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Sample records for ct reconstruction estimation

  1. Mixed Confidence Estimation for Iterative CT Reconstruction.

    PubMed

    Perlmutter, David S; Kim, Soo Mee; Kinahan, Paul E; Alessio, Adam M

    2016-09-01

    Dynamic (4D) CT imaging is used in a variety of applications, but the two major drawbacks of the technique are its increased radiation dose and longer reconstruction time. Here we present a statistical analysis of our previously proposed Mixed Confidence Estimation (MCE) method that addresses both these issues. This method, where framed iterative reconstruction is only performed on the dynamic regions of each frame while static regions are fixed across frames to a composite image, was proposed to reduce computation time. In this work, we generalize the previous method to describe any application where a portion of the image is known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the image space into higher and lower confidence components, MCE can lower the estimator variance in both regions compared to conventional reconstruction. We present a theoretical argument for this reduction in estimator variance and verify this argument with proof-of-principle simulations. We also propose a fast approximation of the variance of images reconstructed with MCE and confirm that this approximation is accurate compared to analytic calculations of and multi-realization image variance. This MCE method requires less computation time and provides reduced image variance for imaging scenarios where portions of the image are known with more certainty than others allowing for potentially reduced radiation dose and/or improved dynamic imaging. PMID:27008663

  2. CT projection estimation and applications to fast and local reconstruction

    NASA Astrophysics Data System (ADS)

    Besson, Guy M.

    1999-05-01

    In this paper, a straightforward method of estimating the CT projections is applied to simplified pre-processing, simplified reconstruction filtering, and to low-dose and local CT image reconstruction. The method relies on the projection- to-projection data redundancy that is shown to exist in CT. In the pre-processing application, the output of a few, angularly sparse fully pre-processed projections, is utilized in a linearization model to estimate directly the output of pre- processing for all the other projections. In the reconstruction filtering application, and with projection i and k being fully filtered, intermediate projection j low frequency components are estimated by a linear combination of projections i and k. That estimate is then subtracted from projection j, and the resulting high-frequency components are then filtered without zeropadding. By linearity the same combination of fully filtered projections i and k is added back to projection j. A factor two simplification is obtained, that can be leveraged for reconstruction speed or cost reduction. The local reconstruction application builds on the filtering method, by showing that truncated data is sufficient for calculating a filtered projection high-frequencies, while a very simple projection completion model is shown to be effective in estimating the low frequencies. Image quality comparisons are described.

  3. Improved total variation based CT reconstruction algorithm with noise estimation

    NASA Astrophysics Data System (ADS)

    Jin, Xin; Li, Liang; Shen, Le; Chen, Zhiqiang

    2012-10-01

    Nowadays a famous way to solve Computed Tomography (CT) inverse problems is to consider a constrained minimization problem following the Compressed Sensing (CS) theory. The CS theory proves the possibility of sparse signal recovery using under sampled measurements which gives a powerful tool for CT problems that have incomplete measurements or contain heavy noise. Among current CS reconstruction methods, one widely accepted reconstruction framework is to perform a total variation (TV) minimization process and a data fidelity constraint process in an alternative way by two separate iteration loops. However because the two processes are done independently certain misbalance may occur which leads to either over-smoothed or noisy reconstructions. Moreover, such misbalance is usually difficult to adjust as it varies according to the scanning objects and protocols. In our work we try to make good balance between the minimization and the constraint processes by estimating the variance of image noise. First, considering that the noise of projection data follows a Poisson distribution, the Anscombe transform (AT) and its inversion is utilized to calculate the unbiased variance of the projections. Second, an estimation of image noise is given through a noise transform model from projections to the image. Finally a modified CS reconstruction method is proposed which guarantees the desired variance on the reconstructed image thus prevents the block-wising or over-noised caused by misbalanced constrained minimizations. Results show the advantage in both image quality and convergence speed.

  4. [Super-resolution reconstruction of lung 4D-CT images based on fast sub-pixel motion estimation].

    PubMed

    Xiao, Shan; Wang, Tingting; Lü, Qingwen; Zhang, Yu

    2015-07-01

    Super-resolution image reconstruction techniques play an important role for improving image resolution of lung 4D-CT. We presents a super-resolution approach based on fast sub-pixel motion estimation to reconstruct lung 4D-CT images. A fast sub-pixel motion estimation method was used to estimate the deformation fields between "frames", and then iterative back projection (IBP) algorithm was employed to reconstruct high-resolution images. Experimental results showed that compared with traditional interpolation method and super-resolution reconstruction algorithm based on full search motion estimation, the proposed method produced clearer images with significantly enhanced image structure details and reduced time for computation.

  5. Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

    NASA Astrophysics Data System (ADS)

    Wang, Jing; Gu, Xuejun

    2014-03-01

    Image reconstruction and motion model estimation in four dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4DCBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: 1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and 2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation minimization. During the forward- and back-projection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

  6. Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

    SciTech Connect

    Wang, Jing; Gu, Xuejun

    2013-10-15

    Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion

  7. Dose reconstruction for real-time patient-specific dose estimation in CT

    SciTech Connect

    De Man, Bruno Yin, Zhye; Wu, Mingye; FitzGerald, Paul; Kalra, Mannudeep

    2015-05-15

    Purpose: Many recent computed tomography (CT) dose reduction approaches belong to one of three categories: statistical reconstruction algorithms, efficient x-ray detectors, and optimized CT acquisition schemes with precise control over the x-ray distribution. The latter category could greatly benefit from fast and accurate methods for dose estimation, which would enable real-time patient-specific protocol optimization. Methods: The authors present a new method for volumetrically reconstructing absorbed dose on a per-voxel basis, directly from the actual CT images. The authors’ specific implementation combines a distance-driven pencil-beam approach to model the first-order x-ray interactions with a set of Gaussian convolution kernels to model the higher-order x-ray interactions. The authors performed a number of 3D simulation experiments comparing the proposed method to a Monte Carlo based ground truth. Results: The authors’ results indicate that the proposed approach offers a good trade-off between accuracy and computational efficiency. The images show a good qualitative correspondence to Monte Carlo estimates. Preliminary quantitative results show errors below 10%, except in bone regions, where the authors see a bigger model mismatch. The computational complexity is similar to that of a low-resolution filtered-backprojection algorithm. Conclusions: The authors present a method for analytic dose reconstruction in CT, similar to the techniques used in radiation therapy planning with megavoltage energies. Future work will include refinements of the proposed method to improve the accuracy as well as a more extensive validation study. The proposed method is not intended to replace methods that track individual x-ray photons, but the authors expect that it may prove useful in applications where real-time patient-specific dose estimation is required.

  8. Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT.

    PubMed

    Müller, K; Maier, A K; Schwemmer, C; Lauritsch, G; De Buck, S; Wielandts, J-Y; Hornegger, J; Fahrig, R

    2014-06-21

    The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical

  9. Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT

    NASA Astrophysics Data System (ADS)

    Müller, K.; Maier, A. K.; Schwemmer, C.; Lauritsch, G.; De Buck, S.; Wielandts, J.-Y.; Hornegger, J.; Fahrig, R.

    2014-06-01

    The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical

  10. Reconstruction of difference in sequential CT studies using penalized likelihood estimation

    PubMed Central

    Pourmorteza, A; Dang, H; Siewerdsen, J H; Stayman, J W

    2016-01-01

    Characterization of anatomical change and other differences is important in sequential computed tomography (CT) imaging, where a high-fidelity patient-specific prior image is typically present, but is not used, in the reconstruction of subsequent anatomical states. Here, we introduce a penalized likelihood (PL) method called reconstruction of difference (RoD) to directly reconstruct a difference image volume using both the current projection data and the (unregistered) prior image integrated into the forward model for the measurement data. The algorithm utilizes an alternating minimization to find both the registration and reconstruction estimates. This formulation allows direct control over the image properties of the difference image, permitting regularization strategies that inhibit noise and structural differences due to inconsistencies between the prior image and the current data.Additionally, if the change is known to be local, RoD allows local acquisition and reconstruction, as opposed to traditional model-based approaches that require a full support field of view (or other modifications). We compared the performance of RoD to a standard PL algorithm, in simulation studies and using test-bench cone-beam CT data. The performances of local and global RoD approaches were similar, with local RoD providing a significant computational speedup. In comparison across a range of data with differing fidelity, the local RoD approach consistently showed lower error (with respect to a truth image) than PL in both noisy data and sparsely sampled projection scenarios. In a study of the prior image registration performance of RoD, a clinically reasonable capture ranges were demonstrated. Lastly, the registration algorithm had a broad capture range and the error for reconstruction of CT data was 35% and 20% less than filtered back-projection for RoD and PL, respectively. The RoD has potential for delivering high-quality difference images in a range of sequential clinical

  11. Reconstruction of difference in sequential CT studies using penalized likelihood estimation

    NASA Astrophysics Data System (ADS)

    Pourmorteza, A.; Dang, H.; Siewerdsen, J. H.; Stayman, J. W.

    2016-03-01

    Characterization of anatomical change and other differences is important in sequential computed tomography (CT) imaging, where a high-fidelity patient-specific prior image is typically present, but is not used, in the reconstruction of subsequent anatomical states. Here, we introduce a penalized likelihood (PL) method called reconstruction of difference (RoD) to directly reconstruct a difference image volume using both the current projection data and the (unregistered) prior image integrated into the forward model for the measurement data. The algorithm utilizes an alternating minimization to find both the registration and reconstruction estimates. This formulation allows direct control over the image properties of the difference image, permitting regularization strategies that inhibit noise and structural differences due to inconsistencies between the prior image and the current data. Additionally, if the change is known to be local, RoD allows local acquisition and reconstruction, as opposed to traditional model-based approaches that require a full support field of view (or other modifications). We compared the performance of RoD to a standard PL algorithm, in simulation studies and using test-bench cone-beam CT data. The performances of local and global RoD approaches were similar, with local RoD providing a significant computational speedup. In comparison across a range of data with differing fidelity, the local RoD approach consistently showed lower error (with respect to a truth image) than PL in both noisy data and sparsely sampled projection scenarios. In a study of the prior image registration performance of RoD, a clinically reasonable capture ranges were demonstrated. Lastly, the registration algorithm had a broad capture range and the error for reconstruction of CT data was 35% and 20% less than filtered back-projection for RoD and PL, respectively. The RoD has potential for delivering high-quality difference images in a range of sequential clinical

  12. Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

    SciTech Connect

    Gang, Grace J.; Stayman, J. Webster; Zbijewski, Wojciech; Siewerdsen, Jeffrey H.

    2014-08-15

    Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP

  13. Reconstruction of limited-angle dual-energy CT using mutual learning and cross-estimation (MLCE)

    NASA Astrophysics Data System (ADS)

    Zhang, Huayu; Xing, Yuxiang

    2016-03-01

    Dual-energy CT (DECT) imaging has gained a lot of attenuation because of its capability to discriminate materials. We proposes a flexible DECT scan strategy which can be realized on a system with general X-ray sources and detectors. In order to lower dose and scanning time, our DECT acquires two projections data sets on two arcs of limited-angular coverage (one for each energy) respectively. Meanwhile, a certain number of rays from two data sets form conjugate sampling pairs. Our reconstruction method for such a DECT scan mainly tackles the consequent limited-angle problem. Using the idea of artificial neural network, we excavate the connection between projections at two different energies by constructing a relationship between the linear attenuation coefficient of the high energy and that of the low one. We use this relationship to cross-estimate missing projections and reconstruct attenuation images from an augmented data set including projections at views covered by itself (projections collected in scanning) and by the other energy (projections estimated) for each energy respectively. Validated by our numerical experiment on a dental phantom with rather complex structures, our DECT is effective in recovering small structures in severe limited-angle situations. This DECT scanning strategy can much broaden DECT design in reality.

