probe microscopy with an undersampled data set include: driving an electron beam or probe to scan across a sample and visit a subset of pixel locations of the sample that are randomly or pseudo-randomly designated; determining actual pixel locations on the sample that are visited by the electron beam or probe; and processing data collected by detectors from the visits of the electron beam or probe at the actual pixel locations and recovering a reconstructed image of the sample.

The Sensitivity of Genetic Connectivity Measures to Unsampled and Under-Sampled Sites

PubMed Central

Koen, Erin L.; Bowman, Jeff; Garroway, Colin J.; Wilson, Paul J.

2013-01-01

Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance (cGD), might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets. We found that pairwise estimates of cGD were sensitive to both unsampled and under-sampled sites, and FST, Dest, and deucl were more sensitive to under-sampled than unsampled sites. We found that the rank order of cGD was also sensitive to unsampled and under-sampled sites, but not enough to affect the outcome of Mantel tests for isolation by distance. We simulated isolation by resistance and found that although cGD estimates were sensitive to unsampled sites, by increasing the number of sites sampled the accuracy of conclusions drawn from landscape genetic analyses increased, a feature that is not possible with pairwise estimates of genetic differentiation such as FST, Dest, and deucl. We suggest that users of cGD assess the sensitivity of this measure by subsampling within their own network and use caution when making extrapolations beyond their sampled network. PMID:23409155

Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

PubMed

Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

2016-04-01

To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shieh, C; Kipritidis, J; OBrien, R

2014-06-15

Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimizationmore » step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.« less

Non-flickering 100 m RGB visible light communication transmission based on a CMOS image sensor.

PubMed

Chow, Chi-Wai; Shiu, Ruei-Jie; Liu, Yen-Chun; Liu, Yang; Yeh, Chien-Hung

2018-03-19

We demonstrate a non-flickering 100 m long-distance RGB visible light communication (VLC) transmission based on a complementary-metal-oxide-semiconductor (CMOS) camera. Experimental bit-error rate (BER) measurements under different camera ISO values and different transmission distances are evaluated. Here, we also experimentally reveal that the rolling shutter effect- (RSE) based VLC system cannot work at long distance transmission, and the under-sampled modulation- (USM) based VLC system is a good choice.

Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction.

PubMed

Wang, Bigong; Li, Liang

2015-01-01

As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.

Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

PubMed Central

Wang, Bigong; Li, Liang

2015-01-01

As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training. PMID:26089956

SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space

PubMed Central

Lustig, Michael; Pauly, John M.

2010-01-01

A new approach to autocalibrating, coil-by-coil parallel imaging reconstruction is presented. It is a generalized reconstruction framework based on self consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k-space sampling patterns. The formulation can flexibly incorporate additional image priors such as off-resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k-space domain are presented. These are based on a projection over convex sets (POCS) and a conjugate gradient (CG) algorithms. Phantom and in-vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off-resonance correction and nonlinear ℓ1-wavelet regularization are also demonstrated. PMID:20665790

Fast Fourier single-pixel imaging via binary illumination.

PubMed

Zhang, Zibang; Wang, Xueying; Zheng, Guoan; Zhong, Jingang

2017-09-20

Fourier single-pixel imaging (FSI) employs Fourier basis patterns for encoding spatial information and is capable of reconstructing high-quality two-dimensional and three-dimensional images. Fourier-domain sparsity in natural scenes allows FSI to recover sharp images from undersampled data. The original FSI demonstration, however, requires grayscale Fourier basis patterns for illumination. This requirement imposes a limitation on the imaging speed as digital micro-mirror devices (DMDs) generate grayscale patterns at a low refreshing rate. In this paper, we report a new strategy to increase the speed of FSI by two orders of magnitude. In this strategy, we binarize the Fourier basis patterns based on upsampling and error diffusion dithering. We demonstrate a 20,000 Hz projection rate using a DMD and capture 256-by-256-pixel dynamic scenes at a speed of 10 frames per second. The reported technique substantially accelerates image acquisition speed of FSI. It may find broad imaging applications at wavebands that are not accessible using conventional two-dimensional image sensors.

New Imaging Strategies Using a Motion-Resistant Liver Sequence in Uncooperative Patients

PubMed Central

Kim, Bong Soo; Lee, Kyung Ryeol; Goh, Myeng Ju

2014-01-01

MR imaging has unique benefits for evaluating the liver because of its high-resolution capability and ability to permit detailed assessment of anatomic lesions. In uncooperative patients, motion artifacts can impair the image quality and lead to the loss of diagnostic information. In this setting, the recent advances in motion-resistant liver MR techniques, including faster imaging protocols (e.g., dual-echo magnetization-prepared rapid-acquisition gradient echo (MP-RAGE), view-sharing technique), the data under-sampling (e.g., gradient recalled echo (GRE) with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA), single-shot echo-train spin-echo (SS-ETSE)), and motion-artifact minimization method (e.g., radial GRE with/without k-space-weighted image contrast (KWIC)), can provide consistent, artifact-free images with adequate image quality and can lead to promising diagnostic performance. Understanding of the different motion-resistant options allows radiologists to adopt the most appropriate technique for their clinical practice and thereby significantly improve patient care. PMID:25243115

Blind compressed sensing image reconstruction based on alternating direction method

NASA Astrophysics Data System (ADS)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Direct and accelerated parameter mapping using the unscented Kalman filter.

PubMed

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

Undersampled digital holographic interferometry

NASA Astrophysics Data System (ADS)

Halaq, H.; Demoli, N.; Sović, I.; Šariri, K.; Torzynski, M.; Vukičević, D.

2008-04-01

In digital holography, primary holographic fringes are recorded using a matricial CCD sensor. Because of the low spatial resolution of currently available CCD arrays, the angle between the reference and object beams must be limited to a few degrees. Namely, due to the digitization involved, the Shannon's criterion imposes that the Nyquist sampling frequency be at least twice the highest signal frequency. This means that, in the case of the recording of an interference fringe pattern by a CCD sensor, the inter-fringe distance must be larger than twice the pixel period. This in turn limits the angle between the object and the reference beams. If this angle, in a practical holographic interferometry measuring setup, cannot be limited to the required value, aliasing will occur in the reconstructed image. In this work, we demonstrate that the low spatial frequency metrology data could nevertheless be efficiently extracted by careful choice of twofold, and even threefold, undersampling of the object field. By combining the time-averaged recording with subtraction digital holography method, we present results for a loudspeaker membrane interferometric study obtained under strong aliasing conditions. High-contrast fringes, as a consequence of the vibration modes of the membrane, are obtained.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Wide-field FTIR microscopy using mid-IR pulse shaping

PubMed Central

Serrano, Arnaldo L.; Ghosh, Ayanjeet; Ostrander, Joshua S.; Zanni, Martin T.

2015-01-01

We have developed a new table-top technique for collecting wide-field Fourier transform infrared (FTIR) microscopic images by combining a femtosecond pulse shaper with a mid-IR focal plane array. The pulse shaper scans the delay between a pulse pair extremely rapidly for high signal-to-noise, while also enabling phase control of the individual pulses to under-sample the interferograms and subtract background. Infrared absorption images were collected for a mixture of W(CO)6 or Mn2(CO)10 absorbed polystyrene beads, demonstrating that this technique can spatially resolve chemically distinct species. The images are sub-diffraction limited, as measured with a USAF test target patterned on CaF2 and verified with scalar wave simulations. We also find that refractive, rather than reflective, objectives are preferable for imaging with coherent radiation. We discuss this method with respect to conventional FTIR microscopes. PMID:26191843

Use of ordinary kriging to interpolate observations of fire radiative heat flux sampled with airborne imagery

NASA Astrophysics Data System (ADS)

Klauberg Silva, C.; Hudak, A. T.; Bright, B. C.; Dickinson, M. B.; Kremens, R.; Paugam, R.; Mell, W.

2016-12-01

Biomass burning has impacts on air pollution at local to regional scales and contributes to greenhouse gases and affects carbon balance at the global scale. Therefore, is important to accurately estimate and manage carbon pools (fuels) and fluxes (gases and particulate emissions having public health implications) associated with wildland fires. Fire radiative energy (FRE) has been shown to be linearly correlated with biomass burned in small-scale experimental fires but not at the landscape level. Characterization of FRE density (FRED) flux in J m-2 from a landscape-level fire presents an undersampling problem. Specifically, airborne acquisitions of long-wave infrared radiation (LWIR) from a nadir-viewing LWIR camera mounted on board fixed-wing aircraft provide only samples of FRED from a landscape-level fire, because of the time required to turn the plane around between passes, and a fire extent that is broader than the camera field of view. This undersampling in time and space produces apparent firelines in an image of observed FRED, capturing the fire spread only whenever and wherever the scene happened to be imaged. We applied ordinary kriging to images of observed FRED from five prescribed burns collected in forested and non-forested management units burned at Eglin Air Force Base in Florida USA in 2011 and 2012. The three objectives were to: 1. more realistically map FRED, 2. more accurately estimate total FRED as predicted from fuel consumption measurements, and 3. compare the sampled and kriged FRED maps to modeled estimates of fire rate of spread (ROS). Observed FRED was integrated from LWIR images calibrated to units of fire radiative flux density (FRFD) in W m-2. Iterating the kriging analysis 2-10 times (depending on the burn unit) led to more accurate FRED estimates, both in map form and in terms of total FRED, as corroborated by independent estimates of fuel consumption and ROS.

Autonomous observing platform CO2 data shed new light on the Southern Ocean carbon cycle

NASA Astrophysics Data System (ADS)

Olsen, Are

2017-06-01

While the number of surface ocean CO2 partial pressure (pCO2) measurements has soared the recent decades, the Southern Ocean remains undersampled. Williams et al. (2017, https://doi.org/10.1002/2016GB005541) now present pCO2 estimates based on data from pH-sensor equipped Bio-Argo floats, which have been measuring in the Southern Ocean since 2014. The authors demonstrate the utility of these data for understanding the carbon cycle in this region, which has a large influence on the distribution of CO2 between the ocean and atmosphere. Biogeochemical sensors deployed on autonomous platforms hold the potential to shape our view of the ocean carbon cycle in the coming decades.

Stellar photometry with the Wide Field/Planetary Camera of the Hubble Space Telescope

NASA Astrophysics Data System (ADS)

Holtzman, Jon A.

1990-07-01

Simulations of Wide Field/Planetary Camera (WF/PC) images are analyzed in order to discover the most effective techniques for stellar photometry and to evaluate the accuracy and limitations of these techniques. The capabilities and operation of the WF/PC and the simulations employed in the study are described. The basic techniques of stellar photometry and methods to improve these techniques for the WF/PC are discussed. The correct parameters for star detection, aperture photometry, and point-spread function (PSF) fitting with the DAOPHOT software of Stetson (1987) are determined. Consideration is given to undersampling of the stellar images by the detector; variations in the PSF; and the crowding of the stellar images. It is noted that, with some changes DAOPHOT, is able to generate photometry almost to the level of photon statistics.

Sparse imaging for fast electron microscopy

NASA Astrophysics Data System (ADS)

Anderson, Hyrum S.; Ilic-Helms, Jovana; Rohrer, Brandon; Wheeler, Jason; Larson, Kurt

2013-02-01

Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, large collections can lead to weeks of around-the-clock imaging time. To increase data collection speed, we propose and demonstrate on an operational SEM a fast method to sparsely sample and reconstruct smooth images. To accurately localize the electron probe position at fast scan rates, we model the dynamics of the scan coils, and use the model to rapidly and accurately visit a randomly selected subset of pixel locations. Images are reconstructed from the undersampled data by compressed sensing inversion using image smoothness as a prior. We report image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes. Our approach is equally applicable to other domains of nanometer microscopy in which the time to position a probe is a limiting factor (e.g., atomic force microscopy), or in which excessive electron doses might otherwise alter the sample being observed (e.g., scanning transmission electron microscopy).

Multichannel Compressive Sensing MRI Using Noiselet Encoding

PubMed Central

Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin

2015-01-01

The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding. PMID:25965548

Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

PubMed

Wang, Haoyu; Miao, Yanwei; Zhou, Kun; Yu, Yanming; Bao, Shanglian; He, Qiang; Dai, Yongming; Xuan, Stephanie Y; Tarabishy, Bisher; Ye, Yongquan; Hu, Jiani

2010-09-01

To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, an in vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution. For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 +/- 0.023 and 0.953 +/- 0.031, respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution. The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

What do you gain from deconvolution? - Observing faint galaxies with the Hubble Space Telescope Wide Field Camera

NASA Technical Reports Server (NTRS)

Schade, David J.; Elson, Rebecca A. W.

1993-01-01

We describe experiments with deconvolutions of simulations of deep HST Wide Field Camera images containing faint, compact galaxies to determine under what circumstances there is a quantitative advantage to image deconvolution, and explore whether it is (1) helpful for distinguishing between stars and compact galaxies, or between spiral and elliptical galaxies, and whether it (2) improves the accuracy with which characteristic radii and integrated magnitudes may be determined. The Maximum Entropy and Richardson-Lucy deconvolution algorithms give the same results. For medium and low S/N images, deconvolution does not significantly improve our ability to distinguish between faint stars and compact galaxies, nor between spiral and elliptical galaxies. Measurements from both raw and deconvolved images are biased and must be corrected; it is easier to quantify and remove the biases for cases that have not been deconvolved. We find no benefit from deconvolution for measuring luminosity profiles, but these results are limited to low S/N images of very compact (often undersampled) galaxies.

Exploiting the wavelet structure in compressed sensing MRI.

PubMed

Chen, Chen; Huang, Junzhou

2014-12-01

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

Monitoring carbonate dissolution using spatially resolved under-sampled NMR propagators and MRI

NASA Astrophysics Data System (ADS)

Sederman, A. J.; Colbourne, A.; Mantle, M. D.; Gladden, L. F.; Oliveira, R.; Bijeljic, B.; Blunt, M. J.

2017-12-01

The dissolution of a porous rock matrix by an acidic flow causes a change in the pore structure and consequently the pattern of fluid flow and rock permeability. This process is relevant to many areas of practical relevance such as enhanced oil recovery, water contaminant migration and sequestration of supercritical CO2. The most important governing factors for the type of change in the pore space are related by the Péclet (Pe) and Damköhler (Da) dimensionless numbers; these compare the transport properties of the fluid in the porous medium with the reactive properties of the solid matrix and the incident fluid respectively. Variation in Pe and Da can cause very different evolution regimes of the pore space and flow can occur, ranging from a uniform dissolution through different "wormholing" regimes (shown on the left hand side of figure 1) to face dissolution. NMR has a unique capability of measuring both the flow and structural changes during such dissolution whilst the characteristics of flow in the highly heterogeneous matrix that is formed can be predicted by the CTRW modelling approach. Here, NMR measurements of displacement probability distributions, or propagators, have been used to monitor the evolution of fluid flow during a reactive dissolution rock core floods. Developments in the NMR method by undersampling the acquisition data enable spatially resolved measurements of the propagators to be done at sufficient displacement resolution and in a timescale that is short enough to capture the changes in structure and flow. The highly under-sampled (4%) data, which typically reduces the acquisition time from 2 hours to 6 minutes, has been shown to produce equivalent propagator results to the fully sampled experiment. Combining these propagator measurements with quantitative and fast imaging techniques a full time-resolved picture of the dissolution reaction is built up. Experiments have been done for both Ketton and Estaillades carbonate rock cores, which exhibit very different dissolution behaviours, and for which experiments and model comparisons will be shown.

Accelerating parallel transmit array B1 mapping in high field MRI with slice undersampling and interpolation by kriging.

PubMed

Ferrand, Guillaume; Luong, Michel; Cloos, Martijn A; Amadon, Alexis; Wackernagel, Hans

2014-08-01

Transmit arrays have been developed to mitigate the RF field inhomogeneity commonly observed in high field magnetic resonance imaging (MRI), typically above 3T. To this end, the knowledge of the RF complex-valued B1 transmit-sensitivities of each independent radiating element has become essential. This paper details a method to speed up a currently available B1-calibration method. The principle relies on slice undersampling, slice and channel interleaving and kriging, an interpolation method developed in geostatistics and applicable in many domains. It has been demonstrated that, under certain conditions, kriging gives the best estimator of a field in a region of interest. The resulting accelerated sequence allows mapping a complete set of eight volumetric field maps of the human head in about 1 min. For validation, the accuracy of kriging is first evaluated against a well-known interpolation technique based on Fourier transform as well as to a B1-maps interpolation method presented in the literature. This analysis is carried out on simulated and decimated experimental B1 maps. Finally, the accelerated sequence is compared to the standard sequence on a phantom and a volunteer. The new sequence provides B1 maps three times faster with a loss of accuracy limited potentially to about 5%.

Spiral trajectory design: a flexible numerical algorithm and base analytical equations.

PubMed

Pipe, James G; Zwart, Nicholas R

2014-01-01

Spiral-based trajectories for magnetic resonance imaging can be advantageous, but are often cumbersome to design or create. This work presents a flexible numerical algorithm for designing trajectories based on explicit definition of radial undersampling, and also gives several analytical expressions for charactering the base (critically sampled) class of these trajectories. Expressions for the gradient waveform, based on slew and amplitude limits, are developed such that a desired pitch in the spiral k-space trajectory is followed. The source code for this algorithm, written in C, is publicly available. Analytical expressions approximating the spiral trajectory (ignoring the radial component) are given to characterize measurement time, gradient heating, maximum gradient amplitude, and off-resonance phase for slew-limited and gradient amplitude-limited cases. Several numerically calculated trajectories are illustrated, and base Archimedean spirals are compared with analytically obtained results. Several different waveforms illustrate that the desired slew and amplitude limits are reached, as are the desired undersampling patterns, using the numerical method. For base Archimedean spirals, the results of the numerical and analytical approaches are in good agreement. A versatile numerical algorithm was developed, and was written in publicly available code. Approximate analytical formulas are given that help characterize spiral trajectories. Copyright © 2013 Wiley Periodicals, Inc.

Correlation between k-space sampling pattern and MTF in compressed sensing MRSI.

PubMed

Heikal, A A; Wachowicz, K; Fallone, B G

2016-10-01

To investigate the relationship between the k-space sampling patterns used for compressed sensing MR spectroscopic imaging (CS-MRSI) and the modulation transfer function (MTF) of the metabolite maps. This relationship may allow the desired frequency content of the metabolite maps to be quantitatively tailored when designing an undersampling pattern. Simulations of a phantom were used to calculate the MTF of Nyquist sampled (NS) 32 × 32 MRSI, and four-times undersampled CS-MRSI reconstructions. The dependence of the CS-MTF on the k-space sampling pattern was evaluated for three sets of k-space sampling patterns generated using different probability distribution functions (PDFs). CS-MTFs were also evaluated for three more sets of patterns generated using a modified algorithm where the sampling ratios are constrained to adhere to PDFs. Strong visual correlation as well as high R 2 was found between the MTF of CS-MRSI and the product of the frequency-dependant sampling ratio and the NS 32 × 32 MTF. Also, PDF-constrained sampling patterns led to higher reproducibility of the CS-MTF, and stronger correlations to the above-mentioned product. The relationship established in this work provides the user with a theoretical solution for the MTF of CS MRSI that is both predictable and customizable to the user's needs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

Compressed sensing for ultrasound computed tomography.

PubMed

van Sloun, Ruud; Pandharipande, Ashish; Mischi, Massimo; Demi, Libertario

2015-06-01

Ultrasound computed tomography (UCT) allows the reconstruction of quantitative tissue characteristics, such as speed of sound, mass density, and attenuation. Lowering its acquisition time would be beneficial; however, this is fundamentally limited by the physical time of flight and the number of transmission events. In this letter, we propose a compressed sensing solution for UCT. The adopted measurement scheme is based on compressed acquisitions, with concurrent randomised transmissions in a circular array configuration. Reconstruction of the image is then obtained by combining the born iterative method and total variation minimization, thereby exploiting variation sparsity in the image domain. Evaluation using simulated UCT scattering measurements shows that the proposed transmission scheme performs better than uniform undersampling, and is able to reduce acquisition time by almost one order of magnitude, while maintaining high spatial resolution.

Model-based quantification of image quality

NASA Technical Reports Server (NTRS)

Hazra, Rajeeb; Miller, Keith W.; Park, Stephen K.

1989-01-01

In 1982, Park and Schowengerdt published an end-to-end analysis of a digital imaging system quantifying three principal degradation components: (1) image blur - blurring caused by the acquisition system, (2) aliasing - caused by insufficient sampling, and (3) reconstruction blur - blurring caused by the imperfect interpolative reconstruction. This analysis, which measures degradation as the square of the radiometric error, includes the sample-scene phase as an explicit random parameter and characterizes the image degradation caused by imperfect acquisition and reconstruction together with the effects of undersampling and random sample-scene phases. In a recent paper Mitchell and Netravelli displayed the visual effects of the above mentioned degradations and presented subjective analysis about their relative importance in determining image quality. The primary aim of the research is to use the analysis of Park and Schowengerdt to correlate their mathematical criteria for measuring image degradations with subjective visual criteria. Insight gained from this research can be exploited in the end-to-end design of optical systems, so that system parameters (transfer functions of the acquisition and display systems) can be designed relative to each other, to obtain the best possible results using quantitative measurements.

A single-pixel X-ray imager concept and its application to secure radiographic inspections

DOE PAGES

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; ...

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

Adaptive Wiener filter super-resolution of color filter array images.

PubMed

Karch, Barry K; Hardie, Russell C

2013-08-12

Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.

Application of Double Spin-Echo Spiral Chemical Shift Imaging to Rapid Metabolic Mapping of Hyperpolarized [1-13C]-Pyruvate

PubMed Central

Josan, Sonal; Yen, Yi-Fen; Hurd, Ralph; Pfefferbaum, Adolf; Spielman, Daniel; Mayer, Dirk

2011-01-01

Undersampled spiral CSI (spCSI) using a free induction decay (FID) acquisition allows sub-second metabolic imaging of hyperpolarized 13C. Phase correction of the FID acquisition can be difficult, especially with contributions from aliased out-of-phase peaks. This work extends the spCSI sequence by incorporating double spin-echo radiofrequency (RF) pulses to eliminate the need for phase correction and obtain high quality spectra in magnitude mode. The sequence also provides an added benefit of attenuating signal from flowing spins, which can otherwise contaminate signal in the organ of interest. The refocusing pulses can potentially lead to a loss of hyperpolarized magnetization in dynamic imaging due to flow of spins through the fringe field of the RF coil, where the refocusing pulses fail to provide complete refocusing. Care must be taken for dynamic imaging to ensure that the spins remain within the B1-homogeneous sensitive volume of the RF coil. PMID:21316280

A single-pixel X-ray imager concept and its application to secure radiographic inspections

NASA Astrophysics Data System (ADS)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; White, Timothy A.; Pitts, William Karl; Jarman, Kenneth D.; Seifert, Allen

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixel optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. In particular, it is found that an inspection with low noise ( < 1 %) and high undersampling ( > 256 ×) exhibits high robustness and security.

Test of 3D CT reconstructions by EM + TV algorithm from undersampled data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evseev, Ivan; Ahmann, Francielle; Silva, Hamilton P. da

2013-05-06

Computerized tomography (CT) plays an important role in medical imaging for diagnosis and therapy. However, CT imaging is connected with ionization radiation exposure of patients. Therefore, the dose reduction is an essential issue in CT. In 2011, the Expectation Maximization and Total Variation Based Model for CT Reconstruction (EM+TV) was proposed. This method can reconstruct a better image using less CT projections in comparison with the usual filtered back projection (FBP) technique. Thus, it could significantly reduce the overall dose of radiation in CT. This work reports the results of an independent numerical simulation for cone beam CT geometry withmore » alternative virtual phantoms. As in the original report, the 3D CT images of 128 Multiplication-Sign 128 Multiplication-Sign 128 virtual phantoms were reconstructed. It was not possible to implement phantoms with lager dimensions because of the slowness of code execution even by the CORE i7 CPU.« less

Single frequency thermal wave radar: A next-generation dynamic thermography for quantitative non-destructive imaging over wide modulation frequency ranges.

PubMed

Melnikov, Alexander; Chen, Liangjie; Ramirez Venegas, Diego; Sivagurunathan, Koneswaran; Sun, Qiming; Mandelis, Andreas; Rodriguez, Ignacio Rojas

2018-04-01

Single-Frequency Thermal Wave Radar Imaging (SF-TWRI) was introduced and used to obtain quantitative thickness images of coatings on an aluminum block and on polyetherketone, and to image blind subsurface holes in a steel block. In SF-TWR, the starting and ending frequencies of a linear frequency modulation sweep are chosen to coincide. Using the highest available camera frame rate, SF-TWRI leads to a higher number of sampled points along the modulation waveform than conventional lock-in thermography imaging because it is not limited by conventional undersampling at high frequencies due to camera frame-rate limitations. This property leads to large reduction in measurement time, better quality of images, and higher signal-noise-ratio across wide frequency ranges. For quantitative thin-coating imaging applications, a two-layer photothermal model with lumped parameters was used to reconstruct the layer thickness from multi-frequency SF-TWR images. SF-TWRI represents a next-generation thermography method with superior features for imaging important classes of thin layers, materials, and components that require high-frequency thermal-wave probing well above today's available infrared camera technology frame rates.

Single frequency thermal wave radar: A next-generation dynamic thermography for quantitative non-destructive imaging over wide modulation frequency ranges

NASA Astrophysics Data System (ADS)

Melnikov, Alexander; Chen, Liangjie; Ramirez Venegas, Diego; Sivagurunathan, Koneswaran; Sun, Qiming; Mandelis, Andreas; Rodriguez, Ignacio Rojas

2018-04-01

Single-Frequency Thermal Wave Radar Imaging (SF-TWRI) was introduced and used to obtain quantitative thickness images of coatings on an aluminum block and on polyetherketone, and to image blind subsurface holes in a steel block. In SF-TWR, the starting and ending frequencies of a linear frequency modulation sweep are chosen to coincide. Using the highest available camera frame rate, SF-TWRI leads to a higher number of sampled points along the modulation waveform than conventional lock-in thermography imaging because it is not limited by conventional undersampling at high frequencies due to camera frame-rate limitations. This property leads to large reduction in measurement time, better quality of images, and higher signal-noise-ratio across wide frequency ranges. For quantitative thin-coating imaging applications, a two-layer photothermal model with lumped parameters was used to reconstruct the layer thickness from multi-frequency SF-TWR images. SF-TWRI represents a next-generation thermography method with superior features for imaging important classes of thin layers, materials, and components that require high-frequency thermal-wave probing well above today's available infrared camera technology frame rates.

3D image restoration for confocal microscopy: toward a wavelet deconvolution for the study of complex biological structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, Jacques; Le Calvez, Sophie; Ulfendahl, Mats

2000-05-01

Image restoration algorithms provide efficient tools for recovering part of the information lost in the imaging process of a microscope. We describe recent progress in the application of deconvolution to confocal microscopy. The point spread function of a Biorad-MRC1024 confocal microscope was measured under various imaging conditions, and used to process 3D-confocal images acquired in an intact preparation of the inner ear developed at Karolinska Institutet. Using these experiments we investigate the application of denoising methods based on wavelet analysis as a natural regularization of the deconvolution process. Within the Bayesian approach to image restoration, we compare wavelet denoising with the use of a maximum entropy constraint as another natural regularization method. Numerical experiments performed with test images show a clear advantage of the wavelet denoising approach, allowing to `cool down' the image with respect to the signal, while suppressing much of the fine-scale artifacts appearing during deconvolution due to the presence of noise, incomplete knowledge of the point spread function, or undersampling problems. We further describe a natural development of this approach, which consists of performing the Bayesian inference directly in the wavelet domain.

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

k-t Acceleration in pure phase encode MRI to monitor dynamic flooding processes in rock core plugs

NASA Astrophysics Data System (ADS)

Xiao, Dan; Balcom, Bruce J.

2014-06-01

Monitoring the pore system in sedimentary rocks with MRI when fluids are introduced is very important in the study of petroleum reservoirs and enhanced oil recovery. However, the lengthy acquisition time of each image, with pure phase encode MRI, limits the temporal resolution. Spatiotemporal correlations can be exploited to undersample the k-t space data. The stacked frames/profiles can be well approximated by an image matrix with rank deficiency, which can be recovered by nonlinear nuclear norm minimization. Sparsity of the x-t image can also be exploited for nonlinear reconstruction. In this work the results of a low rank matrix completion technique were compared with k-t sparse compressed sensing. These methods are demonstrated with one dimensional SPRITE imaging of a Bentheimer rock core plug and SESPI imaging of a Berea rock core plug, but can be easily extended to higher dimensionality and/or other pure phase encode measurements. These ideas will enable higher dimensionality pure phase encode MRI studies of dynamic flooding processes in low magnetic field systems.

Considerations on data acquisition in laser ablation-inductively coupled plasma-mass spectrometry with low-dispersion interfaces

NASA Astrophysics Data System (ADS)

Van Malderen, Stijn J. M.; van Elteren, Johannes T.; Šelih, Vid S.; Vanhaecke, Frank

2018-02-01

This work describes the aliasing effects induced by undersampling the high-frequency signal patterns generated by a laser ablation-inductively coupled plasma-mass spectrometer equipped with a low-dispersion ablation cell and sequential mass analyzer. By characterizing the width of the signal peak generated from a single shot on the sample, critical experimental parameters, such as the laser repetition rate and detector cycle timings for the individual nuclides can be matched so as to avoid these imaging artifacts (spectral skew) induced by an insufficient sampling rate. By increasing the laser repetition rate by a factor 2-3, masses at the end of the mass scan can be sampled at higher sensitivity. Furthermore, the dwell times can be redistributed over the nuclides of interest based on the signal-to-noise ratio to increase the image contrast.

Fast magnetic resonance fingerprinting for dynamic contrast-enhanced studies in mice.

PubMed

Gu, Yuning; Wang, Charlie Y; Anderson, Christian E; Liu, Yuchi; Hu, He; Johansen, Mette L; Ma, Dan; Jiang, Yun; Ramos-Estebanez, Ciro; Brady-Kalnay, Susann; Griswold, Mark A; Flask, Chris A; Yu, Xin

2018-05-09

The goal of this study was to develop a fast MR fingerprinting (MRF) method for simultaneous T 1 and T 2 mapping in DCE-MRI studies in mice. The MRF sequences based on balanced SSFP and fast imaging with steady-state precession were implemented and evaluated on a 7T preclinical scanner. The readout used a zeroth-moment-compensated variable-density spiral trajectory that fully sampled the entire k-space and the inner 10 × 10 k-space with 48 and 4 interleaves, respectively. In vitro and in vivo studies of mouse brain were performed to evaluate the accuracy of MRF measurements with both fully sampled and undersampled data. The application of MRF to dynamic T 1 and T 2 mapping in DCE-MRI studies were demonstrated in a mouse model of heterotopic glioblastoma using gadolinium-based and dysprosium-based contrast agents. The T 1 and T 2 measurements in phantom showed strong agreement between the MRF and the conventional methods. The MRF with spiral encoding allowed up to 8-fold undersampling without loss of measurement accuracy. This enabled simultaneous T 1 and T 2 mapping with 2-minute temporal resolution in DCE-MRI studies. Magnetic resonance fingerprinting provides the opportunity for dynamic quantification of contrast agent distribution in preclinical tumor models on high-field MRI scanners. © 2018 International Society for Magnetic Resonance in Medicine.

Volumetric MRI of the lungs during forced expiration.

PubMed

Berman, Benjamin P; Pandey, Abhishek; Li, Zhitao; Jeffries, Lindsie; Trouard, Theodore P; Oliva, Isabel; Cortopassi, Felipe; Martin, Diego R; Altbach, Maria I; Bilgin, Ali

2016-06-01

Lung function is typically characterized by spirometer measurements, which do not offer spatially specific information. Imaging during exhalation provides spatial information but is challenging due to large movement over a short time. The purpose of this work is to provide a solution to lung imaging during forced expiration using accelerated magnetic resonance imaging. The method uses radial golden angle stack-of-stars gradient echo acquisition and compressed sensing reconstruction. A technique for dynamic three-dimensional imaging of the lungs from highly undersampled data is developed and tested on six subjects. This method takes advantage of image sparsity, both spatially and temporally, including the use of reference frames called bookends. Sparsity, with respect to total variation, and residual from the bookends, enables reconstruction from an extremely limited amount of data. Dynamic three-dimensional images can be captured at sub-150 ms temporal resolution, using only three (or less) acquired radial lines per slice per timepoint. The images have a spatial resolution of 4.6×4.6×10 mm. Lung volume calculations based on image segmentation are compared to those from simultaneously acquired spirometer measurements. Dynamic lung imaging during forced expiration is made possible by compressed sensing accelerated dynamic three-dimensional radial magnetic resonance imaging. Magn Reson Med 75:2295-2302, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.

PubMed

Zhang, Cheng; Zhang, Tao; Zheng, Jian; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified here using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how such an inspection would be made which can maintain high robustness and security. In particular, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

Two-dimensional T2 distribution mapping in rock core plugs with optimal k-space sampling.

PubMed

Xiao, Dan; Balcom, Bruce J

2012-07-01

Spin-echo single point imaging has been employed for 1D T(2) distribution mapping, but a simple extension to 2D is challenging since the time increase is n fold, where n is the number of pixels in the second dimension. Nevertheless 2D T(2) mapping in fluid saturated rock core plugs is highly desirable because the bedding plane structure in rocks often results in different pore properties within the sample. The acquisition time can be improved by undersampling k-space. The cylindrical shape of rock core plugs yields well defined intensity distributions in k-space that may be efficiently determined by new k-space sampling patterns that are developed in this work. These patterns acquire 22.2% and 11.7% of the k-space data points. Companion density images may be employed, in a keyhole imaging sense, to improve image quality. T(2) weighted images are fit to extract T(2) distributions, pixel by pixel, employing an inverse Laplace transform. Images reconstructed with compressed sensing, with similar acceleration factors, are also presented. The results show that restricted k-space sampling, in this application, provides high quality results. Copyright © 2012 Elsevier Inc. All rights reserved.

Super-resolution for imagery from integrated microgrid polarimeters.

PubMed

Hardie, Russell C; LeMaster, Daniel A; Ratliff, Bradley M

2011-07-04

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Propagation phasor approach for holographic image reconstruction

PubMed Central

Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan

2016-01-01

To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671

Computational imaging with a balanced detector.

PubMed

Soldevila, F; Clemente, P; Tajahuerce, E; Uribe-Patarroyo, N; Andrés, P; Lancis, J

2016-06-29

Single-pixel cameras allow to obtain images in a wide range of challenging scenarios, including broad regions of the electromagnetic spectrum and through scattering media. However, there still exist several drawbacks that single-pixel architectures must address, such as acquisition speed and imaging in the presence of ambient light. In this work we introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel camera. This approach enables to acquire information even when the power of the parasite signal is higher than the signal itself. Furthermore, this novel detection scheme increases both the frame rate and the signal-to-noise ratio of the system. By means of a fast digital micromirror device together with a low numerical aperture collecting system, we are able to produce a live-feed video with a resolution of 64 × 64 pixels at 5 Hz. With advanced undersampling techniques, such as compressive sensing, we can acquire information at rates of 25 Hz. By using this strategy, we foresee real-time biological imaging with large area detectors in conditions where array sensors are unable to operate properly, such as infrared imaging and dealing with objects embedded in turbid media.

Computational imaging with a balanced detector

NASA Astrophysics Data System (ADS)

Soldevila, F.; Clemente, P.; Tajahuerce, E.; Uribe-Patarroyo, N.; Andrés, P.; Lancis, J.

2016-06-01

Single-pixel cameras allow to obtain images in a wide range of challenging scenarios, including broad regions of the electromagnetic spectrum and through scattering media. However, there still exist several drawbacks that single-pixel architectures must address, such as acquisition speed and imaging in the presence of ambient light. In this work we introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel camera. This approach enables to acquire information even when the power of the parasite signal is higher than the signal itself. Furthermore, this novel detection scheme increases both the frame rate and the signal-to-noise ratio of the system. By means of a fast digital micromirror device together with a low numerical aperture collecting system, we are able to produce a live-feed video with a resolution of 64 × 64 pixels at 5 Hz. With advanced undersampling techniques, such as compressive sensing, we can acquire information at rates of 25 Hz. By using this strategy, we foresee real-time biological imaging with large area detectors in conditions where array sensors are unable to operate properly, such as infrared imaging and dealing with objects embedded in turbid media.

Computational imaging with a balanced detector

PubMed Central

Soldevila, F.; Clemente, P.; Tajahuerce, E.; Uribe-Patarroyo, N.; Andrés, P.; Lancis, J.

2016-01-01

Single-pixel cameras allow to obtain images in a wide range of challenging scenarios, including broad regions of the electromagnetic spectrum and through scattering media. However, there still exist several drawbacks that single-pixel architectures must address, such as acquisition speed and imaging in the presence of ambient light. In this work we introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel camera. This approach enables to acquire information even when the power of the parasite signal is higher than the signal itself. Furthermore, this novel detection scheme increases both the frame rate and the signal-to-noise ratio of the system. By means of a fast digital micromirror device together with a low numerical aperture collecting system, we are able to produce a live-feed video with a resolution of 64 × 64 pixels at 5 Hz. With advanced undersampling techniques, such as compressive sensing, we can acquire information at rates of 25 Hz. By using this strategy, we foresee real-time biological imaging with large area detectors in conditions where array sensors are unable to operate properly, such as infrared imaging and dealing with objects embedded in turbid media. PMID:27353733

Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images.

PubMed

Elad, M; Feuer, A

1997-01-01

The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images. The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space- and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) motion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem. Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Evaluation of Techniques Used to Estimate Cortical Feature Maps

PubMed Central

Katta, Nalin; Chen, Thomas L.; Watkins, Paul V.; Barbour, Dennis L.

2011-01-01

Functional properties of neurons are often distributed nonrandomly within a cortical area and form topographic maps that reveal insights into neuronal organization and interconnection. Some functional maps, such as in visual cortex, are fairly straightforward to discern with a variety of techniques, while other maps, such as in auditory cortex, have resisted easy characterization. In order to determine appropriate protocols for establishing accurate functional maps in auditory cortex, artificial topographic maps were probed under various conditions, and the accuracy of estimates formed from the actual maps was quantified. Under these conditions, low-complexity maps such as sound frequency can be estimated accurately with as few as 25 total samples (e.g., electrode penetrations or imaging pixels) if neural responses are averaged together. More samples are required to achieve the highest estimation accuracy for higher complexity maps, and averaging improves map estimate accuracy even more than increasing sampling density. Undersampling without averaging can result in misleading map estimates, while undersampling with averaging can lead to the false conclusion of no map when one actually exists. Uniform sample spacing only slightly improves map estimation over nonuniform sample spacing typical of serial electrode penetrations. Tessellation plots commonly used to visualize maps estimated using nonuniform sampling are always inferior to linearly interpolated estimates, although differences are slight at higher sampling densities. Within primary auditory cortex, then, multiunit sampling with at least 100 samples would likely result in reasonable feature map estimates for all but the highest complexity maps and the highest variability that might be expected. PMID:21889537

A non-uniformly under-sampled blade tip-timing signal reconstruction method for blade vibration monitoring.