  14. Maximum-Likelihood Joint Image Reconstruction/Motion Estimation in Attenuation-Corrected Respiratory Gated PET/CT Using a Single Attenuation Map.

    PubMed

    Bousse, Alexandre; Bertolli, Ottavia; Atkinson, David; Arridge, Simon; Ourselin, Sébastien; Hutton, Brian F; Thielemans, Kris

    2016-01-01

    This work provides an insight into positron emission tomography (PET) joint image reconstruction/motion estimation (JRM) by maximization of the likelihood, where the probabilistic model accounts for warped attenuation. Our analysis shows that maximum-likelihood (ML) JRM returns the same reconstructed gates for any attenuation map (μ-map) that is a deformation of a given μ-map, regardless of its alignment with the PET gates. We derived a joint optimization algorithm accordingly, and applied it to simulated and patient gated PET data. We first evaluated the proposed algorithm on simulations of respiratory gated PET/CT data based on the XCAT phantom. Our results show that independently of which μ-map is used as input to JRM: (i) the warped μ-maps correspond to the gated μ-maps, (ii) JRM outperforms the traditional post-registration reconstruction and consolidation (PRRC) for hot lesion quantification and (iii) reconstructed gated PET images are similar to those obtained with gated μ-maps. This suggests that a breath-held μ-map can be used. We then applied JRM on patient data with a μ-map derived from a breath-held high resolution CT (HRCT), and compared the results with PRRC, where each reconstructed PET image was obtained with a corresponding cine-CT gated μ-map. Results show that JRM with breath-held HRCT achieves similar reconstruction to that using PRRC with cine-CT. This suggests a practical low-dose solution for implementation of motion-corrected respiratory gated PET/CT.

  15. Resolution-enhancing hybrid, spectral CT reconstruction

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    Spectral x-ray imaging based on photon-counting x-ray detectors (PCXD) is an area of growing interest. By measuring the energy of x-ray photons, a spectral CT system can better differentiate elements using a single scan. However, the spatial resolution achievable with most PCXDs limits their application, particularly in preclinical CT imaging. Consequently, our group is developing a hybrid micro-CT scanner based on a high-resolution, energy-integrating (EID) detector and a lower-resolution, PCXD. To complement this system, we propose and demonstrate a hybrid, spectral CT reconstruction algorithm which robustly combines the spectral contrast of the PCXD with the spatial resolution of the EID. Specifically, the high-resolution, spectrally resolved data (X) is recovered as the sum of two matrices: one with low column rank (XL) determined from the EID data and one with intensity gradient sparse columns (XS) corresponding to the upsampled spectral contrast obtained from the PCXD data. We test the proposed algorithm in a feasibility study focused on molecular imaging of atherosclerotic plaque using activatable iodine and gold nanoparticles. The results show accurate estimation of material concentrations at increased spatial resolution for a voxel size ratio between the PCXD and the EID of 500 μm3:100 μm3. Specifically, regularized, iterative reconstruction of the MOBY mouse phantom around the K-edges of iodine (33.2 keV) and gold (80.7 keV) reduces the reconstruction error by more than a factor of three relative to least-squares, algebraic reconstruction. Likewise, the material decomposition accuracy into iodine, gold, calcium, and water improves by more than a factor of two.

  16. Maximum-likelihood joint image reconstruction and motion estimation with misaligned attenuation in TOF-PET/CT

    NASA Astrophysics Data System (ADS)

    Bousse, Alexandre; Bertolli, Ottavia; Atkinson, David; Arridge, Simon; Ourselin, Sébastien; Hutton, Brian F.; Thielemans, Kris

    2016-02-01

    This work is an extension of our recent work on joint activity reconstruction/motion estimation (JRM) from positron emission tomography (PET) data. We performed JRM by maximization of the penalized log-likelihood in which the probabilistic model assumes that the same motion field affects both the activity distribution and the attenuation map. Our previous results showed that JRM can successfully reconstruct the activity distribution when the attenuation map is misaligned with the PET data, but converges slowly due to the significant cross-talk in the likelihood. In this paper, we utilize time-of-flight PET for JRM and demonstrate that the convergence speed is significantly improved compared to JRM with conventional PET data.

  17. Maximum-likelihood joint image reconstruction and motion estimation with misaligned attenuation in TOF-PET/CT.

    PubMed

    Bousse, Alexandre; Bertolli, Ottavia; Atkinson, David; Arridge, Simon; Ourselin, Sébastien; Hutton, Brian F; Thielemans, Kris

    2016-02-01

    This work is an extension of our recent work on joint activity reconstruction/motion estimation (JRM) from positron emission tomography (PET) data. We performed JRM by maximization of the penalized log-likelihood in which the probabilistic model assumes that the same motion field affects both the activity distribution and the attenuation map. Our previous results showed that JRM can successfully reconstruct the activity distribution when the attenuation map is misaligned with the PET data, but converges slowly due to the significant cross-talk in the likelihood. In this paper, we utilize time-of-flight PET for JRM and demonstrate that the convergence speed is significantly improved compared to JRM with conventional PET data.

  18. Iterative image reconstruction in spectral CT

    NASA Astrophysics Data System (ADS)

    Hernandez, Daniel; Michel, Eric; Kim, Hye S.; Kim, Jae G.; Han, Byung H.; Cho, Min H.; Lee, Soo Y.

    2012-03-01

    Scan time of spectral-CTs is much longer than conventional CTs due to limited number of x-ray photons detectable by photon-counting detectors. However, the spectral pixel information in spectral-CT has much richer information on physiological and pathological status of the tissues than the CT-number in conventional CT, which makes the spectral- CT one of the promising future imaging modalities. One simple way to reduce the scan time in spectral-CT imaging is to reduce the number of views in the acquisition of projection data. But, this may result in poorer SNR and strong streak artifacts which can severely compromise the image quality. In this work, spectral-CT projection data were obtained from a lab-built spectral-CT consisting of a single CdTe photon counting detector, a micro-focus x-ray tube and scan mechanics. For the image reconstruction, we used two iterative image reconstruction methods, the simultaneous iterative reconstruction technique (SIRT) and the total variation minimization based on conjugate gradient method (CG-TV), along with the filtered back-projection (FBP) to compare the image quality. From the imaging of the iodine containing phantoms, we have observed that SIRT and CG-TV are superior to the FBP method in terms of SNR and streak artifacts.

  19. Quantitative image quality evaluation for cardiac CT reconstructions

    NASA Astrophysics Data System (ADS)

    Tseng, Hsin-Wu; Fan, Jiahua; Kupinski, Matthew A.; Balhorn, William; Okerlund, Darin R.

    2016-03-01

    Maintaining image quality in the presence of motion is always desirable and challenging in clinical Cardiac CT imaging. Different image-reconstruction algorithms are available on current commercial CT systems that attempt to achieve this goal. It is widely accepted that image-quality assessment should be task-based and involve specific tasks, observers, and associated figures of merits. In this work, we developed an observer model that performed the task of estimating the percentage of plaque in a vessel from CT images. We compared task performance of Cardiac CT image data reconstructed using a conventional FBP reconstruction algorithm and the SnapShot Freeze (SSF) algorithm, each at default and optimal reconstruction cardiac phases. The purpose of this work is to design an approach for quantitative image-quality evaluation of temporal resolution for Cardiac CT systems. To simulate heart motion, a moving coronary type phantom synchronized with an ECG signal was used. Three different percentage plaques embedded in a 3 mm vessel phantom were imaged multiple times under motion free, 60 bpm, and 80 bpm heart rates. Static (motion free) images of this phantom were taken as reference images for image template generation. Independent ROIs from the 60 bpm and 80 bpm images were generated by vessel tracking. The observer performed estimation tasks using these ROIs. Ensemble mean square error (EMSE) was used as the figure of merit. Results suggest that the quality of SSF images is superior to the quality of FBP images in higher heart-rate scans.

  20. Beam hardening correction for sparse-view CT reconstruction

    NASA Astrophysics Data System (ADS)

    Liu, Wenlei; Rong, Junyan; Gao, Peng; Liao, Qimei; Lu, HongBing

    2015-03-01

    Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant's modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.

  1. SU-E-T-143: Effect of X-Ray and Cone Beam CT Reconstruction Parameters On Estimation of Bone Volume of Mice Used in Aging Research

    SciTech Connect

    Russ, M; Pang, M; Troen, B; Rudin, S; Ionita, C

    2014-06-01

    Purpose: To investigate the variations in bone volume calculations in mice involved in aging research when changing cone beam micro-CT x-ray and reconstruction parameters. Methods: Mouse spines were placed on an indexed turn table that rotated 0.5° per projection and imaged by a self-built micro CT machine containing a CCD-based high-resolution x-ray detector. After the full 360° rotation data set of object images was obtained, a standard filtered back-projection cone beam reconstruction was performed. Four different kVp's between 40–70 kVp in 10kVp increments were selected. For each kVp two mAs settings were used. Each acquisition was reconstructed using two voxel sizes (12 and 25μm) and two step angles, 0.5° and 1°, respectively. A LabView program was written to determine the total bone volume contained in the mouse's total spine volume (bone plus gaps) as a measure of spine health. First, the user selected the desired 512×512 reconstruction to view the whole spine volume which was then used to select a gray-level threshold that allowed for viewing of the bone structure, then another threshold to include gaps. The program returned bone volume, bone × gap volume, and their ratio, BVF. Results: The calculated bone volume fractions were compared as a function of tube potential. Cases with 25μm slice thickness showed trials with lower kVp's had greater image contrast, which resulted in higher calculated bone volume fractions. Cases with 12μm reconstructed slice thickness were significantly noisier, and showed no clear maximum BVF. Conclusion: Using the projection images and reconstructions acquired from the micro CT, it can be shown that the micro-CT x-ray and reconstruction parameters significantly affect the total bone volume calculations. When comparing mice cohorts treated with different therapies researchers need to be aware of such details and use volumes which were acquired and processed in identical conditions.

  2. Filtered backprojection proton CT reconstruction along most likely paths

    SciTech Connect

    Rit, Simon; Dedes, George; Freud, Nicolas; Sarrut, David; Letang, Jean Michel

    2013-03-15

    Purpose: Proton CT (pCT) has the potential to accurately measure the electron density map of tissues at low doses but the spatial resolution is prohibitive if the curved paths of protons in matter is not accounted for. The authors propose to account for an estimate of the most likely path of protons in a filtered backprojection (FBP) reconstruction algorithm. Methods: The energy loss of protons is first binned in several proton radiographs at different distances to the proton source to exploit the depth-dependency of the estimate of the most likely path. This process is named the distance-driven binning. A voxel-specific backprojection is then used to select the adequate radiograph in the distance-driven binning in order to propagate in the pCT image the best achievable spatial resolution in proton radiographs. The improvement in spatial resolution is demonstrated using Monte Carlo simulations of resolution phantoms. Results: The spatial resolution in the distance-driven binning depended on the distance of the objects from the source and was optimal in the binned radiograph corresponding to that distance. The spatial resolution in the reconstructed pCT images decreased with the depth in the scanned object but it was always better than previous FBP algorithms assuming straight line paths. In a water cylinder with 20 cm diameter, the observed range of spatial resolutions was 0.7 - 1.6 mm compared to 1.0 - 2.4 mm at best with a straight line path assumption. The improvement was strongly enhanced in shorter 200 Degree-Sign scans. Conclusions: Improved spatial resolution was obtained in pCT images with filtered backprojection reconstruction using most likely path estimates of protons. The improvement in spatial resolution combined with the practicality of FBP algorithms compared to iterative reconstruction algorithms makes this new algorithm a candidate of choice for clinical pCT.

  3. Prospects for in vivo estimation of photon linear attenuation coefficients using postprocessing dual-energy CT imaging on a commercial scanner: Comparison of analytic and polyenergetic statistical reconstruction algorithms

    SciTech Connect

    Evans, Joshua D. Yu, Yaduo; Williamson, Jeffrey F.; Whiting, Bruce R.; O’Sullivan, Joseph A.; Politte, David G.; Klahr, Paul H.

    2013-12-15

    Purpose: Accurate patient-specific photon cross-section information is needed to support more accurate model-based dose calculation for low energy photon-emitting modalities in medicine such as brachytherapy and kilovoltage x-ray imaging procedures. A postprocessing dual-energy CT (pDECT) technique for noninvasivein vivo estimation of photon linear attenuation coefficients has been experimentally implemented on a commercial CT scanner and its accuracy assessed in idealized phantom geometries. Methods: Eight test materials of known composition and density were used to compare pDECT-estimated linear attenuation coefficients to NIST reference values over an energy range from 10 keV to 1 MeV. As statistical image reconstruction (SIR) has been shown to reconstruct images with less random and systematic error than conventional filtered backprojection (FBP), the pDECT technique was implemented with both an in-house polyenergetic SIR algorithm, alternating minimization (AM), as well as a conventional FBP reconstruction algorithm. Improvement from increased spectral separation was also investigated by filtering the high-energy beam with an additional 0.5 mm of tin. The law of propagated uncertainty was employed to assess the sensitivity of the pDECT process to errors in reconstructed images. Results: Mean pDECT-estimated linear attenuation coefficients for the eight test materials agreed within 1% of NIST reference values for energies from 1 MeV down to 30 keV, with mean errors rising to between 3% and 6% at 10 keV, indicating that the method is unbiased when measurement and calibration phantom geometries are matched. Reconstruction with FBP and AM algorithms conferred similar mean pDECT accuracy. However, single-voxel pDECT estimates reconstructed on a 1 × 1 × 3 mm{sup 3} grid are shown to be highly sensitive to reconstructed image uncertainty; in some cases pDECT attenuation coefficient estimates exhibited standard deviations on the order of 20% around the mean

  4. Accuracy of quantitative reconstructions in SPECT/CT imaging

    NASA Astrophysics Data System (ADS)

    Shcherbinin, S.; Celler, A.; Belhocine, T.; van der Werf, R.; Driedger, A.

    2008-09-01

    The goal of this study was to determine the quantitative accuracy of our OSEM-APDI reconstruction method based on SPECT/CT imaging for Tc-99m, In-111, I-123, and I-131 isotopes. Phantom studies were performed on a SPECT/low-dose multislice CT system (Infinia-Hawkeye-4 slice, GE Healthcare) using clinical acquisition protocols. Two radioactive sources were centrally and peripherally placed inside an anthropometric Thorax phantom filled with non-radioactive water. Corrections for attenuation, scatter, collimator blurring and collimator septal penetration were applied and their contribution to the overall accuracy of the reconstruction was evaluated. Reconstruction with the most comprehensive set of corrections resulted in activity estimation with error levels of 3-5% for all the isotopes.

  5. Accelerated Compressed Sensing Based CT Image Reconstruction.

    PubMed

    Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R; Paul, Narinder S; Cobbold, Richard S C

    2015-01-01

    In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization. PMID:26167200

  6. Accelerated Compressed Sensing Based CT Image Reconstruction

    PubMed Central

    Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R.; Paul, Narinder S.; Cobbold, Richard S. C.

    2015-01-01

    In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization. PMID:26167200

  7. A noise variance estimation approach for CT

    NASA Astrophysics Data System (ADS)

    Shen, Le; Jin, Xin; Xing, Yuxiang

    2012-10-01

    The Poisson-like noise model has been widely used for noise suppression and image reconstruction in low dose computed tomography. Various noise estimation and suppression approaches have been developed and studied to enhance the image quality. Among them, the recently proposed generalized Anscombe transform (GAT) has been utilized to stabilize the variance of Poisson-Gaussian noise. In this paper, we present a variance estimation approach using GAT. After the transform, the projection data is denoised conventionally with an assumption that the noise variance is uniformly equals to 1. The difference of the original and the denoised projection is treated as pure noise and the global variance σ2 can be estimated from the residual difference. Thus, the final denoising step with the estimated σ2 is performed. The proposed approach is verified on a cone-beam CT system and demonstrated to obtain a more accurate estimation of the actual parameter. We also examine FBP algorithm with the two-step noise suppression in the projection domain using the estimated noise variance. Reconstruction results with simulated and practical projection data suggest that the presented approach could be effective in practical imaging applications.

  8. Influence of Thin Slice Reconstruction on CT Brain Perfusion Analysis

    PubMed Central

    Bennink, Edwin; Oosterbroek, Jaap; Horsch, Alexander D.; Dankbaar, Jan Willem; Velthuis, Birgitta K.; Viergever, Max A.; de Jong, Hugo W. A. M.

    2015-01-01

    Objectives Although CT scanners generally allow dynamic acquisition of thin slices (1 mm), thick slice (≥5 mm) reconstruction is commonly used for stroke imaging to reduce data, processing time, and noise level. Thin slice CT perfusion (CTP) reconstruction may suffer less from partial volume effects, and thus yield more accurate quantitative results with increased resolution. Before thin slice protocols are to be introduced clinically, it needs to be ensured that this does not affect overall CTP constancy. We studied the influence of thin slice reconstruction on average perfusion values by comparing it with standard thick slice reconstruction. Materials and Methods From 50 patient studies, absolute and relative hemisphere averaged estimates of cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and permeability-surface area product (PS) were analyzed using 0.8, 2.4, 4.8, and 9.6 mm slice reconstructions. Specifically, the influence of Gaussian and bilateral filtering, the arterial input function (AIF), and motion correction on the perfusion values was investigated. Results Bilateral filtering gave noise levels comparable to isotropic Gaussian filtering, with less partial volume effects. Absolute CBF, CBV and PS were 22%, 14% and 46% lower with 0.8 mm than with 4.8 mm slices. If the AIF and motion correction were based on thin slices prior to reconstruction of thicker slices, these differences reduced to 3%, 4% and 3%. The effect of slice thickness on relative values was very small. Conclusions This study shows that thin slice reconstruction for CTP with unaltered acquisition protocol gives relative perfusion values without clinically relevant bias. It does however affect absolute perfusion values, of which CBF and CBV are most sensitive. Partial volume effects in large arteries and veins lead to overestimation of these values. The effects of reconstruction slice thickness should be taken into account when absolute perfusion values are

  9. Measuring temporal resolution of cardiac CT reconstructions

    NASA Astrophysics Data System (ADS)

    Matthews, David; Heuscher, Dominic

    2005-04-01

    Multi-slice CT today is capable of imaging the heart with excellent temporal resolution. Algorithms have been developed to perform reconstructions combining data from multiple cardiac cycles. This paper presents a simulation phantom that enables a direct measurement of the actual temporal resolution achieved by these algorithms. This is not only useful for assessing the temporal resolution but also for validating the algorithms themselves. A simulation phantom was developed that consists of a 20 cm. diameter water phantom containing an array of cylinders whose intensities are pulsed for various durations ranging from 10 msec. to 250 msec. The intensity varied between the background value of water (0 HU) and 800 HU. By measuring the nominal attenuation value at the center of each cylinder, a curve can be derived representing the response over the given temporal range. A temporal resolution representing the FWHM value is determined based on the half-max value of this curve. Reconstructions were performed using a multi-cycle cardiac algorithm described previously in the literature. The measured FWHM values agree quite well to the temporal resolution predicted by the cardiac algorithm itself. Even the variation along the longitudinal axis can be accounted for by the predicted values. A simulated phantom can be used to accurately assess the temporal resolution of cardiac reconstruction algorithms. Excellent agreement was achieved between the predicted and measured temporal resolution values for the multi-cycle algorithm used in this study.

  10. Computerized craniofacial reconstruction using CT-derived implicit surface representations.

    PubMed

    Vandermeulen, Dirk; Claes, Peter; Loeckx, Dirk; De Greef, Sven; Willems, Guy; Suetens, Paul

    2006-05-15

    In forensic craniofacial reconstruction, facial features of an unknown individual are estimated from an unidentified skull, based on a mixture of experimentally obtained guidelines on the relationship between soft tissues and the underlying skeleton. In this paper, we investigate the possibility of using full 3D cross-sectional CT images for establishing a reference database of densely sampled distances between the external surfaces of the skull and head for automated craniofacial reconstruction. For each CT image in the reference database, the hard tissue (skull) and soft tissue (head) volumes are automatically segmented and transformed into signed distance transform (sDT) images, representing for each voxel in this image the Euclidean distance to the closest point on the skull and head surface, respectively, distances being positive (negative) for voxels inside (outside) the skull/head. Multiple craniofacial reconstructions are obtained by first warping each reference skull sDT maps to the target skull sDT using a B-spline based free form deformation algorithm and subsequently applying these warps to the reference head sDT maps. A single reconstruction of the target head surface is defined as the zero level set of the arithmetic average of all warped reference head sDT maps, but other reconstructions are possible, biasing the result to subject specific attributes (age, BMI, gender). Both qualitative and quantitative tests (measuring the similarity between the 3D reconstructed and corresponding original head surface) on a small (N = 20) database are presented to proof the validity of the concept.

  11. Cardiac cone-beam CT volume reconstruction using ART

    SciTech Connect

    Nielsen, T.; Manzke, R.; Proksa, R.; Grass, M.

    2005-04-01

    Modern computed tomography systems allow volume imaging of the heart. Up to now, approximately two-dimensional (2D) and 3D algorithms based on filtered backprojection are used for the reconstruction. These algorithms become more sensitive to artifacts when the cone angle of the x-ray beam increases as it is the current trend of computed tomography (CT) technology. In this paper, we investigate the potential of iterative reconstruction based on the algebraic reconstruction technique (ART) for helical cardiac cone-beam CT. Iterative reconstruction has the advantages that it takes the cone angle into account exactly and that it can be combined with retrospective cardiac gating fairly easily. We introduce a modified ART algorithm for cardiac CT reconstruction. We apply it to clinical cardiac data from a 16-slice CT scanner and compare the images to those obtained with a current analytical reconstruction method. In a second part, we investigate the potential of iterative reconstruction for a large area detector with 256 slices. For the clinical cases, iterative reconstruction produces excellent images of diagnostic quality. For the large area detector, iterative reconstruction produces images superior to analytical reconstruction in terms of cone-beam artifacts.

  12. Bayes Estimators for Phylogenetic Reconstruction

    PubMed Central

    Huggins, P. M.; Li, W.; Haws, D.; Friedrich, T.; Liu, J.; Yoshida, R.

    2011-01-01

    Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet, most reconstruction methods like maximum likelihood (ML) do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution. Given tree samples, we propose finding the tree estimate that is closest on average to the samples. This “median” tree is known as the Bayes estimator (BE). The BE literally maximizes posterior expected accuracy, measured in terms of closeness (distance) to the true tree. We discuss a unified framework of BE trees, focusing especially on tree distances that are expressible as squared euclidean distances. Notable examples include Robinson–Foulds (RF) distance, quartet distance, and squared path difference. Using both simulated and real data, we show that BEs can be estimated in practice by hill-climbing. In our simulation, we find that BEs tend to be closer to the true tree, compared with ML and neighbor joining. In particular, the BE under squared path difference tends to perform well in terms of both path difference and RF distances. PMID:21471560

  13. A biological phantom for evaluation of CT image reconstruction algorithms

    NASA Astrophysics Data System (ADS)

    Cammin, J.; Fung, G. S. K.; Fishman, E. K.; Siewerdsen, J. H.; Stayman, J. W.; Taguchi, K.