PubMed

Hu, Zheng; Lin, Jun; Chen, Zhong-Sheng; Yang, Yong-Min; Li, Xue-Jun

2015-01-22

High-speed blades are often prone to fatigue due to severe blade vibrations. In particular, synchronous vibrations can cause irreversible damages to the blade. Blade tip-timing methods (BTT) have become a promising way to monitor blade vibrations. However, synchronous vibrations are unsuitably monitored by uniform BTT sampling. Therefore, non-equally mounted probes have been used, which will result in the non-uniformity of the sampling signal. Since under-sampling is an intrinsic drawback of BTT methods, how to analyze non-uniformly under-sampled BTT signals is a big challenge. In this paper, a novel reconstruction method for non-uniformly under-sampled BTT data is presented. The method is based on the periodically non-uniform sampling theorem. Firstly, a mathematical model of a non-uniform BTT sampling process is built. It can be treated as the sum of certain uniform sample streams. For each stream, an interpolating function is required to prevent aliasing in the reconstructed signal. Secondly, simultaneous equations of all interpolating functions in each sub-band are built and corresponding solutions are ultimately derived to remove unwanted replicas of the original signal caused by the sampling, which may overlay the original signal. In the end, numerical simulations and experiments are carried out to validate the feasibility of the proposed method. The results demonstrate the accuracy of the reconstructed signal depends on the sampling frequency, the blade vibration frequency, the blade vibration bandwidth, the probe static offset and the number of samples. In practice, both types of blade vibration signals can be particularly reconstructed by non-uniform BTT data acquired from only two probes.

A Non-Uniformly Under-Sampled Blade Tip-Timing Signal Reconstruction Method for Blade Vibration Monitoring

PubMed Central

Hu, Zheng; Lin, Jun; Chen, Zhong-Sheng; Yang, Yong-Min; Li, Xue-Jun

2015-01-01

High-speed blades are often prone to fatigue due to severe blade vibrations. In particular, synchronous vibrations can cause irreversible damages to the blade. Blade tip-timing methods (BTT) have become a promising way to monitor blade vibrations. However, synchronous vibrations are unsuitably monitored by uniform BTT sampling. Therefore, non-equally mounted probes have been used, which will result in the non-uniformity of the sampling signal. Since under-sampling is an intrinsic drawback of BTT methods, how to analyze non-uniformly under-sampled BTT signals is a big challenge. In this paper, a novel reconstruction method for non-uniformly under-sampled BTT data is presented. The method is based on the periodically non-uniform sampling theorem. Firstly, a mathematical model of a non-uniform BTT sampling process is built. It can be treated as the sum of certain uniform sample streams. For each stream, an interpolating function is required to prevent aliasing in the reconstructed signal. Secondly, simultaneous equations of all interpolating functions in each sub-band are built and corresponding solutions are ultimately derived to remove unwanted replicas of the original signal caused by the sampling, which may overlay the original signal. In the end, numerical simulations and experiments are carried out to validate the feasibility of the proposed method. The results demonstrate the accuracy of the reconstructed signal depends on the sampling frequency, the blade vibration frequency, the blade vibration bandwidth, the probe static offset and the number of samples. In practice, both types of blade vibration signals can be particularly reconstructed by non-uniform BTT data acquired from only two probes. PMID:25621612

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, Darin P.; Badea, Cristian T., E-mail: cristian.badea@duke.edu; Lee, Chang-Lung

Purpose: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D + dual energy + time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. Methods: The authors approach the 5D reconstruction problem withinmore » the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. Results: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. Conclusions: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.« less

Spectrotemporal CT data acquisition and reconstruction at low dose

PubMed Central

Clark, Darin P.; Lee, Chang-Lung; Kirsch, David G.; Badea, Cristian T.

2015-01-01

Purpose: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D + dual energy + time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. Methods: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. Results: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. Conclusions: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time. PMID:26520724

A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

NASA Astrophysics Data System (ADS)

Melli, S. Ali; Wahid, Khan A.; Babyn, Paul; Cooper, David M. L.; Gopi, Varun P.

2016-12-01

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

Interagency Pacific marten (Martes caurina) distribution study on the Olympic Peninsula, Washington

USGS Publications Warehouse

Moriarty, Katie; Howell, Betsy; Morozumi, Connor; Happe, Patti; Jenkins, Kurt J.; Aubry, Keith B.

2016-01-01

The objective of this study was to determine if the Pacific marten (Martes caurina) still occurs on the Olympic Peninsula in Washington State. We reviewed recent records of marten observations on the Olympic Peninsula since 1998, and conducted new surveys in undersampled regions of the Olympic Peninsula during summer, 2016. We reviewed evidence of fisher presence from 6 previously reported studies of carnivore distribution and presence on the Olympic Peninsula and conducted new surveys in previously undersampled areas of the Peninsula. We documented five highly reliable records of marten observations on the Pensula since 1988. Further, we established 197 camera stations in search of martens, amassing a total of 17,897 camera-nights of survey efforts in previously undersampled regions. We documented presence of one additional marten during summer 2016. This marten, however, was close to a marten detected in 2015, so it was not clear if it represented a different marten. We concluded that five to six martens have been verified present on the Olympic Peninsula since 1988. Pacific martens appear to be very limited in distribution and at critically low numbers throughout much of their former range on the Olympic Peninsula.

Task-based optimization of image reconstruction in breast CT

NASA Astrophysics Data System (ADS)

Sanchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan

2014-03-01

We demonstrate a task-based assessment of image quality in dedicated breast CT in order to optimize the number of projection views acquired. The methodology we employ is based on the Hotelling Observer (HO) and its associated metrics. We consider two tasks: the Rayleigh task of discerning between two resolvable objects and a single larger object, and the signal detection task of classifying an image as belonging to either a signalpresent or signal-absent hypothesis. HO SNR values are computed for 50, 100, 200, 500, and 1000 projection view images, with the total imaging radiation dose held constant. We use the conventional fan-beam FBP algorithm and investigate the effect of varying the width of a Hanning window used in the reconstruction, since this affects both the noise properties of the image and the under-sampling artifacts which can arise in the case of sparse-view acquisitions. Our results demonstrate that fewer projection views should be used in order to increase HO performance, which in this case constitutes an upper-bound on human observer performance. However, the impact on HO SNR of using fewer projection views, each with a higher dose, is not as significant as the impact of employing regularization in the FBP reconstruction through a Hanning filter.

On-line 3D motion estimation using low resolution MRI

NASA Astrophysics Data System (ADS)

Glitzner, M.; de Senneville, B. Denis; Lagendijk, J. J. W.; Raaymakers, B. W.; Crijns, S. P. M.

2015-08-01

Image processing such as deformable image registration finds its way into radiotherapy as a means to track non-rigid anatomy. With the advent of magnetic resonance imaging (MRI) guided radiotherapy, intrafraction anatomy snapshots become technically feasible. MRI provides the needed tissue signal for high-fidelity image registration. However, acquisitions, especially in 3D, take a considerable amount of time. Pushing towards real-time adaptive radiotherapy, MRI needs to be accelerated without degrading the quality of information. In this paper, we investigate the impact of image resolution on the quality of motion estimations. Potentially, spatially undersampled images yield comparable motion estimations. At the same time, their acquisition times would reduce greatly due to the sparser sampling. In order to substantiate this hypothesis, exemplary 4D datasets of the abdomen were downsampled gradually. Subsequently, spatiotemporal deformations are extracted consistently using the same motion estimation for each downsampled dataset. Errors between the original and the respectively downsampled version of the dataset are then evaluated. Compared to ground-truth, results show high similarity of deformations estimated from downsampled image data. Using a dataset with {{≤ft(2.5 \\text{mm}\\right)}3} voxel size, deformation fields could be recovered well up to a downsampling factor of 2, i.e. {{≤ft(5 \\text{mm}\\right)}3} . In a therapy guidance scenario MRI, imaging speed could accordingly increase approximately fourfold, with acceptable loss of estimated motion quality.

Fast l₁-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime.

PubMed

Murphy, Mark; Alley, Marcus; Demmel, James; Keutzer, Kurt; Vasanawala, Shreyas; Lustig, Michael

2012-06-01

We present l₁-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative self-consistent parallel imaging (SPIRiT). Like many iterative magnetic resonance imaging reconstructions, l₁-SPIRiT's image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing l₁-SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of l₁-SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT spoiled gradient echo (SPGR) sequence with up to 8× acceleration via Poisson-disc undersampling in the two phase-encoded directions.

Prototype of a laser guide star wavefront sensor for the Extremely Large Telescope

NASA Astrophysics Data System (ADS)

Patti, M.; Lombini, M.; Schreiber, L.; Bregoli, G.; Arcidiacono, C.; Cosentino, G.; Diolaiti, E.; Foppiani, I.

2018-06-01

The new class of large telescopes, like the future Extremely Large Telescope (ELT), are designed to work with a laser guide star (LGS) tuned to a resonance of atmospheric sodium atoms. This wavefront sensing technique presents complex issues when applied to big telescopes for many reasons, mainly linked to the finite distance of the LGS, the launching angle, tip-tilt indetermination and focus anisoplanatism. The implementation of a laboratory prototype for the LGS wavefront sensor (WFS) at the beginning of the phase study of MAORY (Multi-conjugate Adaptive Optics Relay) for ELT first light has been indispensable in investigating specific mitigation strategies for the LGS WFS issues. This paper presents the test results of the LGS WFS prototype under different working conditions. The accuracy within which the LGS images are generated on the Shack-Hartmann WFS has been cross-checked with the MAORY simulation code. The experiments show the effect of noise on centroiding precision, the impact of LGS image truncation on wavefront sensing accuracy as well as the temporal evolution of the sodium density profile and LGS image under-sampling.

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning

PubMed Central

Zhang, Cheng; Zhang, Tao; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time. PMID:26550024

Low photon count based digital holography for quadratic phase cryptography.

PubMed

Muniraj, Inbarasan; Guo, Changliang; Malallah, Ra'ed; Ryle, James P; Healy, John J; Lee, Byung-Geun; Sheridan, John T

2017-07-15

Recently, the vulnerability of the linear canonical transform-based double random phase encryption system to attack has been demonstrated. To alleviate this, we present for the first time, to the best of our knowledge, a method for securing a two-dimensional scene using a quadratic phase encoding system operating in the photon-counted imaging (PCI) regime. Position-phase-shifting digital holography is applied to record the photon-limited encrypted complex samples. The reconstruction of the complex wavefront involves four sparse (undersampled) dataset intensity measurements (interferograms) at two different positions. Computer simulations validate that the photon-limited sparse-encrypted data has adequate information to authenticate the original data set. Finally, security analysis, employing iterative phase retrieval attacks, has been performed.

Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes

NASA Astrophysics Data System (ADS)

Ng, Theam Foo; Pham, Tuan D.; Zhou, Xiaobo

2010-01-01

With the fast development of multi-dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering-based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype-image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ-HMM) and a well-known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG-HMM) will be carried out. The experimental results show that the performances of both FDVQ-HMM and LBG-HMM are almost similar. Finally, we have justified the competitiveness of FDVQ-HMM in classification of cellular phenotype image database by using hypotheses t-test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome-wide screening image data.

Multilattice sampling strategies for region of interest dynamic MRI.

PubMed

Rilling, Gabriel; Tao, Yuehui; Marshall, Ian; Davies, Mike E

2013-08-01

A multilattice sampling approach is proposed for dynamic MRI with Cartesian trajectories. It relies on the use of sampling patterns composed of several different lattices and exploits an image model where only some parts of the image are dynamic, whereas the rest is assumed static. Given the parameters of such an image model, the methodology followed for the design of a multilattice sampling pattern adapted to the model is described. The multi-lattice approach is compared to single-lattice sampling, as used by traditional acceleration methods such as UNFOLD (UNaliasing by Fourier-Encoding the Overlaps using the temporal Dimension) or k-t BLAST, and random sampling used by modern compressed sensing-based methods. On the considered image model, it allows more flexibility and higher accelerations than lattice sampling and better performance than random sampling. The method is illustrated on a phase-contrast carotid blood velocity mapping MR experiment. Combining the multilattice approach with the KEYHOLE technique allows up to 12× acceleration factors. Simulation and in vivo undersampling results validate the method. Compared to lattice and random sampling, multilattice sampling provides significant gains at high acceleration factors. © 2012 Wiley Periodicals, Inc.

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

Fast ℓ1-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime

PubMed Central

Murphy, Mark; Alley, Marcus; Demmel, James; Keutzer, Kurt; Vasanawala, Shreyas; Lustig, Michael

2012-01-01

We present ℓ1-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the Wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative Self-Consistent Parallel Imaging (SPIRiT). Like many iterative MRI reconstructions, ℓ1-SPIRiT’s image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing ℓ1-SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of ℓ1-SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT Spoiled Gradient Echo (SPGR) sequence with up to 8× acceleration via poisson-disc undersampling in the two phase-encoded directions. PMID:22345529

Interleaved multishot imaging by spatiotemporal encoding: A fast, self-referenced method for high-definition diffusion and functional MRI.

PubMed

Schmidt, Rita; Seginer, Amir; Frydman, Lucio

2016-05-01

Single-shot imaging by spatiotemporal encoding (SPEN) can provide higher immunity to artifacts than its echo planar imaging-based counterparts. Further improvements in resolution and signal-to-noise ratio could be made by rescinding the sequence's single-scan nature. To explore this option, an interleaved SPEN version was developed that was capable of delivering optimized images due to its use of a referenceless correction algorithm. A characteristic element of SPEN encoding is the absence of aliasing when its signals are undersampled along the low-bandwidth dimension. This feature was exploited in this study to segment a SPEN experiment into a number of interleaved shots whose inaccuracies were automatically compared and corrected as part of a navigator-free image reconstruction analysis. This could account for normal phase noises, as well as for object motions during the signal collection. The ensuing interleaved SPEN method was applied to phantoms and human volunteers and delivered high-quality images even in inhomogeneous or mobile environments. Submillimeter functional MRI activation maps confined to gray matter regions as well as submillimeter diffusion coefficient maps of human brains were obtained. We have developed an interleaved SPEN approach for the acquisition of high-definition images that promises a wider range of functional and diffusion MRI applications even in challenging environments. © 2015 Wiley Periodicals, Inc.

Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories

NASA Astrophysics Data System (ADS)

Tolouee, Azar; Alirezaie, Javad; Babyn, Paul

2015-11-01

In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories. We extend k-t sparse algorithm to spiral trajectories to achieve high spatio temporal resolutions in cardiac cine imaging. We have exploited spatiotemporal sparsity of cardiac cine MRI by applying a 2D + time wavelet-Fourier transform. For efficient coverage of k-space, we have used a modified version of multi shot (interleaved) spirals trajectories. In order to reduce incoherent aliasing artifact, we use different random undersampling pattern for each temporal frame. Finally, we have used nonuniform fast Fourier transform (NUFFT) algorithm to reconstruct the image from the non-uniformly acquired samples. The proposed approach was tested in simulated and cardiac cine MRI data. Results show that higher acceleration factors with improved image quality can be obtained with the proposed approach in comparison to the existing state-of-the-art method. The flexibility of the introduced method should allow it to be used not only for the challenging case of cardiac imaging, but also for other patient motion where the patient moves or breathes during acquisition.

Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging.

PubMed

Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-05-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

PubMed

Lai, Zongying; Zhang, Xinlin; Guo, Di; Du, Xiaofeng; Yang, Yonggui; Guo, Gang; Chen, Zhong; Qu, Xiaobo

2018-05-03

Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ 2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

Fast dictionary-based reconstruction for diffusion spectrum imaging.

PubMed

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Complex-Difference Constrained Compressed Sensing Reconstruction for Accelerated PRF Thermometry with Application to MRI Induced RF Heating

PubMed Central

Cao, Zhipeng; Oh, Sukhoon; Otazo, Ricardo; Sica, Christopher T.; Griswold, Mark A.; Collins, Christopher M.

2014-01-01

Purpose Introduce a novel compressed sensing reconstruction method to accelerate proton resonance frequency (PRF) shift temperature imaging for MRI induced radiofrequency (RF) heating evaluation. Methods A compressed sensing approach that exploits sparsity of the complex difference between post-heating and baseline images is proposed to accelerate PRF temperature mapping. The method exploits the intra- and inter-image correlations to promote sparsity and remove shared aliasing artifacts. Validations were performed on simulations and retrospectively undersampled data acquired in ex-vivo and in-vivo studies by comparing performance with previously proposed techniques. Results The proposed complex difference constrained compressed sensing reconstruction method improved the reconstruction of smooth and local PRF temperature change images compared to various available reconstruction methods in a simulation study, a retrospective study with heating of a human forearm in vivo, and a retrospective study with heating of a sample of beef ex vivo . Conclusion Complex difference based compressed sensing with utilization of a fully-sampled baseline image improves the reconstruction accuracy for accelerated PRF thermometry. It can be used to improve the volumetric coverage and temporal resolution in evaluation of RF heating due to MRI, and may help facilitate and validate temperature-based methods for safety assurance. PMID:24753099

Water-fat separation with parallel imaging based on BLADE.

PubMed

Weng, Dehe; Pan, Yanli; Zhong, Xiaodong; Zhuo, Yan

2013-06-01

Uniform suppression of fat signal is desired in clinical applications. Based on phase differences introduced by different chemical shift frequencies, Dixon method and its variations are used as alternatives of fat saturation methods, which are sensitive to B0 inhomogeneities. Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL) separates water and fat images with flexible echo shifting. Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER, alternatively termed as BLADE), in conjunction with IDEAL, yields Turboprop IDEAL (TP-IDEAL) and allows for decomposition of water and fat signal with motion correction. However, the flexibility of its parameter setting is limited, and the related phase correction is complicated. To address these problems, a novel method, BLADE-Dixon, is proposed in this study. This method used the same polarity readout gradients (fly-back gradients) to acquire in-phase and opposed-phases images, which led to less complicated phase correction and more flexible parameter setting compared to TP-IDEAL. Parallel imaging and undersampling were integrated to reduce scan time. Phantom, orbit, neck and knee images were acquired with BLADE-Dixon. Water-fat separation results were compared to those measured with conventional turbo spin echo (TSE) Dixon and TSE with fat saturation, respectively, to demonstrate the performance of BLADE-Dixon. Copyright © 2013 Elsevier Inc. All rights reserved.

Are reconstruction filters necessary?

NASA Astrophysics Data System (ADS)

Holst, Gerald C.

2006-05-01

Shannon's sampling theorem (also called the Shannon-Whittaker-Kotel'nikov theorem) was developed for the digitization and reconstruction of sinusoids. Strict adherence is required when frequency preservation is important. Three conditions must be met to satisfy the sampling theorem: (1) The signal must be band-limited, (2) the digitizer must sample the signal at an adequate rate, and (3) a low-pass reconstruction filter must be present. In an imaging system, the signal is band-limited by the optics. For most imaging systems, the signal is not adequately sampled resulting in aliasing. While the aliasing seems excessive mathematically, it does not significantly affect the perceived image. The human visual system detects intensity differences, spatial differences (shapes), and color differences. The eye is less sensitive to frequency effects and therefore sampling artifacts have become quite acceptable. Indeed, we love our television even though it is significantly undersampled. The reconstruction filter, although absolutely essential, is rarely discussed. It converts digital data (which we cannot see) into a viewable analog signal. There are several reconstruction filters: electronic low-pass filters, the display media (monitor, laser printer), and your eye. These are often used in combination to create a perceived continuous image. Each filter modifies the MTF in a unique manner. Therefore image quality and system performance depends upon the reconstruction filter(s) used. The selection depends upon the application.

Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

PubMed Central

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

QR-decomposition based SENSE reconstruction using parallel architecture.

PubMed

Ullah, Irfan; Nisar, Habab; Raza, Haseeb; Qasim, Malik; Inam, Omair; Omer, Hammad

2018-04-01

Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique that provides essential clinical information about the human body. One major limitation of MRI is its long scan time. Implementation of advance MRI algorithms on a parallel architecture (to exploit inherent parallelism) has a great potential to reduce the scan time. Sensitivity Encoding (SENSE) is a Parallel Magnetic Resonance Imaging (pMRI) algorithm that utilizes receiver coil sensitivities to reconstruct MR images from the acquired under-sampled k-space data. At the heart of SENSE lies inversion of a rectangular encoding matrix. This work presents a novel implementation of GPU based SENSE algorithm, which employs QR decomposition for the inversion of the rectangular encoding matrix. For a fair comparison, the performance of the proposed GPU based SENSE reconstruction is evaluated against single and multicore CPU using openMP. Several experiments against various acceleration factors (AFs) are performed using multichannel (8, 12 and 30) phantom and in-vivo human head and cardiac datasets. Experimental results show that GPU significantly reduces the computation time of SENSE reconstruction as compared to multi-core CPU (approximately 12x speedup) and single-core CPU (approximately 53x speedup) without any degradation in the quality of the reconstructed images. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluating Descriptive Metrics of the Human Cone Mosaic

PubMed Central

Cooper, Robert F.; Wilk, Melissa A.; Tarima, Sergey; Carroll, Joseph

2016-01-01

Purpose To evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification). Methods Using an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power. Results Of the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss. Conclusions The robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic. PMID:27273598

Fast 3D magnetic resonance fingerprinting for a whole-brain coverage.

PubMed

Ma, Dan; Jiang, Yun; Chen, Yong; McGivney, Debra; Mehta, Bhairav; Gulani, Vikas; Griswold, Mark

2018-04-01

The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans. A 3D magnetic resonance fingerprinting scan was accelerated by using a single-shot spiral trajectory with an undersampling factor of 48 in the x-y plane, and an interleaved sampling pattern with an undersampling factor of 3 through plane. Further acceleration came from reducing the waiting time between neighboring partitions. The reconstruction time was accelerated by applying singular value decomposition compression in k-space. Finally, a 3D premeasured B 1 map was used to correct for the B 1 inhomogeneity. The T 1 and T 2 values of the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI phantom showed a good agreement with the standard values, with an average concordance correlation coefficient of 0.99, and coefficient of variation of 7% in the repeatability scans. The results from in vivo scans also showed high image quality in both transverse and coronal views. This study applied a fast acquisition scheme for a fully quantitative 3D magnetic resonance fingerprinting scan with a total acceleration factor of 144 as compared with the Nyquist rate, such that 3D T 1 , T 2 , and proton density maps can be acquired with whole-brain coverage at clinical resolution in less than 5 min. Magn Reson Med 79:2190-2197, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography

PubMed Central

Wang, Tianshi; Pfeiffer, Tom; Regar, Evelyn; Wieser, Wolfgang; van Beusekom, Heleen; Lancee, Charles T.; Springeling, Geert; Krabbendam, Ilona; van der Steen, Antonius F.W.; Huber, Robert; van Soest, Gijs

2015-01-01

Cardiac motion artifacts, non-uniform rotational distortion and undersampling affect the image quality and the diagnostic impact of intravascular optical coherence tomography (IV-OCT). In this study we demonstrate how these limitations of IV-OCT can be addressed by using an imaging system that we called “Heartbeat OCT”, combining a fast Fourier Domain Mode Locked laser, fast pullback, and a micromotor actuated catheter, designed to examine a coronary vessel in less than one cardiac cycle. We acquired in vivo data sets of two coronary arteries in a porcine heart with both Heartbeat OCT, working at 2.88 MHz A-line rate, 4000 frames/s and 100 mm/s pullback speed, and with a commercial system. The in vivo results show that Heartbeat OCT provides faithfully rendered, motion-artifact free, fully sampled vessel wall architecture, unlike the conventional IV-OCT data. We present the Heartbeat OCT system in full technical detail and discuss the steps needed for clinical translation of the technology. PMID:26713214

MO-FG-CAMPUS-JeP2-01: 4D-MRI with 3D Radial Sampling and Self-Gating-Based K-Space Sorting: Image Quality Improvement by Slab-Selective Excitation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Z; Pang, J; Tuli, R

Purpose: A recent 4D MRI technique based on 3D radial sampling and self-gating-based K-space sorting has shown promising results in characterizing respiratory motion. However due to continuous acquisition and potentially drastic k-space undersampling resultant images could suffer from low blood-to-tissue contrast and streaking artifacts. In this study 3D radial sampling with slab-selective excitation (SS) was proposed in attempt to enhance blood-to-tissue contrast by exploiting the in-flow effect and to suppress the excess signal from the peripheral structures particularly in the superior-inferior direction. The feasibility of improving image quality by using this approach was investigated through a comparison with the previouslymore » developed non-selective excitation (NS) approach. Methods: Two excitation approaches SS and NS were compared in 5 cancer patients (1 lung 1 liver 2 pancreas and 1 esophagus) at 3Tesla. Image artifact was assessed in all patients on a 4-point scale (0: poor; 3: excellent). Signal-tonoise ratio (SNR) of the blood vessel (aorta) at the center of field-of-view and its nearby tissue were measured in 3 of the 5 patients (1 liver 2 pancreas) and blood-to-tissue contrast-to-noise ratio (CNR) were then determined. Results: Compared with NS the image quality of SS was visually improved with overall higher signal in all patients (2.6±0.55 vs. 3.4±0.55). SS showed an approximately 2-fold increase of SNR in the blood (aorta: 16.39±1.95 vs. 32.19±7.93) and slight increase in the surrounding tissue (liver/pancreas: 16.91±1.82 vs. 22.31±3.03). As a result the blood-totissue CNR was dramatically higher in the SS method (1.20±1.20 vs. 9.87±6.67). Conclusion: The proposed 3D radial sampling with slabselective excitation allows for reduced image artifact and improved blood SNR and blood-to-tissue CNR. The success of this technique could potentially benefit patients with cancerous tumors that have invaded the surrounding blood vessels where radiation therapy is needed to remove tumor from those regions prior to surgical resection. This work is partially supported by NIH R03CA173273; and CTSI core voucher award.« less

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

PubMed

Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n  = 33), White-naped Crane ( Grus vipio , n  = 40), and Black-necked Crane ( Grus nigricollis , n  = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

PubMed Central

Mi, Chunrong; Huettmann, Falk; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33), White-naped Crane (Grus vipio, n = 40), and Black-necked Crane (Grus nigricollis, n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation. PMID:28097060

SU-E-J-02: 4D Digital Tomosynthesis Based On Algebraic Image Reconstruction and Total-Variation Minimization for the Improvement of Image Quality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D; Kang, S; Kim, T

2014-06-01

Purpose: In this paper, we implemented the four-dimensional (4D) digital tomosynthesis (DTS) imaging based on algebraic image reconstruction technique and total-variation minimization method in order to compensate the undersampled projection data and improve the image quality. Methods: The projection data were acquired as supposed the cone-beam computed tomography system in linear accelerator by the Monte Carlo simulation and the in-house 4D digital phantom generation program. We performed 4D DTS based upon simultaneous algebraic reconstruction technique (SART) among the iterative image reconstruction technique and total-variation minimization method (TVMM). To verify the effectiveness of this reconstruction algorithm, we performed systematic simulation studiesmore » to investigate the imaging performance. Results: The 4D DTS algorithm based upon the SART and TVMM seems to give better results than that based upon the existing method, or filtered-backprojection. Conclusion: The advanced image reconstruction algorithm for the 4D DTS would be useful to validate each intra-fraction motion during radiation therapy. In addition, it will be possible to give advantage to real-time imaging for the adaptive radiation therapy. This research was supported by Leading Foreign Research Institute Recruitment Program (Grant No.2009-00420) and Basic Atomic Energy Research Institute (BAERI); (Grant No. 2009-0078390) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP)« less

Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization

USGS Publications Warehouse

D'Agostino, V.; Greene, E.A.; Passarella, G.; Vurro, M.

1998-01-01

Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter, can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (that is, an undersampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. This paper presents an application of cokriging by using temporal subsets to study the spatial distribution of nitrate concentration in the aquifer of the Lucca Plain, central Italy. Three data sets of nitrate concentration in groundwater were collected during three different periods in 1991. The first set was from 47 wells, but the second and the third are undersampled and represent 28 and 27 wells, respectively. Comparing the result of cokriging with ordinary kriging showed an improvement of the uncertainty in terms of reducing the estimation variance. The application of cokriging to the undersampled data sets reduced the uncertainty in estimating nitrate concentration and at the same time decreased the cost of the field sampling and laboratory analysis.Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter, can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (that is, an undersampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. This paper presents an application of cokriging by using temporal subsets to study the spatial distribution of nitrate concentration in the aquifer of the Lucca Plain, central Italy. Three data sets of nitrate concentration in groundwater were collected during three different periods in 1991. The first set was from 47 wells, but the second and the third are undersampled and represent 28 and 27 wells, respectively. Comparing the result of cokriging with ordinary kriging showed an improvement of the uncertainty in terms of reducing the estimation variance. The application of cokriging to the undersampled data sets reduced the uncertainty in estimating nitrate concentration and at the same time decreased the cost of the field sampling and laboratory analysis.

XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

PubMed

Feng, Li; Axel, Leon; Chandarana, Hersh; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo

2016-02-01

To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. © 2015 Wiley Periodicals, Inc.

XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing

PubMed Central

Feng, Li; Axel, Leon; Chandarana, Hersh; Block, Kai Tobias; Sodickson, Daniel K.; Otazo, Ricardo

2015-01-01

Purpose To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Methods Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting under-sampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. Results XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. Conclusion XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. PMID:25809847

Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging

NASA Astrophysics Data System (ADS)

Rasch, Julian; Brinkmann, Eva-Maria; Burger, Martin

2018-01-01

Joint reconstruction has recently attracted a lot of attention, especially in the field of medical multi-modality imaging such as PET-MRI. Most of the developed methods rely on the comparison of image gradients, or more precisely their location, direction and magnitude, to make use of structural similarities between the images. A challenge and still an open issue for most of the methods is to handle images in entirely different scales, i.e. different magnitudes of gradients that cannot be dealt with by a global scaling of the data. We propose the use of generalized Bregman distances and infimal convolutions thereof with regard to the well-known total variation functional. The use of a total variation subgradient respectively the involved vector field rather than an image gradient naturally excludes the magnitudes of gradients, which in particular solves the scaling behavior. Additionally, the presented method features a weighting that allows to control the amount of interaction between channels. We give insights into the general behavior of the method, before we further tailor it to a particular application, namely PET-MRI joint reconstruction. To do so, we compute joint reconstruction results from blurry Poisson data for PET and undersampled Fourier data from MRI and show that we can gain a mutual benefit for both modalities. In particular, the results are superior to the respective separate reconstructions and other joint reconstruction methods.

MO-DE-207A-06: ECG-Gated CT Reconstruction for a C-Arm Inverse Geometry X-Ray System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slagowski, JM; Dunkerley, DAP

2016-06-15

Purpose: To obtain ECG-gated CT images from truncated projection data acquired with a C-arm based inverse geometry fluoroscopy system, for the purpose of cardiac chamber mapping in interventional procedures. Methods: Scanning-beam digital x-ray (SBDX) is an inverse geometry fluoroscopy system with a scanned multisource x-ray tube and a photon-counting detector mounted to a C-arm. In the proposed method, SBDX short-scan rotational acquisition is performed followed by inverse geometry CT (IGCT) reconstruction and segmentation of contrast-enhanced objects. The prior image constrained compressed sensing (PICCS) framework was adapted for IGCT reconstruction to mitigate artifacts arising from data truncation and angular undersampling duemore » to cardiac gating. The performance of the reconstruction algorithm was evaluated in numerical simulations of truncated and non-truncated thorax phantoms containing a dynamic ellipsoid to represent a moving cardiac chamber. The eccentricity of the ellipsoid was varied at frequencies from 1–1.5 Hz. Projection data were retrospectively sorted into 13 cardiac phases. Each phase was reconstructed using IGCT-PICCS, with a nongated gridded FBP (gFBP) prior image. Surface accuracy was determined using Dice similarity coefficient and a histogram of the point distances between the segmented surface and ground truth surface. Results: The gated IGCT-PICCS algorithm improved surface accuracy and reduced streaking and truncation artifacts when compared to nongated gFBP. For the non-truncated thorax with 1.25 Hz motion, 99% of segmented surface points were within 0.3 mm of the 15 mm diameter ground truth ellipse, versus 1.0 mm for gFBP. For the truncated thorax phantom with a 40 mm diameter ellipse, IGCT-PICCS surface accuracy measured 0.3 mm versus 7.8 mm for gFBP. Dice similarity coefficient was 0.99–1.00 (IGCT-PICCS) versus 0.63–0.75 (gFBP) for intensity-based segmentation thresholds ranging from 25–75% maximum contrast. Conclusions: The PICCS algorithm was successfully applied to reconstruct truncated IGCT projection data with angular undersampling resulting from simulated cardiac gating. Research supported by the National Heart, Lung, and Blood Institute of the NIH under award number R01HL084022. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.« less

Magnetic Resonance Imaging and Velocity Mapping in Chemical Engineering Applications.

PubMed

Gladden, Lynn F; Sederman, Andrew J

2017-06-07

This review aims to illustrate the diversity of measurements that can be made using magnetic resonance techniques, which have the potential to provide insights into chemical engineering systems that cannot readily be achieved using any other method. Perhaps the most notable advantage in using magnetic resonance methods is that both chemistry and transport can be followed in three dimensions, in optically opaque systems, and without the need for tracers to be introduced into the system. Here we focus on hydrodynamics and, in particular, applications to rheology, pipe flow, and fixed-bed and gas-solid fluidized bed reactors. With increasing development of industrially relevant sample environments and undersampling data acquisition strategies that can reduce acquisition times to <1 s, magnetic resonance is finding increasing application in chemical engineering research.

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

PubMed

Knoll, Florian; Hammernik, Kerstin; Kobler, Erich; Pock, Thomas; Recht, Michael P; Sodickson, Daniel K

2018-05-17

Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases. © 2018 International Society for Magnetic Resonance in Medicine.

The effects of navigator distortion and noise level on interleaved EPI DWI reconstruction: a comparison between image- and k-space-based method.

PubMed

Dai, Erpeng; Zhang, Zhe; Ma, Xiaodong; Dong, Zijing; Li, Xuesong; Xiong, Yuhui; Yuan, Chun; Guo, Hua

2018-03-23

To study the effects of 2D navigator distortion and noise level on interleaved EPI (iEPI) DWI reconstruction, using either the image- or k-space-based method. The 2D navigator acquisition was adjusted by reducing its echo spacing in the readout direction and undersampling in the phase encoding direction. A POCS-based reconstruction using image-space sampling function (IRIS) algorithm (POCSIRIS) was developed to reduce the impact of navigator distortion. POCSIRIS was then compared with the original IRIS algorithm and a SPIRiT-based k-space algorithm, under different navigator distortion and noise levels. Reducing the navigator distortion can improve the reconstruction of iEPI DWI. The proposed POCSIRIS and SPIRiT-based algorithms are more tolerable to different navigator distortion levels, compared to the original IRIS algorithm. SPIRiT may be hindered by low SNR of the navigator. Multi-shot iEPI DWI reconstruction can be improved by reducing the 2D navigator distortion. Different reconstruction methods show variable sensitivity to navigator distortion or noise levels. Furthermore, the findings can be valuable in applications such as simultaneous multi-slice accelerated iEPI DWI and multi-slab diffusion imaging. © 2018 International Society for Magnetic Resonance in Medicine.

Illusory motion reversal is caused by rivalry, not by perceptual snapshots of the visual field.

PubMed

Kline, Keith; Holcombe, Alex O; Eagleman, David M

2004-10-01

In stroboscopic conditions--such as motion pictures--rotating objects may appear to rotate in the reverse direction due to under-sampling (aliasing). A seemingly similar phenomenon occurs in constant sunlight, which has been taken as evidence that the visual system processes discrete "snapshots" of the outside world. But if snapshots are indeed taken of the visual field, then when a rotating drum appears to transiently reverse direction, its mirror image should always appeared to reverse direction simultaneously. Contrary to this hypothesis, we found that when observers watched a rotating drum and its mirror image, almost all illusory motion reversals occurred for only one image at a time. This result indicates that the motion reversal illusion cannot be explained by snapshots of the visual field. The same result is found when the two images are presented within one visual hemifield, further ruling out the possibility that discrete sampling of the visual field occurs separately in each hemisphere. The frequency distribution of illusory reversal durations approximates a gamma distribution, suggesting perceptual rivalry as a better explanation for illusory motion reversal. After adaptation of motion detectors coding for the correct direction, the activity of motion-sensitive neurons coding for motion in the reverse direction may intermittently become dominant and drive the perception of motion.

Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography

NASA Astrophysics Data System (ADS)

Kazantsev, Daniil; Jørgensen, Jakob S.; Andersen, Martin S.; Lionheart, William R. B.; Lee, Peter D.; Withers, Philip J.

2018-06-01

Rapid developments in photon-counting and energy-discriminating detectors have the potential to provide an additional spectral dimension to conventional x-ray grayscale imaging. Reconstructed spectroscopic tomographic data can be used to distinguish individual materials by characteristic absorption peaks. The acquired energy-binned data, however, suffer from low signal-to-noise ratio, acquisition artifacts, and frequently angular undersampled conditions. New regularized iterative reconstruction methods have the potential to produce higher quality images and since energy channels are mutually correlated it can be advantageous to exploit this additional knowledge. In this paper, we propose a novel method which jointly reconstructs all energy channels while imposing a strong structural correlation. The core of the proposed algorithm is to employ a variational framework of parallel level sets to encourage joint smoothing directions. In particular, the method selects reference channels from which to propagate structure in an adaptive and stochastic way while preferring channels with a high data signal-to-noise ratio. The method is compared with current state-of-the-art multi-channel reconstruction techniques including channel-wise total variation and correlative total nuclear variation regularization. Realistic simulation experiments demonstrate the performance improvements achievable by using correlative regularization methods.

Helium-3 MR q-space imaging with radial acquisition and iterative highly constrained back-projection.

PubMed

O'Halloran, Rafael L; Holmes, James H; Wu, Yu-Chien; Alexander, Andrew; Fain, Sean B

2010-01-01

An undersampled diffusion-weighted stack-of-stars acquisition is combined with iterative highly constrained back-projection to perform hyperpolarized helium-3 MR q-space imaging with combined regional correction of radiofrequency- and T1-related signal loss in a single breath-held scan. The technique is tested in computer simulations and phantom experiments and demonstrated in a healthy human volunteer with whole-lung coverage in a 13-sec breath-hold. Measures of lung microstructure at three different lung volumes are evaluated using inhaled gas volumes of 500 mL, 1000 mL, and 1500 mL to demonstrate feasibility. Phantom results demonstrate that the proposed technique is in agreement with theoretical values, as well as with a fully sampled two-dimensional Cartesian acquisition. Results from the volunteer study demonstrate that the root mean squared diffusion distance increased significantly from the 500-mL volume to the 1000-mL volume. This technique represents the first demonstration of a spatially resolved hyperpolarized helium-3 q-space imaging technique and shows promise for microstructural evaluation of lung disease in three dimensions. Copyright (c) 2009 Wiley-Liss, Inc.

MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout.

PubMed

Jiang, Yun; Ma, Dan; Seiberlich, Nicole; Gulani, Vikas; Griswold, Mark A

2015-12-01

This study explores the possibility of using gradient echo-based sequences other than balanced steady-state free precession (bSSFP) in the magnetic resonance fingerprinting (MRF) framework to quantify the relaxation parameters . An MRF method based on a fast imaging with steady-state precession (FISP) sequence structure is presented. A dictionary containing possible signal evolutions with physiological range of T1 and T2 was created using the extended phase graph formalism according to the acquisition parameters. The proposed method was evaluated in a phantom and a human brain. T1 , T2 , and proton density were quantified directly from the undersampled data by the pattern recognition algorithm. T1 and T2 values from the phantom demonstrate that the results of MRF FISP are in good agreement with the traditional gold-standard methods. T1 and T2 values in brain are within the range of previously reported values. MRF-FISP enables a fast and accurate quantification of the relaxation parameters. It is immune to the banding artifact of bSSFP due to B0 inhomogeneities, which could improve the ability to use MRF for applications beyond brain imaging. © 2014 Wiley Periodicals, Inc.