    2014-03-01

    In recent years, iterative algorithms have become popular in diagnostic CT imaging to reduce noise or radiation dose to the patient. The non-linear nature of these algorithms leads to non-linearities in the imaging chain. However, the methods to assess the performance of CT imaging systems were developed assuming the linear process of filtered backprojection (FBP). Those methods may not be suitable any longer when applied to non-linear systems. In order to evaluate the imaging performance, a phantom is typically scanned and the image quality is measured using various indices. For reasons of practicality, cost, and durability, those phantoms often consist of simple water containers with uniform cylinder inserts. However, these phantoms do not represent the rich structure and patterns of real tissue accurately. As a result, the measured image quality or detectability performance for lesions may not reflect the performance on clinical images. The discrepancy between estimated and real performance may be even larger for iterative methods which sometimes produce "plastic-like", patchy images with homogeneous patterns. Consequently, more realistic phantoms should be used to assess the performance of iterative algorithms. We designed and constructed a biological phantom consisting of porcine organs and tissue that models a human abdomen, including liver lesions. We scanned the phantom on a clinical CT scanner and compared basic image quality indices between filtered backprojection and an iterative reconstruction algorithm.

  14. Regularized reconstruction in quantitative SPECT using CT side information from hybrid imaging

    NASA Astrophysics Data System (ADS)

    Dewaraja, Yuni K.; Koral, Kenneth F.; Fessler, Jeffrey A.

    2010-05-01

    A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from (1) penalized likelihood employing CT-side information-based regularization (PL-CT), (2) penalized likelihood with edge preserving regularization (no CT) and (3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with (4) ordered subset expectation maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the 'truth', avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with non-uniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets without

  15. Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)

    NASA Astrophysics Data System (ADS)

    McCollough, Cynthia; Zhang, Jie; Bruesewitz, Michael; Bartholmai, Brian

    2006-03-01

    To create a repository of clinical data, CT images and tissue samples and to more clearly understand the pathogenetic features of pulmonary fibrosis and emphysema, the National Heart, Lung, and Blood Institute (NHLBI) launched a cooperative effort known as the Lung Tissue Resource Consortium (LTRC). The CT images for the LTRC effort must contain accurate CT numbers in order to characterize tissues, and must have high-spatial resolution to show fine anatomic structures. This study was performed to optimize the CT image reconstruction algorithms to achieve these criteria. Quantitative analyses of phantom and clinical images were conducted. The ACR CT accreditation phantom containing five regions of distinct CT attenuations (CT numbers of approximately -1000 HU, -80 HU, 0 HU, 130 HU and 900 HU), and a high-contrast spatial resolution test pattern, was scanned using CT systems from two manufacturers (General Electric (GE) Healthcare and Siemens Medical Solutions). Phantom images were reconstructed using all relevant reconstruction algorithms. Mean CT numbers and image noise (standard deviation) were measured and compared for the five materials. Clinical high-resolution chest CT images acquired on a GE CT system for a patient with diffuse lung disease were reconstructed using BONE and STANDARD algorithms and evaluated by a thoracic radiologist in terms of image quality and disease extent. The clinical BONE images were processed with a 3 x 3 x 3 median filter to simulate a thicker slice reconstructed in smoother algorithms, which have traditionally been proven to provide an accurate estimation of emphysema extent in the lungs. Using a threshold technique, the volume of emphysema (defined as the percentage of lung voxels having a CT number lower than -950 HU) was computed for the STANDARD, BONE, and BONE filtered. The CT numbers measured in the ACR CT Phantom images were accurate for all reconstruction kernels for both manufacturers. As expected, visual evaluation of the

  16. Image reconstruction for hybrid true-color micro-CT.

    PubMed

    Xu, Qiong; Yu, Hengyong; Bennett, James; He, Peng; Zainon, Rafidah; Doesburg, Robert; Opie, Alex; Walsh, Mike; Shen, Haiou; Butler, Anthony; Butler, Phillip; Mou, Xuanqin; Wang, Ge

    2012-06-01

    X-ray micro-CT is an important imaging tool for biomedical researchers. Our group has recently proposed a hybrid "true-color" micro-CT system to improve contrast resolution with lower system cost and radiation dose. The system incorporates an energy-resolved photon-counting true-color detector into a conventional micro-CT configuration, and can be used for material decomposition. In this paper, we demonstrate an interior color-CT image reconstruction algorithm developed for this hybrid true-color micro-CT system. A compressive sensing-based statistical interior tomography method is employed to reconstruct each channel in the local spectral imaging chain, where the reconstructed global gray-scale image from the conventional imaging chain served as the initial guess. Principal component analysis was used to map the spectral reconstructions into the color space. The proposed algorithm was evaluated by numerical simulations, physical phantom experiments, and animal studies. The results confirm the merits of the proposed algorithm, and demonstrate the feasibility of the hybrid true-color micro-CT system. Additionally, a "color diffusion" phenomenon was observed whereby high-quality true-color images are produced not only inside the region of interest, but also in neighboring regions. It appears harnessing that this phenomenon could potentially reduce the color detector size for a given ROI, further reducing system cost and radiation dose.

  17. Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT

    SciTech Connect

    Matenine, Dmitri Mascolo-Fortin, Julia; Goussard, Yves

    2015-11-15

    Purpose: The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. Methods: This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. Results: The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. Conclusions: The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can

  18. Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT

    NASA Astrophysics Data System (ADS)

    Chun, Se Young; Fessler, Jeffrey A.; Dewaraja, Yuni K.

    2013-09-01

    Quantitative SPECT techniques are important for many applications including internal emitter therapy dosimetry where accurate estimation of total target activity and activity distribution within targets are both potentially important for dose-response evaluations. We investigated non-local means (NLM) post-reconstruction filtering for accurate I-131 SPECT estimation of both total target activity and the 3D activity distribution. We first investigated activity estimation versus number of ordered-subsets expectation-maximization (OSEM) iterations. We performed simulations using the XCAT phantom with tumors containing a uniform and a non-uniform activity distribution, and measured the recovery coefficient (RC) and the root mean squared error (RMSE) to quantify total target activity and activity distribution, respectively. We observed that using more OSEM iterations is essential for accurate estimation of RC, but may or may not improve RMSE. We then investigated various post-reconstruction filtering methods to suppress noise at high iteration while preserving image details so that both RC and RMSE can be improved. Recently, NLM filtering methods have shown promising results for noise reduction. Moreover, NLM methods using high-quality side information can improve image quality further. We investigated several NLM methods with and without CT side information for I-131 SPECT imaging and compared them to conventional Gaussian filtering and to unfiltered methods. We studied four different ways of incorporating CT information in the NLM methods: two known (NLM CT-B and NLM CT-M) and two newly considered (NLM CT-S and NLM CT-H). We also evaluated the robustness of NLM filtering using CT information to erroneous CT. NLM CT-S and NLM CT-H yielded comparable RC values to unfiltered images while substantially reducing RMSE. NLM CT-S achieved -2.7 to 2.6% increase of RC compared to no filtering and NLM CT-H yielded up to 6% decrease in RC while other methods yielded lower RCs

  19. CEnPiT: Helical cardiac CT reconstruction

    SciTech Connect

    Bontus, Claas; Koken, Peter; Koehler, Thomas; Grass, Michael

    2006-08-15

    Computer tomography (CT) scanners with an increasing number of detector rows offer the potential of shorter scanning times. Nevertheless, the reconstruction problem becomes more challenging, since cone beam artifacts are likely to enter. Here, we consider helical cardiac CT. We analyze how a relationship can be established between exact reconstruction algorithms and the demand to perform a cardiac gating. Utilizing the redundancies requires the consideration of all kinds of Radon planes. For the reconstruction algorithm proposed here, we separate the data into two parts. The first part contains contributions of Radon planes, which are measured with a large number of redundancies. The second part contains the remaining contributions. As it turns out, the second part contributes rather to the low-frequency contents of trans-axial slices. Therefore, we propose to perform a gated back-projection only for the first part, while the second part is back-projected in an ungated way. Data from the complete source trajectory are employed in the reconstruction process in contrary to conventional helical cardiac reconstruction methods. Moreover, all different types of Radon planes are taken into account in the reconstruction, though an ECG-dependent cardiac gating is applied. The reconstruction results, which we present for clinical and simulated data, demonstrate the high potential of CEnPiT for helical cardiac CT with large cone angle systems.

  20. A 3-D industrial CT reconstruction algorithm to directly reconstruct the characteristics

    NASA Astrophysics Data System (ADS)

    Zhao, Ying-Liang; Wang, Li-Ming; Han, Yan

    2011-01-01

    In traditional 3-D CT reconstruction methods, for the projection procedure is low-pass smoothing, the high-frequency characters are difficult to obtain after the projection data are reconstructed. In addition the design and implementation of three-dimensional filter are relatively harder. A new 3D industrial CT reconstruction algorithm to directly reconstruct the characteristics is put forth. Based on the FDK method and the trait of RADON transform, the feasibility of the novel algorithm is theoretically deduced. Combined with the wavelet, it is deduced to extract the characteristics using the 2-D wavelet transform and to directly reconstruct the characteristics in 3-D CT. The experiments show that the algorithm can preferably stand out the useful information, is of engineering practicability and the design of the filter is relatively simpler.

  1. CCG-LCONE CT Reconstruction Code User and Programmer's Guide

    SciTech Connect

    Jackson, J A

    2006-09-27

    This document describes a Computed Tomography (CT) reconstruction code called CCG-LCONE. CCG-LCONE is used to reconstruction objects from projections acquired on a cone beam radiographic system. This document will describe in brief the theory behind parts of the code, as well as detail the structure of the code, so it will function as both a ''User's Guide and a Programmer's Guide''. The Introduction will describe CT in general and cone beam systems in particular. It will explain why CCG-LCONE was developed and give an overview of the design and function. This report discusses the various parts of the system, both theory and code structure.

  2. ANL CT Reconstruction Algorithm for Utilizing Digital X-ray

    2004-05-01

    Reconstructs X-ray computed tomographic images from large data sets known as 16-bit binary sinograms when using a massively parallelized computer architecture such as a Beowuif cluster by parallelizing the X-ray CT reconstruction routine. The algorithm uses the concept of generation of an image from carefully obtained multiple 1-D or 2-D X-ray projections. The individual projections are filtered using a digital Fast Fourier Transform. The literature refers to this as filtered back projection.

  3. Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms.

    PubMed

    Rui, Xue; Cheng, Lishui; Long, Yong; Fu, Lin; Alessio, Adam M; Asma, Evren; Kinahan, Paul E; De Man, Bruno

    2015-10-01

    For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels

  4. Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms.

    PubMed

    Rui, Xue; Cheng, Lishui; Long, Yong; Fu, Lin; Alessio, Adam M; Asma, Evren; Kinahan, Paul E; De Man, Bruno

    2015-10-01

    For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels

  5. Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms

    NASA Astrophysics Data System (ADS)

    Rui, Xue; Cheng, Lishui; Long, Yong; Fu, Lin; Alessio, Adam M.; Asma, Evren; Kinahan, Paul E.; De Man, Bruno

    2015-09-01

    For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition. We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality. With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose

  6. 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.

  7. Uncertainty estimation in reconstructed deformable models

    SciTech Connect

    Hanson, K.M.; Cunningham, G.S.; McKee, R.

    1996-12-31

    One of the hallmarks of the Bayesian approach to modeling is the posterior probability, which summarizes all uncertainties regarding the analysis. Using a Markov Chain Monte Carlo (MCMC) technique, it is possible to generate a sequence of objects that represent random samples drawn from the posterior distribution. We demonstrate this technique for reconstructions of two-dimensional objects from noisy projections taken from two directions. The reconstructed object is modeled in terms of a deformable geometrically-defined boundary with a constant interior density yielding a nonlinear reconstruction problem. We show how an MCMC sequence can be used to estimate uncertainties in the location of the edge of the reconstructed object.