MR Fingerprinting Using Fast Imaging with Steady State Precession (FISP) with Spiral Readout

PubMed Central

Jiang, Yun; Ma, Dan; Seiberlich, Nicole; Gulani, Vikas; Griswold, Mark A.

2015-01-01

Purpose This study explores the possibility of using gradient echo based sequences other than bSSFP in the magnetic resonance fingerprinting (MRF) framework to quantify the relaxation parameters. Methods An MRF method based on a fast imaging with steady state precession (FISP) sequence structure is presented. A dictionary containing possible signal evolutions with physiological range of T1 and T2 was created using the extended phase graph (EPG) formalism according to the acquisition parameters. The proposed method was evaluated in a phantom and a human brain. T1, T2 and proton density were quantified directly from the undersampled data by the pattern recognition algorithm. Results T1 and T2 values from the phantom demonstrate that the results of MRF FISP are in good agreement with the traditional gold-standard methods. T1 and T2 values in brain are within the range of previously reported values. Conclusion MRF FISP enables a fast and accurate quantification of the relaxation parameters, while is immune to the banding artifact of bSSFP due to B0 inhomogeneities, which could improve the ability to use MRF for applications beyond brain imaging. PMID:25491018

Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator.

PubMed

Jiang, Wenwen; Ong, Frank; Johnson, Kevin M; Nagle, Scott K; Hope, Thomas A; Lustig, Michael; Larson, Peder E Z

2018-06-01

To achieve motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data. Five-minute free-breathing scans were acquired with a 3D ultrashort echo time (UTE) sequence with 1.25 mm isotropic resolution. From this data, dynamic 3D self-navigating images were reconstructed under locally low rank (LLR) constraints and used for motion compensation with one of two methods: a soft-gating technique to penalize the respiratory motion induced data inconsistency, and a respiratory motion-resolved technique to provide images of all respiratory motion states. Respiratory motion estimation derived from the proposed dynamic 3D self-navigator of 7.5 mm isotropic reconstruction resolution and a temporal resolution of 300 ms was successful for estimating complex respiratory motion patterns. This estimation improved image quality compared to respiratory belt and DC-based navigators. Respiratory motion compensation with soft-gating and respiratory motion-resolved techniques provided good image quality from highly undersampled data in volunteers and clinical patients. An optimized 3D UTE sequence combined with the proposed reconstruction methods can provide high-resolution motion robust pulmonary MRI. Feasibility was shown in patients who had irregular breathing patterns in which our approach could depict clinically relevant pulmonary pathologies. Magn Reson Med 79:2954-2967, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

TRM4: Range performance model for electro-optical imaging systems

NASA Astrophysics Data System (ADS)

Keßler, Stefan; Gal, Raanan; Wittenstein, Wolfgang

2017-05-01

TRM4 is a commonly used model for assessing device and range performance of electro-optical imagers. The latest version, TRM4.v2, has been released by Fraunhofer IOSB of Germany in June 2016. While its predecessor, TRM3, was developed for thermal imagers, assuming blackbody targets and backgrounds, TRM4 extends the TRM approach to assess three imager categories: imagers that exploit emitted radiation (TRM4 category Thermal), reflected radiation (TRM4 category Visible/NIR/SWIR), and both emitted and reflected radiation (TRM4 category General). Performance assessment in TRM3 and TRM4 is based on the perception of standard four-bar test patterns, whether distorted by under-sampling or not. Spatial and sampling characteristics are taken into account by the Average Modulation at Optimum Phase (AMOP), which replaces the system MTF used in previous models. The Minimum Temperature Difference Perceived (MTDP) figure of merit was introduced in TRM3 for assessing the range performance of thermal imagers. In TRM4, this concept is generalized to the MDSP (Minimum Difference Signal Perceived), which can be applied to all imager categories. In this paper, we outline and discuss the TRM approach and pinpoint differences between TRM4 and TRM3. In addition, an overview of the TRM4 software and its functionality is given. Features newly introduced in TRM4, such as atmospheric turbulence, irradiation sources, and libraries are addressed. We conclude with an outlook on future work and the new module for intensified CCD cameras that is currently under development

Fast susceptibility-weighted imaging with three-dimensional short-axis propeller (SAP)-echo-planar imaging.

PubMed

Holdsworth, Samantha J; Yeom, Kristen W; Moseley, Michael E; Skare, S

2015-05-01

Susceptibility-weighted imaging (SWI) in neuroimaging can be challenging due to long scan times of three-dimensional (3D) gradient recalled echo (GRE), while faster techniques such as 3D interleaved echo-planar imaging (iEPI) are prone to motion artifacts. Here we outline and implement a 3D short-axis propeller echo-planar imaging (SAP-EPI) trajectory as a faster, motion-correctable approach for SWI. Experiments were conducted on a 3T MRI system. The 3D SAP-EPI, 3D iEPI, and 3D GRE SWI scans were acquired on two volunteers. Controlled motion experiments were conducted to test the motion-correction capability of 3D SAP-EPI. The 3D SAP-EPI SWI data were acquired on two pediatric patients as a potential alternative to 2D GRE used clinically. The 3D GRE images had a better target resolution (0.47 × 0.94 × 2 mm, scan time = 5 min), iEPI and SAP-EPI images (resolution = 0.94 × 0.94 × 2 mm) were acquired in a faster scan time (1:52 min) with twice the brain coverage. SAP-EPI showed motion-correction capability and some immunity to undersampling from rejected data. While 3D SAP-EPI suffers from some geometric distortion, its short scan time and motion-correction capability suggest that SAP-EPI may be a useful alternative to GRE and iEPI for use in SWI, particularly in uncooperative patients. © 2014 Wiley Periodicals, Inc.

An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI.

PubMed

Tan, Zhengguo; Hohage, Thorsten; Kalentev, Oleksandr; Joseph, Arun A; Wang, Xiaoqing; Voit, Dirk; Merboldt, K Dietmar; Frahm, Jens

2017-12-01

The purpose of this work is to develop an automatic method for the scaling of unknowns in model-based nonlinear inverse reconstructions and to evaluate its application to real-time phase-contrast (RT-PC) flow magnetic resonance imaging (MRI). Model-based MRI reconstructions of parametric maps which describe a physical or physiological function require the solution of a nonlinear inverse problem, because the list of unknowns in the extended MRI signal equation comprises multiple functional parameters and all coil sensitivity profiles. Iterative solutions therefore rely on an appropriate scaling of unknowns to numerically balance partial derivatives and regularization terms. The scaling of unknowns emerges as a self-adjoint and positive-definite matrix which is expressible by its maximal eigenvalue and solved by power iterations. The proposed method is applied to RT-PC flow MRI based on highly undersampled acquisitions. Experimental validations include numerical phantoms providing ground truth and a wide range of human studies in the ascending aorta, carotid arteries, deep veins during muscular exercise and cerebrospinal fluid during deep respiration. For RT-PC flow MRI, model-based reconstructions with automatic scaling not only offer velocity maps with high spatiotemporal acuity and much reduced phase noise, but also ensure fast convergence as well as accurate and precise velocities for all conditions tested, i.e. for different velocity ranges, vessel sizes and the simultaneous presence of signals with velocity aliasing. In summary, the proposed automatic scaling of unknowns in model-based MRI reconstructions yields quantitatively reliable velocities for RT-PC flow MRI in various experimental scenarios. Copyright © 2017 John Wiley & Sons, Ltd.

High-resolution myocardial T1 mapping using single-shot inversion recovery fast low-angle shot MRI with radial undersampling and iterative reconstruction

PubMed Central

Joseph, Arun A; Kalentev, Oleksandr; Merboldt, Klaus-Dietmar; Voit, Dirk; Roeloffs, Volkert B; van Zalk, Maaike; Frahm, Jens

2016-01-01

Objective: To develop a novel method for rapid myocardial T1 mapping at high spatial resolution. Methods: The proposed strategy represents a single-shot inversion recovery experiment triggered to early diastole during a brief breath-hold. The measurement combines an adiabatic inversion pulse with a real-time readout by highly undersampled radial FLASH, iterative image reconstruction and T1 fitting with automatic deletion of systolic frames. The method was implemented on a 3-T MRI system using a graphics processing unit-equipped bypass computer for online application. Validations employed a T1 reference phantom including analyses at simulated heart rates from 40 to 100 beats per minute. In vivo applications involved myocardial T1 mapping in short-axis views of healthy young volunteers. Results: At 1-mm in-plane resolution and 6-mm section thickness, the inversion recovery measurement could be shortened to 3 s without compromising T1 quantitation. Phantom studies demonstrated T1 accuracy and high precision for values ranging from 300 to 1500 ms and up to a heart rate of 100 beats per minute. Similar results were obtained in vivo yielding septal T1 values of 1246 ± 24 ms (base), 1256 ± 33 ms (mid-ventricular) and 1288 ± 30 ms (apex), respectively (mean ± standard deviation, n = 6). Conclusion: Diastolic myocardial T1 mapping with use of single-shot inversion recovery FLASH offers high spatial resolution, T1 accuracy and precision, and practical robustness and speed. Advances in knowledge: The proposed method will be beneficial for clinical applications relying on native and post-contrast T1 quantitation. PMID:27759423

Convergence Theory for Partial Images and Revision of the Definition of Total Images.

ERIC Educational Resources Information Center

Findeisen, Peter

1979-01-01

Guttman's assumption underlying his definition of "total images" is rejected. Partial images are not generally convergent everywhere. Even divergence everywhere is shown to be possible. The convergence type always found on partial images is convergence in quadratic mean; hence, total images are redefined as quadratic mean-limits.…

Sparse and redundant representations for inverse problems and recognition

NASA Astrophysics Data System (ADS)

Patel, Vishal M.

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.

Four-Dimensional Respiratory Motion-Resolved Whole Heart Coronary MR Angiography

PubMed Central

Piccini, Davide; Feng, Li; Bonanno, Gabriele; Coppo, Simone; Yerly, Jérôme; Lim, Ruth P.; Schwitter, Juerg; Sodickson, Daniel K.; Otazo, Ricardo; Stuber, Matthias

2016-01-01

Purpose Free-breathing whole-heart coronary MR angiography (MRA) commonly uses navigators to gate respiratory motion, resulting in lengthy and unpredictable acquisition times. Conversely, self-navigation has 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We propose replacing navigators and self-navigation with a respiratory motion-resolved reconstruction approach. Methods Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation. Results Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases. Conclusion XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. PMID:27052418

The analysis and rationale behind the upgrading of existing standard definition thermal imagers to high definition

NASA Astrophysics Data System (ADS)

Goss, Tristan M.

2016-05-01

With 640x512 pixel format IR detector arrays having been on the market for the past decade, Standard Definition (SD) thermal imaging sensors have been developed and deployed across the world. Now with 1280x1024 pixel format IR detector arrays becoming readily available designers of thermal imager systems face new challenges as pixel sizes reduce and the demand and applications for High Definition (HD) thermal imaging sensors increases. In many instances the upgrading of existing under-sampled SD thermal imaging sensors into more optimally sampled or oversampled HD thermal imaging sensors provides a more cost effective and reduced time to market option than to design and develop a completely new sensor. This paper presents the analysis and rationale behind the selection of the best suited HD pixel format MWIR detector for the upgrade of an existing SD thermal imaging sensor to a higher performing HD thermal imaging sensor. Several commercially available and "soon to be" commercially available HD small pixel IR detector options are included as part of the analysis and are considered for this upgrade. The impact the proposed detectors have on the sensor's overall sensitivity, noise and resolution is analyzed, and the improved range performance is predicted. Furthermore with reduced dark currents due to the smaller pixel sizes, the candidate HD MWIR detectors are operated at higher temperatures when compared to their SD predecessors. Therefore, as an additional constraint and as a design goal, the feasibility of achieving upgraded performance without any increase in the size, weight and power consumption of the thermal imager is discussed herein.

A New Submersible Imaging-in-flow Instrument to Monitor Nano- and Microplankton: Imaging FlowCytobot

NASA Astrophysics Data System (ADS)

Olson, R. J.; Sosik, H. M.; Shalapyonok, A.

2004-12-01

Understanding of how coastal plankton communities are regulated has traditionally been limited by undersampling, but cabled observatories now provide opportunities to deploy submersible sensors that have high power and data transmission requirements. We have developed an in situ instrument to carry out high-resolution, long term monitoring of phytoplankton and microzooplankton in the size range 10 to100 micrometers, to be deployed at cabled research facilities such as the Martha's Vineyard Coastal Observatory (MVCO). The new instrument is designed to complement FlowCytobot, a submersible flow cytometer currently deployed at MVCO that uses fluorescence and light scattering signals from a laser beam to characterize the smallest phytoplankton cells (less than 10 micrometers). Imaging FlowCytobot uses a combination of flow cytometric and video technology to capture images of organisms for identification and to measure chlorophyll fluorescence associated with each image. Images will be classified using neural net software, while the measurements of chlorophyll fluorescence will allow us to discriminate heterotrophic from phototrophic cells. The new instrument, like the original FlowCytobot is autonomous but remotely programmable. It utilizes a computer controlled syringe pump and distribution valve that allows periodic anti-fouling treatment and analysis of standard beads. Samples are analyzed continuously (0.25 to 2.5 ml per min) and data is sent over a fiber optic link to a remote computer for analysis. Preliminary results indicate that we can detect cells as small as 5 micrometers and discriminate several taxa of diatoms and dinoflagellates.

An Incremental Weighted Least Squares Approach to Surface Lights Fields

NASA Astrophysics Data System (ADS)

Coombe, Greg; Lastra, Anselmo

An Image-Based Rendering (IBR) approach to appearance modelling enables the capture of a wide variety of real physical surfaces with complex reflectance behaviour. The challenges with this approach are handling the large amount of data, rendering the data efficiently, and previewing the model as it is being constructed. In this paper, we introduce the Incremental Weighted Least Squares approach to the representation and rendering of spatially and directionally varying illumination. Each surface patch consists of a set of Weighted Least Squares (WLS) node centers, which are low-degree polynomial representations of the anisotropic exitant radiance. During rendering, the representations are combined in a non-linear fashion to generate a full reconstruction of the exitant radiance. The rendering algorithm is fast, efficient, and implemented entirely on the GPU. The construction algorithm is incremental, which means that images are processed as they arrive instead of in the traditional batch fashion. This human-in-the-loop process enables the user to preview the model as it is being constructed and to adapt to over-sampling and under-sampling of the surface appearance.

Optimizing 4-Dimensional Magnetic Resonance Imaging Data Sampling for Respiratory Motion Analysis of Pancreatic Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stemkens, Bjorn, E-mail: b.stemkens@umcutrecht.nl; Tijssen, Rob H.N.; Senneville, Baudouin D. de

2015-03-01

Purpose: To determine the optimum sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data for nonrigid motion characterization of tumor and organs at risk for radiation therapy purposes. Methods and Materials: For optimization, we compared 2 surrogate signals (external respiratory bellows and internal MRI navigators) and 2 MR sampling strategies (Cartesian and radial) in terms of image quality and robustness. Using the optimized protocol, 6 pancreatic cancer patients were scanned to calculate the 4D motion. Region of interest analysis was performed to characterize the respiratory-induced motion of the tumor and organs at risk simultaneously. Results: The MRI navigator was foundmore » to be a more reliable surrogate for pancreatic motion than the respiratory bellows signal. Radial sampling is most benign for undersampling artifacts and intraview motion. Motion characterization revealed interorgan and interpatient variation, as well as heterogeneity within the tumor. Conclusions: A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.« less

Demonstrating the Value of Fine-resolution Optical Data for Minimising Aliasing Impacts on Biogeochemical Models of Surface Waters

NASA Astrophysics Data System (ADS)

Chappell, N. A.; Jones, T.; Young, P.; Krishnaswamy, J.

2015-12-01

There is increasing awareness that under-sampling may have resulted in the omission of important physicochemical information present in water quality signatures of surface waters - thereby affecting interpretation of biogeochemical processes. For dissolved organic carbon (DOC) and nitrogen this under-sampling can now be avoided using UV-visible spectroscopy measured in-situ and continuously at a fine-resolution e.g. 15 minutes ("real time"). Few methods are available to extract biogeochemical process information directly from such high-frequency data. Jones, Chappell & Tych (2014 Environ Sci Technol: 13289-97) developed one such method using optically-derived DOC data based upon a sophisticated time-series modelling tool. Within this presentation we extend the methodology to quantify the minimum sampling interval required to avoid distortion of model structures and parameters that describe fundamental biogeochemical processes. This shifting of parameters which results from under-sampling is called "aliasing". We demonstrate that storm dynamics at a variety of sites dominate over diurnal and seasonal changes and that these must be characterised by sampling that may be sub-hourly to avoid aliasing. This is considerably shorter than that used by other water quality studies examining aliasing (e.g. Kirchner 2005 Phys Rev: 069902). The modelling approach presented is being developed into a generic tool to calculate the minimum sampling for water quality monitoring in systems driven primarily by hydrology. This is illustrated with fine-resolution, optical data from watersheds in temperate Europe through to the humid tropics.

Field Measurements of Trace Gases and Aerosols Emitted by Undersampled Combustion Sources Including Wood and Dung Cooking Fires, Garbage and Crop Residue Burning, and Indonesian Peat Fires

NASA Astrophysics Data System (ADS)

Stockwell, C.; Jayarathne, T. S.; Goetz, D.; Simpson, I. J.; Selimovic, V.; Bhave, P.; Blake, D. R.; Cochrane, M. A.; Ryan, K. C.; Putra, E. I.; Saharjo, B.; Stone, E. A.; DeCarlo, P. F.; Yokelson, R. J.

2017-12-01

Field measurements were conducted in Nepal and in the Indonesian province of Central Kalimantan to improve characterization of trace gases and aerosols emitted by undersampled combustion sources. The sources targeted included cooking with a variety of stoves, garbage burning, crop residue burning, and authentic peat fires. Trace gas and aerosol emissions were studied using a land-based Fourier transform infrared spectrometer, whole air sampling, photoacoustic extinctiometers (405 and 870nm), and filter samples that were analyzed off-line. These measurements were used to calculate fuel-based emission factors (EFs) for up to 90 gases, PM2.5, and PM2.5 constituents. The aerosol optical data measured included EFs for the scattering and absorption coefficients, the single scattering albedo (at 870 and 405 nm), as well as the absorption Ångström exponent. The emissions varied significantly by source, although light absorption by both brown and black carbon (BrC and BC, respectively) was important for all non-peat sources. For authentic peat combustion, the emissions of BC were negligible and absorption was dominated by organic aerosol. The field results from peat burning were in reasonable agreement with recent lab measurements of smoldering Kalimantan peat and compare well to the limited data available from other field studies. The EFs can be used with estimates of fuel consumption to improve regional emissions inventories and assessments of the climate and health impacts of these undersampled sources.

Wavelength scanning achieves pixel super-resolution in holographic on-chip microscopy

NASA Astrophysics Data System (ADS)

Luo, Wei; Göröcs, Zoltan; Zhang, Yibo; Feizi, Alborz; Greenbaum, Alon; Ozcan, Aydogan

2016-03-01

Lensfree holographic on-chip imaging is a potent solution for high-resolution and field-portable bright-field imaging over a wide field-of-view. Previous lensfree imaging approaches utilize a pixel super-resolution technique, which relies on sub-pixel lateral displacements between the lensfree diffraction patterns and the image sensor's pixel-array, to achieve sub-micron resolution under unit magnification using state-of-the-art CMOS imager chips, commonly used in e.g., mobile-phones. Here we report, for the first time, a wavelength scanning based pixel super-resolution technique in lensfree holographic imaging. We developed an iterative super-resolution algorithm, which generates high-resolution reconstructions of the specimen from low-resolution (i.e., under-sampled) diffraction patterns recorded at multiple wavelengths within a narrow spectral range (e.g., 10-30 nm). Compared with lateral shift-based pixel super-resolution, this wavelength scanning approach does not require any physical shifts in the imaging setup, and the resolution improvement is uniform in all directions across the sensor-array. Our wavelength scanning super-resolution approach can also be integrated with multi-height and/or multi-angle on-chip imaging techniques to obtain even higher resolution reconstructions. For example, using wavelength scanning together with multi-angle illumination, we achieved a halfpitch resolution of 250 nm, corresponding to a numerical aperture of 1. In addition to pixel super-resolution, the small scanning steps in wavelength also enable us to robustly unwrap phase, revealing the specimen's optical path length in our reconstructed images. We believe that this new wavelength scanning based pixel super-resolution approach can provide competitive microscopy solutions for high-resolution and field-portable imaging needs, potentially impacting tele-pathology applications in resource-limited-settings.

Reduced aliasing artifacts using shaking projection k-space sampling trajectory

NASA Astrophysics Data System (ADS)

Zhu, Yan-Chun; Du, Jiang; Yang, Wen-Chao; Duan, Chai-Jie; Wang, Hao-Yu; Gao, Song; Bao, Shang-Lian

2014-03-01

Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-T2 imaging. They are less sensitive to flow and motion artifacts, and support fast imaging with short echo times. However, aliasing and streaking artifacts are two main sources which degrade radial imaging quality. For a given fixed number of k-space projections, data distributions along radial and angular directions will influence the level of aliasing and streaking artifacts. Conventional radial k-space sampling trajectory introduces an aliasing artifact at the first principal ring of point spread function (PSF). In this paper, a shaking projection (SP) k-space sampling trajectory was proposed to reduce aliasing artifacts in MR images. SP sampling trajectory shifts the projection alternately along the k-space center, which separates k-space data in the azimuthal direction. Simulations based on conventional and SP sampling trajectories were compared with the same number projections. A significant reduction of aliasing artifacts was observed using the SP sampling trajectory. These two trajectories were also compared with different sampling frequencies. A SP trajectory has the same aliasing character when using half sampling frequency (or half data) for reconstruction. SNR comparisons with different white noise levels show that these two trajectories have the same SNR character. In conclusion, the SP trajectory can reduce the aliasing artifact without decreasing SNR and also provide a way for undersampling reconstruction. Furthermore, this method can be applied to three-dimensional (3D) hybrid or spherical radial k-space sampling for a more efficient reduction of aliasing artifacts.

Wavefront Sensing for WFIRST with a Linear Optical Model

NASA Technical Reports Server (NTRS)

Jurling, Alden S.; Content, David A.

2012-01-01

In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.

Low-Cost High-Performance MRI

NASA Astrophysics Data System (ADS)

Sarracanie, Mathieu; Lapierre, Cristen D.; Salameh, Najat; Waddington, David E. J.; Witzel, Thomas; Rosen, Matthew S.

2015-10-01

Magnetic Resonance Imaging (MRI) is unparalleled in its ability to visualize anatomical structure and function non-invasively with high spatial and temporal resolution. Yet to overcome the low sensitivity inherent in inductive detection of weakly polarized nuclear spins, the vast majority of clinical MRI scanners employ superconducting magnets producing very high magnetic fields. Commonly found at 1.5-3 tesla (T), these powerful magnets are massive and have very strict infrastructure demands that preclude operation in many environments. MRI scanners are costly to purchase, site, and maintain, with the purchase price approaching $1 M per tesla (T) of magnetic field. We present here a remarkably simple, non-cryogenic approach to high-performance human MRI at ultra-low magnetic field, whereby modern under-sampling strategies are combined with fully-refocused dynamic spin control using steady-state free precession techniques. At 6.5 mT (more than 450 times lower than clinical MRI scanners) we demonstrate (2.5 × 3.5 × 8.5) mm3 imaging resolution in the living human brain using a simple, open-geometry electromagnet, with 3D image acquisition over the entire brain in 6 minutes. We contend that these practical ultra-low magnetic field implementations of MRI (<10 mT) will complement traditional MRI, providing clinically relevant images and setting new standards for affordable (<$50,000) and robust portable devices.

Progress in sensor performance testing, modeling and range prediction using the TOD method: an overview

NASA Astrophysics Data System (ADS)

Bijl, Piet; Hogervorst, Maarten A.; Toet, Alexander

2017-05-01

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.

GPU-accelerated compressed-sensing (CS) image reconstruction in chest digital tomosynthesis (CDT) using CUDA programming

NASA Astrophysics Data System (ADS)

Choi, Sunghoon; Lee, Haenghwa; Lee, Donghoon; Choi, Seungyeon; Shin, Jungwook; Jang, Woojin; Seo, Chang-Woo; Kim, Hee-Joung

2017-03-01

A compressed-sensing (CS) technique has been rapidly applied in medical imaging field for retrieving volumetric data from highly under-sampled projections. Among many variant forms, CS technique based on a total-variation (TV) regularization strategy shows fairly reasonable results in cone-beam geometry. In this study, we implemented the TV-based CS image reconstruction strategy in our prototype chest digital tomosynthesis (CDT) R/F system. Due to the iterative nature of time consuming processes in solving a cost function, we took advantage of parallel computing using graphics processing units (GPU) by the compute unified device architecture (CUDA) programming to accelerate our algorithm. In order to compare the algorithmic performance of our proposed CS algorithm, conventional filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) reconstruction schemes were also studied. The results indicated that the CS produced better contrast-to-noise ratios (CNRs) in the physical phantom images (Teflon region-of-interest) by factors of 3.91 and 1.93 than FBP and SART images, respectively. The resulted human chest phantom images including lung nodules with different diameters also showed better visual appearance in the CS images. Our proposed GPU-accelerated CS reconstruction scheme could produce volumetric data up to 80 times than CPU programming. Total elapsed time for producing 50 coronal planes with 1024×1024 image matrix using 41 projection views were 216.74 seconds for proposed CS algorithms on our GPU programming, which could match the clinically feasible time ( 3 min). Consequently, our results demonstrated that the proposed CS method showed a potential of additional dose reduction in digital tomosynthesis with reasonable image quality in a fast time.

Condenser optics, partial coherence, and imaging for soft-x-ray projection lithography.

PubMed

Sommargren, G E; Seppala, L G

1993-12-01

A condenser system couples the radiation source to an imaging system, controlling the uniformity and partial coherence at the object, which ultimately affects the characteristics of the aerial image. A soft-x-ray projection lithography system based on a ring-field imaging system and a laser-produced plasma x-ray source places considerable constraints on the design of a condenser system. Two designs are proposed, critical illumination and Köhler illumination, each of which requires three mirrors and scanning for covering the entire ring field with the required uniformity and partial coherence. Images based on Hopkins' formulation of partially coherent imaging are simulated.

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

Four-dimensional respiratory motion-resolved whole heart coronary MR angiography.

PubMed

Piccini, Davide; Feng, Li; Bonanno, Gabriele; Coppo, Simone; Yerly, Jérôme; Lim, Ruth P; Schwitter, Juerg; Sodickson, Daniel K; Otazo, Ricardo; Stuber, Matthias

2017-04-01

Free-breathing whole-heart coronary MR angiography (MRA) commonly uses navigators to gate respiratory motion, resulting in lengthy and unpredictable acquisition times. Conversely, self-navigation has 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We propose replacing navigators and self-navigation with a respiratory motion-resolved reconstruction approach. Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation. Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases. XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. Magn Reson Med 77:1473-1484, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

A COMPARISON OF GALAXY COUNTING TECHNIQUES IN SPECTROSCOPICALLY UNDERSAMPLED REGIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Specian, Mike A.; Szalay, Alex S., E-mail: mspecia1@jhu.edu, E-mail: szalay@jhu.edu

2016-11-01

Accurate measures of galactic overdensities are invaluable for precision cosmology. Obtaining these measurements is complicated when members of one’s galaxy sample lack radial depths, most commonly derived via spectroscopic redshifts. In this paper, we utilize the Sloan Digital Sky Survey’s Main Galaxy Sample to compare seven methods of counting galaxies in cells when many of those galaxies lack redshifts. These methods fall into three categories: assigning galaxies discrete redshifts, scaling the numbers counted using regions’ spectroscopic completeness properties, and employing probabilistic techniques. We split spectroscopically undersampled regions into three types—those inside the spectroscopic footprint, those outside but adjacent to it,more » and those distant from it. Through Monte Carlo simulations, we demonstrate that the preferred counting techniques are a function of region type, cell size, and redshift. We conclude by reporting optimal counting strategies under a variety of conditions.« less

A fully redundant double difference algorithm for obtaining minimum variance estimates from GPS observations

NASA Technical Reports Server (NTRS)

Melbourne, William G.

1986-01-01

In double differencing a regression system obtained from concurrent Global Positioning System (GPS) observation sequences, one either undersamples the system to avoid introducing colored measurement statistics, or one fully samples the system incurring the resulting non-diagonal covariance matrix for the differenced measurement errors. A suboptimal estimation result will be obtained in the undersampling case and will also be obtained in the fully sampled case unless the color noise statistics are taken into account. The latter approach requires a least squares weighting matrix derived from inversion of a non-diagonal covariance matrix for the differenced measurement errors instead of inversion of the customary diagonal one associated with white noise processes. Presented is the so-called fully redundant double differencing algorithm for generating a weighted double differenced regression system that yields equivalent estimation results, but features for certain cases a diagonal weighting matrix even though the differenced measurement error statistics are highly colored.

Using a local low rank plus sparse reconstruction to accelerate dynamic hyperpolarized 13C imaging using the bSSFP sequence

NASA Astrophysics Data System (ADS)

Milshteyn, Eugene; von Morze, Cornelius; Reed, Galen D.; Shang, Hong; Shin, Peter J.; Larson, Peder E. Z.; Vigneron, Daniel B.

2018-05-01

Acceleration of dynamic 2D (T2 Mapping) and 3D hyperpolarized 13C MRI acquisitions using the balanced steady-state free precession sequence was achieved with a specialized reconstruction method, based on the combination of low rank plus sparse and local low rank reconstructions. Methods were validated using both retrospectively and prospectively undersampled in vivo data from normal rats and tumor-bearing mice. Four-fold acceleration of 1-2 mm isotropic 3D dynamic acquisitions with 2-5 s temporal resolution and two-fold acceleration of 0.25-1 mm2 2D dynamic acquisitions was achieved. This enabled visualization of the biodistribution of [2-13C]pyruvate, [1-13C]lactate, [13C, 15N2]urea, and HP001 within heart, kidneys, vasculature, and tumor, as well as calculation of high resolution T2 maps.

Measurement of subcellular texture by optical Gabor-like filtering with a digital micromirror device

PubMed Central

Pasternack, Robert M.; Qian, Zhen; Zheng, Jing-Yi; Metaxas, Dimitris N.; White, Eileen; Boustany, Nada N.

2010-01-01

We demonstrate an optical Fourier processing method to quantify object texture arising from subcellular feature orientation within unstained living cells. Using a digital micromirror device as a Fourier spatial filter, we measured cellular responses to two-dimensional optical Gabor-like filters optimized to sense orientation of nonspherical particles, such as mitochondria, with a width around 0.45 μm. Our method showed significantly rounder structures within apoptosis-defective cells lacking the proapoptotic mitochondrial effectors Bax and Bak, when compared with Bax/Bak expressing cells functional for apoptosis, consistent with reported differences in mitochondrial shape in these cells. By decoupling spatial frequency resolution from image resolution, this method enables rapid analysis of nonspherical submicrometer scatterers in an under-sampled large field of view and yields spatially localized morphometric parameters that improve the quantitative assessment of biological function. PMID:18830354

LCAMP: Location Constrained Approximate Message Passing for Compressed Sensing MRI

PubMed Central

Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

2016-01-01

Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random 1D signals and then applied to dynamic contrast-enhanced breast MRI to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5 - 10 seconds using Matlab) with highly undersampled 3D data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen; Wald, Lawrence L.

2017-01-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization. PMID:26915119

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

PubMed

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L

2016-08-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

Time multiplexing for increased FOV and resolution in virtual reality

NASA Astrophysics Data System (ADS)

Miñano, Juan C.; Benitez, Pablo; Grabovičkić, Dejan; Zamora, Pablo; Buljan, Marina; Narasimhan, Bharathwaj

2017-06-01

We introduce a time multiplexing strategy to increase the total pixel count of the virtual image seen in a VR headset. This translates into an improvement of the pixel density or the Field of View FOV (or both) A given virtual image is displayed by generating a succession of partial real images, each representing part of the virtual image and together representing the virtual image. Each partial real image uses the full set of physical pixels available in the display. The partial real images are successively formed and combine spatially and temporally to form a virtual image viewable from the eye position. Partial real images are imaged through different optical channels depending of its time slot. Shutters or other schemes are used to avoid that a partial real image be imaged through the wrong optical channels or at the wrong time slot. This time multiplexing strategy needs real images be shown at high frame rates (>120fps). Available display and shutters technologies are discussed. Several optical designs for achieving this time multiplexing scheme in a compact format are shown. This time multiplexing scheme allows increasing the resolution/FOV of the virtual image not only by increasing the physical pixel density but also by decreasing the pixels switching time, a feature that may be simpler to achieve in certain circumstances.

Adaptive foveated single-pixel imaging with dynamic supersampling

PubMed Central

Phillips, David B.; Sun, Ming-Jie; Taylor, Jonathan M.; Edgar, Matthew P.; Barnett, Stephen M.; Gibson, Graham M.; Padgett, Miles J.

2017-01-01

In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom—a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements. PMID:28439538

Observing the ExoEarth: Simulating the Retrieval of Exoplanet Parameters Using DSCOVR

NASA Astrophysics Data System (ADS)

Kane, S.; Cowan, N. B.; Domagal-Goldman, S. D.; Herman, J. R.; Robinson, T.; Stine, A.

2017-12-01

The field of exoplanets has rapidly expanded from detection to include exoplanet characterization. This has been enabled by developments such as the detection of terrestrial-sized planets and the use of transit spectroscopy to study exoplanet atmospheres. Studies of rocky planets are leading towards the direct imaging of exoplanets and the development of techniques to extract their intrinsic properties. The importance of properties such as rotation, albedo, and obliquity are significant since they inform planet formation theories and are key input parameters for Global Circulation Models used to determine surface conditions, including habitability. Thus, a complete characterization of exoplanets for understanding habitable climates requires the ability to measure these key planetary parameters. The retrieval of planetary rotation rates, albedos, and obliquities from highly undersampled imaging data can be honed using satellites designed to study the Earth's atmosphere. In this talk I will describe how the Deep Space Climate Observatory (DSCOVR) provides a unique opportunity to test such retrieval methods using data for the sunlit hemisphere of the Earth. Our methods use the high-resolution DSCOVR-EPIC images to simulate the Earth as an exoplanet, by deconvolving the images to match a variety of expected exoplanet mission requirements, and by comparing EPIC data with the cavity radiometer data from DSCOVR-NISTAR that views the Earth as a single pixel. Through this methodology, we are creating a grid of retrieval states as a function of image resolution, observing cadence, passband, etc. Our modeling of the DSCOVR data will provide an effective baseline from which to develop tools that can be applied to a variety of exoplanet imaging data.

Effects of Regularisation Priors and Anatomical Partial Volume Correction on Dynamic PET Data

NASA Astrophysics Data System (ADS)

Caldeira, Liliana L.; Silva, Nuno da; Scheins, Jürgen J.; Gaens, Michaela E.; Shah, N. Jon

2015-08-01

Dynamic PET provides temporal information about the tracer uptake. However, each PET frame has usually low statistics, resulting in noisy images. Furthermore, PET images suffer from partial volume effects. The goal of this study is to understand the effects of prior regularisation on dynamic PET data and subsequent anatomical partial volume correction. The Median Root Prior (MRP) regularisation method was used in this work during reconstruction. The quantification and noise in image-domain and time-domain (time-activity curves) as well as the impact on parametric images is assessed and compared with Ordinary Poisson Ordered Subset Expectation Maximisation (OP-OSEM) reconstruction with and without Gaussian filter. This study shows the improvement in PET images and time-activity curves (TAC) in terms of noise as well as in the parametric images when using prior regularisation in dynamic PET data. Anatomical partial volume correction improves the TAC and consequently, parametric images. Therefore, the use of MRP with anatomical partial volume correction is of interest for dynamic PET studies.

Using the phase-space imager to analyze partially coherent imaging systems: bright-field, phase contrast, differential interference contrast, differential phase contrast, and spiral phase contrast

NASA Astrophysics Data System (ADS)

Mehta, Shalin B.; Sheppard, Colin J. R.

2010-05-01

Various methods that use large illumination aperture (i.e. partially coherent illumination) have been developed for making transparent (i.e. phase) specimens visible. These methods were developed to provide qualitative contrast rather than quantitative measurement-coherent illumination has been relied upon for quantitative phase analysis. Partially coherent illumination has some important advantages over coherent illumination and can be used for measurement of the specimen's phase distribution. However, quantitative analysis and image computation in partially coherent systems have not been explored fully due to the lack of a general, physically insightful and computationally efficient model of image formation. We have developed a phase-space model that satisfies these requirements. In this paper, we employ this model (called the phase-space imager) to elucidate five different partially coherent systems mentioned in the title. We compute images of an optical fiber under these systems and verify some of them with experimental images. These results and simulated images of a general phase profile are used to compare the contrast and the resolution of the imaging systems. We show that, for quantitative phase imaging of a thin specimen with matched illumination, differential phase contrast offers linear transfer of specimen information to the image. We also show that the edge enhancement properties of spiral phase contrast are compromised significantly as the coherence of illumination is reduced. The results demonstrate that the phase-space imager model provides a useful framework for analysis, calibration, and design of partially coherent imaging methods.

Determination of mango fruit from binary image using randomized Hough transform

NASA Astrophysics Data System (ADS)

Rizon, Mohamed; Najihah Yusri, Nurul Ain; Abdul Kadir, Mohd Fadzil; bin Mamat, Abd. Rasid; Abd Aziz, Azim Zaliha; Nanaa, Kutiba

2015-12-01

A method of detecting mango fruit from RGB input image is proposed in this research. From the input image, the image is processed to obtain the binary image using the texture analysis and morphological operations (dilation and erosion). Later, the Randomized Hough Transform (RHT) method is used to find the best ellipse fits to each binary region. By using the texture analysis, the system can detect the mango fruit that is partially overlapped with each other and mango fruit that is partially occluded by the leaves. The combination of texture analysis and morphological operator can isolate the partially overlapped fruit and fruit that are partially occluded by leaves. The parameters derived from RHT method was used to calculate the center of the ellipse. The center of the ellipse acts as the gripping point for the fruit picking robot. As the results, the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.

High-resolution, time-resolved MRA provides superior definition of lower-extremity arterial segments compared to 2D time-of-flight imaging.

PubMed

Thornton, F J; Du, J; Suleiman, S A; Dieter, R; Tefera, G; Pillai, K R; Korosec, F R; Mistretta, C A; Grist, T M

2006-08-01

To evaluate a novel time-resolved contrast-enhanced (CE) projection reconstruction (PR) magnetic resonance angiography (MRA) method for identifying potential bypass graft target vessels in patients with Class II-IV peripheral vascular disease. Twenty patients (M:F = 15:5, mean age = 58 years, range = 48-83 years), were recruited from routine MRA referrals. All imaging was performed on a 1.5 T MRI system with fast gradients (Signa LX; GE Healthcare, Waukesha, WI). Images were acquired with a novel technique that combined undersampled PR with a time-resolved acquisition to yield an MRA method with high temporal and spatial resolution. The method is called PR hyper time-resolved imaging of contrast kinetics (PR-hyperTRICKS). Quantitative and qualitative analyses were used to compare two-dimensional (2D) time-of-flight (TOF) and PR-hyperTRICKS in 13 arterial segments per lower extremity. Statistical analysis was performed with the Wilcoxon signed-rank test. Fifteen percent (77/517) of the vessels were scored as missing or nondiagnostic with 2D TOF, but were scored as diagnostic with PR-hyperTRICKS. Image quality was superior with PR-hyperTRICKS vs. 2D TOF (on a four-point scale, mean rank = 3.3 +/- 1.2 vs. 2.9 +/- 1.2, P < 0.0001). PR-hyperTRICKS produced images with high contrast-to-noise ratios (CNR) and high spatial and temporal resolution. 2D TOF images were of inferior quality due to moderate spatial resolution, inferior CNR, greater flow-related artifacts, and absence of temporal resolution. PR-hyperTRICKS provides superior preoperative assessment of lower limb ischemia compared to 2D TOF.

Acoustic Inversion in Optoacoustic Tomography: A Review

PubMed Central

Rosenthal, Amir; Ntziachristos, Vasilis; Razansky, Daniel

2013-01-01

Optoacoustic tomography enables volumetric imaging with optical contrast in biological tissue at depths beyond the optical mean free path by the use of optical excitation and acoustic detection. The hybrid nature of optoacoustic tomography gives rise to two distinct inverse problems: The optical inverse problem, related to the propagation of the excitation light in tissue, and the acoustic inverse problem, which deals with the propagation and detection of the generated acoustic waves. Since the two inverse problems have different physical underpinnings and are governed by different types of equations, they are often treated independently as unrelated problems. From an imaging standpoint, the acoustic inverse problem relates to forming an image from the measured acoustic data, whereas the optical inverse problem relates to quantifying the formed image. This review focuses on the acoustic aspects of optoacoustic tomography, specifically acoustic reconstruction algorithms and imaging-system practicalities. As these two aspects are intimately linked, and no silver bullet exists in the path towards high-performance imaging, we adopt a holistic approach in our review and discuss the many links between the two aspects. Four classes of reconstruction algorithms are reviewed: time-domain (so called back-projection) formulae, frequency-domain formulae, time-reversal algorithms, and model-based algorithms. These algorithms are discussed in the context of the various acoustic detectors and detection surfaces which are commonly used in experimental studies. We further discuss the effects of non-ideal imaging scenarios on the quality of reconstruction and review methods that can mitigate these effects. Namely, we consider the cases of finite detector aperture, limited-view tomography, spatial under-sampling of the acoustic signals, and acoustic heterogeneities and losses. PMID:24772060

Detection of partial-thickness tears in ligaments and tendons by Stokes-polarimetry imaging

NASA Astrophysics Data System (ADS)

Kim, Jihoon; John, Raheel; Walsh, Joseph T.

2008-02-01

A Stokes polarimetry imaging (SPI) system utilizes an algorithm developed to construct degree of polarization (DoP) image maps from linearly polarized light illumination. Partial-thickness tears of turkey tendons were imaged by the SPI system in order to examine the feasibility of the system to detect partial-thickness rotator cuff tear or general tendon pathology. The rotating incident polarization angle (IPA) for the linearly polarized light provides a way to analyze different tissue types which may be sensitive to IPA variations. Degree of linear polarization (DoLP) images revealed collagen fiber structure, related to partial-thickness tears, better than standard intensity images. DoLP images also revealed structural changes in tears that are related to the tendon load. DoLP images with red-wavelength-filtered incident light may show tears and related organization of collagen fiber structure at a greater depth from the tendon surface. Degree of circular polarization (DoCP) images exhibited well the horizontal fiber orientation that is not parallel to the vertically aligned collagen fibers of the tendon. The SPI system's DOLP images reveal alterations in tendons and ligaments, which have a tissue matrix consisting largely of collagen, better than intensity images. All polarized images showed modulated intensity as the IPA was varied. The optimal detection of the partial-thickness tendon tears at a certain IPA was observed. The SPI system with varying IPA and spectral information can improve the detection of partial-thickness rotator cuff tears by higher visibility of fiber orientations and thereby improve diagnosis and treatment of tendon related injuries.

CFHT's SkyProbe: True Atmospheric Attenuation Measurement in the Telescope Field

NASA Astrophysics Data System (ADS)

Cuillandre, J.-C.; Magnier, E. A.; Isani, S.; Sabin, D.; Knight, W.; Kras, S.; Lai, K.

Developed at the Canada France Hawaii Telescope (CFHT), SkyProbe is a system that allows the direct measurement of the true attenuation by clouds. This measurement is performed approximately once per min, directly on the field viewed by the telescope. It has been possible to make this system relatively inexpensively due to low cost CCD cameras available on the amateur market. A crucial addition to this hardware is the recent availability of a full-sky photometry catalog at the appropriate depth: the Tycho catalog from the Hipparcos mission. A very important element in the SkyProbe data set creation is the automatic data analysis pipeline, Elixir, developed at CFHT for the improved operation of the CFHT wide-field imagers CFH12K and MegaCam. SkyProbe's FITS images are processed in real time, and the pipeline output (a zero point attenuation) provides the current sky transmission to the observers and aids immediate decision making. These measurements are also attached to the archived data, adding a key tool for future use by other astronomers. Specific features of the detector, such as intra pixel quantum efficiency variations, must be taken into consideration since the data are strongly undersampled.

Sparse reconstruction of breast MRI using homotopic L0 minimization in a regional sparsified domain.

PubMed

Wong, Alexander; Mishra, Akshaya; Fieguth, Paul; Clausi, David A

2013-03-01

The use of MRI for early breast examination and screening of asymptomatic women has become increasing popular, given its ability to provide detailed tissue characteristics that cannot be obtained using other imaging modalities such as mammography and ultrasound. Recent application-oriented developments in compressed sensing theory have shown that certain types of magnetic resonance images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled k-space data below Nyquist sampling rates using homotopic L0 minimization schemes, which holds great potential for significantly reducing acquisition time. An important consideration in the use of such homotopic L0 minimization schemes is the choice of sparsifying transform. In this paper, a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI. By taking local regional characteristics into account, the regional differential sparsifying transform can better account for signal variations and fine details that are characteristic of breast MRI than the popular finite differential transform, while still maintaining strong structure fidelity. Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods.

Dictionary Learning for Data Recovery in Positron Emission Tomography

PubMed Central

Valiollahzadeh, SeyyedMajid; Clark, John W.; Mawlawi, Osama

2015-01-01

Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as Total variation (TV), wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline). A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions. For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients’ studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications towards reducing scanner cost while maintaining accurate PET image quantification. PMID:26161630

Dictionary learning for data recovery in positron emission tomography

NASA Astrophysics Data System (ADS)

Valiollahzadeh, SeyyedMajid; Clark, John W., Jr.; Mawlawi, Osama

2015-08-01

Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline). A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions. For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients’ studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.

A mixed-order nonlinear diffusion compressed sensing MR image reconstruction.

PubMed

Joy, Ajin; Paul, Joseph Suresh

2018-03-07

Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity region but also a planar region. Therefore, a controlled application of fourth-order diffusivity function is used to encourage second-order diffusion to reconstruct the smooth regions of the image as a plane rather than a group of blocks, while not being strong enough to introduce the undesirable speckle effect. Proposed method is compared with second- and fourth-order nonlinear diffusion reconstruction, total variation (TV), total generalized variation, and higher degree TV using in vivo data sets for different undersampling levels with application to dictionary learning-based reconstruction. It is observed that the proposed technique preserves sharp boundaries in the image while preventing the formation of staircase artifacts in the regions of smoothly varying pixel intensities. It also shows reduced error measures compared with second-order nonlinear diffusion reconstruction or TV and converges faster than TV-based methods. Because nonlinear diffusion is known to be an effective alternative to TV for edge-preserving reconstruction, the crucial aspect of staircase artifact removal is addressed. Reconstruction is found to be stable for the experimentally determined range of fourth-order regularization parameter, and therefore not does not introduce a parameter search. Hence, the computational simplicity of second-order diffusion is retained. © 2018 International Society for Magnetic Resonance in Medicine.

Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model.

PubMed

Ting, Samuel T; Ahmad, Rizwan; Jin, Ning; Craft, Jason; Serafim da Silveira, Juliana; Xue, Hui; Simonetti, Orlando P

2017-04-01

Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Development of ball surface acoustic wave trace moisture analyzer using burst waveform undersampling circuit

NASA Astrophysics Data System (ADS)

Tsuji, Toshihiro; Oizumi, Toru; Fukushi, Hideyuki; Takeda, Nobuo; Akao, Shingo; Tsukahara, Yusuke; Yamanaka, Kazushi

2018-05-01

The measurement and control of trace moisture, where the water concentration is lower than 1 ppmv [-76.2 °C for the frost point (°CFP)], are essential for improving the yield rate of semiconductor devices and for ensuring their reliability. A ball surface acoustic wave (SAW) sensor with a sol-gel silica coating exhibited useful characteristics for a trace moisture analyzer (TMA) when the temperature drift of the delay time output was precisely compensated using two-frequency measurement (TFM), where the temperature-compensated relative delay time change (RDTC) was obtained by subtracting the RDTC at the fundamental frequency from that at the third harmonic frequency on an identical propagation path. However, the cost of the measurement circuit was a problem. In this study, a burst waveform undersampling (BUS) circuit based on the theory of undersampling measurement was developed as a practical means. The BUS circuit was useful for precise temperature compensation of the RDTC, and the ball SAW TMA was prototyped by calibrating the RDTC using a TMA based on cavity ring-down spectroscopy (CRDS), which is the most reliable method for trace moisture measurement. The ball SAW TMA outputted a similar concentration to that obtained by the CRDS TMA, and its response time at a set concentration in N2 with a flow rate of 1 l/min was about half that of the CRDS TMA, suggesting that moisture of -80 °CFP was measured within only 1 min. The detection limit at a signal-to-noise ratio of 3 was estimated to be 0.05 ppbv, comparable with that of the CRDS TMA. From these results, it was demonstrated that a practical ball SAW TMA can be realized using the developed BUS circuit.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

PubMed

Ichikawa, Daisuke; Saito, Toki; Ujita, Waka; Oyama, Hiroshi

2016-12-01

Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test. Data were collected during annual health check-ups performed in Japan between 2011 and 2013 (inclusive). We prepared training and test datasets from the health check-up data to build prediction models; these were composed of 43,524 and 17,789 persons, respectively. Gradient-boosting decision tree (GBDT), random forest (RF), and logistic regression (LR) approaches were trained using the training dataset and were then used to predict hyperuricemia in the test dataset. Undersampling was applied to build the prediction models to deal with the imbalanced class dataset. The results showed that the RF and GBDT approaches afforded the best performances in terms of sensitivity and specificity, respectively. The area under the curve (AUC) values of the models, which reflected the total discriminative ability of the classification, were 0.796 [95% confidence interval (CI): 0.766-0.825] for the GBDT, 0.784 [95% CI: 0.752-0.815] for the RF, and 0.785 [95% CI: 0.752-0.819] for the LR approaches. No significant differences were observed between pairs of each approach. Small changes occurred in the AUCs after applying undersampling to build the models. We developed a virtual health check-up that predicted the development of hyperuricemia using machine-learning methods. The GBDT, RF, and LR methods had similar predictive capability. Undersampling did not remarkably improve predictive power. Copyright © 2016 Elsevier Inc. All rights reserved.

Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Duarte-Carvajalino, Julio M; Sapiro, Guillermo; Lenglet, Christophe

2018-02-15

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

NASA Astrophysics Data System (ADS)

Bustillo, Andrés; Rodríguez, Juan J.

2014-12-01

Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in the bibliography.

A morphing-based scheme for large deformation analysis with stereo-DIC

NASA Astrophysics Data System (ADS)

Genovese, Katia; Sorgente, Donato

2018-05-01

A key step in the DIC-based image registration process is the definition of the initial guess for the non-linear optimization routine aimed at finding the parameters describing the pixel subset transformation. This initialization may result very challenging and possibly fail when dealing with pairs of largely deformed images such those obtained from two angled-views of not-flat objects or from the temporal undersampling of rapidly evolving phenomena. To address this problem, we developed a procedure that generates a sequence of intermediate synthetic images for gradually tracking the pixel subset transformation between the two extreme configurations. To this scope, a proper image warping function is defined over the entire image domain through the adoption of a robust feature-based algorithm followed by a NURBS-based interpolation scheme. This allows a fast and reliable estimation of the initial guess of the deformation parameters for the subsequent refinement stage of the DIC analysis. The proposed method is described step-by-step by illustrating the measurement of the large and heterogeneous deformation of a circular silicone membrane undergoing axisymmetric indentation. A comparative analysis of the results is carried out by taking as a benchmark a standard reference-updating approach. Finally, the morphing scheme is extended to the most general case of the correspondence search between two largely deformed textured 3D geometries. The feasibility of this latter approach is demonstrated on a very challenging case: the full-surface measurement of the severe deformation (> 150% strain) suffered by an aluminum sheet blank subjected to a pneumatic bulge test.

Underwater partial polarization signatures from the shallow water real-time imaging polarimeter (SHRIMP)

NASA Astrophysics Data System (ADS)

Taylor, James S., Jr.; Davis, P. S.; Wolff, Lawrence B.

2003-09-01

Research has shown that naturally occurring light outdoors and underwater is partially linearly polarized. The polarized components can be combined to form an image that describes the polarization of the light in the scene. This image is known as the degree of linear polarization (DOLP) image or partial polarization image. These naturally occurring polarization signatures can provide a diver or an unmanned underwater vehicle (UUV) with more information to detect, classify, and identify threats such as obstacles and/or mines in the shallow water environment. The SHallow water Real-time IMaging Polarimeter (SHRIMP), recently developed under sponsorship of Dr. Tom Swean at the Office of Naval Research (Code 321OE), can measure underwater partial polarization imagery. This sensor is a passive, three-channel device that simultaneously measures the three components of the Stokes vector needed to determine the partial linear polarization of the scene. The testing of this sensor has been completed and the data has been analyzed. This paper presents performance results from the field-testing and quantifies the gain provided by the partial polarization signature of targets in the Very Shallow Water (VSW) and Surf Zone (SZ) regions.

Atmospheric Concentrations of Persistent Organic Pollutants in the Southern Ocean

NASA Astrophysics Data System (ADS)

Vlahos, P.; Edson, J.; Cifuentes, A.; McGillis, W. R.; Zappa, C.

2008-12-01

Long-range transport of persistent organic pollutant (POPs) is a global concern. Remote regions such as the Southern Ocean are greatly under-sampled though critical components in understanding POPs cycling. Over 20 high-volume air samples were collected in the Southern Ocean aboard the RV Brown during the GASEX III experiment between Mar 05 to April 9 2008. The relatively stationary platform (51S,38W) enabled the collection of a unique atmospheric time series at this open ocean station. Air sampling was also conducted across transects from Punto Arenas, Chile and to Montevideo, Uruguay. Samples were collected using glass sleeves packed with poly-urethane foam plugs and C-18 resin in order to collect target organic pollutants (per-fluorinated compounds, currently and historically used pesticides) in this under-sampled region. Here we present POPs concentrations and trends over the sampled period and compare variations with air parcel back trajectories to establish potential origins of their long-range transport.

A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

PubMed

Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

2014-08-25

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

Discriminative motif discovery via simulated evolution and random under-sampling.

PubMed

Song, Tao; Gu, Hong

2014-01-01

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.

Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm.

PubMed

Levine, Rebecca S; Peterson, A Townsend; Benedict, Mark Q

2004-02-01

The distribution of the Anopheles gambiae complex of malaria vectors in Africa is uncertain due to under-sampling of vast regions. We use ecologic niche modeling to predict the potential distribution of three members of the complex (A. gambiae, A. arabiensis, and A. quadriannulatus) and demonstrate the statistical significance of the models. Predictions correspond well to previous estimates, but provide detail regarding spatial discontinuities in the distribution of A. gambiae s.s. that are consistent with population genetic studies. Our predictions also identify large areas of Africa where the presence of A. arabiensis is predicted, but few specimens have been obtained, suggesting under-sampling of the species. Finally, we project models developed from African distribution data for the late 1900s into the past and to South America to determine retrospectively whether the deadly 1929 introduction of A. gambiae sensu lato into Brazil was more likely that of A. gambiae sensu stricto or A. arabiensis.

VLC-beacon detection with an under-sampled ambient light sensor

NASA Astrophysics Data System (ADS)

Green, Jacob; Pérez-Olivas, Huetzin; Martínez-Díaz, Saúl; García-Márquez, Jorge; Domínguez-González, Carlos; Santiago-Montero, Raúl; Guan, Hongyu; Rozenblat, Marc; Topsu, Suat

2017-08-01

LEDs will replace in a near future the current worldwide lighting mainly due to their low production-cost and energy-saving assets. Visible light communications (VLC) will turn gradually the existing lighting network into a communication network. Nowadays VLC transceivers can be found in some commercial centres in Europe; some of them broadcast continuously an identification tag that contains its coordinate position. In such a case, the transceiver acts as a geolocation beacon. Nevertheless, mobile transceivers represent a challenge in the VLC communication chain, as smartphones have not integrated yet a VLC customized detection stage. In order to make current smartphones capable to detect VLC broadcasted signals, their Ambient Light Sensor (ALS) is adapted as a VLC detector. For this to be achieved, lighting transceivers need to adapt their modulation scheme. For instance, frequencies representing start bit, 1, and 0 logic values can be set to avoid flicker from illumination and to permit detecting the under-sampled signal. Decoding the signal requires a multiple steps real-time signal processing as shown here.

Low rank magnetic resonance fingerprinting.

PubMed

Mazor, Gal; Weizman, Lior; Tal, Assaf; Eldar, Yonina C

2016-08-01

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.

Limited diagnostic accuracy of magnetic resonance imaging and clinical tests for detecting partial-thickness tears of the rotator cuff.

PubMed

Brockmeyer, Matthias; Schmitt, Cornelia; Haupert, Alexander; Kohn, Dieter; Lorbach, Olaf

2017-12-01

The reliable diagnosis of partial-thickness tears of the rotator cuff is still elusive in clinical practise. Therefore, the purpose of the study was to determine the diagnostic accuracy of MR imaging and clinical tests for detecting partial-thickness tears of the rotator cuff as well as the combination of these parameters. 334 consecutive shoulder arthroscopies for rotator cuff pathologies performed during the time period between 2010 and 2012 were analyzed retrospectively for the findings of common clinical signs for rotator cuff lesions and preoperative MR imaging. These were compared with the intraoperative arthroscopic findings as "gold standard". The reports of the MR imaging were evaluated with regard to the integrity of the rotator cuff. The Ellman Classification was used to define partial-thickness tears of the rotator cuff in accordance with the arthroscopic findings. Descriptive statistics, sensitivity, specificity, positive and negative predictive value were calculated. MR imaging showed 80 partial-thickness and 70 full-thickness tears of the rotator cuff. The arthroscopic examination confirmed 64 partial-thickness tears of which 52 needed debridement or refixation of the rotator cuff. Sensitivity for MR imaging to identify partial-thickness tears was 51.6%, specificity 77.2%, positive predictive value 41.3% and negative predictive value 83.7%. For the Jobe-test, sensitivity was 64.1%, specificity 43.2%, positive predictive value 25.9% and negative predictive value 79.5%. Sensitivity for the Impingement-sign was 76.7%, specificity 46.6%, positive predictive value 30.8% and negative predictive value 86.5%. For the combination of MR imaging, Jobe-test and Impingement-sign sensitivity was 46.9%, specificity 85.4%, positive predictive value 50% and negative predictive value 83.8%. The diagnostic accuracy of MR imaging and clinical tests (Jobe-test and Impingement-sign) alone is limited for detecting partial-thickness tears of the rotator cuff. Additionally, the combination of MR imaging and clinical tests does not improve diagnostic accuracy. Level II, Diagnostic study.

Considering low-rank, sparse and gas-inflow effects constraints for accelerated pulmonary dynamic hyperpolarized 129Xe MRI

NASA Astrophysics Data System (ADS)

Xiao, Sa; Deng, He; Duan, Caohui; Xie, Junshuai; Zhang, Huiting; Sun, Xianping; Ye, Chaohui; Zhou, Xin

2018-05-01

Dynamic hyperpolarized (HP) 129Xe MRI is able to visualize the process of lung ventilation, which potentially provides unique information about lung physiology and pathophysiology. However, the longitudinal magnetization of HP 129Xe is nonrenewable, making it difficult to achieve high image quality while maintaining high temporal-spatial resolution in the pulmonary dynamic MRI. In this paper, we propose a new accelerated dynamic HP 129Xe MRI scheme incorporating the low-rank, sparse and gas-inflow effects (L + S + G) constraints. According to the gas-inflow effects of HP gas during the lung inspiratory process, a variable-flip-angle (VFA) strategy is designed to compensate for the rapid attenuation of the magnetization. After undersampling k-space data, an effective reconstruction algorithm considering the low-rank, sparse and gas-inflow effects constraints is developed to reconstruct dynamic MR images. In this way, the temporal and spatial resolution of dynamic MR images is improved and the artifacts are lessened. Simulation and in vivo experiments implemented on the phantom and healthy volunteers demonstrate that the proposed method is not only feasible and effective to compensate for the decay of the magnetization, but also has a significant improvement compared with the conventional reconstruction algorithms (P-values are less than 0.05). This confirms the superior performance of the proposed designs and their ability to maintain high quality and temporal-spatial resolution.

Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting.

PubMed

Cao, Xiaozhi; Liao, Congyu; Wang, Zhixing; Chen, Ying; Ye, Huihui; He, Hongjian; Zhong, Jianhui

2017-10-01

To develop a method for accelerated and robust MR fingerprinting (MRF) with improved image reconstruction and parameter matching processes. A sliding-window (SW) strategy was applied to MRF, in which signal and dictionary matching was conducted between fingerprints consisting of mixed-contrast image series reconstructed from consecutive data frames segmented by a sliding window, and a precalculated mixed-contrast dictionary. The effectiveness and performance of this new method, dubbed SW-MRF, was evaluated in both phantom and in vivo. Error quantifications were conducted on results obtained with various settings of SW reconstruction parameters. Compared with the original MRF strategy, the results of both phantom and in vivo experiments demonstrate that the proposed SW-MRF strategy either provided similar accuracy with reduced acquisition time, or improved accuracy with equal acquisition time. Parametric maps of T 1 , T 2 , and proton density of comparable quality could be achieved with a two-fold or more reduction in acquisition time. The effect of sliding-window width on dictionary sensitivity was also estimated. The novel SW-MRF recovers high quality image frames from highly undersampled MRF data, which enables more robust dictionary matching with reduced numbers of data frames. This time efficiency may facilitate MRF applications in time-critical clinical settings. Magn Reson Med 78:1579-1588, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Multiscale reconstruction for MR fingerprinting.

PubMed

Pierre, Eric Y; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A

2016-06-01

To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Zone of Avoidance Tully-Fisher Survey

NASA Astrophysics Data System (ADS)

Williams, Wendy; Woudt, Patrick; Kraan-Korteweg, Renee

2009-10-01

We propose to use the Parkes telescope to obtain narrowband HI spectra of a sample of galaxies in the Galactic Zone of Avoidance (ZOA). These observations, combined with high-quality near infrared photometry, will provide both the uniform coverage and accurate distance determinations (via the Tully-Fisher relation) required to map the peculiar velocity flow fields in the ZOA. The mass distribution in this region has a significant effect on the motion of the Local Group. Dynamically important structures, including the Great Attractor and the Local Void, are partially hidden behind our Galaxy. Even the most recent systematic all-sky surveys, such as the 2MASS Redshift Survey (2MRS; Huchra et al. 2005), undersample the ZOA due to stellar crowding and high dust extinction. While statistical reconstruction methods have been used to extrapolate the density field in the ZOA, they are unlikely to truely re?ect the velocity field (Loeb & Narayan 2008). Our project aims for the ?rst time to directly determine the velocity flow fields in this part of the sky. Our sample is taken from the Parkes HIZOA survey (Henning et al. 2005) and is unbiased with respect to extinction and star density.

Use of (N-1)-D expansions for N-D phase unwrapping in MRI

NASA Astrophysics Data System (ADS)

Bones, Philip J.; King, Laura J.; Millane, Rick P.

2017-09-01

In MRI the presence of metal implants causes severe artifacts in images and interferes with the usual techniques used to separate fat signals from other tissues. In the Dixon method, three images are acquired at different echo times to enable the variation in the magnetic field to be estimated. However, the estimate is represented as the phase of a complex quantity and therefore suffers from wrapping. High field gradients near the metal mean that the phase estimate is undersampled and therefore challenging to unwrap. We have developed POP, phase estimation by onion peeling, an algorithm which unwraps the phase along 1-D paths for a 2-D image obtained with the Dixon method. The unwrapping is initially performed along a closed path enclosing the implant and well separated from it. The recovered phase is expanded using a smooth periodic basis along the path. Then, path-by-path, the estimate is applied to the next path and then the expansion coefficients are estimated to best fit the wrapped measurements. We have successfully tested POP on MRI images of specially constructed phantoms and on a group of patients with hip implants. In principle, POP can be extended to 3-D imaging. In that case, POP would entail representing phase with a suitably smooth basis over a series of surfaces enclosing the implant (the "onion skins"), again beginning the phase estimation well away from the implant. An approach for this is proposed. Results are presented for fat and water separation for 2-D images of phantoms and actual patients. The practicality of the method and its employment in clinical MRI are discussed.

Shading correction assisted iterative cone-beam CT reconstruction

NASA Astrophysics Data System (ADS)

Yang, Chunlin; Wu, Pengwei; Gong, Shutao; Wang, Jing; Lyu, Qihui; Tang, Xiangyang; Niu, Tianye

2017-11-01

Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under-sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non-ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low-dose CBCT reconstruction and propose a clinically acceptable and stable three-dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low-pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage-thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan© 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low-dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low-dose CBCT imaging in the clinic.

Partial scan artifact reduction (PSAR) for the assessment of cardiac perfusion in dynamic phase-correlated CT.

PubMed

Stenner, Philip; Schmidt, Bernhard; Bruder, Herbert; Allmendinger, Thomas; Haberland, Ulrike; Flohr, Thomas; Kachelriess, Marc

2009-12-01

Cardiac CT achieves its high temporal resolution by lowering the scan range from 2pi to pi plus fan angle (partial scan). This, however, introduces CT-value variations, depending on the angular position of the pi range. These partial scan artifacts are of the order of a few HU and prevent the quantitative evaluation of perfusion measurements. The authors present the new algorithm partial scan artifact reduction (PSAR) that corrects a dynamic phase-correlated scan without a priori information. In general, a full scan does not suffer from partial scan artifacts since all projections in [0, 2pi] contribute to the data. To maintain the optimum temporal resolution and the phase correlation, PSAR creates an artificial full scan pn(AF) by projectionwise averaging a set of neighboring partial scans pn(P) from the same perfusion examination (typically N approximately 30 phase-correlated partial scans distributed over 20 s and n = 1, ..., N). Corresponding to the angular range of each partial scan, the authors extract virtual partial scans pn(V) from the artificial full scan pn(AF). A standard reconstruction yields the corresponding images fn(P), fn(AF), and fn(V). Subtracting the virtual partial scan image fn(V) from the artificial full scan image fn(AF) yields an artifact image that can be used to correct the original partial scan image: fn(C) = fn(P) - fn(V) + fn(AF), where fn(C) is the corrected image. The authors evaluated the effects of scattered radiation on the partial scan artifacts using simulated and measured water phantoms and found a strong correlation. The PSAR algorithm has been validated with a simulated semianthropomorphic heart phantom and with measurements of a dynamic biological perfusion phantom. For the stationary phantoms, real full scans have been performed to provide theoretical reference values. The improvement in the root mean square errors between the full and the partial scans with respect to the errors between the full and the corrected scans is up to 54% for the simulations and 90% for the measurements. The phase-correlated data now appear accurate enough for a quantitative analysis of cardiac perfusion.

Image Reconstruction for a Partially Collimated Whole Body PET Scanner

PubMed Central

Alessio, Adam M.; Schmitz, Ruth E.; MacDonald, Lawrence R.; Wollenweber, Scott D.; Stearns, Charles W.; Ross, Steven G.; Ganin, Alex; Lewellen, Thomas K.; Kinahan, Paul E.

2008-01-01

Partially collimated PET systems have less collimation than conventional 2-D systems and have been shown to offer count rate improvements over 2-D and 3-D systems. Despite this potential, previous efforts have not established image-based improvements with partial collimation and have not customized the reconstruction method for partially collimated data. This work presents an image reconstruction method tailored for partially collimated data. Simulated and measured sensitivity patterns are presented and provide a basis for modification of a fully 3-D reconstruction technique. The proposed method uses a measured normalization correction term to account for the unique sensitivity to true events. This work also proposes a modified scatter correction based on simulated data. Measured image quality data supports the use of the normalization correction term for true events, and suggests that the modified scatter correction is unnecessary. PMID:19096731

Image Reconstruction for a Partially Collimated Whole Body PET Scanner.

PubMed

Alessio, Adam M; Schmitz, Ruth E; Macdonald, Lawrence R; Wollenweber, Scott D; Stearns, Charles W; Ross, Steven G; Ganin, Alex; Lewellen, Thomas K; Kinahan, Paul E

2008-06-01

Partially collimated PET systems have less collimation than conventional 2-D systems and have been shown to offer count rate improvements over 2-D and 3-D systems. Despite this potential, previous efforts have not established image-based improvements with partial collimation and have not customized the reconstruction method for partially collimated data. This work presents an image reconstruction method tailored for partially collimated data. Simulated and measured sensitivity patterns are presented and provide a basis for modification of a fully 3-D reconstruction technique. The proposed method uses a measured normalization correction term to account for the unique sensitivity to true events. This work also proposes a modified scatter correction based on simulated data. Measured image quality data supports the use of the normalization correction term for true events, and suggests that the modified scatter correction is unnecessary.

77 FR 2299 - Agency Information Collection Activities; Proposed Collection; Comment Request; Healthcare...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-17

... survey is designed to explore the opinions and perceptions of physicians, nurse practitioners, and... assistants. Such a design will help to ensure our ability to discuss not only healthcare professional... well as any noncoverage or undersampling and oversampling resulting from the sample design...

Partially converted stereoscopic images and the effects on visual attention and memory

NASA Astrophysics Data System (ADS)

Kim, Sanghyun; Morikawa, Hiroyuki; Mitsuya, Reiko; Kawai, Takashi; Watanabe, Katsumi

2015-03-01

This study contained two experimental examinations of the cognitive activities such as visual attention and memory in viewing stereoscopic (3D) images. For this study, partially converted 3D images were used with binocular parallax added to a specific region of the image. In Experiment 1, change blindness was used as a presented stimulus. The visual attention and impact on memory were investigated by measuring the response time to accomplish the given task. In the change blindness task, an 80 ms blank was intersected between the original and altered images, and the two images were presented alternatingly for 240 ms each. Subjects were asked to temporarily memorize the two switching images and to compare them, visually recognizing the difference between the two. The stimuli for four conditions (2D, 3D, Partially converted 3D, distracted partially converted 3D) were randomly displayed for 20 subjects. The results of Experiment 1 showed that partially converted 3D images tend to attract visual attention and are prone to remain in viewer's memory in the area where moderate negative parallax has been added. In order to examine the impact of a dynamic binocular disparity on partially converted 3D images, an evaluation experiment was conducted that applied learning, distraction, and recognition tasks for 33 subjects. The learning task involved memorizing the location of cells in a 5 × 5 matrix pattern using two different colors. Two cells were positioned with alternating colors, and one of the gray cells was moved up, down, left, or right by one cell width. Experimental conditions was set as a partially converted 3D condition in which a gray cell moved diagonally for a certain period of time with a dynamic binocular disparity added, a 3D condition in which binocular disparity was added to all gray cells, and a 2D condition. The correct response rates for recognition of each task after the distraction task were compared. The results of Experiment 2 showed that the correct response rate in the partial 3D condition was significantly higher with the recognition task than in the other conditions. These results showed that partially converted 3D images tended to have a visual attraction and affect viewer's memory.

Simulation of the modulation transfer function dependent on the partial Fourier fraction in dynamic contrast enhancement magnetic resonance imaging.

PubMed

Takatsu, Yasuo; Ueyama, Tsuyoshi; Miyati, Tosiaki; Yamamura, Kenichirou

2016-12-01

The image characteristics in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) depend on the partial Fourier fraction and contrast medium concentration. These characteristics were assessed and the modulation transfer function (MTF) was calculated by computer simulation. A digital phantom was created from signal intensity data acquired at different contrast medium concentrations on a breast model. The frequency images [created by fast Fourier transform (FFT)] were divided into 512 parts and rearranged to form a new image. The inverse FFT of this image yielded the MTF. From the reference data, three linear models (low, medium, and high) and three exponential models (slow, medium, and rapid) of the signal intensity were created. Smaller partial Fourier fractions, and higher gradients in the linear models, corresponded to faster MTF decline. The MTF more gradually decreased in the exponential models than in the linear models. The MTF, which reflects the image characteristics in DCE-MRI, was more degraded as the partial Fourier fraction decreased.

Introducing OTUshuff and DwOdum: A new set of tools for estimating beta diversity for under-sampled communities

USDA-ARS?s Scientific Manuscript database

Characterization of complex microbial communities by DNA sequencing has become a standard technique in microbial ecology. Yet, particular features of this approach render traditional methods of community comparison problematic. In particular, a very low proportion of community members are typically ...

Estimating beta diversity for under-sampled communities using the variably weighted Odum dissimilarity index and OTUshuff

USDA-ARS?s Scientific Manuscript database

Characterization of complex microbial communities by DNA sequencing has become a standard technique in microbial ecology. Yet, particular features of this approach render traditional methods of community comparison problematic. In particular, a very low proportion of community members are typically ...

Self-imaging of partially coherent light in graded-index media.

PubMed

Ponomarenko, Sergey A

2015-02-15

We demonstrate that partially coherent light beams of arbitrary intensity and spectral degree of coherence profiles can self-image in linear graded-index media. The results can be applicable to imaging with noisy spatial or temporal light sources.

Fast template matching with polynomials.

PubMed

Omachi, Shinichiro; Omachi, Masako

2007-08-01

Template matching is widely used for many applications in image and signal processing. This paper proposes a novel template matching algorithm, called algebraic template matching. Given a template and an input image, algebraic template matching efficiently calculates similarities between the template and the partial images of the input image, for various widths and heights. The partial image most similar to the template image is detected from the input image for any location, width, and height. In the proposed algorithm, a polynomial that approximates the template image is used to match the input image instead of the template image. The proposed algorithm is effective especially when the width and height of the template image differ from the partial image to be matched. An algorithm using the Legendre polynomial is proposed for efficient approximation of the template image. This algorithm not only reduces computational costs, but also improves the quality of the approximated image. It is shown theoretically and experimentally that the computational cost of the proposed algorithm is much smaller than the existing methods.

Using a local low rank plus sparse reconstruction to accelerate dynamic hyperpolarized 13C imaging using the bSSFP sequence.

PubMed

Milshteyn, Eugene; von Morze, Cornelius; Reed, Galen D; Shang, Hong; Shin, Peter J; Larson, Peder E Z; Vigneron, Daniel B

2018-05-01

Acceleration of dynamic 2D (T 2 Mapping) and 3D hyperpolarized 13 C MRI acquisitions using the balanced steady-state free precession sequence was achieved with a specialized reconstruction method, based on the combination of low rank plus sparse and local low rank reconstructions. Methods were validated using both retrospectively and prospectively undersampled in vivo data from normal rats and tumor-bearing mice. Four-fold acceleration of 1-2 mm isotropic 3D dynamic acquisitions with 2-5 s temporal resolution and two-fold acceleration of 0.25-1 mm 2 2D dynamic acquisitions was achieved. This enabled visualization of the biodistribution of [2- 13 C]pyruvate, [1- 13 C]lactate, [ 13 C,  15 N 2 ]urea, and HP001 within heart, kidneys, vasculature, and tumor, as well as calculation of high resolution T 2 maps. Copyright © 2018 Elsevier Inc. All rights reserved.

Joint synthetic aperture radar plus ground moving target indicator from single-channel radar using compressive sensing

DOEpatents

Thompson, Douglas; Hallquist, Aaron; Anderson, Hyrum

2017-10-17

The various embodiments presented herein relate to utilizing an operational single-channel radar to collect and process synthetic aperture radar (SAR) and ground moving target indicator (GMTI) imagery from a same set of radar returns. In an embodiment, data is collected by randomly staggering a slow-time pulse repetition interval (PRI) over a SAR aperture such that a number of transmitted pulses in the SAR aperture is preserved with respect to standard SAR, but many of the pulses are spaced very closely enabling movers (e.g., targets) to be resolved, wherein a relative velocity of the movers places them outside of the SAR ground patch. The various embodiments of image reconstruction can be based on compressed sensing inversion from undersampled data, which can be solved efficiently using such techniques as Bregman iteration. The various embodiments enable high-quality SAR reconstruction, and high-quality GMTI reconstruction from the same set of radar returns.

Motion video compression system with neural network having winner-take-all function

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi (Inventor); Sheu, Bing J. (Inventor)

1997-01-01

A motion video data system includes a compression system, including an image compressor, an image decompressor correlative to the image compressor having an input connected to an output of the image compressor, a feedback summing node having one input connected to an output of the image decompressor, a picture memory having an input connected to an output of the feedback summing node, apparatus for comparing an image stored in the picture memory with a received input image and deducing therefrom pixels having differences between the stored image and the received image and for retrieving from the picture memory a partial image including the pixels only and applying the partial image to another input of the feedback summing node, whereby to produce at the output of the feedback summing node an updated decompressed image, a subtraction node having one input connected to received the received image and another input connected to receive the partial image so as to generate a difference image, the image compressor having an input connected to receive the difference image whereby to produce a compressed difference image at the output of the image compressor.

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE PAGES

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

2014-12-01

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Research on interpolation methods in medical image processing.

PubMed

Pan, Mei-Sen; Yang, Xiao-Li; Tang, Jing-Tian

2012-04-01

Image interpolation is widely used for the field of medical image processing. In this paper, interpolation methods are divided into three groups: filter interpolation, ordinary interpolation and general partial volume interpolation. Some commonly-used filter methods for image interpolation are pioneered, but the interpolation effects need to be further improved. When analyzing and discussing ordinary interpolation, many asymmetrical kernel interpolation methods are proposed. Compared with symmetrical kernel ones, the former are have some advantages. After analyzing the partial volume and generalized partial volume estimation interpolations, the new concept and constraint conditions of the general partial volume interpolation are defined, and several new partial volume interpolation functions are derived. By performing the experiments of image scaling, rotation and self-registration, the interpolation methods mentioned in this paper are compared in the entropy, peak signal-to-noise ratio, cross entropy, normalized cross-correlation coefficient and running time. Among the filter interpolation methods, the median and B-spline filter interpolations have a relatively better interpolating performance. Among the ordinary interpolation methods, on the whole, the symmetrical cubic kernel interpolations demonstrate a strong advantage, especially the symmetrical cubic B-spline interpolation. However, we have to mention that they are very time-consuming and have lower time efficiency. As for the general partial volume interpolation methods, from the total error of image self-registration, the symmetrical interpolations provide certain superiority; but considering the processing efficiency, the asymmetrical interpolations are better.

30 Doradus: The Low-Mass Stars

NASA Astrophysics Data System (ADS)

Zinnecker, H.; Brandl, B.; Brandner, W.; Moneti, A.; Hunter, D.