  8. Efficient iterative image reconstruction algorithm for dedicated breast CT

    NASA Astrophysics Data System (ADS)

    Antropova, Natalia; Sanchez, Adrian; Reiser, Ingrid S.; Sidky, Emil Y.; Boone, John; Pan, Xiaochuan

    2016-03-01

    Dedicated breast computed tomography (bCT) is currently being studied as a potential screening method for breast cancer. The X-ray exposure is set low to achieve an average glandular dose comparable to that of mammography, yielding projection data that contains high levels of noise. Iterative image reconstruction (IIR) algorithms may be well-suited for the system since they potentially reduce the effects of noise in the reconstructed images. However, IIR outcomes can be difficult to control since the algorithm parameters do not directly correspond to the image properties. Also, IIR algorithms are computationally demanding and have optimal parameter settings that depend on the size and shape of the breast and positioning of the patient. In this work, we design an efficient IIR algorithm with meaningful parameter specifications and that can be used on a large, diverse sample of bCT cases. The flexibility and efficiency of this method comes from having the final image produced by a linear combination of two separately reconstructed images - one containing gray level information and the other with enhanced high frequency components. Both of the images result from few iterations of separate IIR algorithms. The proposed algorithm depends on two parameters both of which have a well-defined impact on image quality. The algorithm is applied to numerous bCT cases from a dedicated bCT prototype system developed at University of California, Davis.

  9. 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

  10. Spectrotemporal CT data acquisition and reconstruction at low dose

    SciTech Connect

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

    2015-11-15

    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

  11. Fast reconstruction of low dose proton CT by sinogram interpolation

    NASA Astrophysics Data System (ADS)

    Hansen, David C.; Sangild Sørensen, Thomas; Rit, Simon

    2016-08-01

    Proton computed tomography (CT) has been demonstrated as a promising image modality in particle therapy planning. It can reduce errors in particle range calculations and consequently improve dose calculations. Obtaining a high imaging resolution has traditionally required computationally expensive iterative reconstruction techniques to account for the multiple scattering of the protons. Recently, techniques for direct reconstruction have been developed, but these require a higher imaging dose than the iterative methods. No previous work has compared the image quality of the direct and the iterative methods. In this article, we extend the methodology for direct reconstruction to be applicable for low imaging doses and compare the obtained results with three state-of-the-art iterative algorithms. We find that the direct method yields comparable resolution and image quality to the iterative methods, even at 1 mSv dose levels, while yielding a twentyfold speedup in reconstruction time over previously published iterative algorithms.

  12. CT x-ray tube voltage optimisation and image reconstruction evaluation using visual grading analysis

    NASA Astrophysics Data System (ADS)

    Zheng, Xiaoming; Kim, Ted M.; Davidson, Rob; Lee, Seongju; Shin, Cheongil; Yang, Sook

    2014-03-01

    The purposes of this work were to find an optimal x-ray voltage for CT imaging and to determine the diagnostic effectiveness of image reconstruction techniques by using the visual grading analysis (VGA). Images of the PH-5 CT abdomen phantom (Kagaku Co, Kyoto) were acquired by the Toshiba Aquillion One 320 slices CT system with various exposures (from 10 to 580 mAs) under different tube peak voltages (80, 100 and 120 kVp). The images were reconstructed by employing the FBP and the AIDR 3D iterative reconstructions with Mild, Standard and Strong FBP blending. Image quality was assessed by measuring noise, contrast to noise ratio and human observer's VGA scores. The CT dose index CTDIv was obtained from the values displayed on the images. The best fit for the curves of the image quality VGA vs dose CTDIv is a logistic function from the SPSS estimation. A threshold dose Dt is defined as the CTDIv at the just acceptable for diagnostic image quality and a figure of merit (FOM) is defined as the slope of the standardised logistic function. The Dt and FOM were found to be 5.4, 8.1 and 9.1 mGy and 0.47, 0.51 and 0.38 under the tube voltages of 80, 100 and 120 kVp, respectively, from images reconstructed by the FBP technique. The Dt and FOM values were lower from the images reconstructed by the AIDR 3D in comparison with the FBP technique. The optimal xray peak voltage for the imaging of the PH-5 abdomen phantom by the Aquillion One CT system was found to be at 100 kVp. The images reconstructed by the FBP are more diagnostically effective than that by the AIDR 3D but with a higher dose Dt to the patients.

  13. Joint regularization for spectro-temporal CT reconstruction

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    X-ray 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. In previous work, we proposed and demonstrated a projection acquisition and reconstruction strategy for 5D CT (3D + dual-energy + time) which recovered spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. The approach relied on the approximate separability of the temporal and spectral reconstruction sub-problems, which enabled substantial projection undersampling and effective regularization. Here, we extend this previous work to more general, nonseparable 5D CT reconstruction cases (3D + muti-energy + time) with applicability to K-edge imaging of exogenous contrast agents. We apply the newly proposed algorithm in phantom simulations using a realistic system and noise model for a photon counting x-ray detector with six energy thresholds. The MOBY mouse phantom used contains realistic concentrations of iodine, gold, and calcium in water. Relative to weighted least-squares reconstruction, the proposed 5D reconstruction algorithm improved reconstruction and material decomposition accuracy by 3-18 times. Furthermore, by exploiting joint, low rank image structure between time points and energies, ~80 HU of contrast associated with the Kedge of gold and ~35 HU of contrast associated with the blood pool and myocardium were recovered from more than 400 HU of noise.

  14. Investigation of statistical iterative reconstruction for dedicated breast CT

    NASA Astrophysics Data System (ADS)

    Makeev, Andrey; Das, Mini; Glick, Stephen J.

    2012-03-01

    Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. In this study, statistical iterative reconstruction with a penalized likelihood objective function and a Huber prior are investigated for use with breast CT. This prior has two free parameters, the penalty weight and the edgepreservation threshold, that need to be evaluated to determine those values that give optimal performance. Computer simulations with breast-like phantoms were used to study these parameters using various figuresof- merit that relate to performance in detecting microcalcifications. Results suggested that a narrow range of Huber prior parameters give optimal performance. Furthermore, iterative reconstruction provided improved performance measures as compared to conventional filtered back-projection.

  15. Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery

    NASA Astrophysics Data System (ADS)

    Reiter, A.; Leonard, S.; Sinha, A.; Ishii, M.; Taylor, R. H.; Hager, G. D.

    2016-03-01

    In this work we present a method for dense reconstruction of anatomical structures using white light endoscopic imagery based on a learning process that estimates a mapping between light reflectance and surface geometry. Our method is unique in that few unrealistic assumptions are considered (i.e., we do not assume a Lambertian reflectance model nor do we assume a point light source) and we learn a model on a per-patient basis, thus increasing the accuracy and extensibility to different endoscopic sequences. The proposed method assumes accurate video-CT registration through a combination of Structure-from-Motion (SfM) and Trimmed-ICP, and then uses the registered 3D structure and motion to generate training data with which to learn a multivariate regression of observed pixel values to known 3D surface geometry. We demonstrate with a non-linear regression technique using a neural network towards estimating depth images and surface normal maps, resulting in high-resolution spatial 3D reconstructions to an average error of 0.53mm (on the low side, when anatomy matches the CT precisely) to 1.12mm (on the high side, when the presence of liquids causes scene geometry that is not present in the CT for evaluation). Our results are exhibited on patient data and validated with associated CT scans. In total, we processed 206 total endoscopic images from patient data, where each image yields approximately 1 million reconstructed 3D points per image.

  16. Adaptive phase-coded reconstruction for cardiac CT

    NASA Astrophysics Data System (ADS)

    Hsieh, Jiang; Mayo, John; Acharya, Kishor; Pan, Tin-Su

    2000-04-01

    Cardiac imaging with conventional computed tomography (CT) has gained significant attention in recent years. New hardware development enables a CT scanner to rotate at a faster speed so that less cardiac motion is present in acquired projection data. Many new tomographic reconstruction techniques have also been developed to reduce the artifacts induced by the cardiac motion. Most of the algorithms make use of the projection data collected over several cardiac cycles to formulate a single projection data set. Because the data set is formed with samples collected roughly in the same phase of a cardiac cycle, the temporal resolution of the newly formed data set is significantly improved compared with projections collected continuously. In this paper, we present an adaptive phase- coded reconstruction scheme (APR) for cardiac CT. Unlike the previously proposed schemes where the projection sector size is identical, APR determines each sector size based on the tomographic reconstruction algorithm. The newly proposed scheme ensures that the temporal resolution of each sector is substantially equal. In addition, the scan speed is selected based on the measured EKG signal of the patient.

  17. Filtered back-projection reconstruction for attenuation proton CT along most likely paths

    NASA Astrophysics Data System (ADS)

    Quiñones, C. T.; Létang, J. M.; Rit, S.

    2016-05-01

    This work investigates the attenuation of a proton beam to reconstruct the map of the linear attenuation coefficient of a material which is mainly caused by the inelastic interactions of protons with matter. Attenuation proton computed tomography (pCT) suffers from a poor spatial resolution due to multiple Coulomb scattering (MCS) of protons in matter, similarly to the conventional energy-loss pCT. We therefore adapted a recent filtered back-projection algorithm along the most likely path (MLP) of protons for energy-loss pCT (Rit et al 2013) to attenuation pCT assuming a pCT scanner that can track the position and the direction of protons before and after the scanned object. Monte Carlo simulations of pCT acquisitions of density and spatial resolution phantoms were performed to characterize the new algorithm using Geant4 (via Gate). Attenuation pCT assumes an energy-independent inelastic cross-section, and the impact of the energy dependence of the inelastic cross-section below 100 MeV showed a capping artifact when the residual energy was below 100 MeV behind the object. The statistical limitation has been determined analytically and it was found that the noise in attenuation pCT images is 411 times and 278 times higher than the noise in energy-loss pCT images for the same imaging dose at 200 MeV and 300 MeV, respectively. Comparison of the spatial resolution of attenuation pCT images with a conventional straight-line path binning showed that incorporating the MLP estimates during reconstruction improves the spatial resolution of attenuation pCT. Moreover, regardless of the significant noise in attenuation pCT images, the spatial resolution of attenuation pCT was better than that of conventional energy-loss pCT in some studied situations thanks to the interplay of MCS and attenuation known as the West-Sherwood effect.

  18. Computed Tomographic (CT) Reconstruction From Limited Projection Angles

    NASA Astrophysics Data System (ADS)

    Hanson, Kenneth M.

    1982-12-01

    When the available CT projection data are incomplete, there exists a null space in the space of possible reconstructions about which the data provide no information. Deterministic CT reconstructions are impotent in regard to this null space. Furthermore, it is shown that consistency conditions based on projection moments do not provide the missing projections. When the projection data consist of a set of parallel projections that do not encompass a complete 180° rotation, the null space corresponds to a missing sector in the Fourier transform of the original 2-D function. The long-range streak artifacts created by the missing sector can be reduced by attenuating the Fourier transform of the reconstruction smoothly to zero at the sector boundary. It is shown that the Fourier transform of a reconstruction obtained under a maximum entropy constraint is nearly zero in the missing sector. Hence, maximum entropy does not overcome the basic lack of information. It is suggested that some portion of the null space might be filled in by use of a priori knowledge of the type of image expected.