We have obtained HST/NICMOS H-band images of the central 1'x1' field around the R136 starburst cluster in the 30 Doradus HII region, in an attempt to reveal the presence (or absence) of a low-mass stellar population (M < 1 Mo). We will discuss the fascinating prospect of 30 Dor/R136 being a proto-globular cluster and a template starburst unit. At the time of writing, we are still working to determine which method and photometry package is best suited to our 0.15" NICMOS images, which are characterised by extreme crowding in the cluster center and a peculiar and slightly undersampled NICMOS PSF. The main difficulty with the PSF is identifying the many "dots" that appear outside the Airy ring as PSF features and not as faint stars. Prelimininary analysis suggests that the H-band luminosity function rises at least until H = 20 (2 Mo). We have detected numerous stars with 20.0 < H < 22.5 (the latter corresponding to 0.4 Mo) beyond about 7" from the cluster centre, but we have not yet determined the completeness in that magnitude range, and we are not yet in a position to make a statement about the shape of the H-band luminosity function there. We have combined our infrared data with the optical WFPC2 images of Hunter et al. (1995) to produce a VIH 3-colour image of the central 30" x 30" area. The result clearly shows unexpected patches of extinction, with one patch only about 5" from the cluster core.

Deep learning with domain adaptation for accelerated projection-reconstruction MR.

PubMed

Han, Yoseob; Yoo, Jaejun; Kim, Hak Hee; Shin, Hee Jung; Sung, Kyunghyun; Ye, Jong Chul

2018-09-01

The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory requires a large number of radial lines for high-resolution reconstruction. Increasing the number of radial lines causes longer acquisition time, making it more difficult for routine clinical use. On the other hand, if we reduce the number of radial lines, streaking artifact patterns are unavoidable. To solve this problem, we propose a novel deep learning approach with domain adaptation to restore high-resolution MR images from under-sampled k-space data. The proposed deep network removes the streaking artifacts from the artifact corrupted images. To address the situation given the limited available data, we propose a domain adaptation scheme that employs a pre-trained network using a large number of X-ray computed tomography (CT) or synthesized radial MR datasets, which is then fine-tuned with only a few radial MR datasets. The proposed method outperforms existing compressed sensing algorithms, such as the total variation and PR-FOCUSS methods. In addition, the calculation time is several orders of magnitude faster than the total variation and PR-FOCUSS methods. Moreover, we found that pre-training using CT or MR data from similar organ data is more important than pre-training using data from the same modality for different organ. We demonstrate the possibility of a domain-adaptation when only a limited amount of MR data is available. The proposed method surpasses the existing compressed sensing algorithms in terms of the image quality and computation time. © 2018 International Society for Magnetic Resonance in Medicine.

Evaluation of fast highly undersampled contrast-enhanced MR angiography (sparse CE-MRA) in intracranial applications - initial study.

PubMed

Gratz, Marcel; Schlamann, Marc; Goericke, Sophia; Maderwald, Stefan; Quick, Harald H

2017-03-01

To assess the image quality of sparsely sampled contrast-enhanced MR angiography (sparse CE-MRA) providing high spatial resolution and whole-head coverage. Twenty-three patients scheduled for contrast-enhanced MR imaging of the head, (N = 19 with intracranial pathologies, N = 9 with vascular diseases), were included. Sparse CE-MRA at 3 Tesla was conducted using a single dose of contrast agent. Two neuroradiologists independently evaluated the data regarding vascular visibility and diagnostic value of overall 24 parameters and vascular segments on a 5-point ordinary scale (5 = very good, 1 = insufficient vascular visibility). Contrast bolus timing and the resulting arterio-venous overlap was also evaluated. Where available (N = 9), sparse CE-MRA was compared to intracranial Time-of-Flight MRA. The overall rating across all patients for sparse CE-MRA was 3.50 ± 1.07. Direct influence of the contrast bolus timing on the resulting image quality was observed. Overall mean vascular visibility and image quality across different features was rated good to intermediate (3.56 ± 0.95). The average performance of intracranial Time-of-Flight was rated 3.84 ± 0.87 across all patients and 3.54 ± 0.62 across all features. Sparse CE-MRA provides high-quality 3D MRA with high spatial resolution and whole-head coverage within short acquisition time. Accurate contrast bolus timing is mandatory. • Sparse CE-MRA enables fast vascular imaging with full brain coverage. • Volumes with sub-millimetre resolution can be acquired within 10 seconds. • Reader's ratings are good to intermediate and dependent on contrast bolus timing. • The method provides an excellent overview and allows screening for vascular pathologies.

Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data

NASA Astrophysics Data System (ADS)

Humphries, T.; Winn, J.; Faridani, A.

2017-08-01

Recent work in CT image reconstruction has seen increasing interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to ‘superiorize’ an iterative image reconstruction algorithm with respect to a chosen objective function, such as TV. Under certain conditions, the superiorized algorithm is guaranteed to find a solution that is as satisfactory as any found by the original algorithm with respect to satisfying the constraints of the problem; this solution is also expected to be superior with respect to the chosen objective. Most work on superiorization has used reconstruction algorithms which assume a linear measurement model, which in the case of CT corresponds to data generated from a monoenergetic x-ray beam. Many CT systems generate x-rays from a polyenergetic spectrum, however, in which the measured data represent an integral of object attenuation over all energies in the spectrum. This inconsistency with the linear model produces the well-known beam hardening artifacts, which impair analysis of CT images. In this work we superiorize an iterative algorithm for reconstruction from polyenergetic data, using both TV and an anisotropic TV (ATV) penalty. We apply the superiorized algorithm in numerical phantom experiments modeling both sparse-view and limited-angle scenarios. In our experiments, the superiorized algorithm successfully finds solutions which are as constraints-compatible as those found by the original algorithm, with significantly reduced TV and ATV values. The superiorized algorithm thus produces images with greatly reduced sparse-view and limited angle artifacts, which are also largely free of the beam hardening artifacts that would be present if a superiorized version of a monoenergetic algorithm were used.

LANDSAT-D data format control book. Volume 6, appendix C: Partially processed multispectral scanner high density tape (HDT-AM)

NASA Technical Reports Server (NTRS)

Andersen, K. E.

1982-01-01

The format of high density tapes which contain partially processed LANDSAT 4 and LANDSAT D prime MSS image data is defined. This format is based on and is compatible with the existing format for partially processed LANDSAT 3 MSS image data HDTs.

Analytical-Based Partial Volume Recovery in Mouse Heart Imaging

NASA Astrophysics Data System (ADS)

Dumouchel, Tyler; deKemp, Robert A.

2011-02-01

Positron emission tomography (PET) is a powerful imaging modality that has the ability to yield quantitative images of tracer activity. Physical phenomena such as photon scatter, photon attenuation, random coincidences and spatial resolution limit quantification potential and must be corrected to preserve the accuracy of reconstructed images. This study focuses on correcting the partial volume effects that arise in mouse heart imaging when resolution is insufficient to resolve the true tracer distribution in the myocardium. The correction algorithm is based on fitting 1D profiles through the myocardium in gated PET images to derive myocardial contours along with blood, background and myocardial activity. This information is interpolated onto a 2D grid and convolved with the tomograph's point spread function to derive regional recovery coefficients enabling partial volume correction. The point spread function was measured by placing a line source inside a small animal PET scanner. PET simulations were created based on noise properties measured from a reconstructed PET image and on the digital MOBY phantom. The algorithm can estimate the myocardial activity to within 5% of the truth when different wall thicknesses, backgrounds and noise properties are encountered that are typical of healthy FDG mouse scans. The method also significantly improves partial volume recovery in simulated infarcted tissue. The algorithm offers a practical solution to the partial volume problem without the need for co-registered anatomic images and offers a basis for improved quantitative 3D heart imaging.

Adaptive optics images restoration based on frame selection and multi-framd blind deconvolution

NASA Astrophysics Data System (ADS)

Tian, Y.; Rao, C. H.; Wei, K.

2008-10-01

The adaptive optics can only partially compensate the image blurred by atmospheric turbulent due to the observing condition and hardware restriction. A post-processing method based on frame selection and multi-frame blind deconvolution to improve images partially corrected by adaptive optics is proposed. The appropriate frames which are picked out by frame selection technique is deconvolved. There is no priori knowledge except the positive constraint. The method has been applied in the image restoration of celestial bodies which were observed by 1.2m telescope equipped with 61-element adaptive optical system in Yunnan Observatory. The results showed that the method can effectively improve the images partially corrected by adaptive optics.

Lung morphometry using hyperpolarized 129 Xe multi-b diffusion MRI with compressed sensing in healthy subjects and patients with COPD.

PubMed

Zhang, Huiting; Xie, Junshuai; Xiao, Sa; Zhao, Xiuchao; Zhang, Ming; Shi, Lei; Wang, Ke; Wu, Guangyao; Sun, Xianping; Ye, Chaohui; Zhou, Xin

2018-05-04

To demonstrate the feasibility of compressed sensing (CS) to accelerate the acquisition of hyperpolarized (HP) 129 Xe multi-b diffusion MRI for quantitative assessments of lung microstructural morphometry. Six healthy subjects and six chronic obstructive pulmonary disease (COPD) subjects underwent HP 129 Xe multi-b diffusion MRI (b = 0, 10, 20, 30, and 40 s/cm 2 ). First, a fully sampled (FS) acquisition of HP 129 Xe multi-b diffusion MRI was conducted in one healthy subject. The acquired FS dataset was retrospectively undersampled in the phase encoding direction, and an optimal twofold undersampled pattern was then obtained by minimizing mean absolute error (MAE) between retrospective CS (rCS) and FS MR images. Next, the FS and CS acquisitions during separate breath holds were performed on five healthy subjects (including the above one). Additionally, the FS and CS synchronous acquisitions during a single breath hold were performed on the sixth healthy subject and one COPD subject. However, only CS acquisitions were conducted in the rest of the five COPD subjects. Finally, all the acquired FS, rCS and CS MR images were used to obtain morphometric parameters, including acinar duct radius (R), acinar lumen radius (r), alveolar sleeve depth (h), mean linear intercept (L m ), and surface-to-volume ratio (SVR). The Wilcoxon signed-rank test and the Bland-Altman plot were employed to assess the fidelity of the CS reconstruction. Moreover, the t-test was used to demonstrate the effectiveness of the multi-b diffusion MRI with CS in clinical applications. The retrospective results demonstrated that there was no statistically significant difference between rCS and FS measurements using the Wilcoxon signed-rank test (P > 0.05). Good agreement between measurements obtained with the CS and FS acquisitions during separate breath holds was demonstrated in Bland-Altman plots of slice differences. Specifically, the mean biases of the R, r, h, L m , and SVR between the CS and FS acquisitions were 1.0%, 2.6%, -0.03%, 1.5%, and -5.5%, respectively. Good agreement between measurements with the CS and FS acquisitions was also observed during the single breath-hold experiments. Furthermore, there were significant differences between the morphometric parameters for the healthy and COPD subjects (P < 0.05). Our study has shown that HP 129 Xe multi-b diffusion MRI with CS could be beneficial in lung microstructural assessments by acquiring less data while maintaining the consistent results with the FS acquisitions. © 2018 American Association of Physicists in Medicine.

Frequency Domain Design of Robust Controllers for Space Structures

DTIC Science & Technology

1989-08-01

A natural assumption for structural problems [1] is that A0 is self -adjoint with compact resolvent and discrete (real) spectrum which is bounded from...the same controller using only 256 points. The curves are very similiar, but in both cases, the low frequency end of the respose is under-sampled due

Under-sampling in a Multiple-Channel Laser Vibrometry System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corey, Jordan

2007-03-01

Laser vibrometry is a technique used to detect vibrations on objects using the interference of coherent light with itself. Most vibrometry systems process only one target location at a time, but processing multiple locations simultaneously provides improved detection capabilities. Traditional laser vibrometry systems employ oversampling to sample the incoming modulated-light signal, however as the number of channels increases in these systems, certain issues arise such a higher computational cost, excessive heat, increased power requirements, and increased component cost. This thesis describes a novel approach to laser vibrometry that utilizes undersampling to control the undesirable issues associated with over-sampled systems. Undersamplingmore » allows for significantly less samples to represent the modulated-light signals, which offers several advantages in the overall system design. These advantages include an improvement in thermal efficiency, lower processing requirements, and a higher immunity to the relative intensity noise inherent in laser vibrometry applications. A unique feature of this implementation is the use of a parallel architecture to increase the overall system throughput. This parallelism is realized using a hierarchical multi-channel architecture based on off-the-shelf programmable logic devices (PLDs).« less

In vitro evaluation of flow patterns and turbulent kinetic energy in trans-catheter aortic valve prostheses.

PubMed

Giese, Daniel; Weiss, Kilian; Baeßler, Bettina; Madershahian, Navid; Choi, Yeong-Hoon; Maintz, David; Bunck, Alexander C

2018-02-01

The objective of the current work was to evaluate flow and turbulent kinetic energy in different transcatheter aortic valve implants using highly undersampled time-resolved multi-point 3-directional phase-contrast measurements (4D Flow MRI) in an in vitro setup. A pulsatile flow setup was used with a compliant tubing mimicking a stiff left ventricular outflow tract and ascending aorta. Five different implants were measured using a highly undersampled multi-point 4D Flow MRI sequence. Velocities and turbulent kinetic energy values were analysed and compared. Strong variations of turbulent kinetic energy distributions between the valves were observed. Maximum turbulent kinetic energy values ranged from 100 to over 500 J/m 3 while through-plane velocities were similar between all valves. Highly accelerated 4D Flow MRI for the measurement of velocities and turbulent kinetic energy values allowed for the assessment of hemodynamic parameters in five different implant models. The presented setup, measurement protocol and analysis methods provides an efficient approach to compare different valve implants and could aid future novel valve designs.

Use of pattern recognition for unaliasing simultaneously acquired slices in simultaneous multislice MR fingerprinting.

PubMed

Jiang, Yun; Ma, Dan; Bhat, Himanshu; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L; Setsompop, Kawin; Griswold, Mark A

2017-11-01

The purpose of this study is to accelerate an MR fingerprinting (MRF) acquisition by using a simultaneous multislice method. A multiband radiofrequency (RF) pulse was designed to excite two slices with different flip angles and phases. The signals of two slices were driven to be as orthogonal as possible. The mixed and undersampled MRF signal was matched to two dictionaries to retrieve T 1 and T 2 maps of each slice. Quantitative results from the proposed method were validated with the gold-standard spin echo methods in a phantom. T 1 and T 2 maps of in vivo human brain from two simultaneously acquired slices were also compared to the results of fast imaging with steady-state precession based MRF method (MRF-FISP) with a single-band RF excitation. The phantom results showed that the simultaneous multislice imaging MRF-FISP method quantified the relaxation properties accurately compared to the gold-standard spin echo methods. T 1 and T 2 values of in vivo brain from the proposed method also matched the results from the normal MRF-FISP acquisition. T 1 and T 2 values can be quantified at a multiband acceleration factor of two using our proposed acquisition even in a single-channel receive coil. Further acceleration could be achieved by combining this method with parallel imaging or iterative reconstruction. Magn Reson Med 78:1870-1876, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

SU-F-J-23: Field-Of-View Expansion in Cone-Beam CT Reconstruction by Use of Prior Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haga, A; Magome, T; Nakano, M

Purpose: Cone-beam CT (CBCT) has become an integral part of online patient setup in an image-guided radiation therapy (IGRT). In addition, the utility of CBCT for dose calculation has actively been investigated. However, the limited size of field-of-view (FOV) and resulted CBCT image with a lack of peripheral area of patient body prevents the reliability of dose calculation. In this study, we aim to develop an FOV expanded CBCT in IGRT system to allow the dose calculation. Methods: Three lung cancer patients were selected in this study. We collected the cone-beam projection images in the CBCT-based IGRT system (X-ray volumemore » imaging unit, ELEKTA), where FOV size of the provided CBCT with these projections was 410 × 410 mm{sup 2} (normal FOV). Using these projections, CBCT with a size of 728 × 728 mm{sup 2} was reconstructed by a posteriori estimation algorithm including a prior image constrained compressed sensing (PICCS). The treatment planning CT was used as a prior image. To assess the effectiveness of FOV expansion, a dose calculation was performed on the expanded CBCT image with region-of-interest (ROI) density mapping method, and it was compared with that of treatment planning CT as well as that of CBCT reconstructed by filtered back projection (FBP) algorithm. Results: A posteriori estimation algorithm with PICCS clearly visualized an area outside normal FOV, whereas the FBP algorithm yielded severe streak artifacts outside normal FOV due to under-sampling. The dose calculation result using the expanded CBCT agreed with that using treatment planning CT very well; a maximum dose difference was 1.3% for gross tumor volumes. Conclusion: With a posteriori estimation algorithm, FOV in CBCT can be expanded. Dose comparison results suggested that the use of expanded CBCTs is acceptable for dose calculation in adaptive radiation therapy. This study has been supported by KAKENHI (15K08691).« less

Highly accelerated intracranial 4D flow MRI: evaluation of healthy volunteers and patients with intracranial aneurysms.

PubMed

Liu, Jing; Koskas, Louise; Faraji, Farshid; Kao, Evan; Wang, Yan; Haraldsson, Henrik; Kefayati, Sarah; Zhu, Chengcheng; Ahn, Sinyeob; Laub, Gerhard; Saloner, David

2018-04-01

To evaluate an accelerated 4D flow MRI method that provides high temporal resolution in a clinically feasible acquisition time for intracranial velocity imaging. Accelerated 4D flow MRI was developed by using a pseudo-random variable-density Cartesian undersampling strategy (CIRCUS) with the combination of k-t, parallel imaging and compressed sensing image reconstruction techniques (k-t SPARSE-SENSE). Four-dimensional flow data were acquired on five healthy volunteers and eight patients with intracranial aneurysms using CIRCUS (acceleration factor of R = 4, termed CIRCUS4) and GRAPPA (R = 2, termed GRAPPA2) as the reference method. Images with three times higher temporal resolution (R = 12, CIRCUS12) were also reconstructed from the same acquisition as CIRCUS4. Qualitative and quantitative image assessment was performed on the images acquired with different methods, and complex flow patterns in the aneurysms were identified and compared. Four-dimensional flow MRI with CIRCUS was achieved in 5 min and allowed further improved temporal resolution of <30 ms. Volunteer studies showed similar qualitative and quantitative evaluation obtained with the proposed approach compared to the reference (overall image scores: GRAPPA2 3.2 ± 0.6; CIRCUS4 3.1 ± 0.7; CIRCUS12 3.3 ± 0.4; difference of the peak velocities: -3.83 ± 7.72 cm/s between CIRCUS4 and GRAPPA2, -1.72 ± 8.41 cm/s between CIRCUS12 and GRAPPA2). In patients with intracranial aneurysms, the higher temporal resolution improved capturing of the flow features in intracranial aneurysms (pathline visualization scores: GRAPPA2 2.2 ± 0.2; CIRCUS4 2.5 ± 0.5; CIRCUS12 2.7 ± 0.6). The proposed rapid 4D flow MRI with a high temporal resolution is a promising tool for evaluating intracranial aneurysms in a clinically feasible acquisition time.

SU-E-T-217: Intrinsic Respiratory Gating in Small Animal CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Smith, M; Mistry, N

Purpose: Preclinical animal models of lung cancer can provide a controlled test-bed for testing dose escalation or function-based-treatment-planning studies. However, to extract lung function, i.e. ventilation, one needs to be able to image the lung at different phases of ventilation (in-hale / ex-hale). Most respiratory-gated imaging using micro-CT involves using an external ventilator and surgical intervention limiting the utility in longitudinal studies. A new intrinsic respiratory retrospective gating method was developed and tested in mice. Methods: A fixed region of interest (ROI) that covers the diaphragm was selected on all projection images to estimate the mean intensity (M). The meanmore » intensity depends on the projection angle and diaphragm position. A 3-point moving average (A) of consecutive M values: Mpre, Mcurrent and Mpost, was calculated to be subtracted from Mcurrent. A fixed threshold was used to enable amplitude based sorting into 4 different phases of respiration. Images at full-inhale and end-exhale phases of respiration were reconstructed using the open source OSCaR. Lung volumes estimated at the 2 phases of respiration were validated against literature values. Results: Intrinsic retrospective gating was accomplished without the use of any external breathing waveform. While projection images were acquired at 360 different angles. Only 138 and 104 projections were used to reconstruct images at full-inhale and end-exhale. This often results in non-uniform under-sampled angular projections leading to some minor streaking artifacts. The calculated expiratory, inspiratory and tidal lung volumes correlated well with the values known from the literature. Conclusion: Our initial result demonstrates an intrinsic gating method that is suitable for flat panel cone beam small animal CT systems. Reduction in streaking artifacts can be accomplished by oversampling the data or using iterative reconstruction methods. This initial experience will enable freebreathing small animal micro-CT imaging to fuel longitudinal studies of lung function.« less

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

PubMed

Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cervino, Laura; Jiang, Steve B; Jia, Xun

2014-07-01

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

Using compressive sensing to recover images from PET scanners with partial detector rings.

PubMed

Valiollahzadeh, SeyyedMajid; Clark, John W; Mawlawi, Osama

2015-01-01

Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors' aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition. The model was designed to minimize the total variation (TV) and L1-norm of wavelet coefficients while constrained by the partially observed data. The CS model also incorporated a Poisson noise term that modeled the observed noise while suppressing its contribution by penalizing the Poisson log likelihood function. Three experiments were performed to evaluate the proposed CS recovery algorithm: a simulation study, a phantom study, and six patient studies. The simulation dataset comprised six disks of various sizes in a uniform background with an activity concentration of 5:1. The simulated image was multiplied by the system matrix to obtain the corresponding sinogram and then Poisson noise was added. The resultant sinogram was masked to create the effect of partial detector removal and then the proposed CS algorithm was applied to recover the missing PET data. In addition, different levels of noise were simulated to assess the performance of the proposed algorithm. For the phantom study, an IEC phantom with six internal spheres each filled with F-18 at an activity-to-background ratio of 10:1 was used. The phantom was imaged twice on a RX PET/CT scanner: once with all detectors operational (baseline) and once with four detector blocks (11%) turned off at each of 0 ˚, 90 ˚, 180 ˚, and 270° (partially sampled). The partially acquired sinograms were then recovered using the proposed algorithm. For the third test, PET images from six patient studies were investigated using the same strategy of the phantom study. The recovered images using WTV and TV as well as the partially sampled images from all three experiments were then compared with the fully sampled images (the baseline). Comparisons were done by calculating the mean error (%bias), root mean square error (RMSE), contrast recovery (CR), and SNR of activity concentration in regions of interest drawn in the background as well as the disks, spheres, and lesions. For the simulation study, the mean error, RMSE, and CR for the WTV (TV) recovered images were 0.26% (0.48%), 2.6% (2.9%), 97% (96%), respectively, when compared to baseline. For the partially sampled images, these results were 22.5%, 45.9%, and 64%, respectively. For the simulation study, the average SNR for the baseline was 41.7 while for WTV (TV), recovered image was 44.2 (44.0). The phantom study showed similar trends with 5.4% (18.2%), 15.6% (18.8%), and 78% (60%), respectively, for the WTV (TV) images and 33%, 34.3%, and 69% for the partially sampled images. For the phantom study, the average SNR for the baseline was 14.7 while for WTV (TV) recovered image was 13.7 (11.9). Finally, the average of these values for the six patient studies for the WTV-recovered, TV, and partially sampled images was 1%, 7.2%, 92% and 1.3%, 15.1%, 87%, and 27%, 25.8%, 45%, respectively. CS with WTV is capable of recovering PET images with good quantitative accuracy from partially sampled data. Such an approach can be used to potentially reduce the cost of scanners while maintaining good image quality.

Using compressive sensing to recover images from PET scanners with partial detector rings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valiollahzadeh, SeyyedMajid, E-mail: sv4@rice.edu; Clark, John W.; Mawlawi, Osama

2015-01-15

Purpose: Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors’ aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. Methods: A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition. The model was designed to minimize the total variation (TV) and L1-norm of wavelet coefficients while constrained by the partially observed data. The CSmore » model also incorporated a Poisson noise term that modeled the observed noise while suppressing its contribution by penalizing the Poisson log likelihood function. Three experiments were performed to evaluate the proposed CS recovery algorithm: a simulation study, a phantom study, and six patient studies. The simulation dataset comprised six disks of various sizes in a uniform background with an activity concentration of 5:1. The simulated image was multiplied by the system matrix to obtain the corresponding sinogram and then Poisson noise was added. The resultant sinogram was masked to create the effect of partial detector removal and then the proposed CS algorithm was applied to recover the missing PET data. In addition, different levels of noise were simulated to assess the performance of the proposed algorithm. For the phantom study, an IEC phantom with six internal spheres each filled with F-18 at an activity-to-background ratio of 10:1 was used. The phantom was imaged twice on a RX PET/CT scanner: once with all detectors operational (baseline) and once with four detector blocks (11%) turned off at each of 0 °, 90 °, 180 °, and 270° (partially sampled). The partially acquired sinograms were then recovered using the proposed algorithm. For the third test, PET images from six patient studies were investigated using the same strategy of the phantom study. The recovered images using WTV and TV as well as the partially sampled images from all three experiments were then compared with the fully sampled images (the baseline). Comparisons were done by calculating the mean error (%bias), root mean square error (RMSE), contrast recovery (CR), and SNR of activity concentration in regions of interest drawn in the background as well as the disks, spheres, and lesions. Results: For the simulation study, the mean error, RMSE, and CR for the WTV (TV) recovered images were 0.26% (0.48%), 2.6% (2.9%), 97% (96%), respectively, when compared to baseline. For the partially sampled images, these results were 22.5%, 45.9%, and 64%, respectively. For the simulation study, the average SNR for the baseline was 41.7 while for WTV (TV), recovered image was 44.2 (44.0). The phantom study showed similar trends with 5.4% (18.2%), 15.6% (18.8%), and 78% (60%), respectively, for the WTV (TV) images and 33%, 34.3%, and 69% for the partially sampled images. For the phantom study, the average SNR for the baseline was 14.7 while for WTV (TV) recovered image was 13.7 (11.9). Finally, the average of these values for the six patient studies for the WTV-recovered, TV, and partially sampled images was 1%, 7.2%, 92% and 1.3%, 15.1%, 87%, and 27%, 25.8%, 45%, respectively. Conclusions: CS with WTV is capable of recovering PET images with good quantitative accuracy from partially sampled data. Such an approach can be used to potentially reduce the cost of scanners while maintaining good image quality.« less

An Optimized Online Verification Imaging Procedure for External Beam Partial Breast Irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Willis, David J., E-mail: David.Willis@petermac.or; Royal Melbourne Institute of Technology University, Melbourne, Victoria; Kron, Tomas

2011-07-01

The purpose of this study was to evaluate the capabilities of a kilovoltage (kV) on-board imager (OBI)-equipped linear accelerator in the setting of on-line verification imaging for external-beam partial breast irradiation. Available imaging techniques were optimized and assessed for image quality using a modified anthropomorphic phantom. Imaging dose was also assessed. Imaging techniques were assessed for physical clearance between patient and treatment machine using a volunteer. Nonorthogonal kV image pairs were identified as optimal in terms of image quality, clearance, and dose. After institutional review board approval, this approach was used for 17 patients receiving accelerated partial breast irradiation. Imagingmore » was performed before every fraction verification with online correction of setup deviations >5 mm (total image sessions = 170). Treatment staff rated risk of collision and visibility of tumor bed surgical clips where present. Image session duration and detected setup deviations were recorded. For all cases, both image projections (n = 34) had low collision risk. Surgical clips were rated as well as visualized in all cases where they were present (n = 5). The average imaging session time was 6 min, 16 sec, and a reduction in duration was observed as staff became familiar with the technique. Setup deviations of up to 1.3 cm were detected before treatment and subsequently confirmed offline. Nonorthogonal kV image pairs allowed effective and efficient online verification for partial breast irradiation. It has yet to be tested in a multicenter study to determine whether it is dependent on skilled treatment staff.« less

Analytic image reconstruction from partial data for a single-scan cone-beam CT with scatter correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Min, Jonghwan; Pua, Rizza; Cho, Seungryong, E-mail: scho@kaist.ac.kr

Purpose: A beam-blocker composed of multiple strips is a useful gadget for scatter correction and/or for dose reduction in cone-beam CT (CBCT). However, the use of such a beam-blocker would yield cone-beam data that can be challenging for accurate image reconstruction from a single scan in the filtered-backprojection framework. The focus of the work was to develop an analytic image reconstruction method for CBCT that can be directly applied to partially blocked cone-beam data in conjunction with the scatter correction. Methods: The authors developed a rebinned backprojection-filteration (BPF) algorithm for reconstructing images from the partially blocked cone-beam data in amore » circular scan. The authors also proposed a beam-blocking geometry considering data redundancy such that an efficient scatter estimate can be acquired and sufficient data for BPF image reconstruction can be secured at the same time from a single scan without using any blocker motion. Additionally, scatter correction method and noise reduction scheme have been developed. The authors have performed both simulation and experimental studies to validate the rebinned BPF algorithm for image reconstruction from partially blocked cone-beam data. Quantitative evaluations of the reconstructed image quality were performed in the experimental studies. Results: The simulation study revealed that the developed reconstruction algorithm successfully reconstructs the images from the partial cone-beam data. In the experimental study, the proposed method effectively corrected for the scatter in each projection and reconstructed scatter-corrected images from a single scan. Reduction of cupping artifacts and an enhancement of the image contrast have been demonstrated. The image contrast has increased by a factor of about 2, and the image accuracy in terms of root-mean-square-error with respect to the fan-beam CT image has increased by more than 30%. Conclusions: The authors have successfully demonstrated that the proposed scanning method and image reconstruction algorithm can effectively estimate the scatter in cone-beam projections and produce tomographic images of nearly scatter-free quality. The authors believe that the proposed method would provide a fast and efficient CBCT scanning option to various applications particularly including head-and-neck scan.« less

Full-range k-domain linearization in spectral-domain optical coherence tomography.

PubMed

Jeon, Mansik; Kim, Jeehyun; Jung, Unsang; Lee, Changho; Jung, Woonggyu; Boppart, Stephen A

2011-03-10

A full-bandwidth k-domain linearization method for spectral-domain optical coherence tomography (SD-OCT) is demonstrated. The method uses information of the wavenumber-pixel-position provided by a translating-slit-based wavelength filter. For calibration purposes, the filter is placed either after a broadband source or at the end of the sample path, and the filtered spectrum with a narrowed line width (∼0.5 nm) is incident on a line-scan camera in the detection path. The wavelength-swept spectra are co-registered with the pixel positions according to their central wavelengths, which can be automatically measured with an optical spectrum analyzer. For imaging, the method does not require a filter or a software recalibration algorithm; it simply resamples the OCT signal from the detector array without employing rescaling or interpolation methods. The accuracy of k-linearization is maximized by increasing the k-linearization order, which is known to be a crucial parameter for maintaining a narrow point-spread function (PSF) width at increasing depths. The broadening effect is studied by changing the k-linearization order by undersampling to search for the optimal value. The system provides more position information, surpassing the optimum without compromising the imaging speed. The proposed full-range k-domain linearization method can be applied to SD-OCT systems to simplify their hardware/software, increase their speed, and improve the axial image resolution. The experimentally measured width of PSF in air has an FWHM of 8 μm at the edge of the axial measurement range. At an imaging depth of 2.5 mm, the sensitivity of the full-range calibration case drops less than 10 dB compared with the uncompensated case.

SU-D-206-01: Employing a Novel Consensus Optimization Strategy to Achieve Iterative Cone Beam CT Reconstruction On a Multi-GPU Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, B; Southern Medical University, Guangzhou, Guangdong; Tian, Z

Purpose: While compressed sensing-based cone-beam CT (CBCT) iterative reconstruction techniques have demonstrated tremendous capability of reconstructing high-quality images from undersampled noisy data, its long computation time still hinders wide application in routine clinic. The purpose of this study is to develop a reconstruction framework that employs modern consensus optimization techniques to achieve CBCT reconstruction on a multi-GPU platform for improved computational efficiency. Methods: Total projection data were evenly distributed to multiple GPUs. Each GPU performed reconstruction using its own projection data with a conventional total variation regularization approach to ensure image quality. In addition, the solutions from GPUs were subjectmore » to a consistency constraint that they should be identical. We solved the optimization problem with all the constraints considered rigorously using an alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework was implemented using OpenCL on a platform with two Nvidia GTX590 GPU cards, each with two GPUs. We studied the performance of our method and demonstrated its advantages through a simulation case with a NCAT phantom and an experimental case with a Catphan phantom. Result: Compared with the CBCT images reconstructed using conventional FDK method with full projection datasets, our proposed method achieved comparable image quality with about one third projection numbers. The computation time on the multi-GPU platform was ∼55 s and ∼ 35 s in the two cases respectively, achieving a speedup factor of ∼ 3.0 compared with single GPU reconstruction. Conclusion: We have developed a consensus ADMM-based CBCT reconstruction method which enabled performing reconstruction on a multi-GPU platform. The achieved efficiency made this method clinically attractive.« less

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

PubMed Central

Muhammad, Khan; Baik, Sung Wook

2017-01-01

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods. PMID:28859140

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging.

PubMed

Truong, Trong-Kha; Song, Allen W; Chen, Nan-Kuei

2015-01-01

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T2(∗) -weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed.

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging

PubMed Central

Truong, Trong-Kha; Song, Allen W.; Chen, Nan-kuei

2015-01-01

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T 2 ∗-weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed. PMID:26413505

Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

NASA Astrophysics Data System (ADS)

Johnston, Brian; Atkins, M. Stella; Booth, Kellogg S.

1994-05-01

An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.

First-pass myocardial perfusion MRI with reduced subendocardial dark-rim artifact using optimized Cartesian sampling.

PubMed

Zhou, Zhengwei; Bi, Xiaoming; Wei, Janet; Yang, Hsin-Jung; Dharmakumar, Rohan; Arsanjani, Reza; Bairey Merz, C Noel; Li, Debiao; Sharif, Behzad

2017-02-01

The presence of subendocardial dark-rim artifact (DRA) remains an ongoing challenge in first-pass perfusion (FPP) cardiac magnetic resonance imaging (MRI). We propose a free-breathing FPP imaging scheme with Cartesian sampling that is optimized to minimize the DRA and readily enables near-instantaneous image reconstruction. The proposed FPP method suppresses Gibbs ringing effects-a major underlying factor for the DRA-by "shaping" the underlying point spread function through a two-step process: 1) an undersampled Cartesian sampling scheme that widens the k-space coverage compared to the conventional scheme; and 2) a modified parallel-imaging scheme that incorporates optimized apodization (k-space data filtering) to suppress Gibbs-ringing effects. Healthy volunteer studies (n = 10) were performed to compare the proposed method against the conventional Cartesian technique-both using a saturation-recovery gradient-echo sequence at 3T. Furthermore, FPP imaging studies using the proposed method were performed in infarcted canines (n = 3), and in two symptomatic patients with suspected coronary microvascular dysfunction for assessment of myocardial hypoperfusion. Width of the DRA and the number of DRA-affected myocardial segments were significantly reduced in the proposed method compared to the conventional approach (width: 1.3 vs. 2.9 mm, P < 0.001; number of segments: 2.6 vs. 8.7; P < 0.0001). The number of slices with severe DRA was markedly lower for the proposed method (by 10-fold). The reader-assigned image quality scores were similar (P = 0.2), although the quantified myocardial signal-to-noise ratio was lower for the proposed method (P < 0.05). Animal studies showed that the proposed method can detect subendocardial perfusion defects and patient results were consistent with the gold-standard invasive test. The proposed free-breathing Cartesian FPP imaging method significantly reduces the prevalence of severe DRAs compared to the conventional approach while maintaining similar resolution and image quality. 2 J. Magn. Reson. Imaging 2017;45:542-555. © 2016 International Society for Magnetic Resonance in Medicine.

[Evaluation of Image Quality of Readout Segmented EPI with Readout Partial Fourier Technique].

PubMed

Yoshimura, Yuuki; Suzuki, Daisuke; Miyahara, Kanae

Readout segmented EPI (readout segmentation of long variable echo-trains: RESOLVE) segmented k-space in the readout direction. By using the partial Fourier method in the readout direction, the imaging time was shortened. However, the influence on image quality due to insufficient data sampling is concerned. The setting of the partial Fourier method in the readout direction in each segment was changed. Then, we examined signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and distortion ratio for changes in image quality due to differences in data sampling. As the number of sampling segments decreased, SNR and CNR showed a low value. In addition, the distortion ratio did not change. The image quality of minimum sampling segments is greatly different from full data sampling, and caution is required when using it.

Photonic compressed sensing nyquist folding receiver

DTIC Science & Technology

2017-09-01

filter . Two independent photonic receiver architectures are designed and analyzed over the course of this research. Both receiver designs are...undersamples the signals using an opti- cal modulator configuration at 1550 nm and collects the detected samples in a low pass interpolation filter ...Electronic Intelligence EW Electronic Warfare FM Frequency Modulated LNA Low Noise Amplifier LPF Low Pass Filter MZI Mach-Zehnder Interferometer NYFR Nyquist

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

NASA Astrophysics Data System (ADS)

Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

2018-02-01

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

An artifact caused by undersampling optimal trees in supermatrix analyses of locally sampled characters.

PubMed

Simmons, Mark P; Goloboff, Pablo A

2013-10-01

Empirical and simulated examples are used to demonstrate an artifact caused by undersampling optimal trees in data matrices that consist mostly or entirely of locally sampled (as opposed to globally, for most or all terminals) characters. The artifact is that unsupported clades consisting entirely of terminals scored for the same locally sampled partition may be resolved and assigned high resampling support-despite their being properly unsupported (i.e., not resolved in the strict consensus of all optimal trees). This artifact occurs despite application of random-addition sequences for stepwise terminal addition. The artifact is not necessarily obviated with thorough conventional branch swapping methods (even tree-bisection-reconnection) when just a single tree is held, as is sometimes implemented in parsimony bootstrap pseudoreplicates, and in every GARLI, PhyML, and RAxML pseudoreplicate and search for the most likely tree for the matrix as a whole. Hence GARLI, RAxML, and PhyML-based likelihood results require extra scrutiny, particularly when they provide high resolution and support for clades that are entirely unsupported by methods that perform more thorough searches, as in most parsimony analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Incremental isometric embedding of high-dimensional data using connected neighborhood graphs.

PubMed

Zhao, Dongfang; Yang, Li

2009-01-01

Most nonlinear data embedding methods use bottom-up approaches for capturing the underlying structure of data distributed on a manifold in high dimensional space. These methods often share the first step which defines neighbor points of every data point by building a connected neighborhood graph so that all data points can be embedded to a single coordinate system. These methods are required to work incrementally for dimensionality reduction in many applications. Because input data stream may be under-sampled or skewed from time to time, building connected neighborhood graph is crucial to the success of incremental data embedding using these methods. This paper presents algorithms for updating $k$-edge-connected and $k$-connected neighborhood graphs after a new data point is added or an old data point is deleted. It further utilizes a simple algorithm for updating all-pair shortest distances on the neighborhood graph. Together with incremental classical multidimensional scaling using iterative subspace approximation, this paper devises an incremental version of Isomap with enhancements to deal with under-sampled or unevenly distributed data. Experiments on both synthetic and real-world data sets show that the algorithm is efficient and maintains low dimensional configurations of high dimensional data under various data distributions.

WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, K; Kuo, J; Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio

Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation andmore » R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the accuracy of MR-AC and MR-RTP applications. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.« less

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Compressed Sensing for Resolution Enhancement of Hyperpolarized 13C Flyback 3D-MRSI

PubMed Central

Hu, Simon; Lustig, Michael; Chen, Albert P.; Crane, Jason; Kerr, Adam; Kelley, Douglas A.C.; Hurd, Ralph; Kurhanewicz, John; Nelson, Sarah J.; Pauly, John M.; Vigneron, Daniel B.

2008-01-01

High polarization of nuclear spins in liquid state through dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at very high signal to noise, allowing for rapid assessment of tissue metabolism. The abundant SNR afforded by this hyperpolarization technique makes high resolution 13C 3D-MRSI feasible. However, the number of phase encodes that can be fit into the short acquisition time for hyperpolarized imaging limits spatial coverage and resolution. To take advantage of the high SNR available from hyperpolarization, we have applied compressed sensing to achieve a factor of 2 enhancement in spatial resolution without increasing acquisition time or decreasing coverage. In this paper, the design and testing of compressed sensing suited for a flyback 13C 3D-MRSI sequence are presented. The key to this design was the undersampling of spectral k-space using a novel blipped scheme, thus taking advantage of the considerable sparsity in typical hyperpolarized 13C spectra. Phantom tests validated the accuracy of the compressed sensing approach and initial mouse experiments demonstrated in vivo feasibility. PMID:18367420

Two-dimensional sparse wavenumber recovery for guided wavefields

NASA Astrophysics Data System (ADS)

Sabeti, Soroosh; Harley, Joel B.

2018-04-01

The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

Partial volume correction and image analysis methods for intersubject comparison of FDG-PET studies

NASA Astrophysics Data System (ADS)

Yang, Jun

2000-12-01

Partial volume effect is an artifact mainly due to the limited imaging sensor resolution. It creates bias in the measured activity in small structures and around tissue boundaries. In brain FDG-PET studies, especially for Alzheimer's disease study where there is serious gray matter atrophy, accurate estimate of cerebral metabolic rate of glucose is even more problematic due to large amount of partial volume effect. In this dissertation, we developed a framework enabling inter-subject comparison of partial volume corrected brain FDG-PET studies. The framework is composed of the following image processing steps: (1)MRI segmentation, (2)MR-PET registration, (3)MR based PVE correction, (4)MR 3D inter-subject elastic mapping. Through simulation studies, we showed that the newly developed partial volume correction methods, either pixel based or ROI based, performed better than previous methods. By applying this framework to a real Alzheimer's disease study, we demonstrated that the partial volume corrected glucose rates vary significantly among the control, at risk and disease patient groups and this framework is a promising tool useful for assisting early identification of Alzheimer's patients.

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction

NASA Astrophysics Data System (ADS)

Schwemmer, C.; Rohkohl, C.; Lauritsch, G.; Müller, K.; Hornegger, J.

2013-06-01

Three-dimensional reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-the-art approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts. We present an algorithm to correct for residual motion. Our approach is based on a deformable 2D-2D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available CAVAREV platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the CAVAREV ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13 ± 6%. Vessel sharpness was improved by 25 ± 12% on average.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

High-performance 3D compressive sensing MRI reconstruction.

PubMed

Kim, Daehyun; Trzasko, Joshua D; Smelyanskiy, Mikhail; Haider, Clifton R; Manduca, Armando; Dubey, Pradeep

2010-01-01

Compressive Sensing (CS) is a nascent sampling and reconstruction paradigm that describes how sparse or compressible signals can be accurately approximated using many fewer samples than traditionally believed. In magnetic resonance imaging (MRI), where scan duration is directly proportional to the number of acquired samples, CS has the potential to dramatically decrease scan time. However, the computationally expensive nature of CS reconstructions has so far precluded their use in routine clinical practice - instead, more-easily generated but lower-quality images continue to be used. We investigate the development and optimization of a proven inexact quasi-Newton CS reconstruction algorithm on several modern parallel architectures, including CPUs, GPUs, and Intel's Many Integrated Core (MIC) architecture. Our (optimized) baseline implementation on a quad-core Core i7 is able to reconstruct a 256 × 160×80 volume of the neurovasculature from an 8-channel, 10 × undersampled data set within 56 seconds, which is already a significant improvement over existing implementations. The latest six-core Core i7 reduces the reconstruction time further to 32 seconds. Moreover, we show that the CS algorithm benefits from modern throughput-oriented architectures. Specifically, our CUDA-base implementation on NVIDIA GTX480 reconstructs the same dataset in 16 seconds, while Intel's Knights Ferry (KNF) of the MIC architecture even reduces the time to 12 seconds. Such level of performance allows the neurovascular dataset to be reconstructed within a clinically viable time.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients.

PubMed

Guo, Yi; Lebel, R Marc; Zhu, Yinghua; Lingala, Sajan Goud; Shiroishi, Mark S; Law, Meng; Nayak, Krishna

2016-05-01

To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm(3), FOV 22 × 22 × 4.2 cm(3), and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm(3), and broader coverage 22 × 22 × 19 cm(3). Temporal resolution was 5 s for both protocols. Time-resolved images and blood-brain barrier permeability maps were qualitatively evaluated by two radiologists. The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.

The collaboration of grouping laws in vision.

PubMed

Grompone von Gioi, Rafael; Delon, Julie; Morel, Jean-Michel

2012-01-01

Gestalt theory gives a list of geometric grouping laws that could in principle give a complete account of human image perception. Based on an extensive thesaurus of clever graphical images, this theory discusses how grouping laws collaborate, and conflict toward a global image understanding. Unfortunately, as shown in the bibliographical analysis herewith, the attempts to formalize the grouping laws in computer vision and psychophysics have at best succeeded to compute individual partial structures (or partial gestalts), such as alignments or symmetries. Nevertheless, we show here that a never formalized clever Gestalt experimental procedure, the Nachzeichnung suggests a numerical set up to implement and test the collaboration of partial gestalts. The new computational procedure proposed here analyzes a digital image, and performs a numerical simulation that we call Nachtanz or Gestaltic dance. In this dance, the analyzed digital image is gradually deformed in a random way, but maintaining the detected partial gestalts. The resulting dancing images should be perceptually indistinguishable if and only if the grouping process was complete. Like the Nachzeichnung, the Nachtanz permits a visual exploration of the degrees of freedom still available to a figure after all partial groups (or gestalts) have been detected. In the new proposed procedure, instead of drawing themselves, subjects will be shown samples of the automatic Gestalt dances and required to evaluate if the figures are similar. Several numerical preliminary results with this new Gestaltic experimental setup are thoroughly discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI.

PubMed

Gholipour, Ali; Afacan, Onur; Aganj, Iman; Scherrer, Benoit; Prabhu, Sanjay P; Sahin, Mustafa; Warfield, Simon K

2015-12-01

To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.

SC-GRAPPA: Self-constraint noniterative GRAPPA reconstruction with closed-form solution.

PubMed

Ding, Yu; Xue, Hui; Ahmad, Rizwan; Ting, Samuel T; Simonetti, Orlando P

2012-12-01

Parallel MRI (pMRI) reconstruction techniques are commonly used to reduce scan time by undersampling the k-space data. GRAPPA, a k-space based pMRI technique, is widely used clinically because of its robustness. In GRAPPA, the missing k-space data are estimated by solving a set of linear equations; however, this set of equations does not take advantage of the correlations within the missing k-space data. All k-space data in a neighborhood acquired from a phased-array coil are correlated. The correlation can be estimated easily as a self-constraint condition, and formulated as an extra set of linear equations to improve the performance of GRAPPA. The authors propose a modified k-space based pMRI technique called self-constraint GRAPPA (SC-GRAPPA) which combines the linear equations of GRAPPA with these extra equations to solve for the missing k-space data. Since SC-GRAPPA utilizes a least-squares solution of the linear equations, it has a closed-form solution that does not require an iterative solver. The SC-GRAPPA equation was derived by incorporating GRAPPA as a prior estimate. SC-GRAPPA was tested in a uniform phantom and two normal volunteers. MR real-time cardiac cine images with acceleration rate 5 and 6 were reconstructed using GRAPPA and SC-GRAPPA. SC-GRAPPA showed a significantly lower artifact level, and a greater than 10% overall signal-to-noise ratio (SNR) gain over GRAPPA, with more significant SNR gain observed in low-SNR regions of the images. SC-GRAPPA offers improved pMRI reconstruction, and is expected to benefit clinical imaging applications in the future.

Quantifying vegetation distribution and structure using high resolution drone-based structure-from-motion photogrammetry

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2017-12-01

Vegetation is one of the most important driving factors of different ecosystem processes in drylands. The structure of vegetation controls the spatial distribution of moisture and heat in the canopy and the surrounding area. Also, the structure of vegetation influences both airflow and boundary layer resistance above the land surface. Multispectral satellite remote sensing has been widely used to monitor vegetation coverage and its change; however, it can only capture 2D images, which do not contain the vertical information of vegetation. In situ observation uses different methods to measure the structure of vegetation, and their results are accurate; however, these methods are laborious and time-consuming, and susceptible to undersampling in spatial heterogeneity. Drylands are sparsely covered by short plants, which allows the drone fly at a relatively low height to obtain ultra-high resolution images. Structure-from-motion (SfM) is a photogrammetric method that was proved to produce 3D model based on 2D images. Drone-based remote sensing can obtain the multiangle images for one object, which can be used to constructed 3D models of vegetation in drylands. Using these images detected by the drone, the orthomosaics and digital surface model (DSM) can be built. In this study, the drone-based remote sensing was conducted in Jornada Basin, New Mexico, in the spring of 2016 and 2017, and three derived vegetation parameters (i.e., canopy size, bare soil gap size, and plant height) were compared with those obtained with field measurement. The correlation coefficient of canopy size, bare soil gap size, and plant height between drone images and field data are 0.91, 0.96, and 0.84, respectively. The two-year averaged root-mean-square error (RMSE) of canopy size, bare soil gap size, and plant height between drone images and field data are 0.61 m, 1.21 m, and 0.25 cm, respectively. The two-year averaged measure error (ME) of canopy size, bare soil gap size, and plant height between drone images and field data are 0.02 m, -0.03, and -0.1 m, respectively. These results indicate a good agreement between drone-based remote sensing and field measurement.

Quantitative phase imaging method based on an analytical nonparaxial partially coherent phase optical transfer function.

PubMed

Bao, Yijun; Gaylord, Thomas K

2016-11-01

Multifilter phase imaging with partially coherent light (MFPI-PC) is a promising new quantitative phase imaging method. However, the existing MFPI-PC method is based on the paraxial approximation. In the present work, an analytical nonparaxial partially coherent phase optical transfer function is derived. This enables the MFPI-PC to be extended to the realistic nonparaxial case. Simulations over a wide range of test phase objects as well as experimental measurements on a microlens array verify higher levels of imaging accuracy compared to the paraxial method. Unlike the paraxial version, the nonparaxial MFPI-PC with obliquity factor correction exhibits no systematic error. In addition, due to its analytical expression, the increase in computation time compared to the paraxial version is negligible.

Post-processing of adaptive optics images based on frame selection and multi-frame blind deconvolution

NASA Astrophysics Data System (ADS)

Tian, Yu; Rao, Changhui; Wei, Kai

2008-07-01

The adaptive optics can only partially compensate the image blurred by atmospheric turbulence due to the observing condition and hardware restriction. A post-processing method based on frame selection and multi-frames blind deconvolution to improve images partially corrected by adaptive optics is proposed. The appropriate frames which are suitable for blind deconvolution from the recorded AO close-loop frames series are selected by the frame selection technique and then do the multi-frame blind deconvolution. There is no priori knowledge except for the positive constraint in blind deconvolution. It is benefit for the use of multi-frame images to improve the stability and convergence of the blind deconvolution algorithm. The method had been applied in the image restoration of celestial bodies which were observed by 1.2m telescope equipped with 61-element adaptive optical system at Yunnan Observatory. The results show that the method can effectively improve the images partially corrected by adaptive optics.

A Virtual Reality System for PTCD Simulation Using Direct Visuo-Haptic Rendering of Partially Segmented Image Data.

PubMed

Fortmeier, Dirk; Mastmeyer, Andre; Schröder, Julian; Handels, Heinz

2016-01-01

This study presents a new visuo-haptic virtual reality (VR) training and planning system for percutaneous transhepatic cholangio-drainage (PTCD) based on partially segmented virtual patient models. We only use partially segmented image data instead of a full segmentation and circumvent the necessity of surface or volume mesh models. Haptic interaction with the virtual patient during virtual palpation, ultrasound probing and needle insertion is provided. Furthermore, the VR simulator includes X-ray and ultrasound simulation for image-guided training. The visualization techniques are GPU-accelerated by implementation in Cuda and include real-time volume deformations computed on the grid of the image data. Computation on the image grid enables straightforward integration of the deformed image data into the visualization components. To provide shorter rendering times, the performance of the volume deformation algorithm is improved by a multigrid approach. To evaluate the VR training system, a user evaluation has been performed and deformation algorithms are analyzed in terms of convergence speed with respect to a fully converged solution. The user evaluation shows positive results with increased user confidence after a training session. It is shown that using partially segmented patient data and direct volume rendering is suitable for the simulation of needle insertion procedures such as PTCD.

Apparent CBF decrease with normal aging due to partial volume effects: MR-based partial volume correction on CBF SPECT.

PubMed

Inoue, Kentaro; Ito, Hiroshi; Goto, Ryoi; Nakagawa, Manabu; Kinomura, Shigeo; Sato, Tachio; Sato, Kazunori; Fukuda, Hiroshi

2005-06-01

Several studies using single photon emission tomography (SPECT) have shown changes in cerebral blood flow (CBF) with age, which were associated with partial volume effects by some authors. Some studies have also demonstrated gender-related differences in CBF. The present study aimed to examine age and gender effects on CBF SPECT images obtained using the 99mTc-ethyl cysteinate dimer and a SPECT scanner, before and after partial volume correction (PVC) using magnetic resonance (MR) imaging. Forty-four healthy subjects (29 males and 15 females; age range, 27-64 y; mean age, 50.0 +/- 9.8 y) participated. Each MR image was segmented to yield grey and white matter images and coregistered to a corresponding SPECT image, followed by convolution to approximate the SPECT spatial resolution. PVC-SPECT images were produced using the convoluted grey matter MR (GM-MR) and white matter MR images. The age and gender effects were assessed using SPM99. Decreases with age were detected in the anterolateral prefrontal cortex and in areas along the lateral sulcus and the lateral ventricle, bilaterally, in the GM-MR images and the SPECT images. In the PVC-SPECT images, decreases in CBF in the lateral prefrontal cortex lost their statistical significance. Decreases in CBF with age found along the lateral sulcus and the lateral ventricle, on the other hand, remained statistically significant, but observation of the spatially normalized MR images suggests that these findings are associated with the dilatation of the lateral sulcus and lateral ventricle, which was not completely compensated for by the spatial normalization procedure. Our present study demonstrated that age effects on CBF in healthy subjects could reflect morphological differences with age in grey matter.

Experimental evidence for partial spatial coherence in imaging Mueller polarimetry.

PubMed

Ossikovski, Razvigor; Arteaga, Oriol; Yoo, Sang Hyuk; Garcia-Caurel, Enric; Hingerl, Kurt

2017-11-15

We demonstrate experimentally the validity of the partial spatial coherence formalism in Mueller polarimetry and show that, in a finite spatial resolution experiment, the measured response is obtained through convolving the theoretical one with the instrument function. The reported results are of primary importance for Mueller imaging systems.

Femtosecond laser subsurface scleral treatment in cadaver human sclera and evaluation using two-photon and confocal microscopy

NASA Astrophysics Data System (ADS)

Sun, Hui; Fan, Zhongwei; Yan, Ying; Lian, Fuqiang; Kurtz, Ron; Juhasz, Tibor

2016-03-01

Glaucoma is the second-leading cause of blindness worldwide and is often associated with elevated intraocular pressure (IOP). Partial-thickness drainage channels can be created with femtosecond laser in the translucent sclera for the potential treatment of glaucoma. We demonstrate the creation of partial-thickness subsurface drainage channels with the femtosecond laser in the cadaver human eyeballs and describe the application of two-photon microscopy and confocal microscopy for noninvasive imaging of the femtosecond laser created partial-thickness scleral channels in cadaver human eyes. A femtosecond laser operating at a wavelength of 1700 nm was scanned along a rectangular raster pattern to create the partial thickness subsurface drainage channels in the sclera of cadaver human eyes. Analysis of the dimensions and location of these channels is important in understanding their effects. We describe the application of two-photon microscopy and confocal microscopy for noninvasive imaging of the femtosecond laser created partial-thickness scleral channels in cadaver human eyes. High-resolution images, hundreds of microns deep in the sclera, were obtained to allow determination of the shape and dimension of such partial thickness subsurface scleral channels. Our studies suggest that the confocal and two-photon microscopy can be used to investigate femtosecond-laser created partial-thickness drainage channels in the sclera of cadaver human eyes.

Real-time cardiovascular magnetic resonance at high temporal resolution: radial FLASH with nonlinear inverse reconstruction.

PubMed

Zhang, Shuo; Uecker, Martin; Voit, Dirk; Merboldt, Klaus-Dietmar; Frahm, Jens

2010-07-08

Functional assessments of the heart by dynamic cardiovascular magnetic resonance (CMR) commonly rely on (i) electrocardiographic (ECG) gating yielding pseudo real-time cine representations, (ii) balanced gradient-echo sequences referred to as steady-state free precession (SSFP), and (iii) breath holding or respiratory gating. Problems may therefore be due to the need for a robust ECG signal, the occurrence of arrhythmia and beat to beat variations, technical instabilities (e.g., SSFP "banding" artefacts), and limited patient compliance and comfort. Here we describe a new approach providing true real-time CMR with image acquisition times as short as 20 to 30 ms or rates of 30 to 50 frames per second. The approach relies on a previously developed real-time MR method, which combines a strongly undersampled radial FLASH CMR sequence with image reconstruction by regularized nonlinear inversion. While iterative reconstructions are currently performed offline due to limited computer speed, online monitoring during scanning is accomplished using gridding reconstructions with a sliding window at the same frame rate but with lower image quality. Scans of healthy young subjects were performed at 3 T without ECG gating and during free breathing. The resulting images yield T1 contrast (depending on flip angle) with an opposed-phase or in-phase condition for water and fat signals (depending on echo time). They completely avoid (i) susceptibility-induced artefacts due to the very short echo times, (ii) radiofrequency power limitations due to excitations with flip angles of 10 degrees or less, and (iii) the risk of peripheral nerve stimulation due to the use of normal gradient switching modes. For a section thickness of 8 mm, real-time images offer a spatial resolution and total acquisition time of 1.5 mm at 30 ms and 2.0 mm at 22 ms, respectively. Though awaiting thorough clinical evaluation, this work describes a robust and flexible acquisition and reconstruction technique for real-time CMR at the ultimate limit of this technology.

The Thing We Really Need

NASA Astrophysics Data System (ADS)

Donovan, E.

2017-12-01

I'm known for hyperbole and provocative statements. "What do you get from 10,000 magnetometers distributed around the globe? A low resolution image of currents." To a great extent, I'm having fun with such statements, I truly value the phenomenal armada of spacecraft - the HSO - with which we are exploring geospace, and certainly we need the in situ measurements to unravel the fundamental (small scale) processes at the heart of space weather. But, for me there is underlying truth for my light-hearted rhetoric. The geospace community has been too accepting of arguments against significant investment in imaging. Some say "you can't do science with images." My answer? "Tell that to the astronomers". Some say "we've flown an auroral imager… been there, done that." My answer? "The fact that we've flown ten magnetometers seems to not preclude flying another ten." We are comfortable spending multiple billions of dollars on in situ observations, yet uncomfortable spending even just tens of millions on imaging. Why does this matter? In situ focused missions are giving us a profoundly important of the small scale physics by which everything in geospace happens. However, these effects spin up via space/time scales that are poorly sampled. We are often left piecing together the critically important mesoscale picture with dramatically under-sampled "images". Consider the images below. Consider the SDO images showing a flare in exquisite detail in multiple wavelengths. With these, solar physicists have made dramatic advances relating small scale with mesoscales and their consequences. Through these the public has been inspired and awe stricken. Consider the ASI images with overlaid SuperDARN observations. Such images have allowed us to make great advances, but it's time for us to acknowledge that our imaging leaves too much to the imagination, and falls well short of creating visuals that inspire. It's time for us to make the movie of our system. In this presentation, I suggest a $3B "mission to geospace" with three pillars: (1) a continent-size ground-based network of innovative new instruments; (2) four satellites on Molniya orbits outfitted with next-generation multi-wavelength optical, ENA and X-Ray imagers; and (3) 100 satellites in formation in the magnetosphere providing radio sounding and in situ observations of key regions.

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hao; Folkerts, Michael; Jiang, Steve B., E-mail: xun.jia@utsouthwestern.edu, E-mail: steve.jiang@UTSouthwestern.edu

2014-07-15

Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is inventedmore » to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.« less

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gholipour, Ali, E-mail: ali.gholipour@childrens.harvard.edu; Afacan, Onur; Scherrer, Benoit

Purpose: To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. Methods: The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) ofmore » image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Results: Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Conclusions: Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.« less

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

PubMed Central

Gholipour, Ali; Afacan, Onur; Aganj, Iman; Scherrer, Benoit; Prabhu, Sanjay P.; Sahin, Mustafa; Warfield, Simon K.

2015-01-01

Purpose: To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. Methods: The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Results: Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Conclusions: Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans. PMID:26632048

76 FR 22386 - Availability for Exclusive, Non-Exclusive, or Partially-Exclusive Licensing of an Invention...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-21

... Partially-Exclusive Licensing of an Invention Concerning the Method and Apparatus for Stereo Imaging AGENCY... ``Method and Apparatus for Stereo Imaging,'' filed on March 11, 2011. The United States Government, as...: The invention relates to a method and apparatus for the generation of macro scale extremely high...

A comparison of five standard methods for evaluating image intensity uniformity in partially parallel imaging MRI

PubMed Central

Goerner, Frank L.; Duong, Timothy; Stafford, R. Jason; Clarke, Geoffrey D.

2013-01-01

Purpose: To investigate the utility of five different standard measurement methods for determining image uniformity for partially parallel imaging (PPI) acquisitions in terms of consistency across a variety of pulse sequences and reconstruction strategies. Methods: Images were produced with a phantom using a 12-channel head matrix coil in a 3T MRI system (TIM TRIO, Siemens Medical Solutions, Erlangen, Germany). Images produced using echo-planar, fast spin echo, gradient echo, and balanced steady state free precession pulse sequences were evaluated. Two different PPI reconstruction methods were investigated, generalized autocalibrating partially parallel acquisition algorithm (GRAPPA) and modified sensitivity-encoding (mSENSE) with acceleration factors (R) of 2, 3, and 4. Additionally images were acquired with conventional, two-dimensional Fourier imaging methods (R = 1). Five measurement methods of uniformity, recommended by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) were considered. The methods investigated were (1) an ACR method and a (2) NEMA method for calculating the peak deviation nonuniformity, (3) a modification of a NEMA method used to produce a gray scale uniformity map, (4) determining the normalized absolute average deviation uniformity, and (5) a NEMA method that focused on 17 areas of the image to measure uniformity. Changes in uniformity as a function of reconstruction method at the same R-value were also investigated. Two-way analysis of variance (ANOVA) was used to determine whether R-value or reconstruction method had a greater influence on signal intensity uniformity measurements for partially parallel MRI. Results: Two of the methods studied had consistently negative slopes when signal intensity uniformity was plotted against R-value. The results obtained comparing mSENSE against GRAPPA found no consistent difference between GRAPPA and mSENSE with regard to signal intensity uniformity. The results of the two-way ANOVA analysis suggest that R-value and pulse sequence type produce the largest influences on uniformity and PPI reconstruction method had relatively little effect. Conclusions: Two of the methods of measuring signal intensity uniformity, described by the (NEMA) MRI standards, consistently indicated a decrease in uniformity with an increase in R-value. Other methods investigated did not demonstrate consistent results for evaluating signal uniformity in MR images obtained by partially parallel methods. However, because the spatial distribution of noise affects uniformity, it is recommended that additional uniformity quality metrics be investigated for partially parallel MR images. PMID:23927345

Poster — Thur Eve — 10: Partial kV CBCT, complete kV CBCT and EPID in breast treatment: a dose comparison study for skin, breasts, heart and lungs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roussin, E; Archambault, L K; Wierzbicki, W

The advantages of kilovoltage cone beam CT (kV CBCT) imaging over electronic portal imaging device (EPID) such as accurate 3D anatomy, soft tissue visualization, fast rigid registration and enhanced precision on patient positioning has lead to its increasing use in clinics. The benefits of this imaging technique are at the cost of increasing the dose to healthy surrounding organs. Our center has moved toward the use of daily partial rotation kV CBCT to restrict the dose to healthy tissues. This study aims to better quantify radiation doses from different image-guidance techniques such as tangential EPID, complete and partial kV CBCTmore » for breast treatments. Cross-calibrated ionization chambers and kV calibrated Gafchromic films were used to measure the dose to the heart, lungs, breasts and skin. It was found that performing partial kV CBCT decreases the heart dose by about 36%, the lungs dose by 31%, the contralateral breast dose by 41% and the ipsilateral breast dose by 43% when compared to a full rotation CBCT. The skin dose measured for a full rotation CBCT was about 0.8 cGy for the contralateral breast and about 0.3 cGy for the ipsilateral breast. The study is still ongoing and results on skin doses for partial rotation kV CBCT as well as for tangential EPID images are upcoming.« less

Mapping stand-age distribution of Russian forests from satellite data

NASA Astrophysics Data System (ADS)

Chen, D.; Loboda, T. V.; Hall, A.; Channan, S.; Weber, C. Y.

2013-12-01

Russian boreal forest is a critical component of the global boreal biome as approximately two thirds of the boreal forest is located in Russia. Numerous studies have shown that wildfire and logging have led to extensive modifications of forest cover in the region since 2000. Forest disturbance and subsequent regrowth influences carbon and energy budgets and, in turn, affect climate. Several global and regional satellite-based data products have been developed from coarse (>100m) and moderate (10-100m) resolution imagery to monitor forest cover change over the past decade, record of forest cover change pre-dating year 2000 is very fragmented. Although by using stacks of Landsat images, some information regarding the past disturbances can be obtained, the quantity and locations of such stacks with sufficient number of images are extremely limited, especially in Eastern Siberia. This paper describes a modified method which is built upon previous work to hindcast the disturbance history and map stand-age distribution in the Russian boreal forest. Utilizing data from both Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), a wall-to-wall map indicating the estimated age of forest in the Russian boreal forest is created. Our previous work has shown that disturbances can be mapped successfully up to 30 years in the past as the spectral signature of regrowing forests is statistically significantly different from that of mature forests. The presented algorithm ingests 55 multi-temporal stacks of Landsat imagery available over Russian forest before 2001 and processes through a standardized and semi-automated approach to extract training and validation data samples. Landsat data, dating back to 1984, are used to generate maps of forest disturbance using temporal shifts in Disturbance Index through the multi-temporal stack of imagery in selected locations. These maps are then used as reference data to train a decision tree classifier on 50 MODIS-based indices. The resultant map provides an estimate of forest age based on the regrowth curves observed from Landsat imagery. The accuracy of the resultant map is assessed against three datasets: 1) subset of the disturbance maps developed within the algorithm, 2) independent disturbance maps created by the Northern Eurasia Land Dynamics Analysis (NELDA) project, and 3) field-based stand-age distribution from forestry inventory units. The current version of the product presents a considerable improvement on the previous version which used Landsat data samples at a set of randomly selected locations, resulting a strong bias of the training samples towards the Landsat-rich regions (e.g. European Russia) whereas regions such as Siberia were under-sampled. Aiming at improving accuracy, the current method significantly increases the number of training Landsat samples compared to the previous work. Aside from the previously used data, the current method uses all available Landsat data for the under-sampled regions in order to increase the representativeness of the total samples. The finial accuracy assessment is still ongoing, however, the initial results suggested an overall accuracy expressed in Kappa > 0.8. We plan to release both the training data and the final disturbance map of the Russian boreal forest to the public after the validation is completed.

A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings.

PubMed

Tudorica, Luminita A; Oh, Karen Y; Roy, Nicole; Kettler, Mark D; Chen, Yiyi; Hemmingson, Stephanie L; Afzal, Aneela; Grinstead, John W; Laub, Gerhard; Li, Xin; Huang, Wei

2012-11-01

Three dimensional bilateral imaging is the standard for most clinical breast dynamic contrast-enhanced (DCE) MRI protocols. Because of high spatial resolution (sRes) requirement, the typical 1-2 min temporal resolution (tRes) afforded by a conventional full-k-space-sampling gradient echo (GRE) sequence precludes meaningful and accurate pharmacokinetic analysis of DCE time-course data. The commercially available, GRE-based, k-space undersampling and data sharing TWIST (time-resolved angiography with stochastic trajectories) sequence was used in this study to perform DCE-MRI exams on thirty one patients (with 36 suspicious breast lesions) before their biopsies. The TWIST DCE-MRI was immediately followed by a single-frame conventional GRE acquisition. Blinded from each other, three radiologist readers assessed agreements in multiple lesion morphology categories between the last set of TWIST DCE images and the conventional GRE images. Fleiss' κ test was used to evaluate inter-reader agreement. The TWIST DCE time-course data were subjected to quantitative pharmacokinetic analyses. With a four-channel phased-array breast coil, the TWIST sequence produced DCE images with 20 s or less tRes and ~ 1.0×1.0×1.4 mm(3) sRes. There were no significant differences in signal-to-noise (P=.45) and contrast-to-noise (P=.51) ratios between the TWIST and conventional GRE images. The agreements in morphology evaluations between the two image sets were excellent with the intra-reader agreement ranging from 79% for mass margin to 100% for mammographic density and the inter-reader κ value ranging from 0.54 (P<.0001) for lesion size to 1.00 (P<.0001) for background parenchymal enhancement. Quantitative analyses of the DCE time-course data provided higher breast cancer diagnostic accuracy (91% specificity at 100% sensitivity) than the current clinical practice of morphology and qualitative kinetics assessments. The TWIST sequence may be used in clinical settings to acquire high spatiotemporal resolution breast DCE-MRI images for both precise lesion morphology characterization and accurate pharmacokinetic analysis. Copyright © 2012 Elsevier Inc. All rights reserved.

Optimizing 4DCBCT projection allocation to respiratory bins.

PubMed

O'Brien, Ricky T; Kipritidis, John; Shieh, Chun-Chien; Keall, Paul J

2014-10-07

4D cone beam computed tomography (4DCBCT) is an emerging image guidance strategy used in radiotherapy where projections acquired during a scan are sorted into respiratory bins based on the respiratory phase or displacement. 4DCBCT reduces the motion blur caused by respiratory motion but increases streaking artefacts due to projection under-sampling as a result of the irregular nature of patient breathing and the binning algorithms used. For displacement binning the streak artefacts are so severe that displacement binning is rarely used clinically. The purpose of this study is to investigate if sharing projections between respiratory bins and adjusting the location of respiratory bins in an optimal manner can reduce or eliminate streak artefacts in 4DCBCT images. We introduce a mathematical optimization framework and a heuristic solution method, which we will call the optimized projection allocation algorithm, to determine where to position the respiratory bins and which projections to source from neighbouring respiratory bins. Five 4DCBCT datasets from three patients were used to reconstruct 4DCBCT images. Projections were sorted into respiratory bins using equispaced, equal density and optimized projection allocation. The standard deviation of the angular separation between projections was used to assess streaking and the consistency of the segmented volume of a fiducial gold marker was used to assess motion blur. The standard deviation of the angular separation between projections using displacement binning and optimized projection allocation was 30%-50% smaller than conventional phase based binning and 59%-76% smaller than conventional displacement binning indicating more uniformly spaced projections and fewer streaking artefacts. The standard deviation in the marker volume was 20%-90% smaller when using optimized projection allocation than using conventional phase based binning suggesting more uniform marker segmentation and less motion blur. Images reconstructed using displacement binning and the optimized projection allocation algorithm were clearer, contained visibly fewer streak artefacts and produced more consistent marker segmentation than those reconstructed with either equispaced or equal-density binning. The optimized projection allocation algorithm significantly improves image quality in 4DCBCT images and provides, for the first time, a method to consistently generate high quality displacement binned 4DCBCT images in clinical applications.

Partial achilles tendon rupture presenting with giant hematoma; MRI findings of 4 year follow up.

PubMed

Sarsilmaz, Aysegul; Varer, Makbule; Coskun, Gulten; Apaydın, Melda; Oyar, Orhan

2011-12-01

In the young population, spontaneous rupture of Achilles tendon is very rare. The big hematoma is also rare finding of the Achilles tendon partial rupture. It is usually seen with complete rupture. We presented imaging findings of 4 years follow up of the spontaneous partial rupture of Achilles tendon presenting with giant expanding hematoma and mimicking complete rupture radiologically. We discussed the alterations of tendon signal intensity and result of conservative therapy after partial rupture with big hematoma in the long term. A 29 year-old man, applied with pain and swelling in the retrocalcaneal region of left ankle. He did not have chronic metabolic disease. He was not active in physical activities. X-ray radiograms were normal. At magnetic resonance images (MRI), there was an intratendinous big hematoma, subcutanous fat planes were edematous around tendon. The diagnosis was partial rupture and giant hematoma. Hematoma was drained. The conservative treatment was applied and his complaints disappeared. After treatment, approximately 4 years later, control MRI showed thickened and hypointense tendon in all images. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Partial anomalous pulmonary venous return in Turner syndrome.

PubMed

van den Hoven, Allard T; Chelu, Raluca G; Duijnhouwer, Anthonie L; Demulier, Laurent; Devos, Daniel; Nieman, Koen; Witsenburg, Maarten; van den Bosch, Annemien E; Loeys, Bart L; van Hagen, Iris M; Roos-Hesselink, Jolien W

2017-10-01

The aim of this study is to describe the prevalence, anatomy, associations and clinical impact of partial anomalous pulmonary venous return in patients with Turner syndrome. All Turner patients who presented at our Turner clinic, between January 2007 and October 2015 were included in this study and underwent ECG, echocardiography and advanced imaging such as cardiac magnetic resonance or computed tomography as part of their regular clinical workup. All imaging was re-evaluated and detailed anatomy was described. Partial anomalous pulmonary venous return was diagnosed in 24 (25%) out of 96 Turner patients included and 14 (58%) of these 24 partial anomalous pulmonary venous return had not been reported previously. Right atrial or ventricular dilatation was present in 11 (46%) of 24 partial anomalous pulmonary venous return patients. When studied with advanced imaging modalities and looked for with specific attention, PAPVR is found in 1 out of 4 Turner patients. Half of these patients had right atrial and/or ventricular dilatation. Evaluation of pulmonary venous return should be included in the standard protocol in all Turner patients. Copyright © 2017. Published by Elsevier B.V.

Noninvasive oxygen partial pressure measurement of human body fluids in vivo using magnetic resonance imaging.

PubMed

Zaharchuk, Greg; Busse, Reed F; Rosenthal, Guy; Manley, Geoffery T; Glenn, Orit A; Dillon, William P

2006-08-01

The oxygen partial pressure (pO2) of human body fluids reflects the oxygenation status of surrounding tissues. All existing fluid pO2 measurements are invasive, requiring either microelectrode/optode placement or fluid removal. The purpose of this study is to develop a noninvasive magnetic resonance imaging method to measure the pO2 of human body fluids. We developed an imaging paradigm that exploits the paramagnetism of molecular oxygen to create quantitative images of fluid oxygenation. A single-shot fast spin echo pulse sequence was modified to minimize artifacts from motion, fluid flow, and partial volume. Longitudinal relaxation rate (R1 = 1/T1) was measured with a time-efficient nonequilibrium saturation recovery method and correlated with pO2 measured in phantoms. pO2 images of human and fetal cerebrospinal fluid, bladder urine, and vitreous humor are presented and quantitative oxygenation levels are compared with prior literature estimates, where available. Significant pO2 increases are shown in cerebrospinal fluid and vitreous following 100% oxygen inhalation. Potential errors due to temperature, fluid flow, and partial volume are discussed. Noninvasive measurements of human body fluid pO2 in vivo are presented, which yield reasonable values based on prior literature estimates. This rapid imaging-based measurement of fluid oxygenation may provide insight into normal physiology as well as changes due to disease or during treatment.

Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates

Treesearch

C. Klauberg; A. T. Hudak; B. C. Bright; L. Boschetti; M. B. Dickinson; R. L. Kremens; C. A. Silva

2018-01-01

Fire radiative energy density (FRED, J m-2) integrated from fire radiative power density (FRPD, W m-2) observations of landscape-level fires can present an undersampling problem when collected from fixed-wing aircraft. In the present study, the aircraft made multiple passes over the fire at ~3 min intervals, thus failing to observe most of the FRPD emitted as the flame...

A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images.

PubMed

McClelland, Jamie R; Modat, Marc; Arridge, Simon; Grimes, Helen; D'Souza, Derek; Thomas, David; Connell, Dylan O'; Low, Daniel A; Kaza, Evangelia; Collins, David J; Leach, Martin O; Hawkes, David J

2017-06-07

Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated.

Timescales alter the inferred strength and temporal consistency of intraspecific diet specialization

USGS Publications Warehouse

Novak, Mark; Tinker, M. Tim

2015-01-01

Many populations consist of individuals that differ substantially in their diets. Quantification of the magnitude and temporal consistency of such intraspecific diet variation is needed to understand its importance, but the extent to which different approaches for doing so reflect instantaneous vs. time-aggregated measures of individual diets may bias inferences. We used direct observations of sea otter individuals (Enhydra lutris nereis) to assess how: (1) the timescale of sampling, (2) under-sampling, and (3) the incidence- vs. frequency-based consideration of prey species affect the inferred strength and consistency of intraspecific diet variation. Analyses of feeding observations aggregated over hourly to annual intervals revealed a substantial bias associated with time aggregation that decreases the inferred magnitude of specialization and increases the inferred consistency of individuals’ diets. Time aggregation also made estimates of specialization more sensitive to the consideration of prey frequency, which decreased estimates relative to the use of prey incidence; time aggregation did not affect the extent to which under-sampling contributed to its overestimation. Our analyses demonstrate the importance of studying intraspecific diet variation with an explicit consideration of time and thereby suggest guidelines for future empirical efforts. Failure to consider time will likely produce inconsistent predictions regarding the effects of intraspecific variation on predator–prey interactions.

Sparse Representation Based Frequency Detection and Uncertainty Reduction in Blade Tip Timing Measurement for Multi-Mode Blade Vibration Monitoring

PubMed Central

Pan, Minghao; Yang, Yongmin; Guan, Fengjiao; Hu, Haifeng; Xu, Hailong

2017-01-01

The accurate monitoring of blade vibration under operating conditions is essential in turbo-machinery testing. Blade tip timing (BTT) is a promising non-contact technique for the measurement of blade vibrations. However, the BTT sampling data are inherently under-sampled and contaminated with several measurement uncertainties. How to recover frequency spectra of blade vibrations though processing these under-sampled biased signals is a bottleneck problem. A novel method of BTT signal processing for alleviating measurement uncertainties in recovery of multi-mode blade vibration frequency spectrum is proposed in this paper. The method can be divided into four phases. First, a single measurement vector model is built by exploiting that the blade vibration signals are sparse in frequency spectra. Secondly, the uniqueness of the nonnegative sparse solution is studied to achieve the vibration frequency spectrum. Thirdly, typical sources of BTT measurement uncertainties are quantitatively analyzed. Finally, an improved vibration frequency spectra recovery method is proposed to get a guaranteed level of sparse solution when measurement results are biased. Simulations and experiments are performed to prove the feasibility of the proposed method. The most outstanding advantage is that this method can prevent the recovered multi-mode vibration spectra from being affected by BTT measurement uncertainties without increasing the probe number. PMID:28758952

Physiological plasticity increases resilience of ectothermic animals to climate change

NASA Astrophysics Data System (ADS)

Seebacher, Frank; White, Craig R.; Franklin, Craig E.

2015-01-01

Understanding how climate change affects natural populations remains one of the greatest challenges for ecology and management of natural resources. Animals can remodel their physiology to compensate for the effects of temperature variation, and this physiological plasticity, or acclimation, can confer resilience to climate change. The current lack of a comprehensive analysis of the capacity for physiological plasticity across taxonomic groups and geographic regions, however, constrains predictions of the impacts of climate change. Here, we assembled the largest database to date to establish the current state of knowledge of physiological plasticity in ectothermic animals. We show that acclimation decreases the sensitivity to temperature and climate change of freshwater and marine animals, but less so in terrestrial animals. Animals from more stable environments have greater capacity for acclimation, and there is a significant trend showing that the capacity for thermal acclimation increases with decreasing latitude. Despite the capacity for acclimation, climate change over the past 20 years has already resulted in increased physiological rates of up to 20%, and we predict further future increases under climate change. The generality of these predictions is limited, however, because much of the world is drastically undersampled in the literature, and these undersampled regions are the areas of greatest need for future research efforts.

Suppression of imaging crack caused by the gap between micromirrors in maskless lithography

NASA Astrophysics Data System (ADS)

Liang, Liwen; Zhou, Jinyun; Lei, Liang; Wang, Bo; Wang, Qu; Wen, Kunhua

2017-10-01

The digital micromirror device (DMD) is the key device in maskless lithography. However, because of the machinery manufacturing limit of DMDs, the gap between the micromirrors may destroy the continuity of the graphic. This work presents a simple way to fill the imaging crack by controlling the partial coherence factor σ of the light source. A crack can be regarded as the image of a dark space. By considering the resolving power for such cracks under partially coherent illumination, the images of such dark spaces can be covered, preventing them from being imaged on the substrate. By using mathematical derivations of the light intensity distribution exposed to the substrate, and by utilizing the diffraction effect induced by the finite aperture of the optical projection system, an appropriate σ value can be determined for eliminating the image of the crack in an actual scene. The numerical simulation results demonstrate that this method can ensure the continuity of the graphic at the critical partial coherence factor σc regardless of the shape of the target graphic.

Partially Covered Lenses and Additive Color Mixing

ERIC Educational Resources Information Center

Razpet, Nada; Kranjc, Tomaž

2017-01-01

When doing experimental work of image formation by mirrors and (thin) lenses, it turns out again and again that students often have partially incorrect preconceptions about how the light emerging from an object passes through a lens and how the image is formed on a screen or directly in the eye. To check students' prior knowledge and help get a…

Apparatus for generating partially coherent radiation

DOEpatents

Naulleau, Patrick P.

2004-09-28

The effective coherence of an undulator beamline can be tailored to projection lithography requirements by using a simple single moving element and a simple stationary low-cost spherical mirror. The invention is particularly suited for use in an illuminator device for an optical image processing system requiring partially coherent illumination. The illuminator includes: (i) source of coherent or partially coherent radiation which has an intrinsic coherence that is higher than the desired coherence; (ii) a reflective surface that receives incident radiation from said source; (iii) means for moving the reflective surface through a desired range of angles in two dimensions wherein the rate of the motion is fast relative to integration time of said image processing system; and (iv) a condenser optic that re-images the moving reflective surface to the entrance plane of said image processing system, thereby, making the illumination spot in said entrance plane essentially stationary.

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies.

PubMed

Koch, Lisa M; Rajchl, Martin; Bai, Wenjia; Baumgartner, Christian F; Tong, Tong; Passerat-Palmbach, Jonathan; Aljabar, Paul; Rueckert, Daniel

2017-08-22

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing.

PubMed

Leong, Siow Hoo; Ong, Seng Huat

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

PubMed Central

Leong, Siow Hoo

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index. PMID:28686634

Defocusing effects of lensless ghost imaging and ghost diffraction with partially coherent sources

NASA Astrophysics Data System (ADS)

Zhou, Shuang-Xi; Sheng, Wei; Bi, Yu-Bo; Luo, Chun-Ling

2018-04-01

The defocusing effect is inevitable and degrades the image quality in the conventional optical imaging process significantly due to the close confinement of the imaging lens. Based on classical optical coherent theory and linear algebra, we develop a unified formula to describe the defocusing effects of both lensless ghost imaging (LGI) and lensless ghost diffraction (LGD) systems with a partially coherent source. Numerical examples are given to illustrate the influence of defocusing length on the quality of LGI and LGD. We find that the defocusing effects of the test and reference paths in the LGI or LGD systems are entirely different, while the LGD system is more robust against defocusing than the LGI system. Specifically, we find that the imaging process for LGD systems can be viewed as pinhole imaging, which may find applications in ultra-short-wave band imaging without imaging lenses, e.g. x-ray diffraction and γ-ray imaging.

Blind compressive sensing dynamic MRI

PubMed Central

Lingala, Sajan Goud; Jacob, Mathews

2013-01-01

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the non-orthogonal nature of the dictionary basis functions. Since the number of degrees of freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting ℓ1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler sub problems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the ℓ1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the ℓ0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic MRI applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes. PMID:23542951

High-resolution three-dimensional partially coherent diffraction imaging.

PubMed

Clark, J N; Huang, X; Harder, R; Robinson, I K

2012-01-01

The wave properties of light, particularly its coherence, are responsible for interference effects, which can be exploited in powerful imaging applications. Coherent diffractive imaging relies heavily on coherence and has recently experienced rapid growth. Coherent diffractive imaging recovers an object from its diffraction pattern by computational phasing with the potential of wavelength-limited resolution. Diminished coherence results in reconstructions that suffer from artefacts or fail completely. Here we demonstrate ab initio phasing of partially coherent diffraction patterns in three dimensions, while simultaneously determining the coherence properties of the illuminating wavefield. Both the dramatic improvements in image interpretability and the three-dimensional evaluation of the coherence will have broad implications for quantitative imaging of nanostructures and wavefield characterization with X-rays and electrons.

Accelerated echo planar J-resolved spectroscopic imaging in prostate cancer: a pilot validation of non-linear reconstruction using total variation and maximum entropy.

PubMed

Nagarajan, Rajakumar; Iqbal, Zohaib; Burns, Brian; Wilson, Neil E; Sarma, Manoj K; Margolis, Daniel A; Reiter, Robert E; Raman, Steven S; Thomas, M Albert

2015-11-01

The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites. Copyright © 2015 John Wiley & Sons, Ltd.

Accelerated dual-contrast first-pass perfusion MRI of the mouse heart: development and application to diet-induced obese mice.

PubMed

Naresh, Nivedita K; Chen, Xiao; Roy, Rene J; Antkowiak, Patrick F; Annex, Brian H; Epstein, Frederick H

2015-03-01

Gene-modified mice may be used to elucidate molecular mechanisms underlying abnormal myocardial blow flow (MBF). We sought to develop a quantitative myocardial perfusion imaging technique for mice and to test the hypothesis that myocardial perfusion reserve (MPR) is reduced in a mouse model of diet-induced obesity (DIO). A dual-contrast saturation-recovery sequence with ky -t undersampling and a motion-compensated compressed sensing reconstruction algorithm was developed for first-pass MRI on a small-bore 7 Tesla system. Control mice were imaged at rest and with the vasodilators ATL313 and Regadenoson (n = 6 each). In addition, we imaged mice fed a high-fat diet (HFD) for 24 weeks. In control mice, MBF was 5.7 ± 0.8 mL/g/min at rest and it increased to 11.8 ± 0.6 mL/g/min with ATL313 and to 10.4 ± 0.3 mL/g/min with Regadenoson. In HFD mice, we detected normal resting MBF (5.6 ± 0.4 versus 5.0 ± 0.3 on control diet), low MBF at stress (7.7 ± 0.4 versus 10.4 ± 0.3 on control diet, P < 0.05), and reduced MPR (1.4 ± 0.2 versus 2.0 ± 0.3 on control diet, P < 0.05). Accelerated dual-contrast first-pass MRI with motion-compensated compressed sensing provides spatiotemporal resolution suitable for measuring MBF in free-breathing mice, and detected reduced MPR in DIO mice. These techniques may be used to study molecular mechanisms that underlie abnormal myocardial perfusion. © 2014 Wiley Periodicals, Inc.

Accelerated Dual-contrast First-pass Perfusion MRI of the Mouse Heart: Development and Application to Diet-induced Obese Mice

PubMed Central

Naresh, Nivedita K.; Chen, Xiao; Roy, Rene J.; Antkowiak, Patrick F.; Annex, Brian H.; Epstein, Frederick H.

2014-01-01

Background Gene-modified mice may be used to elucidate molecular mechanisms underlying abnormal myocardial blood flow (MBF). We sought to develop a quantitative myocardial perfusion imaging technique for mice and to test the hypothesis that myocardial perfusion reserve (MPR) is reduced in a mouse model of diet-induced obesity (DIO). Methods A dual-contrast saturation-recovery sequence with ky-t undersampling and a motion-compensated compressed sensing reconstruction algorithm was developed for first-pass MRI on a small-bore 7T system. Control mice were imaged at rest and with the vasodilators ATL313 and Regadenoson (n=6 each). In addition, we imaged mice fed a high-fat diet (HFD) for 24 weeks. Results In control mice, MBF was 5.7±0.8 ml/g/min at rest and it increased to 11.8±0.6 ml/g/min with ATL313 and to 10.4±0.3 ml/g/min with Regadenoson. In HFD mice we detected normal resting MBF (5.6±0.4 vs. 5.0±0.3 on control diet), low MBF at stress (7.7±0.4 vs. 10.4±0.3 on control diet, p<0.05), and reduced MPR (1.4±0.2 vs. 2.0±0.3 on control diet, p<0.05). Conclusions Accelerated dual-contrast first-pass MRI with motion-compensated compressed sensing provides spatiotemporal resolution suitable for measuring MBF in free-breathing mice, and detected reduced MPR in DIO mice. These techniques may be used to study molecular mechanisms that underlie abnormal myocardial perfusion. PMID:24760707

Quantifying the accuracy of the tumor motion and area as a function of acceleration factor for the simulation of the dynamic keyhole magnetic resonance imaging method.

PubMed

Lee, Danny; Greer, Peter B; Pollock, Sean; Kim, Taeho; Keall, Paul

2016-05-01

The dynamic keyhole is a new MR image reconstruction method for thoracic and abdominal MR imaging. To date, this method has not been investigated with cancer patient magnetic resonance imaging (MRI) data. The goal of this study was to assess the dynamic keyhole method for the task of lung tumor localization using cine-MR images reconstructed in the presence of respiratory motion. The dynamic keyhole method utilizes a previously acquired a library of peripheral k-space datasets at similar displacement and phase (where phase is simply used to determine whether the breathing is inhale to exhale or exhale to inhale) respiratory bins in conjunction with central k-space datasets (keyhole) acquired. External respiratory signals drive the process of sorting, matching, and combining the two k-space streams for each respiratory bin, thereby achieving faster image acquisition without substantial motion artifacts. This study was the first that investigates the impact of k-space undersampling on lung tumor motion and area assessment across clinically available techniques (zero-filling and conventional keyhole). In this study, the dynamic keyhole, conventional keyhole and zero-filling methods were compared to full k-space dataset acquisition by quantifying (1) the keyhole size required for central k-space datasets for constant image quality across sixty four cine-MRI datasets from nine lung cancer patients, (2) the intensity difference between the original and reconstructed images in a constant keyhole size, and (3) the accuracy of tumor motion and area directly measured by tumor autocontouring. For constant image quality, the dynamic keyhole method, conventional keyhole, and zero-filling methods required 22%, 34%, and 49% of the keyhole size (P < 0.0001), respectively, compared to the full k-space image acquisition method. Compared to the conventional keyhole and zero-filling reconstructed images with the keyhole size utilized in the dynamic keyhole method, an average intensity difference of the dynamic keyhole reconstructed images (P < 0.0001) was minimal, and resulted in the accuracy of tumor motion within 99.6% (P < 0.0001) and the accuracy of tumor area within 98.0% (P < 0.0001) for lung tumor monitoring applications. This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiation therapy requiring the MR monitoring of thoracic tumors. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by up to a factor of five compared with full k-space methods for real-time lung tumor MRI.

Spatially encoded phase-contrast MRI-3D MRI movies of 1D and 2D structures at millisecond resolution.

PubMed

Merboldt, Klaus-Dietmar; Uecker, Martin; Voit, Dirk; Frahm, Jens

2011-10-01

This work demonstrates that the principles underlying phase-contrast MRI may be used to encode spatial rather than flow information along a perpendicular dimension, if this dimension contains an MRI-visible object at only one spatial location. In particular, the situation applies to 3D mapping of curved 2D structures which requires only two projection images with different spatial phase-encoding gradients. These phase-contrast gradients define the field of view and mean spin-density positions of the object in the perpendicular dimension by respective phase differences. When combined with highly undersampled radial fast low angle shot (FLASH) and image reconstruction by regularized nonlinear inversion, spatial phase-contrast MRI allows for dynamic 3D mapping of 2D structures in real time. First examples include 3D MRI movies of the acting human hand at a temporal resolution of 50 ms. With an even simpler technique, 3D maps of curved 1D structures may be obtained from only three acquisitions of a frequency-encoded MRI signal with two perpendicular phase encodings. Here, 3D MRI movies of a rapidly rotating banana were obtained at 5 ms resolution or 200 frames per second. In conclusion, spatial phase-contrast 3D MRI of 2D or 1D structures is respective two or four orders of magnitude faster than conventional 3D MRI. Copyright © 2011 Wiley-Liss, Inc.

Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.

PubMed

Wahab, Noorul; Khan, Asifullah; Lee, Yeon Soo

2017-06-01

Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While detecting cancer, one important factor is mitotic count - showing how rapidly the cells are dividing. But the class imbalance problem, due to the small number of mitotic nuclei in comparison to the overwhelming number of non-mitotic nuclei, affects the performance of classification models. This work presents a two-phase model to mitigate the class biasness issue while classifying mitotic and non-mitotic nuclei in breast cancer histopathology images through a deep convolutional neural network (CNN). First, nuclei are segmented out using blue ratio and global binary thresholding. In Phase-1 a CNN is then trained on the segmented out 80×80 pixel patches based on a standard dataset. Hard non-mitotic examples are identified and augmented; mitotic examples are oversampled by rotation and flipping; whereas non-mitotic examples are undersampled by blue ratio histogram based k-means clustering. Based on this information from Phase-1, the dataset is modified for Phase-2 in order to reduce the effects of class imbalance. The proposed CNN architecture and data balancing technique yielded an F-measure of 0.79, and outperformed all the methods relying on specific handcrafted features, as well as those using a combination of handcrafted and CNN-generated features. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm

NASA Astrophysics Data System (ADS)

Gilat Schmidt, Taly; Barber, Rina F.; Sidky, Emil Y.

2017-03-01

Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained `One-Step' Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Yi, E-mail: yiguo@usc.edu; Zhu, Yinghua; Lingala, Sajan Goud

Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm{sup 3}, FOV 22 × 22 × 4.2 cm{sup 3}, and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm{sup 3}, and broader coverage 22 × 22 × 19 cm{sup 3}. Temporal resolution was 5 smore » for both protocols. Time-resolved images and blood–brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.« less

An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising.

PubMed

Khanian, Maryam; Feizi, Awat; Davari, Ali

2014-01-01

Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

A theory of fine structure image models with an application to detection and classification of dementia.

PubMed

O'Neill, William; Penn, Richard; Werner, Michael; Thomas, Justin

2015-06-01

Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible.

[Image-guided radiotherapy and partial delegation to radiotherapy technicians: Clermont-Ferrand experience].

PubMed

Loos, G; Moreau, J; Miroir, J; Benhaïm, C; Biau, J; Caillé, C; Bellière, A; Lapeyre, M

2013-10-01

The various image-guided radiotherapy techniques raise the question of how to achieve the control of patient positioning before irradiation session and sharing of tasks between radiation oncologists and radiotherapy technicians. We have put in place procedures and operating methods to make a partial delegation of tasks to radiotherapy technicians and secure the process in three situations: control by orthogonal kV imaging (kV-kV) of bony landmarks, control by kV-kV imaging of intraprostatic fiducial goldmarkers and control by cone beam CT (CBCT) imaging for prostate cancer. Significant medical overtime is required to control these three IGRT techniques. Because of their competence in imaging, these daily controls can be delegated to radiotherapy technicians. However, to secure the process, initial training and regular evaluation are essential. The analysis of the comparison of the use of kV/kV on bone structures allowed us to achieve a partial delegation of control to radiotherapy technicians. Controlling the positioning of the prostate through the use and automatic registration of fiducial goldmarkers allows better tracking of the prostate and can be easily delegated to radiotherapy technicians. The analysis of the use of daily cone beam CT for patients treated with intensity modulated irradiation is underway, and a comparison of practices between radiotherapy technicians and radiation oncologists is ongoing to know if a partial delegation of this control is possible. Copyright © 2013. Published by Elsevier SAS.

Parahippocampal epilepsy with subtle dysplasia: A cause of "imaging negative" partial epilepsy.

PubMed

Pillay, Neelan; Fabinyi, Gavin C A; Myles, Terry S; Fitt, Gregory J; Berkovic, Samuel F; Jackson, Graeme D

2009-12-01

Lesion-negative refractory partial epilepsy is a major challenge in the assessment of patients for potential surgery. Finding a potential epileptogenic lesion simplifies assessment and is associated with good outcome. Here we describe imaging features of subtle parahippocampal dysplasia in five cases that were initially assessed as having imaging-negative frontal or temporal lobe epilepsy. We analyzed the clinical and imaging features of five patients with seizures from the parahippocampal region. Five patients had subtle but distinctive magnetic resonance imaging (MRI) abnormalities in the parahippocampal gyrus. This was a unilateral signal abnormality in the parahippocampal white matter extending into gray matter on heavily T(1)- and T(2)-weighted images with relative preservation of the gray-white matter boundary on T(1)-weighted volume sequences. Only one of these patients had typical electroclinical unilateral temporal lobe epilepsy (TLE); one mimicked frontal lobe epilepsy, two showed bitemporal seizures, and one had unlocalized partial seizures. All have had surgery; four are seizure-free (one has occasional auras only, follow-up 6 months to 10 years), and one has a >50% seizure reduction. Histopathologic evaluation suggested dysplastic features in the surgical specimens in all. In patients with lesion-negative partial epilepsy with frontal or temporal semiology, or in cases with apparent bitemporal seizures, subtle parahippocampal abnormalities should be carefully excluded. Recognizing the MRI findings of an abnormal parahippocampal gyrus can lead to successful surgery without invasive monitoring, despite apparently incongruent electroclinical features.

Phased array ghost elimination.

PubMed

Kellman, Peter; McVeigh, Elliot R

2006-05-01

Parallel imaging may be applied to cancel ghosts caused by a variety of distortion mechanisms, including distortions such as off-resonance or local flow, which are space variant. Phased array combining coefficients may be calculated that null ghost artifacts at known locations based on a constrained optimization, which optimizes SNR subject to the nulling constraint. The resultant phased array ghost elimination (PAGE) technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation is applied to full field-of-view (FOV) images. The phased array method for ghost elimination may result in greater flexibility in designing acquisition strategies. For example, in multi-shot EPI applications ghosts are typically mitigated by the use of an interleaved phase encode acquisition order. An alternative strategy is to use a sequential, non-interleaved phase encode order and cancel the resultant ghosts using PAGE parallel imaging. Cancellation of ghosts by means of phased array processing makes sequential, non-interleaved phase encode acquisition order practical, and permits a reduction in repetition time, TR, by eliminating the need for echo-shifting. Sequential, non-interleaved phase encode order has benefits of reduced distortion due to off-resonance, in-plane flow and EPI delay misalignment. Furthermore, the use of EPI with PAGE has inherent fat-water separation and has been used to provide off-resonance correction using a technique referred to as lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), and may further generalized using the multi-point Dixon method. Other applications of PAGE include cancelling ghosts which arise due to amplitude or phase variation during the approach to steady state. Parallel imaging requires estimates of the complex coil sensitivities. In vivo estimates may be derived by temporally varying the phase encode ordering to obtain a full k-space dataset in a scheme similar to the autocalibrating TSENSE method. This scheme is a generalization of the UNFOLD method used for removing aliasing in undersampled acquisitions. The more general scheme may be used to modulate each EPI ghost image to a separate temporal frequency as described in this paper. Copyright (c) 2006 John Wiley & Sons, Ltd.

Phased array ghost elimination

PubMed Central

Kellman, Peter; McVeigh, Elliot R.

2007-01-01

Parallel imaging may be applied to cancel ghosts caused by a variety of distortion mechanisms, including distortions such as off-resonance or local flow, which are space variant. Phased array combining coefficients may be calculated that null ghost artifacts at known locations based on a constrained optimization, which optimizes SNR subject to the nulling constraint. The resultant phased array ghost elimination (PAGE) technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation is applied to full field-of-view (FOV) images. The phased array method for ghost elimination may result in greater flexibility in designing acquisition strategies. For example, in multi-shot EPI applications ghosts are typically mitigated by the use of an interleaved phase encode acquisition order. An alternative strategy is to use a sequential, non-interleaved phase encode order and cancel the resultant ghosts using PAGE parallel imaging. Cancellation of ghosts by means of phased array processing makes sequential, non-interleaved phase encode acquisition order practical, and permits a reduction in repetition time, TR, by eliminating the need for echo-shifting. Sequential, non-interleaved phase encode order has benefits of reduced distortion due to off-resonance, in-plane flow and EPI delay misalignment. Furthermore, the use of EPI with PAGE has inherent fat-water separation and has been used to provide off-resonance correction using a technique referred to as lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), and may further generalized using the multi-point Dixon method. Other applications of PAGE include cancelling ghosts which arise due to amplitude or phase variation during the approach to steady state. Parallel imaging requires estimates of the complex coil sensitivities. In vivo estimates may be derived by temporally varying the phase encode ordering to obtain a full k-space dataset in a scheme similar to the autocalibrating TSENSE method. This scheme is a generalization of the UNFOLD method used for removing aliasing in undersampled acquisitions. The more general scheme may be used to modulate each EPI ghost image to a separate temporal frequency as described in this paper. PMID:16705636

SU-E-I-36: A KWIC and Dirty Look at Dose Savings and Perfusion Metrics in Simulated CT Neuro Perfusion Exams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, J; Martin, T; Young, S

Purpose: CT neuro perfusion scans are one of the highest dose exams. Methods to reduce dose include decreasing the number of projections acquired per gantry rotation, however conventional reconstruction of such scans leads to sampling artifacts. In this study we investigated a projection view-sharing reconstruction algorithm used in dynamic MRI – “K-space Weighted Image Contrast” (KWIC) – applied to simulated perfusion exams and evaluated dose savings and impacts on perfusion metrics. Methods: A FORBILD head phantom containing simulated time-varying objects was developed and a set of parallel-beam CT projection data was created. The simulated scans were 60 seconds long, 1152more » projections per turn, with a rotation time of one second. No noise was simulated. 5mm, 10mm, and 50mm objects were modeled in the brain. A baseline, “full dose” simulation used all projections and reduced dose cases were simulated by downsampling the number of projections per turn from 1152 to 576 (50% dose), 288 (25% dose), and 144 (12.5% dose). KWIC was further evaluated at 72 projections per rotation (6.25%). One image per second was reconstructed using filtered backprojection (FBP) and KWIC. KWIC reconstructions utilized view cores of 36, 72, 144, and 288 views and 16, 8, 4, and 2 subapertures respectively. From the reconstructed images, time-to-peak (TTP), cerebral blood flow (CBF) and the FWHM of the perfusion curve were calculated and compared against reference values from the full-dose FBP data. Results: TTP, CBF, and the FWHM were unaffected by dose reduction (to 12.5%) and reconstruction method, however image quality was improved when using KWIC. Conclusion: This pilot study suggests that KWIC preserves image quality and perfusion metrics when under-sampling projections and that the unique contrast weighting of KWIC could provided substantial dose-savings for perfusion CT scans. Evaluation of KWIC in clinical CT data will be performed in the near future. R01 EB014922, NCI Grant U01 CA181156 (Quantitative Imaging Network), and Tobacco Related Disease Research Project grant 22RT-0131.« less

Blind Bayesian restoration of adaptive optics telescope images using generalized Gaussian Markov random field models

NASA Astrophysics Data System (ADS)

Jeffs, Brian D.; Christou, Julian C.

1998-09-01

This paper addresses post processing for resolution enhancement of sequences of short exposure adaptive optics (AO) images of space objects. The unknown residual blur is removed using Bayesian maximum a posteriori blind image restoration techniques. In the problem formulation, both the true image and the unknown blur psf's are represented by the flexible generalized Gaussian Markov random field (GGMRF) model. The GGMRF probability density function provides a natural mechanism for expressing available prior information about the image and blur. Incorporating such prior knowledge in the deconvolution optimization is crucial for the success of blind restoration algorithms. For example, space objects often contain sharp edge boundaries and geometric structures, while the residual blur psf in the corresponding partially corrected AO image is spectrally band limited, and exhibits while the residual blur psf in the corresponding partially corrected AO image is spectrally band limited, and exhibits smoothed, random , texture-like features on a peaked central core. By properly choosing parameters, GGMRF models can accurately represent both the blur psf and the object, and serve to regularize the deconvolution problem. These two GGMRF models also serve as discriminator functions to separate blur and object in the solution. Algorithm performance is demonstrated with examples from synthetic AO images. Results indicate significant resolution enhancement when applied to partially corrected AO images. An efficient computational algorithm is described.

Partial report and other sampling procedures overestimate the duration of iconic memory.

PubMed

Appelman, I B

1980-03-01

In three experiments, subjects estimated the duration of a brief visual image (iconic memory) either directly by adjusting onset of a click to offset of the visual image, or indirectly with a Sperling partial report (sampling) procedure. The results indicated that partial report and other sampling procedures may reflect other brief phenomena along with iconic memory. First, the partial report procedure yields a greater estimate of the duration of iconic memory than the more direct click method. Second, the partial report estimate of the duration of iconic memory is affected if the subject is required to simultaneously retain a list of distractor items (memory load), while the click method estimate of the duration of iconic memory is not affected by a memory load. Finally, another sampling procedure based on visual cuing yields different estimates of the duration of iconic memory depending on how many items are cued. It was concluded that partial report and other sampling procedures overestimate the duration of iconic memory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Masiero, Joseph R.; Mainzer, A. K.; Nugent, C. R.

We present revised near-infrared albedo fits of 2835 main-belt asteroids observed by WISE/NEOWISE over the course of its fully cryogenic survey in 2010. These fits are derived from reflected-light near-infrared images taken simultaneously with thermal emission measurements, allowing for more accurate measurements of the near-infrared albedos than is possible for visible albedo measurements. Because our sample requires reflected light measurements, it undersamples small, low-albedo asteroids, as well as those with blue spectral slopes across the wavelengths investigated. We find that the main belt separates into three distinct groups of 6%, 16%, and 40% reflectance at 3.4 μm. Conversely, the 4.6more » μm albedo distribution spans the full range of possible values with no clear grouping. Asteroid families show a narrow distribution of 3.4 μm albedos within each family that map to one of the three observed groupings, with the (221) Eos family being the sole family associated with the 16% reflectance 3.4 μm albedo group. We show that near-infrared albedos derived from simultaneous thermal emission and reflected light measurements are important indicators of asteroid taxonomy and can identify interesting targets for spectroscopic follow-up.« less

How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography

PubMed Central

Jørgensen, J. S.; Sidky, E. Y.

2015-01-01

We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sampling patterns. Finally, we show preliminary results of how well phase-diagram analysis can predict the sufficient number of projections for accurately reconstructing a large-scale image of a given sparsity by means of total-variation regularization. PMID:25939620

How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography.

PubMed

Jørgensen, J S; Sidky, E Y

2015-06-13

We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sampling patterns. Finally, we show preliminary results of how well phase-diagram analysis can predict the sufficient number of projections for accurately reconstructing a large-scale image of a given sparsity by means of total-variation regularization.

Reducing Interpolation Artifacts for Mutual Information Based Image Registration

PubMed Central

Soleimani, H.; Khosravifard, M.A.

2011-01-01

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. Mutual Information is a basic concept of Information theory which indicates the dependency of two random variables (or two images). In order to evaluate the mutual information of two images their joint probability distribution is required. Several interpolation methods, such as Partial Volume (PV) and bilinear, are used to estimate joint probability distribution. Both of these two methods yield some artifacts on mutual information function. Partial Volume-Hanning window (PVH) and Generalized Partial Volume (GPV) methods are introduced to remove such artifacts. In this paper we show that the acceptable performance of these methods is not due to their kernel function. It's because of the number of pixels which incorporate in interpolation. Since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as PV and bilinear interpolations) and removes most of the artifacts. Experimental results of the registration of Computed Tomography (CT) images show superiority of the proposed scheme. PMID:22606673

Enhancements for digital imaging of gusset plate connections : fisheye and image stitching.

DOT National Transportation Integrated Search

2011-09-01

This report describes techniques to convert fisheye digital images of gusset plates to perspective images (defish). It also describes methods to stitch together partial images of a gusset plate into a composite of the complete gusset plate. The conve...

The Postself and Richard Nixon's Partial Death.

ERIC Educational Resources Information Center

Nelson, Phillips

1980-01-01

Correlates Nixon's actions during his final days in office to the concepts of postself and partial death. Postself is the image one wants to remain after death. Partial death is a transitory state in which one faces a major alteration in his/her relationship to the world. (JMF)

Rapid whole-brain resting-state fMRI at 3 T: Efficiency-optimized three-dimensional EPI versus repetition time-matched simultaneous-multi-slice EPI.

PubMed

Stirnberg, Rüdiger; Huijbers, Willem; Brenner, Daniel; Poser, Benedikt A; Breteler, Monique; Stöcker, Tony

2017-12-01

State-of-the-art simultaneous-multi-slice (SMS-)EPI and 3D-EPI share several properties that benefit functional MRI acquisition. Both sequences employ equivalent parallel imaging undersampling with controlled aliasing to achieve high temporal sampling rates. As a volumetric imaging sequence, 3D-EPI offers additional means of acceleration complementary to 2D-CAIPIRINHA sampling, such as fast water excitation and elliptical sampling. We performed an application-oriented comparison between a tailored, six-fold CAIPIRINHA-accelerated 3D-EPI protocol at 530 ms temporal and 2.4 mm isotropic spatial resolution and an SMS-EPI protocol with identical spatial and temporal resolution for whole-brain resting-state fMRI at 3 T. The latter required eight-fold slice acceleration to compensate for the lack of elliptical sampling and fast water excitation. Both sequences used vendor-supplied on-line image reconstruction. We acquired test/retest resting-state fMRI scans in ten volunteers, with simultaneous acquisition of cardiac and respiration data, subsequently used for optional physiological noise removal (nuisance regression). We found that the 3D-EPI protocol has significantly increased temporal signal-to-noise ratio throughout the brain as compared to the SMS-EPI protocol, especially when employing motion and nuisance regression. Both sequence types reliably identified known functional networks with stronger functional connectivity values for the 3D-EPI protocol. We conclude that the more time-efficient 3D-EPI primarily benefits from reduced parallel imaging noise due to a higher, actual k-space sampling density compared to SMS-EPI. The resultant BOLD sensitivity increase makes 3D-EPI a valuable alternative to SMS-EPI for whole-brain fMRI at 3 T, with voxel sizes well below 3 mm isotropic and sampling rates high enough to separate dominant cardiac signals from BOLD signals in the frequency domain. Copyright © 2017 Elsevier Inc. All rights reserved.

Using the Optical Mouse Sensor as a Two-Euro Counterfeit Coin Detector

PubMed Central

Tresanchez, Marcel; Pallejà, Tomàs; Teixidó, Mercè; Palacín, Jordi

2009-01-01

In this paper, the sensor of an optical mouse is presented as a counterfeit coin detector applied to the two-Euro case. The detection process is based on the short distance image acquisition capabilities of the optical mouse sensor where partial images of the coin under analysis are compared with some partial reference coin images for matching. Results show that, using only the vision sense, the counterfeit acceptance and rejection rates are very similar to those of a trained user and better than those of an untrained user. PMID:22399987

Phase-Sensitive Coherence and the Classical-Quantum Boundary in Ghost Imaging

NASA Technical Reports Server (NTRS)

Erkmen, Baris I.; Hardy, Nicholas D.; Venkatraman, Dheera; Wong, Franco N. C.; Shapiro, Jeffrey H.

2011-01-01

The theory of partial coherence has a long and storied history in classical statistical optics. the vast majority of this work addresses fields that are statistically stationary in time, hence their complex envelopes only have phase-insensitive correlations. The quantum optics of squeezed-state generation, however, depends on nonlinear interactions producing baseband field operators with phase-insensitive and phase-sensitive correlations. Utilizing quantum light to enhance imaging has been a topic of considerable current interest, much of it involving biphotons, i.e., streams of entangled-photon pairs. Biphotons have been employed for quantum versions of optical coherence tomography, ghost imaging, holography, and lithography. However, their seemingly quantum features have been mimicked with classical-sate light, questioning wherein lies the classical-quantum boundary. We have shown, for the case of Gaussian-state light, that this boundary is intimately connected to the theory of phase-sensitive partial coherence. Here we present that theory, contrasting it with the familiar case of phase-insensitive partial coherence, and use it to elucidate the classical-quantum boundary of ghost imaging. We show, both theoretically and experimentally, that classical phase-sensitive light produces ghost imaging most closely mimicking those obtained in biphotons, and we derived the spatial resolution, image contrast, and signal-to-noise ratio of a standoff-sensing ghost imager, taking into account target-induced speckle.

Species undersampling in tropical bat surveys: effects on emerging biodiversity patterns.

PubMed

Meyer, Christoph F J; Aguiar, Ludmilla M S; Aguirre, Luis F; Baumgarten, Julio; Clarke, Frank M; Cosson, Jean-François; Estrada Villegas, Sergio; Fahr, Jakob; Faria, Deborah; Furey, Neil; Henry, Mickaël; Jenkins, Richard K B; Kunz, Thomas H; Cristina MacSwiney González, M; Moya, Isabel; Pons, Jean-Marc; Racey, Paul A; Rex, Katja; Sampaio, Erica M; Stoner, Kathryn E; Voigt, Christian C; von Staden, Dietrich; Weise, Christa D; Kalko, Elisabeth K V

2015-01-01

Undersampling is commonplace in biodiversity surveys of species-rich tropical assemblages in which rare taxa abound, with possible repercussions for our ability to implement surveys and monitoring programmes in a cost-effective way. We investigated the consequences of information loss due to species undersampling (missing subsets of species from the full species pool) in tropical bat surveys for the emerging patterns of species richness (SR) and compositional variation across sites. For 27 bat assemblage data sets from across the tropics, we used correlations between original data sets and subsets with different numbers of species deleted either at random, or according to their rarity in the assemblage, to assess to what extent patterns in SR and composition in data subsets are congruent with those in the initial data set. We then examined to what degree high sample representativeness (r ≥ 0·8) was influenced by biogeographic region, sampling method, sampling effort or structural assemblage characteristics. For SR, correlations between random subsets and original data sets were strong (r ≥ 0·8) with moderate (ca. 20%) species loss. Bias associated with information loss was greater for species composition; on average ca. 90% of species in random subsets had to be retained to adequately capture among-site variation. For nonrandom subsets, removing only the rarest species (on average c. 10% of the full data set) yielded strong correlations (r > 0·95) for both SR and composition. Eliminating greater proportions of rare species resulted in weaker correlations and large variation in the magnitude of observed correlations among data sets. Species subsets that comprised ca. 85% of the original set can be considered reliable surrogates, capable of adequately revealing patterns of SR and temporal or spatial turnover in many tropical bat assemblages. Our analyses thus demonstrate the potential as well as limitations for reducing survey effort and streamlining sampling protocols, and consequently for increasing the cost-effectiveness in tropical bat surveys or monitoring programmes. The dependence of the performance of species subsets on structural assemblage characteristics (total assemblage abundance, proportion of rare species), however, underscores the importance of adaptive monitoring schemes and of establishing surrogate performance on a site by site basis based on pilot surveys. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

Isotropic differential phase contrast microscopy for quantitative phase bio-imaging.

PubMed

Chen, Hsi-Hsun; Lin, Yu-Zi; Luo, Yuan

2018-05-16

Quantitative phase imaging (QPI) has been investigated to retrieve optical phase information of an object and applied to biological microscopy and related medical studies. In recent examples, differential phase contrast (DPC) microscopy can recover phase image of thin sample under multi-axis intensity measurements in wide-field scheme. Unlike conventional DPC, based on theoretical approach under partially coherent condition, we propose a new method to achieve isotropic differential phase contrast (iDPC) with high accuracy and stability for phase recovery in simple and high-speed fashion. The iDPC is simply implemented with a partially coherent microscopy and a programmable thin-film transistor (TFT) shield to digitally modulate structured illumination patterns for QPI. In this article, simulation results show consistency of our theoretical approach for iDPC under partial coherence. In addition, we further demonstrate experiments of quantitative phase images of a standard micro-lens array, as well as label-free live human cell samples. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation.

PubMed

Chen, Chao; Zare, Alina; Trinh, Huy N; Omotara, Gbenga O; Cobb, James Tory; Lagaunne, Timotius A

2017-12-01

Topic models [e.g., probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and supervised LDA] have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership LDA (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery, where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.

Partially Covered Lenses and Additive Color Mixing

NASA Astrophysics Data System (ADS)

Razpet, Nada; Kranjc, Tomaž

2017-12-01

When doing experimental work of image formation by mirrors and (thin) lenses, it turns out again and again that students often have partially incorrect preconceptions about how the light emerging from an object passes through a lens and how the image is formed on a screen or directly in the eye. To check students' prior knowledge and help get a better understanding of geometrical optics, we decided to start classes with a pre-test to assess their knowledge and understanding. Then we performed a series of experiments (to be described in the paper) with (thin) converging lenses, partially covered with either an opaque screen or with (one or more) color filters. In the end, students' knowledge and understanding were tested again with a post-test. The main goal of the experiments was to convey to students a clearer picture about the image formation, and to help them recognize the fact that every small part of a lens participates in the formation of the whole image.

Quantification of left ventricular functional parameter values using 3D spiral bSSFP and through-time non-Cartesian GRAPPA.