  19. Ultra Low Dose CT Pulmonary Angiography with Iterative Reconstruction

    PubMed Central

    Koehler, Thomas; Fingerle, Alexander A.; Brendel, Bernhard; Richter, Vivien; Rasper, Michael; Rummeny, Ernst J.; Noël, Peter B.; Münzel, Daniela

    2016-01-01

    Objective Evaluation of a new iterative reconstruction algorithm (IMR) for detection/rule-out of pulmonary embolism (PE) in ultra-low dose computed tomography pulmonary angiography (CTPA). Methods Lower dose CT data sets were simulated based on CTPA examinations of 16 patients with pulmonary embolism (PE) with dose levels (DL) of 50%, 25%, 12.5%, 6.3% or 3.1% of the original tube current setting. Original CT data sets and simulated low-dose data sets were reconstructed with three reconstruction algorithms: the standard reconstruction algorithm “filtered back projection” (FBP), the first generation iterative reconstruction algorithm iDose and the next generation iterative reconstruction algorithm “Iterative Model Reconstruction” (IMR). In total, 288 CTPA data sets (16 patients, 6 tube current levels, 3 different algorithms) were evaluated by two blinded radiologists regarding image quality, diagnostic confidence, detectability of PE and contrast-to-noise ratio (CNR). Results iDose and IMR showed better detectability of PE than FBP. With IMR, sensitivity for detection of PE was 100% down to a dose level of 12.5%. iDose and IMR showed superiority to FBP regarding all characteristics of subjective (diagnostic confidence in detection of PE, image quality, image noise, artefacts) and objective image quality. The minimum DL providing acceptable diagnostic performance was 12.5% (= 0.45 mSv) for IMR, 25% (= 0.89 mSv) for iDose and 100% (= 3.57 mSv) for FBP. CNR was significantly (p < 0.001) improved by IMR compared to FBP and iDose at all dose levels. Conclusion By using IMR for detection of PE, dose reduction for CTPA of up to 75% is possible while maintaining full diagnostic confidence. This would result in a mean effective dose of approximately 0.9 mSv for CTPA. PMID:27611830

  20. System matrix analysis for sparse-view iterative image reconstruction in X-ray CT.

    PubMed

    Wang, Linyuan; Zhang, Hanming; Cai, Ailong; Li, Yongl; Yan, Bin; Li, Lei; Hu, Guoen

    2015-01-01

    Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, used for investigations in compressive sensing (CS) claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is non-trivial, as both the singularity of undersampled reconstruction and the sufficient view number for sparse-view reconstruction are ill-defined. In this paper, the singular value decomposition method is used to study the condition number and singularity of the system matrix and the regularized matrix. An estimation method of the empirical lower bound is proposed, which is helpful for estimating the number of projection views required for exact reconstruction. Simulation studies show that the singularity of the system matrices for different projection views is effectively reduced by regularization. Computing the condition number of a regularized matrix is necessary to provide a reference for evaluating the singularity and recovery potential of reconstruction algorithms using regularization. The empirical lower bound is helpful for estimating the projections view number with a sparse reconstruction algorithm. PMID:25567402

  1. Nonlocal means-based regularizations for statistical CT reconstruction

    NASA Astrophysics Data System (ADS)

    Zhang, Hao; Ma, Jianhua; Liu, Yan; Han, Hao; Li, Lihong; Wang, Jing; Liang, Zhengrong

    2014-03-01

    Statistical iterative reconstruction (SIR) methods have shown remarkable gains over the conventional filtered backprojection (FBP) method in improving image quality for low-dose computed tomography (CT). They reconstruct the CT images by maximizing/minimizing a cost function in a statistical sense, where the cost function usually consists of two terms: the data-fidelity term modeling the statistics of measured data, and the regularization term reflecting a prior information. The regularization term in SIR plays a critical role for successful image reconstruction, and an established family of regularizations is based on the Markov random field (MRF) model. Inspired by the success of nonlocal means (NLM) algorithm in image processing applications, we proposed, in this work, a family of generic and edgepreserving NLM-based regularizations for SIR. We evaluated one of them where the potential function takes the quadratic-form. Experimental results with both digital and physical phantoms clearly demonstrated that SIR with the proposed regularization can achieve more significant gains than SIR with the widely-used Gaussian MRF regularization and the conventional FBP method, in terms of image noise reduction and resolution preservation.

  2. Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET.

    PubMed

    Rezaei, Ahmadreza; Michel, Christian; Casey, Michael E; Nuyts, Johan

    2016-02-21

    Previously, maximum-likelihood methods have been proposed to jointly estimate the activity image and the attenuation image or the attenuation sinogram from time-of-flight (TOF) positron emission tomography (PET) data. In this contribution, we propose a method that addresses the possible alignment problem of the TOF-PET emission data and the computed tomography (CT) attenuation data, by combining reconstruction and registration. The method, called MLRR, iteratively reconstructs the activity image while registering the available CT-based attenuation image, so that the pair of activity and attenuation images maximise the likelihood of the TOF emission sinogram. The algorithm is slow to converge, but some acceleration could be achieved by using Nesterov's momentum method and by applying a multi-resolution scheme for the non-rigid displacement estimation. The latter also helps to avoid local optima, although convergence to the global optimum cannot be guaranteed. The results are evaluated on 2D and 3D simulations as well as a respiratory gated clinical scan. Our experiments indicate that the proposed method is able to correct for possible misalignment of the CT-based attenuation image, and is therefore a very promising approach to suppressing attenuation artefacts in clinical PET/CT. When applied to respiratory gated data of a patient scan, it produced deformations that are compatible with breathing motion and which reduced the well known attenuation artefact near the dome of the liver. Since the method makes use of the energy-converted CT attenuation image, the scale problem of joint reconstruction is automatically solved. PMID:26854817

  3. 3D-guided CT reconstruction using time-of-flight camera

    NASA Astrophysics Data System (ADS)

    Ismail, Mahmoud; Taguchi, Katsuyuki; Xu, Jingyan; Tsui, Benjamin M. W.; Boctor, Emad M.

    2011-03-01

    We propose the use of a time-of-flight (TOF) camera to obtain the patient's body contour in 3D guided imaging reconstruction scheme in CT and C-arm imaging systems with truncated projection. In addition to pixel intensity, a TOF camera provides the 3D coordinates of each point in the captured scene with respect to the camera coordinates. Information from the TOF camera was used to obtain a digitized surface of the patient's body. The digitization points are transformed to X-Ray detector coordinates by registering the two coordinate systems. A set of points corresponding to the slice of interest are segmented to form a 2D contour of the body surface. Radon transform is applied to the contour to generate the 'trust region' for the projection data. The generated 'trust region' is integrated as an input to augment the projection data. It is used to estimate the truncated, unmeasured projections using linear interpolation. Finally the image is reconstructed using the combination of the estimated and the measured projection data. The proposed method is evaluated using a physical phantom. Projection data for the phantom were obtained using a C-arm system. Significant improvement in the reconstructed image quality near the truncation edges was observed using the proposed method as compared to that without truncation correction. This work shows that the proposed 3D guided CT image reconstruction using a TOF camera represents a feasible solution to the projection data truncation problem.

  4. Reconstruction of 4D-CT data sets acquired during free breathing for the analysis of respiratory motion

    NASA Astrophysics Data System (ADS)

    Ehrhardt, Jan; Werner, Rene; Frenzel, Thorsten; Säring, Dennis; Lu, Wei; Low, Daniel; Handels, Heinz

    2006-03-01

    Respiratory motion is a significant source of error in radiotherapy treatment planning. 4D-CT data sets can be useful to measure the impact of organ motion caused by breathing. But modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. For studying free breathing motion multislice CT scans can be collected simultaneously with digital spirometry over several breathing cycles. The 4D data set is assembled by sorting the free breathing multislice CT scans according to the couch position and the tidal volume. But artifacts can occur because there are no data segments for exactly the same tidal volume and all couch positions. We present an optical flow based method for the reconstruction of 4D-CT data sets from multislice CT scans, which are collected simultaneously with digital spirometry. The optical flow between the scans is estimated by a non-linear registration method. The calculated velocity field is used to reconstruct a 4D-CT data set by interpolating data at user-defined tidal volumes. By this technique, artifacts can be reduced significantly. The reconstructed 4D-CT data sets are used for studying inner organ motion during the respiratory cycle. The procedures described were applied to reconstruct 4D-CT data sets for four tumour patients who have been scanned during free breathing. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.

  5. An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging.

    PubMed

    Van Eyndhoven, Geert; Batenburg, K Joost; Kazantsev, Daniil; Van Nieuwenhove, Vincent; Lee, Peter D; Dobson, Katherine J; Sijbers, Jan

    2015-11-01

    The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.

  6. On proton CT reconstruction using MVCT-converted virtual proton projections

    SciTech Connect

    Wang Dongxu; Mackie, T. Rockwell; Tome, Wolfgang A.

    2012-06-15

    . If these images are used for treatment planning, the average proton range uncertainty is estimated to be less than 1.5% for an imaging dose in the milligray range. Conclusions: The proposed method can be used to convert x-ray projections into virtual proton projections. The converted proton projections can be blended with existing proton projections or can be used solely for pCT reconstruction, addressing the range limit problem of pCT using current therapeutic proton machines.

  7. Investigation of statistical iterative reconstruction for dedicated breast CT

    SciTech Connect

    Makeev, Andrey; Glick, Stephen J.

    2013-08-15

    Purpose: Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue.Methods: Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters.Results: The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved

  8. Investigation of a one-step spectral CT reconstruction algorithm for direct inversion into basis material images

    NASA Astrophysics Data System (ADS)

    Gilat Schmidt, Taly; Sidky, Emil Y.

    2015-03-01

    Photon-counting detectors with pulse-height analysis have shown promise for improved spectral CT imaging. This study investigated a novel spectral CT reconstruction method that directly estimates basis-material images from the measured energy-bin data (i.e., `one-step' reconstruction). The proposed algorithm can incorporate constraints to stabilize the reconstruction and potentially reduce noise. The algorithm minimizes the error between the measured energy-bin data and the data estimated from the reconstructed basis images. A total variation (TV) constraint was also investigated for additional noise reduction. The proposed one-step algorithm was applied to simulated data of an anthropomorphic phantom with heterogeneous tissue composition. Reconstructed water, bone, and gadolinium basis images were compared for the proposed one-step algorithm and the conventional `two-step' method of decomposition followed by reconstruction. The unconstrained algorithm provided a 30% to 60% reduction in noise standard deviation compared to the two-step algorithm. The fTV =0.8 constraint provided a small reduction in noise (˜ 1%) compared to the unconstrained reconstruction. Images reconstructed with the fTV =0.5 constraint demonstrated 77% to 94% standard deviation reduction compared to the two-step reconstruction, however with increased blurring. There were no significant differences in the mean values reconstructed by the investigated algorithms. Overall, the proposed one-step spectral CT reconstruction algorithm provided three-material-decomposition basis images with reduced noise compared to the conventional two-step approach. When using a moderate TV constraint factor (fTV = 0.8), a 30%-60% reduction in noise standard deviation was achieved while preserving the edge profile for this simulated phantom.

  9. CT reconstruction techniques for improved accuracy of lung CT airway measurement

    SciTech Connect

    Rodriguez, A.; Ranallo, F. N.; Judy, P. F.; Gierada, D. S.; Fain, S. B.