PubMed

Barkauskas, Kestutis J; Rajiah, Prabhakar; Ashwath, Ravi; Hamilton, Jesse I; Chen, Yong; Ma, Dan; Wright, Katherine L; Gulani, Vikas; Griswold, Mark A; Seiberlich, Nicole

2014-09-11

The standard clinical acquisition for left ventricular functional parameter analysis with cardiovascular magnetic resonance (CMR) uses a multi-breathhold multi-slice segmented balanced SSFP sequence. Performing multiple long breathholds in quick succession for ventricular coverage in the short-axis orientation can lead to fatigue and is challenging in patients with severe cardiac or respiratory disorders. This study combines the encoding efficiency of a six-fold undersampled 3D stack of spirals balanced SSFP sequence with 3D through-time spiral GRAPPA parallel imaging reconstruction. This 3D spiral method requires only one breathhold to collect the dynamic data. Ten healthy volunteers were recruited for imaging at 3 T. The 3D spiral technique was compared against 2D imaging in terms of systolic left ventricular functional parameter values (Bland-Altman plots), total scan time (Welch's t-test) and qualitative image rating scores (Wilcoxon signed-rank test). Systolic left ventricular functional values were not significantly different (i.e. 3D-2D) between the methods. The 95% confidence interval for ejection fraction was -0.1 ± 1.6% (mean ± 1.96*SD). The total scan time for the 3D spiral technique was 48 s, which included one breathhold with an average duration of 14 s for the dynamic scan, plus 34 s to collect the calibration data under free-breathing conditions. The 2D method required an average of 5 min 40s for the same coverage of the left ventricle. The difference between 3D and 2D image rating scores was significantly different from zero (Wilcoxon signed-rank test, p < 0.05); however, the scores were at least 3 (i.e. average) or higher for 3D spiral imaging. The 3D through-time spiral GRAPPA method demonstrated equivalent systolic left ventricular functional parameter values, required significantly less total scan time and yielded acceptable image quality with respect to the 2D segmented multi-breathhold standard in this study. Moreover, the 3D spiral technique used just one breathhold for dynamic imaging, which is anticipated to reduce patient fatigue as part of the complete cardiac examination in future studies that include patients.

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

PubMed

Hu, Weiming; Xie, Nianhua; Hu, Ruiguang; Ling, Haibin; Chen, Qiang; Yan, Shuicheng; Maybank, Stephen

2014-12-01

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.

Fundus Photography as a Screening Method for Diabetic Retinopathy in Children With Type 1 Diabetes: Outcome of the Initial Photography.

PubMed

Gräsbeck, Thomas C; Gräsbeck, Sophia V; Miettinen, Päivi J; Summanen, Paula A

2016-09-01

To determine the success rate of the initial fundus photography session in producing gradable images for screening diabetic retinopathy in children <18 years of age with type 1 diabetes (T1D), and to analyze outcome-associated factors. Retrospective observational cohort study. Mydriatic red-free monochromatic 60-degree digital fundus images centered on the macula and optic disc of 213 patients were graded. Photography success was classified as "complete" if both images of both eyes were gradable, "partial" if both images of 1 eye were gradable, "macula-centered image(s) only" if only the macula-centered image of one or both eyes was gradable, and "unsuccessful" if neither macula-centered image was gradable. Complete success was reached in 97 (46%; 95% confidence interval [CI], 39-52) patients, at least partial success in 153 (72%; 95% CI, 65-78) patients, success of macula-centered image(s) only in 47 (22%; 95% CI, 17-28) patients, and in 13 (6%; 95%CI, 3-10) patients fundus photography was unsuccessful. Macula-centered images were more often gradable in both eyes than optic disc-centered images (P < .001). Success of photography did not differ between right and left eye. Sex, age at diagnosis of T1D, and the duration of diabetes, age, and glycemic control at the time of initial photography were unassociated with complete success. Partial success tended to decrease with increasing age category (P = .093), and the frequency of gradable macula-centered image(s) only increased with increasing age (P = .043). Less than half of the children achieved complete success, but in only 6% initial fundus photography was unsuccessful, indicating its value in assessing retinopathy in the pediatric setting. Copyright © 2016 Elsevier Inc. All rights reserved.

Separation of Undersampled Composite Signals Using the Dantzig Selector with Overcomplete Dictionaries

DTIC Science & Technology

2014-06-02

2011). [22] Li, Q., Micchelli, C., Shen, L., and Xu, Y. A proximity algorithm acelerated by Gauss - Seidel iterations for L1/TV denoising models. Inverse...system of equations and their relationship to the solution of Model (2) and present an algorithm with an iterative approach for finding these solutions...Using the fixed-point characterization above, the (k + 1)th iteration of the prox- imity operator algorithm to find the solution of the Dantzig

A Pilot Study to Examine Exposure to Residential Radon in Under-Sampled Census Tracts of DeKalb County, Georgia, in 2015.

PubMed

Stauber, Christine E; Dai, Dajun; Chan, Sydney R; Diem, Jeremy E; Weaver, Scott R; Rothenberg, Richard

2017-03-22

While DeKalb County, Georgia, offers free radon screening for all eligible residents, portions of the county remain relatively under-sampled. This pilot study focused on 10% of the census tracts in the county with the lowest proportion of radon testing; most were in southern DeKalb County. In total, 217 households were recruited and homes were tested for indoor radon concentrations on the lowest livable floor over an eight-week period from March-May 2015. Tract-level characteristics were examined to understand the differences in socio-demographic and economic factors between the pilot study area and the rest of the county. The pilot study tracts had a higher proportion of African Americans compared to the rest of DeKalb County (82% versus 47%). Radon was detected above 11.1 Bq/m³ (0.3 pCi/L) in 73% of the indoor samples and 4% of samples were above 148 Bq/m³ (4 pCi/L). Having a basement was the strongest predictive factor for detectable and hazardous levels of radon. Radon screening can identify problems and spur homeowners to remediate but more research should be done to identify why screening rates vary across the county and how that varies with radon levels in homes to reduce radon exposure.

A Pilot Study to Examine Exposure to Residential Radon in Under-Sampled Census Tracts of DeKalb County, Georgia, in 2015

PubMed Central

Stauber, Christine E.; Dai, Dajun; Chan, Sydney R.; Diem, Jeremy E.; Weaver, Scott R.; Rothenberg, Richard

2017-01-01

While DeKalb County, Georgia, offers free radon screening for all eligible residents, portions of the county remain relatively under-sampled. This pilot study focused on 10% of the census tracts in the county with the lowest proportion of radon testing; most were in southern DeKalb County. In total, 217 households were recruited and homes were tested for indoor radon concentrations on the lowest livable floor over an eight-week period from March–May 2015. Tract-level characteristics were examined to understand the differences in socio-demographic and economic factors between the pilot study area and the rest of the county. The pilot study tracts had a higher proportion of African Americans compared to the rest of DeKalb County (82% versus 47%). Radon was detected above 11.1 Bq/m3 (0.3 pCi/L) in 73% of the indoor samples and 4% of samples were above 148 Bq/m3 (4 pCi/L). Having a basement was the strongest predictive factor for detectable and hazardous levels of radon. Radon screening can identify problems and spur homeowners to remediate but more research should be done to identify why screening rates vary across the county and how that varies with radon levels in homes to reduce radon exposure. PMID:28327511

Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI.

PubMed

Valvano, Giuseppe; Martini, Nicola; Landini, Luigi; Santarelli, Maria Filomena

2016-07-01

To develop a 3D sampling strategy based on a stack of variable density spirals for compressive sensing MRI. A random sampling pattern was obtained by rotating each spiral by a random angle and by delaying for few time steps the gradient waveforms of the different interleaves. A three-dimensional (3D) variable sampling density was obtained by designing different variable density spirals for each slice encoding. The proposed approach was tested with phantom simulations up to a five-fold undersampling factor. Fully sampled 3D dataset of a human knee, and of a human brain, were obtained from a healthy volunteer. The proposed approach was tested with off-line reconstructions of the knee dataset up to a four-fold acceleration and compared with other noncoherent trajectories. The proposed approach outperformed the standard stack of spirals for various undersampling factors. The level of coherence and the reconstruction quality of the proposed approach were similar to those of other trajectories that, however, require 3D gridding for the reconstruction. The variable density randomized stack of spirals (VDR-SoS) is an easily implementable trajectory that could represent a valid sampling strategy for 3D compressive sensing MRI. It guarantees low levels of coherence without requiring 3D gridding. Magn Reson Med 76:59-69, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Image based method for aberration measurement of lithographic tools

NASA Astrophysics Data System (ADS)

Xu, Shuang; Tao, Bo; Guo, Yongxing; Li, Gongfa

2018-01-01

Information of lens aberration of lithographic tools is important as it directly affects the intensity distribution in the image plane. Zernike polynomials are commonly used for a mathematical description of lens aberrations. Due to the advantage of lower cost and easier implementation of tools, image based measurement techniques have been widely used. Lithographic tools are typically partially coherent systems that can be described by a bilinear model, which entails time consuming calculations and does not lend a simple and intuitive relationship between lens aberrations and the resulted images. Previous methods for retrieving lens aberrations in such partially coherent systems involve through-focus image measurements and time-consuming iterative algorithms. In this work, we propose a method for aberration measurement in lithographic tools, which only requires measuring two images of intensity distribution. Two linear formulations are derived in matrix forms that directly relate the measured images to the unknown Zernike coefficients. Consequently, an efficient non-iterative solution is obtained.

Large-Scale Partial-Duplicate Image Retrieval and Its Applications

DTIC Science & Technology

2016-04-23

SECURITY CLASSIFICATION OF: The explosive growth of Internet Media (partial-duplicate/similar images, 3D objects, 3D models, etc.) sheds bright...light on many promising applications in forensics, surveillance, 3D animation, mobile visual search, and 3D model/object search. Compared with the...and stable spatial configuration. Compared with the general 2D objects, 3D models/objects consist of 3D data information (typically a list of

When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies.

PubMed

Dukart, Juergen; Bertolino, Alessandro

2014-01-01

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality--an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

Correlation between hepatobiliary scintigraphy and surgery or postmortem examination findings in dogs and cats with extrahepatic biliary obstruction, partial obstruction, or patency of the biliary system: 18 cases (1995-2004).

PubMed

Head, Laurie L; Daniel, Gregory B

2005-11-15

To evaluate the usefulness of serum biochemical variables and scintigraphic study results for differentiating between dogs and cats with complete extrahepatic biliary obstruction (EHO) and those with partial EHO or patent bile ducts. Retrospective case series. 17 dogs and 1 cat. Animals that underwent hepatobiliary scintigraphy and had either surgical or postmortem confirmation of the degree of bile duct patency were included. Scintigraphic images were evaluated and biliary tracts were classified as patent, partially obstructed but patent, or obstructed. Surgery or postmortem examination was considered the gold standard for diagnosis, and compared with those findings, sensitivity and specificity of scintigraphy were calculated. With absence of radioactivity in the intestinal tract as the diagnostic criterion for EHO, the sensitivity and specificity of scintigraphic diagnosis were both 83% when final images were acquired at 19 to 24 hours, compared with 100% and 33%, respectively, when 180 minutes was used as the cutoff time. Animals with partial biliary obstruction had less intestinal radioactivity that arrived later than that observed in animals with patent biliary tracts. Animals in which intestinal radioactivity has not been observed after the standard 3 to 4 hours should undergo additional scintigraphic imaging. Findings in animals with partial biliary obstruction include delayed arrival of radioactivity and less radioactivity in the intestine. Distinguishing between complete and partial biliary tract obstruction is important because animals with partial obstruction may respond favorably to medical management and should not be given an erroneous diagnosis of complete obstruction.

NASA's Aqua Satellite Sees Partial Solar Eclipse Effect in Western Canada

NASA Image and Video Library

2017-12-08

This image shows how a partial solar eclipse darkened clouds over the Yukon and British Columbia in western Canada. It was taken on Oct. 23 at 21:20 UTC (5:20 p.m. EDT) by the Moderate Resolution Imaging Spectroradiometer instrument that flies aboard NASA's Aqua satellite. Credit: NASA Goddard MODIS Rapid Response Team Unlabeled image NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Preschoolers' use of reflective properties: identification of reflections on partially transparent surfaces.

PubMed

Costanzo, E S; Wittgenstein, K M; Benson, K

2001-12-01

This exploratory study extended past studies of children's ability to reference the mirror as a tool in locating the source of reflected images to preschoolers' ability to use the affordances of a transparency. Thirty-six children (3.5 to 5 years old) were shown nonreflected lights and lights reflected on a partially transparent, glassy surface. Children did not spontaneously locate the source of the reflected image. However, they were able to verbally discriminate reflected from nonreflected images following training. These findings indicate that, although preschoolers may not spontaneously use transparencies as a perceptual tool, the ability to distinguish visual differences of reflected from nonreflected images on transparencies is likely within preschool children's developmental capacity.

Robust reflective ghost imaging against different partially polarized thermal light

NASA Astrophysics Data System (ADS)

Li, Hong-Guo; Wang, Yan; Zhang, Rui-Xue; Zhang, De-Jian; Liu, Hong-Chao; Li, Zong-Guo; Xiong, Jun

2018-03-01

We theoretically study the influence of degree of polarization (DOP) of thermal light on the contrast-to-noise ratio (CNR) of the reflective ghost imaging (RGI), which is a novel and indirect imaging modality. An expression for the CNR of RGI with partially polarized thermal light is carefully derived, which suggests a weak dependence of CNR on the DOP, especially when the ratio of the object size to the speckle size of thermal light has a large value. Different from conventional imaging approaches, our work reveals that RGI is much more robust against the DOP of the light source, which thereby has advantages in practical applications, such as remote sensing.

Simultaneous imaging of cerebral partial pressure of oxygen and blood flow during functional activation and cortical spreading depression

PubMed Central

Sakadžić, Sava; Yuan, Shuai; Dilekoz, Ergin; Ruvinskaya, Svetlana; Vinogradov, Sergei A.; Ayata, Cenk; Boas, David A.

2009-01-01

We developed a novel imaging technique that provides real-time two-dimensional maps of the absolute partial pressure of oxygen and relative cerebral blood flow in rats by combining phosphorescence lifetime imaging with laser speckle contrast imaging. Direct measurement of blood oxygenation based on phosphorescence lifetime is not significantly affected by changes in the optical parameters of the tissue during the experiment. The potential of the system as a novel tool for quantitative analysis of the dynamic delivery of oxygen to support brain metabolism was demonstrated in rats by imaging cortical responses to forepaw stimulation and the propagation of cortical spreading depression waves. This new instrument will enable further study of neurovascular coupling in normal and diseased brain. PMID:19340106

Partially-overlapped viewing zone based integral imaging system with super wide viewing angle.

PubMed

Xiong, Zhao-Long; Wang, Qiong-Hua; Li, Shu-Li; Deng, Huan; Ji, Chao-Chao

2014-09-22

In this paper, we analyze the relationship between viewer and viewing zones of integral imaging (II) system and present a partially-overlapped viewing zone (POVZ) based integral imaging system with a super wide viewing angle. In the proposed system, the viewing angle can be wider than the viewing angle of the conventional tracking based II system. In addition, the POVZ can eliminate the flipping and time delay of the 3D scene as well. The proposed II system has a super wide viewing angle of 120° without flipping effect about twice as wide as the conventional one.

Evaluation of Partial k-space strategies to speed up Time-domain EPR Imaging

PubMed Central

Subramanian, Sankaran; Chandramouli, Gadisetti VR; McMillan, Alan; Gullapalli, Rao P; Devasahayam, Nallathamby; Mitchell, James B.; Matsumoto, Shingo; Krishna, Murali C

2012-01-01

Narrow-line spin probes derived from the trityl radical have led to the development of fast in vivo time-domain EPR imaging. Pure phase-encoding imaging modalities based on the Single Point Imaging scheme (SPI) have demonstrated the feasibility of 3D oximetric images with functional information in minutes. In this paper, we explore techniques to improve the temporal resolution and circumvent the relatively short biological half-lives of trityl probes using partial k-space strategies. There are two main approaches: one involves the use of the Hermitian character of the k-space by which only part of the k-space is measured and the unmeasured part is generated using the Hermitian symmetry. This approach is limited in success by the accuracy of numerical estimate of the phase roll in the k-space that corrupts the Hermiticy. The other approach is to measure only a judicially chosen reduced region of k-space (a centrosymmetric ellipsoid region) that more or less accounts for >70% of the k-space energy. Both of these aspects were explored in FT-EPR imaging with a doubling of scan speed demonstrated by considering ellipsoid geometry of the k-space. Partial k-space strategies help improve the temporal resolution in studying fast dynamics of functional aspects in vivo with infused spin probes. PMID:23045171

Which Way is Up?

NASA Image and Video Library

2014-10-29

This image NASA Mars Reconnaissance Orbiter shows an impact crater that was cut by lava in the Elysium Planitia region of Mars. It looks relatively flat, with a shallow floor, rough surface texture, and possible cooling cracks seem to indicate that the crater was partially filled with lava. The northern part of the image also shows a more extensive lava flow deposit that surrounds the impact ejecta of the largest impact crater in the image. Which way did the lava flow? It might appear that the lava flowed from the north through the channel into the partially filled crater. However, if you look at the anaglyph with your red and blue 3D glasses, it becomes clear that the partially filled crater sits on top of the large crater's ejecta blanket, making it higher than the lava flow to the north. Since lava does not flow uphill, that means the explanation isn't so simple. http://photojournal.jpl.nasa.gov/catalog/PIA18887

Phase-and-amplitude recovery from a single phase-contrast image using partially spatially coherent x-ray radiation

NASA Astrophysics Data System (ADS)

Beltran, Mario A.; Paganin, David M.; Pelliccia, Daniele

2018-05-01

A simple method of phase-and-amplitude extraction is derived that corrects for image blurring induced by partially spatially coherent incident illumination using only a single intensity image as input. The method is based on Fresnel diffraction theory for the case of high Fresnel number, merged with the space-frequency description formalism used to quantify partially coherent fields and assumes the object under study is composed of a single-material. A priori knowledge of the object’s complex refractive index and information obtained by characterizing the spatial coherence of the source is required. The algorithm was applied to propagation-based phase-contrast data measured with a laboratory-based micro-focus x-ray source. The blurring due to the finite spatial extent of the source is embedded within the algorithm as a simple correction term to the so-called Paganin algorithm and is also numerically stable in the presence of noise.

Multi-tissue partial volume quantification in multi-contrast MRI using an optimised spectral unmixing approach.

PubMed

Collewet, Guylaine; Moussaoui, Saïd; Deligny, Cécile; Lucas, Tiphaine; Idier, Jérôme

2018-06-01

Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry. Copyright © 2018 Elsevier Inc. All rights reserved.

A theory of fine structure image models with an application to detection and classification of dementia

PubMed Central

Penn, Richard; Werner, Michael; Thomas, Justin

2015-01-01

Background Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. Methods In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. Results We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Conclusions Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible. PMID:26029638

k-t SENSE-accelerated Myocardial Perfusion MR Imaging at 3.0 Tesla - comparison with 1.5 Tesla

PubMed Central

Plein, Sven; Schwitter, Juerg; Suerder, Daniel; Greenwood, John P.; Boesiger, Peter; Kozerke, Sebastian

2008-01-01

Purpose To determine the feasibility and diagnostic accuracy of high spatial resolution myocardial perfusion MR at 3.0 Tesla using k-space and time domain undersampling with sensitivity encoding (k-t SENSE). Materials and Methods The study was reviewed and approved by the local ethic review board. k-t SENSE perfusion MR was performed at 1.5 Tesla and 3.0 Tesla (saturation recovery gradient echo pulse sequence, repetition time/echo time 3.0ms/1.0ms, flip angle 15°, 5x k-t SENSE acceleration, spatial resolution 1.3×1.3×10mm3). Fourteen volunteers were studied at rest and 37 patients during adenosine stress. In volunteers, comparison was also made with standard-resolution (2.5×2.5×10mm3) 2x SENSE perfusion MR at 3.0 Tesla. Image quality, artifact scores, signal-to-noise ratios (SNR) and contrast-enhancement ratios (CER) were derived. In patients, diagnostic accuracy of visual analysis to detect >50% diameter stenosis on quantitative coronary angiography was determined by receiver-operator-characteristics (ROC). Results In volunteers, image quality and artifact scores were similar for 3.0 Tesla and 1.5 Tesla, while SNR was higher (11.6 vs. 5.6) and CER lower (1.1 vs. 1.5, p=0.012) at 3.0 Tesla. Compared with standard-resolution perfusion MR, image quality was higher for k-t SENSE (3.6 vs. 3.1, p=0.04), endocardial dark rim artifacts were reduced (artifact thickness 1.6mm vs. 2.4mm, p<0.001) and CER similar. In patients, area under the ROC curve for detection of coronary stenosis was 0.89 and 0.80, p=0.21 for 3.0 Tesla and 1.5 Tesla, respectively. Conclusions k-t SENSE accelerated high-resolution perfusion MR at 3.0 Tesla is feasible with similar artifacts and diagnostic accuracy as at 1.5 Tesla. Compared with standard-resolution perfusion MR, image quality is improved and artifacts are reduced. PMID:18936311

Variable Sampling Mapping

NASA Technical Reports Server (NTRS)

Smith, Jeffrey, S.; Aronstein, David L.; Dean, Bruce H.; Lyon, Richard G.

2012-01-01

The performance of an optical system (for example, a telescope) is limited by the misalignments and manufacturing imperfections of the optical elements in the system. The impact of these misalignments and imperfections can be quantified by the phase variations imparted on light traveling through the system. Phase retrieval is a methodology for determining these variations. Phase retrieval uses images taken with the optical system and using a light source of known shape and characteristics. Unlike interferometric methods, which require an optical reference for comparison, and unlike Shack-Hartmann wavefront sensors that require special optical hardware at the optical system's exit pupil, phase retrieval is an in situ, image-based method for determining the phase variations of light at the system s exit pupil. Phase retrieval can be used both as an optical metrology tool (during fabrication of optical surfaces and assembly of optical systems) and as a sensor used in active, closed-loop control of an optical system, to optimize performance. One class of phase-retrieval algorithms is the iterative transform algorithm (ITA). ITAs estimate the phase variations by iteratively enforcing known constraints in the exit pupil and at the detector, determined from modeled or measured data. The Variable Sampling Mapping (VSM) technique is a new method for enforcing these constraints in ITAs. VSM is an open framework for addressing a wide range of issues that have previously been considered detrimental to high-accuracy phase retrieval, including undersampled images, broadband illumination, images taken at or near best focus, chromatic aberrations, jitter or vibration of the optical system or detector, and dead or noisy detector pixels. The VSM is a model-to-data mapping procedure. In VSM, fully sampled electric fields at multiple wavelengths are modeled inside the phase-retrieval algorithm, and then these fields are mapped to intensities on the light detector, using the properties of the detector and optical system, for comparison with measured data. Ultimately, this model-to-data mapping procedure enables a more robust and accurate way of incorporating the exit-pupil and image detector constraints, which are fundamental to the general class of ITA phase retrieval algorithms.

Low-dose cardio-respiratory phase-correlated cone-beam micro-CT of small animals.

PubMed

Sawall, Stefan; Bergner, Frank; Lapp, Robert; Mronz, Markus; Karolczak, Marek; Hess, Andreas; Kachelriess, Marc

2011-03-01

Micro-CT imaging of animal hearts typically requires a double gating procedure because scans during a breath-hold are not possible due to the long scan times and the high respiratory rates, Simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. True five-dimensional information can be either retrieved with retrospective gating or with prospective gating if several prospective gates are acquired. In any case, the amount of information available to reconstruct one volume for a given respiratory and cardiac phase is orders of magnitud lower than the total amount of information acquired. For example, the reconstruction of a volume from a 10% wide respiratory and a 20% wide cardiac window uses only 2% of the data acquired. Achieving a similar image quality as a nongated scan would therefore require to increase the amount of data and thereby the dose to the animal by up to a factor of 50. To achieve the goal of low-dose phase-correlated (LDPC) imaging, the authors propose to use a highly efficient combination of slightly modified existing algorithms. In particular, the authors developed a variant of the McKinnon-Bates image reconstruction algorithm and combined it with bilateral filtering in up to five dimensions to significantly reduce image noise without impairing spatial or temporal resolution. The preliminary results indicate that the proposed LDPC reconstruction method typically reduces image noise by a factor of up to 6 (e.g., from 170 to 30 HU), while the dose values lie in a range from 60 to 500 mGy. Compared to other publications that apply 250-1800 mGy for the same task [C. T. Badea et al., "4D micro-CT of the mouse heart," Mol. Imaging 4(2), 110-116 (2005); M. Drangova et al., "Fast retrospectively gated quantitative four-dimensional (4D) cardiac micro computed tomography imaging of free-breathing mice," Invest. Radiol. 42(2), 85-94 (2007); S. H. Bartling et al., "Retrospective motion gating in small animal CT of mice and rats," Invest. Radiol. 42(10), 704-714 (2007)], the authors' LDPC approach therefore achieves a more than tenfold dose usage improvement. The LDPC reconstruction method improves phase-correlated imaging from highly undersampled data. Artifacts caused by sparse angular sampling are removed and the image noise is decreased, while spatial and temporal resolution are preserved. Thus, the administered dose per animal can be decreased allowing for long-term studies with reduced metabolic inference.

Fourth-order partial differential equation noise removal on welding images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halim, Suhaila Abd; Ibrahim, Arsmah; Sulong, Tuan Nurul Norazura Tuan

2015-10-22

Partial differential equation (PDE) has become one of the important topics in mathematics and is widely used in various fields. It can be used for image denoising in the image analysis field. In this paper, a fourth-order PDE is discussed and implemented as a denoising method on digital images. The fourth-order PDE is solved computationally using finite difference approach and then implemented on a set of digital radiographic images with welding defects. The performance of the discretized model is evaluated using Peak Signal to Noise Ratio (PSNR). Simulation is carried out on the discretized model on different level of Gaussianmore » noise in order to get the maximum PSNR value. The convergence criteria chosen to determine the number of iterations required is measured based on the highest PSNR value. Results obtained show that the fourth-order PDE model produced promising results as an image denoising tool compared with median filter.« less

Signal-to-Noise Ratio Requirements for Half-Wave and Full-Wave Nonlinear Detectors with Arbitrary Power Laws, Sampling Rates, Input Spectra, and Filter Characteristics

DTIC Science & Technology

1986-06-10

system consisting of a sampler, a nonlinear rectifier, and a low-pass filter is evaluated generally , for arbitrary half-wave or full-wave v-th law...spectra, the possibility of using deliberate undersampling with no loss of performance is illustrated. The use of a half-wave rectifier generally ... some cases, significantly so. Programs for all procedures employed are presented so that investigation of additional cases or combinations of

New records of bee flies (Diptera, Bombyliidae) from Cuatro Ciénegas, Coahuila, Mexico

PubMed Central

Ávalos-Hernández, Omar; Kits, Joel; Trujano-Ortega, Marysol; García-Vázquez, Uri Omar; Cano-Santana, Zenón

2014-01-01

Abstract Forty one new records of species of Bombyliidae are reported for Coahuila in northeastern Mexico. Nine of these species are reported for the first time for the country. The specimens were collected in the Cuatro Ciénegas Basin and Sierra La Madera mountains during 2007–2013. The modified distributions of species are discussed. The gaps in the distribution of many species suggest an undersampling of this group of insects in the north of Mexico. PMID:25061389

Quantifying the accuracy of the tumor motion and area as a function of acceleration factor for the simulation of the dynamic keyhole magnetic resonance imaging method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Danny; Pollock, Sean; Keall, Paul, E-mail: paul.keall@sydney.edu.au

2016-05-15

Purpose: The dynamic keyhole is a new MR image reconstruction method for thoracic and abdominal MR imaging. To date, this method has not been investigated with cancer patient magnetic resonance imaging (MRI) data. The goal of this study was to assess the dynamic keyhole method for the task of lung tumor localization using cine-MR images reconstructed in the presence of respiratory motion. Methods: The dynamic keyhole method utilizes a previously acquired a library of peripheral k-space datasets at similar displacement and phase (where phase is simply used to determine whether the breathing is inhale to exhale or exhale to inhale)more » respiratory bins in conjunction with central k-space datasets (keyhole) acquired. External respiratory signals drive the process of sorting, matching, and combining the two k-space streams for each respiratory bin, thereby achieving faster image acquisition without substantial motion artifacts. This study was the first that investigates the impact of k-space undersampling on lung tumor motion and area assessment across clinically available techniques (zero-filling and conventional keyhole). In this study, the dynamic keyhole, conventional keyhole and zero-filling methods were compared to full k-space dataset acquisition by quantifying (1) the keyhole size required for central k-space datasets for constant image quality across sixty four cine-MRI datasets from nine lung cancer patients, (2) the intensity difference between the original and reconstructed images in a constant keyhole size, and (3) the accuracy of tumor motion and area directly measured by tumor autocontouring. Results: For constant image quality, the dynamic keyhole method, conventional keyhole, and zero-filling methods required 22%, 34%, and 49% of the keyhole size (P < 0.0001), respectively, compared to the full k-space image acquisition method. Compared to the conventional keyhole and zero-filling reconstructed images with the keyhole size utilized in the dynamic keyhole method, an average intensity difference of the dynamic keyhole reconstructed images (P < 0.0001) was minimal, and resulted in the accuracy of tumor motion within 99.6% (P < 0.0001) and the accuracy of tumor area within 98.0% (P < 0.0001) for lung tumor monitoring applications. Conclusions: This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiation therapy requiring the MR monitoring of thoracic tumors. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by up to a factor of five compared with full k-space methods for real-time lung tumor MRI.« less

Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group.

PubMed

Su, Yi; Blazey, Tyler M; Owen, Christopher J; Christensen, Jon J; Friedrichsen, Karl; Joseph-Mathurin, Nelly; Wang, Qing; Hornbeck, Russ C; Ances, Beau M; Snyder, Abraham Z; Cash, Lisa A; Koeppe, Robert A; Klunk, William E; Galasko, Douglas; Brickman, Adam M; McDade, Eric; Ringman, John M; Thompson, Paul M; Saykin, Andrew J; Ghetti, Bernardino; Sperling, Reisa A; Johnson, Keith A; Salloway, Stephen P; Schofield, Peter R; Masters, Colin L; Villemagne, Victor L; Fox, Nick C; Förster, Stefan; Chen, Kewei; Reiman, Eric M; Xiong, Chengjie; Marcus, Daniel S; Weiner, Michael W; Morris, John C; Bateman, Randall J; Benzinger, Tammie L S

2016-01-01

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer's Network (DIAN), an autosomal dominant Alzheimer's disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer's disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.

Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group

PubMed Central

Su, Yi; Blazey, Tyler M.; Owen, Christopher J.; Christensen, Jon J.; Friedrichsen, Karl; Joseph-Mathurin, Nelly; Wang, Qing; Hornbeck, Russ C.; Ances, Beau M.; Snyder, Abraham Z.; Cash, Lisa A.; Koeppe, Robert A.; Klunk, William E.; Galasko, Douglas; Brickman, Adam M.; McDade, Eric; Ringman, John M.; Thompson, Paul M.; Saykin, Andrew J.; Ghetti, Bernardino; Sperling, Reisa A.; Johnson, Keith A.; Salloway, Stephen P.; Schofield, Peter R.; Masters, Colin L.; Villemagne, Victor L.; Fox, Nick C.; Förster, Stefan; Chen, Kewei; Reiman, Eric M.; Xiong, Chengjie; Marcus, Daniel S.; Weiner, Michael W.; Morris, John C.; Bateman, Randall J.; Benzinger, Tammie L. S.

2016-01-01

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer’s Network (DIAN), an autosomal dominant Alzheimer’s disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer’s disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted. PMID:27010959

3D FSE Cube and VIPR-aTR 3.0 Tesla magnetic resonance imaging predicts canine cranial cruciate ligament structural properties.

PubMed

Racette, Molly; Al saleh, Habib; Waller, Kenneth R; Bleedorn, Jason A; McCabe, Ronald P; Vanderby, Ray; Markel, Mark D; Brounts, Sabrina H; Block, Walter F; Muir, Peter

2016-03-01

Estimation of cranial cruciate ligament (CrCL) structural properties in client-owned dogs with incipient cruciate rupture would be advantageous. The objective of this study was to determine whether magnetic resonance imaging (MRI) measurement of normal CrCL volume in an ex-vivo canine model predicts structural properties. Stifles from eight dogs underwent 3.0 Tesla 3D MRI. CrCL volume and normalized median grayscale values were determined using 3D Fast Spin Echo (FSE) Cube and Vastly under-sampled Isotropic PRojection (VIPR)-alternative repetition time (aTR) sequences. Stifles were then mechanically tested. After joint laxity testing, CrCL structural properties were determined, including displacement at yield, yield load, load to failure, and stiffness. Yield load and load to failure (R(2)=0.56, P <0.01) were correlated with CrCL volume determined by VIPR-aTR. Yield load was also correlated with CrCL volume determined by 3D FSE Cube (R(2)=0.32, P <0.05). Structural properties were not related to median grayscale values. Joint laxity and CrCL stiffness were not related to MRI parameters, but displacement at yield load was related to CrCL volume for both sequences during testing (R(2)>0.57, P <0.005). In conclusion, 3D MRI offers a predictive method for estimating canine CrCL structural properties. 3D MRI may be useful for monitoring CrCL properties in clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simultaneous total electron content and all-sky camera measurements of an auroral arc

NASA Astrophysics Data System (ADS)

Kintner, P. M.; Kil, H.; Deehr, C.; Schuck, P.

2002-07-01

We present an example of Global Positioning System (GPS) derived total electron content (TEC) and all-sky camera (ASC) images that show increases of TEC by ~10 × 1016 electrons m-2 (10 TEC units) occurring simultaneously with auroral light in ASC images. The TEC example appears to be an E region density enhancement produced by two discrete auroral arcs occurring in the late morning auroral oval at 1000 LT. This suggests that GPS signal TEC measurements can be used to detect individual auroral arcs and that individual discrete auroral arcs are responsible for some high-latitude phase scintillations. The specific auroral feature detected was a poleward moving auroral form believed to occur in the polar cap where the ionosphere is convecting antisunward. The magnitude of the rate of change of TEC (dTEC/dt) is comparable to that previously reported. However, the timescales associated with the event, the order of 1 min, suggest that the data sampling technique commonly used by chain GPS TEC receivers (averaging and time decimation) will undersample E region TEC perturbations produced by active auroral displays. The localized nature of this example implies that L1 ranging errors of at least 1.6 m will be introduced by auroral arcs into systems relying on differential GPS for navigation or augmentation. Although the TEC and auroral arcs presented herein occurred in the late morning auroral oval, we expect that the effects of discrete auroral arcs on GPS TEC and subsequent ranging errors should occur at all local times. Furthermore, GPS receivers can be used to detect individual discrete arcs.

Precision attachments for the partially dentate mouth

PubMed Central

Preiskel, H W

1974-01-01

Some uses of precision attachments in restoring the partially dentate mouth are considered. These devices are indicated where neither the clasp-retained denture nor the fixed bridge is entirely suitable. ImagesFig. 2 PMID:4614689

Assessment of female breast dose for thoracic cone-beam CT using MOSFET dosimeters.

PubMed

Sun, Wenzhao; Wang, Bin; Qiu, Bo; Liang, Jian; Xie, Weihao; Deng, Xiaowu; Qi, Zhenyu

2017-03-21

To assess the breast dose during a routine thoracic cone-beam CT (CBCT) check with the efforts to explore the possible dose reduction strategy. Metal oxide semiconductor field-effect transistor (MOSFET) dosimeters were used to measure breast surface doses during a thorax kV CBCT scan in an anthropomorphic phantom. Breast doses for different scanning protocols and breast sizes were compared. Dose reduction was attempted by using partial arc CBCT scan with bowtie filter. The impact of this dose reduction strategy on image registration accuracy was investigated. The average breast surface doses were 20.02 mGy and 11.65 mGy for thoracic CBCT without filtration and with filtration, respectively. This indicates a dose reduction of 41.8% by use of bowtie filter. It was found 220° partial arc scanning significantly reduced the dose to contralateral breast (44.4% lower than ipsilateral breast), while the image registration accuracy was not compromised. Breast dose reduction can be achieved by using ipsilateral 220° partial arc scan with bowtie filter. This strategy also provides sufficient image quality for thorax image registration in daily patient positioning verification.

Partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

PubMed Central

Barbee, David L; Flynn, Ryan T; Holden, James E; Nickles, Robert J; Jeraj, Robert

2010-01-01

Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised of partial volume effects which may affect treatment prognosis, assessment, or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discover LS at positions of increasing radii from the scanner’s center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method’s correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom, and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of ±30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated that similar results could be reached using both methods, but large differences result for the arbitrary selection of SINV-PVC parameters. The presented SV-PVC method was performed without user intervention, requiring only a tumor mask as input. Research involving PET-imaged tumor heterogeneity should include correcting for partial volume effects to improve the quantitative accuracy of results. PMID:20009194

Generation of various partially coherent beams and their propagation properties in turbulent atmosphere: a review

NASA Astrophysics Data System (ADS)

Cai, Yangjian

2011-03-01

Partially coherent beams, such as Gaussian Schell-model beam, partially coherent dark hollow beam, partially coherent flat-topped beam and electromagnetic Gaussian Schell-model beam, have important applications in free space optical communications, optical imaging, optical trapping, inertial confinement fusion and nonlinear optics. In this paper, experimental generations of various partially coherent beams are introduced. Furthermore, with the help of a tensor method, analytical formulae for such beams propagating in turbulent atmosphere are derived, and the propagation properties of such beams in turbulent atmosphere are reviewed.

NASA's Aqua Satellite Sees Partial Solar Eclipse Effect in Alaska

NASA Image and Video Library

2017-12-08

This image shows how the partial solar eclipse darkened clouds over Alaska. It was taken on Oct. 23 at 21:10 UTC (5:10 p.m. EDT) by the Moderate Resolution Imaging Spectroradiometer instrument that flies aboard NASA's Aqua satellite. Credit: NASA Goddard MODIS Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Numerical Simulation of Partially-Coherent Broadband Optical Imaging Using the FDTD Method

PubMed Central

Çapoğlu, İlker R.; White, Craig A.; Rogers, Jeremy D.; Subramanian, Hariharan; Taflove, Allen; Backman, Vadim

2012-01-01

Rigorous numerical modeling of optical systems has attracted interest in diverse research areas ranging from biophotonics to photolithography. We report the full-vector electromagnetic numerical simulation of a broadband optical imaging system with partially-coherent and unpolarized illumination. The scattering of light from the sample is calculated using the finite-difference time-domain (FDTD) numerical method. Geometrical optics principles are applied to the scattered light to obtain the intensity distribution at the image plane. Multilayered object spaces are also supported by our algorithm. For the first time, numerical FDTD calculations are directly compared to and shown to agree well with broadband experimental microscopy results. PMID:21540939

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters).

PubMed

Sisniega, A; Zbijewski, W; Stayman, J W; Xu, J; Taguchi, K; Fredenberg, E; Lundqvist, Mats; Siewerdsen, J H

2016-01-07

Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm  ×  25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8  ×  higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.

Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation

PubMed Central

Yan, Hao; Wang, Xiaoyu; Shi, Feng; Bai, Ti; Folkerts, Michael; Cervino, Laura; Jiang, Steve B.; Jia, Xun

2014-01-01

Purpose: Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superior–inferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). Methods: Image quality improvement methods are proposed to mitigate the cone and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases. Results: Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superior–inferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan cases, respectively. In terms of efficiency boost, an overall 3.1 × speedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases. Conclusions: The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation. PMID:25370645

Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hao, E-mail: steve.jiang@utsouthwestern.edu, E-mail: xun.jia@utsouthwestern.edu; Shi, Feng; Jiang, Steve B.

Purpose: Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superior–inferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). Methods: Image quality improvement methods are proposed to mitigate the conemore » and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases. Results: Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superior–inferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan cases, respectively. In terms of efficiency boost, an overall 3.1 × speedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases. Conclusions: The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation.« less