    2014-11-01

    Purpose: To determine the impact of constrained reconstruction techniques on quantitative CT (qCT) of the lung parenchyma and airways for low x-ray radiation dose. Methods: Measurement of small airways with qCT remains a challenge, especially for low x-ray dose protocols. Images of the COPDGene quality assurance phantom (CTP698, The Phantom Laboratory, Salem, NY) were obtained using a GE discovery CT750 HD scanner for helical scans at x-ray radiation dose-equivalents ranging from 1 to 4.12 mSv (12–100 mA s current–time product). Other parameters were 40 mm collimation, 0.984 pitch, 0.5 s rotation, and 0.625 mm thickness. The phantom was sandwiched between 7.5 cm thick water attenuating phantoms for a total length of 20 cm to better simulate the scatter conditions of patient scans. Image data sets were reconstructed using STANDARD (STD), DETAIL, BONE, and EDGE algorithms for filtered back projection (FBP), 100% adaptive statistical iterative reconstruction (ASIR), and Veo reconstructions. Reduced (half) display field of view (DFOV) was used to increase sampling across airway phantom structures. Inner diameter (ID), wall area percent (WA%), and wall thickness (WT) measurements of eight airway mimicking tubes in the phantom, including a 2.5 mm ID (42.6 WA%, 0.4 mm WT), 3 mm ID (49.0 WA%, 0.6 mm WT), and 6 mm ID (49.0 WA%, 1.2 mm WT) were performed with Airway Inspector (Surgical Planning Laboratory, Brigham and Women’s Hospital, Boston, MA) using the phase congruency edge detection method. The average of individual measures at five central slices of the phantom was taken to reduce measurement error. Results: WA% measures were greatly overestimated while IDs were underestimated for the smaller airways, especially for reconstructions at full DFOV (36 cm) using the STD kernel, due to poor sampling and spatial resolution (0.7 mm pixel size). Despite low radiation dose, the ID of the 6 mm ID airway was consistently measured accurately for all methods other than STD

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

    NASA Astrophysics Data System (ADS)

    Evseev, Ivan; Ahmann, Francielle; da Silva, Hamilton P.; Schelin, Hugo R.; Yevseyeva, Olga; Klock, Márgio C. L.

    2013-05-01

    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 with alternative virtual phantoms. As in the original report, the 3D CT images of 128×128×128 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.

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

    SciTech Connect

    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 with 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.

  12. Fourier-based reconstruction via alternating direction total variation minimization in linear scan CT

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    In this study, we consider a novel form of computed tomography (CT), that is, linear scan CT (LCT), which applies a straight line trajectory. Furthermore, an iterative algorithm is proposed for pseudo-polar Fourier reconstruction through total variation minimization (PPF-TVM). Considering that the sampled Fourier data are distributed in pseudo-polar coordinates, the reconstruction model minimizes the TV of the image subject to the constraint that the estimated 2D Fourier data for the image are consistent with the 1D Fourier transform of the projection data. PPF-TVM employs the alternating direction method (ADM) to develop a robust and efficient iteration scheme, which ensures stable convergence provided that appropriate parameter values are given. In the ADM scheme, PPF-TVM applies the pseudo-polar fast Fourier transform and its adjoint to iterate back and forth between the image and frequency domains. Thus, there is no interpolation in the Fourier domain, which makes the algorithm both fast and accurate. PPF-TVM is particularly useful for limited angle reconstruction in LCT and it appears to be robust against artifacts. The PPF-TVM algorithm was tested with the FORBILD head phantom and real data in comparisons with state-of-the-art algorithms. Simulation studies and real data verification suggest that PPF-TVM can reconstruct higher accuracy images with lower time consumption.

  13. Bayesian reconstruction strategy of fluorescence-mediated tomography using an integrated SPECT-CT-OT system

    NASA Astrophysics Data System (ADS)

    Cao, Liji; Peter, Jörg

    2010-05-01

    Following the assembly of a triple-modality SPECT-CT-OT small animal imaging system providing intrinsically co-registered projection data of all three submodalities and under the assumption and investigation of dual-labeled probes consisting of both fluorophores and radionuclides, a novel multi-modal reconstruction strategy is presented in this paper aimed at improving fluorescence-mediated tomography (FMT). The following reconstruction procedure is proposed: firstly, standard x-ray CT image reconstruction is performed employing the FDK algorithm. Secondly, standard SPECT image reconstruction is performed using OSEM. Thirdly, from the reconstructed CT volume data the surface boundary of the imaged object is extracted for finite element definition. Finally, the reconstructed SPECT data are used as a priori information within a Bayesian reconstruction framework for optical (FMT) reconstruction. We provide results of this multi-modal approach using phantom experimental data and illustrate that this strategy does suppress artifacts and facilitates quantitative analysis for optical imaging studies.

  14. 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.

  15. Registration based super-resolution reconstruction for lung 4D-CT.

    PubMed

    Wu, Xiuxiu; Xiao, Shan; Zhang, Yu

    2014-01-01

    Lung 4D-CT plays an important role in lung cancer radiotherapy for tumor localization and treatment planning. In lung 4D-CT data, the resolution in the slice direction is often much lower than the in-plane resolution. For multi-plane display, isotropic resolution is necessary, but the commonly used interpolation operation will blur the images. In this paper, we present a registration based method for super resolution enhancement of the 4D-CT multi-plane images. Our working premise is that the low-resolution images of different phases at the corresponding position can be regarded as input "frames" to reconstruct high resolution images. First, we employ the Demons registration algorithm to estimate the motion field between different "frames". Then, the projections onto convex sets (POCS) approach is employed to reconstruction high-resolution lung images. We show that our method can get clearer lung images and enhance image structure, compared with the cubic spline interpolation and back projection method. PMID:25570484

  16. Registration based super-resolution reconstruction for lung 4D-CT.

    PubMed

    Wu, Xiuxiu; Xiao, Shan; Zhang, Yu

    2014-01-01

    Lung 4D-CT plays an important role in lung cancer radiotherapy for tumor localization and treatment planning. In lung 4D-CT data, the resolution in the slice direction is often much lower than the in-plane resolution. For multi-plane display, isotropic resolution is necessary, but the commonly used interpolation operation will blur the images. In this paper, we present a registration based method for super resolution enhancement of the 4D-CT multi-plane images. Our working premise is that the low-resolution images of different phases at the corresponding position can be regarded as input "frames" to reconstruct high resolution images. First, we employ the Demons registration algorithm to estimate the motion field between different "frames". Then, the projections onto convex sets (POCS) approach is employed to reconstruction high-resolution lung images. We show that our method can get clearer lung images and enhance image structure, compared with the cubic spline interpolation and back projection method.

  17. A feature refinement approach for statistical interior CT reconstruction.

    PubMed

    Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong

    2016-07-21

    Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements. PMID:27362527

  18. A feature refinement approach for statistical interior CT reconstruction.

    PubMed

    Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong

    2016-07-21

    Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

  19. A feature refinement approach for statistical interior CT reconstruction

    NASA Astrophysics Data System (ADS)

    Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong

    2016-07-01

    Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)—minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

  20. Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

    SciTech Connect

    Eck, Brendan L.; Fahmi, Rachid; Miao, Jun; Brown, Kevin M.; Zabic, Stanislav; Raihani, Nilgoun; Wilson, David L.

    2015-10-15

    Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit

  1. Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

    PubMed Central

    Eck, Brendan L.; Fahmi, Rachid; Brown, Kevin M.; Zabic, Stanislav; Raihani, Nilgoun; Miao, Jun; Wilson, David L.

    2015-01-01

    Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, PC. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and

  2. [3D Super-resolution Reconstruction and Visualization of Pulmonary Nodules from CT Image].

    PubMed

    Wang, Bing; Fan, Xing; Yang, Ying; Tian, Xuedong; Gu, Lixu

    2015-08-01

    The aim of this study was to propose an algorithm for three-dimensional projection onto convex sets (3D POCS) to achieve super resolution reconstruction of 3D lung computer tomography (CT) images, and to introduce multi-resolution mixed display mode to make 3D visualization of pulmonary nodules. Firstly, we built the low resolution 3D images which have spatial displacement in sub pixel level between each other and generate the reference image. Then, we mapped the low resolution images into the high resolution reference image using 3D motion estimation and revised the reference image based on the consistency constraint convex sets to reconstruct the 3D high resolution images iteratively. Finally, we displayed the different resolution images simultaneously. We then estimated the performance of provided method on 5 image sets and compared them with those of 3 interpolation reconstruction methods. The experiments showed that the performance of 3D POCS algorithm was better than that of 3 interpolation reconstruction methods in two aspects, i.e., subjective and objective aspects, and mixed display mode is suitable to the 3D visualization of high resolution of pulmonary nodules.

  3. Optimization of SPECT-CT Hybrid Imaging Using Iterative Image Reconstruction for Low-Dose CT: A Phantom Study

    PubMed Central

    Grosser, Oliver S.; Kupitz, Dennis; Ruf, Juri; Czuczwara, Damian; Steffen, Ingo G.; Furth, Christian; Thormann, Markus; Loewenthal, David; Ricke, Jens; Amthauer, Holger

    2015-01-01

    Background Hybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) with computed tomography (CT). Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR) on the image quality of the low-dose CT images. Methodology/Principal Findings Examinations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU) values reconstructed by ASIR were correlated to the FBP HU values(R2 ≥ 0.88) and the contrast-to-noise ratio (CNR) was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04). In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001). Conclusion/Significance In general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality. PMID:26390216

  4. Automatic estimation of detector radial position for contoured SPECT acquisition using CT images on a SPECT/CT system.

    PubMed

    Liu, Ruijie Rachel; Erwin, William D

    2006-08-01

    An algorithm was developed to estimate noncircular orbit (NCO) single-photon emission computed tomography (SPECT) detector radius on a SPECT/CT imaging system using the CT images, for incorporation into collimator resolution modeling for iterative SPECT reconstruction. Simulated male abdominal (arms up), male head and neck (arms down) and female chest (arms down) anthropomorphic phantom, and ten patient, medium-energy SPECT/CT scans were acquired on a hybrid imaging system. The algorithm simulated inward SPECT detector radial motion and object contour detection at each projection angle, employing the calculated average CT image and a fixed Hounsfield unit (HU) threshold. Calculated radii were compared to the observed true radii, and optimal CT threshold values, corresponding to patient bed and clothing surfaces, were found to be between -970 and -950 HU. The algorithm was constrained by the 45 cm CT field-of-view (FOV), which limited the detected radii to < or = 22.5 cm and led to occasional radius underestimation in the case of object truncation by CT. Two methods incorporating the algorithm were implemented: physical model (PM) and best fit (BF). The PM method computed an offset that produced maximum overlap of calculated and true radii for the phantom scans, and applied that offset as a calculated-to-true radius transformation. For the BF method, the calculated-to-true radius transformation was based upon a linear regression between calculated and true radii. For the PM method, a fixed offset of +2.75 cm provided maximum calculated-to-true radius overlap for the phantom study, which accounted for the camera system's object contour detect sensor surface-to-detector face distance. For the BF method, a linear regression of true versus calculated radius from a reference patient scan was used as a calculated-to-true radius transform. Both methods were applied to ten patient scans. For -970 and -950 HU thresholds, the combined overall average root-mean-square (rms

  5. Effect of tube current modulation for dose estimation using a simulation tool on body CT examination.

    PubMed

    Kawaguchi, Ai; Matsunaga, Yuta; Kobayashi, Masanao; Suzuki, Shoichi; Matsubara, Kosuke; Chida, Koichi

    2015-12-01

    The purpose of this study was to evaluate the effect of tube current modulation for dose estimation of a body computed tomography (CT) examination using a simulation tool. The authors also compared longitudinal variations in tube current values between iterative reconstruction (IR) and filtered back-projection (FBP) reconstruction algorithms. One hundred patients underwent body CT examinations. The tube current values around 10 organ regions were recorded longitudinally from tube current information. The organ and effective doses were simulated by average tube current values and longitudinal modulated tube current values. The organ doses for the bladder and breast estimated by longitudinal modulated tube current values were 20 % higher and 25 % lower than those estimated using the average tube current values, respectively. The differences in effective doses were small (mean, 0.7 mSv). The longitudinal variations in tube current values were almost the same for the IR and FBP algorithms.

  6. A limited-angle CT reconstruction method based on anisotropic TV minimization

    NASA Astrophysics Data System (ADS)

    Chen, Zhiqiang; Jin, Xin; Li, Liang; Wang, Ge

    2013-04-01

    This paper presents a compressed sensing (CS)-inspired reconstruction method for limited-angle computed tomography (CT). Currently, CS-inspired CT reconstructions are often performed by minimizing the total variation (TV) of a CT image subject to data consistency. A key to obtaining high image quality is to optimize the balance between TV-based smoothing and data fidelity. In the case of the limited-angle CT problem, the strength of data consistency is angularly varying. For example, given a parallel beam of x-rays, information extracted in the Fourier domain is mostly orthogonal to the direction of x-rays, while little is probed otherwise. However, the TV minimization process is isotropic, suggesting that it is unfit for limited-angle CT. Here we introduce an anisotropic TV minimization method to address this challenge. The advantage of our approach is demonstrated in numerical simulation with both phantom and real CT images, relative to the TV-based reconstruction.

  7. Spectral X-Ray CT Image Reconstruction with a Combination of Energy-Integrating and Photon-Counting Detectors

    PubMed Central

    Yang, Qingsong; Cong, Wenxiang; Xi, Yan; Wang, Ge

    2016-01-01

    The purpose of this paper is to develop an algorithm for hybrid spectral computed tomography (CT) which combines energy-integrating and photon-counting detectors. While the energy-integrating scan is global, the photon-counting scan can have a local field of view (FOV). The algorithm synthesizes both spectral data and energy-integrating data. Low rank and sparsity prior is used for spectral CT reconstruction. An initial estimation is obtained from the projection data based on physical principles of x-ray interaction with the matter, which provides a more accurate Taylor expansion than previous work and can guarantee the convergence of the algorithm. Numerical simulation with clinical CT images are performed. The proposed algorithm produces very good spectral features outside the FOV when no K-edge material exists. Exterior reconstruction of K-edge material can be partially achieved. PMID:27171153

  8. WE-G-18A-03: Cone Artifacts Correction in Iterative Cone Beam CT Reconstruction

    SciTech Connect

    Yan, H; Folkerts, M; Jiang, S; Jia, X; Wang, X; Bai, T; Lu, W

    2014-06-15

    Purpose: For iterative reconstruction (IR) in cone-beam CT (CBCT) imaging, data truncation along the superior-inferior (SI) direction causes severe cone artifacts in the reconstructed CBCT volume images. Not only does it reduce the effective SI coverage of the reconstructed volume, it also hinders the IR algorithm convergence. This is particular a problem for regularization based IR, where smoothing type regularization operations tend to propagate the artifacts to a large area. It is our purpose to develop a practical cone artifacts correction solution. Methods: We found it is the missing data residing in the truncated cone area that leads to inconsistency between the calculated forward projections and measured projections. We overcome this problem by using FDK type reconstruction to estimate the missing data and design weighting factors to compensate the inconsistency caused by the missing data. We validate the proposed methods in our multi-GPU low-dose CBCT reconstruction system on multiple patients' datasets. Results: Compared to the FDK reconstruction with full datasets, while IR is able to reconstruct CBCT images using a subset of projection data, the severe cone artifacts degrade overall image quality. For head-neck case under a full-fan mode, 13 out of 80 slices are contaminated. It is even more severe in pelvis case under half-fan mode, where 36 out of 80 slices are affected, leading to inferior soft-tissue delineation. By applying the proposed method, the cone artifacts are effectively corrected, with a mean intensity difference decreased from ∼497 HU to ∼39HU for those contaminated slices. Conclusion: A practical and effective solution for cone artifacts correction is proposed and validated in CBCT IR algorithm. This study is supported in part by NIH (1R01CA154747-01)

  9. SU-E-I-73: Clinical Evaluation of CT Image Reconstructed Using Interior Tomography

    SciTech Connect

    Zhang, J; Ge, G; Winkler, M; Cong, W; Wang, G

    2014-06-01

    Purpose: Radiation dose reduction has been a long standing challenge in CT imaging of obese patients. Recent advances in interior tomography (reconstruction of an interior region of interest (ROI) from line integrals associated with only paths through the ROI) promise to achieve significant radiation dose reduction without compromising image quality. This study is to investigate the application of this technique in CT imaging through evaluating imaging quality reconstructed from patient data. Methods: Projection data were directly obtained from patients who had CT examinations in a Dual Source CT scanner (DSCT). Two detectors in a DSCT acquired projection data simultaneously. One detector provided projection data for full field of view (FOV, 50 cm) while another detectors provided truncated projection data for a FOV of 26 cm. Full FOV CT images were reconstructed using both filtered back projection and iterative algorithm; while interior tomography algorithm was implemented to reconstruct ROI images. For comparison reason, FBP was also used to reconstruct ROI images. Reconstructed CT images were evaluated by radiologists and compared with images from CT scanner. Results: The results show that the reconstructed ROI image was in excellent agreement with the truth inside the ROI, obtained from images from CT scanner, and the detailed features in the ROI were quantitatively accurate. Radiologists evaluation shows that CT images reconstructed with interior tomography met diagnosis requirements. Radiation dose may be reduced up to 50% using interior tomography, depending on patient size. Conclusion: This study shows that interior tomography can be readily employed in CT imaging for radiation dose reduction. It may be especially useful in imaging obese patients, whose subcutaneous tissue is less clinically relevant but may significantly increase radiation dose.

  10. 4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling.

    PubMed

    Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing

    2016-02-01

    A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

  11. 4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

    NASA Astrophysics Data System (ADS)

    Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing

    2016-02-01

    A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

  12. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

    SciTech Connect

    Solomon, Justin; Samei, Ehsan

    2014-09-15

    Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was

  13. Iterative ct reconstruction from few projections for the nondestructive post irradiation examination of nuclear fuel assemblies

    NASA Astrophysics Data System (ADS)

    Abir, Muhammad Imran Khan

    The core components (e.g. fuel assemblies, spacer grids, control rods) of the nuclear reactors encounter harsh environment due to high temperature, physical stress, and a tremendous level of radiation. The integrity of these elements is crucial for safe operation of the nuclear power plants. The Post Irradiation Examination (PIE) can reveal information about the integrity of the elements during normal operations and off?normal events. Computed tomography (CT) is a tool for evaluating the structural integrity of elements non-destructively. CT requires many projections to be acquired from different view angles after which a mathematical algorithm is adopted for reconstruction. Obtaining many projections is laborious and expensive in nuclear industries. Reconstructions from a small number of projections are explored to achieve faster and cost-efficient PIE. Classical reconstruction algorithms (e.g. filtered back projection) cannot offer stable reconstructions from few projections and create severe streaking artifacts. In this thesis, conventional algorithms are reviewed, and new algorithms are developed for reconstructions of the nuclear fuel assemblies using few projections. CT reconstruction from few projections falls into two categories: the sparse-view CT and the limited-angle CT or tomosynthesis. Iterative reconstruction algorithms are developed for both cases in the field of compressed sensing (CS). The performance of the algorithms is assessed using simulated projections and validated through real projections. The thesis also describes the systematic strategy towards establishing the conditions of reconstructions and finds the optimal imaging parameters for reconstructions of the fuel assemblies from few projections.

  14. Texture-preserving Bayesian image reconstruction for low-dose CT

    NASA Astrophysics Data System (ADS)

    Zhang, Hao; Han, Hao; Hu, Yifan; Liu, Yan; Ma, Jianhua; Li, Lihong; Moore, William; Liang, Zhengrong

    2016-03-01

    Markov random field (MRF) model has been widely used in Bayesian image reconstruction to reconstruct piecewise smooth images in the presence of noise, such as in low-dose X-ray computed tomography (LdCT). While it can preserve edge sharpness via edge-preserving potential function, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it compromises clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodule or colon polyp. This study aims to shift the edge preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of lung, bone, fat, muscle, etc. from previous full-dose CT scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of proposed reconstruction framework, experiments using clinical patient scans (with lung nodule or colon polyp) were conducted. The experimental outcomes showed noticeable gain by the a priori knowledge for LdCT image reconstruction with the well-known Haralick texture measures. Thus, it is conjectured that texture-preserving LdCT reconstruction has advantages over edge-preserving regional smoothing paradigm for texture-specific clinical applications.

  15. Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction

    SciTech Connect

    Brady, Samuel L.; Shulkin, Barry L.

    2015-02-15

    Purpose: To develop ultralow dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT). Methods: A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultralow doses (10–35 mA s). CT quantitation: noise, low-contrast resolution, and CT numbers for 11 tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% volume computed tomography dose index (0.39/3.64; mGy) from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET images were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUV{sub bw}) of various diameter targets (range 8–37 mm), background uniformity, and spatial resolution. Radiation dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative dose reduction and noise control. Results: CT numbers were constant to within 10% from the nondose reduced CTAC image for 90% dose reduction. No change in SUV{sub bw}, background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols was found down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62% and 86% (3.2/8.3–0.9/6.2). Noise magnitude in dose-reduced patient images increased but was not statistically different from predose-reduced patient images. Conclusions: Using ASiR allowed for aggressive reduction in CT dose with no change in PET reconstructed images while maintaining sufficient image quality for colocalization of hybrid CT anatomy and PET radioisotope uptake.

  16. WE-G-18A-05: Cone-Beam CT Reconstruction with Deformed Prior Image

    SciTech Connect

    Zhang, H; Huang, J; Ma, J; Chen, W; Ouyang, L; Wang, J

    2014-06-15

    Purpose: Prior image can be incorporated into image reconstruction process to improve the quality of on-treatment cone-beam CT (CBCT) from sparseview or low-dose projections. However, the deformation between the prior image and on-treatment CBCT are not considered in current prior image based reconstructions (e.g., prior image constrained compressed sensing (PICCS)). The purpose of this work is to develop a deformed-prior-imagebased- reconstruction strategy (DPIR) to address the mismatch problem between the prior image and target image. Methods: The deformed prior image is obtained by a projection based registration approach. Specifically, the deformation vector fields (DVF) used to deform the prior image is estimated through matching the forward projection of the prior image and the measured on-treatment projection. The deformed prior image is then used as the prior image in the standard PICCS algorithm. Simulation studies on the XCAT phantom was conducted to evaluate the performance of the projection based registration procedure and the proposed DPIR strategy. Results: The deformed prior image matches the geometry of on-treatment CBCT closer as compared to the original prior image. Using the deformed prior image, the quality of the image reconstructed by DPIR from few-view projection data is greatly improved as compared to the standard PICCS algorithm. The relative image reconstruction error is reduced to 11.13% in the proposed DPIR from 17.57% in the original PICCS. Conclusion: The proposed DPIR approach can solve the mismatch problem between the prior image and target image, which overcomes the limitation of the original PICCS algorithm for CBCT reconstruction from sparse-view or low-dose projections.

  17. A novel reconstruction algorithm to extend the CT scan field-of-view.

    PubMed

    Hsieh, J; Chao, E; Thibault, J; Grekowicz, B; Horst, A; McOlash, S; Myers, T J

    2004-09-01

    For various reasons, a projection dataset acquired on a computed tomography (CT) scanner can be truncated. That is, a portion of the scanned object is positioned outside the scan field-of-view (SFOV) and the line integrals corresponding to those regions are not measured. A projection truncation problem causes imaging artifacts that lead to suboptimal image quality. In this paper, we propose a reconstruction algorithm that enables an adequate estimation of the projection outside the SFOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over views. We use the magnitudes and slopes of the projection samples at the location of truncation to estimate water cylinders that can best fit to the projection data outside the SFOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm.

  18. Sparse signal reconstruction from polychromatic X-ray CT measurements via mass attenuation discretization

    SciTech Connect

    Gu, Renliang; Dogandžić, Aleksandar

    2014-02-18

    We propose a method for reconstructing sparse images from polychromatic x-ray computed tomography (ct) measurements via mass attenuation coefficient discretization. The material of the inspected object and the incident spectrum are assumed to be unknown. We rewrite the Lambert-Beer’s law in terms of integral expressions of mass attenuation and discretize the resulting integrals. We then present a penalized constrained least-squares optimization approach for reconstructing the underlying object from log-domain measurements, where an active set approach is employed to estimate incident energy density parameters and the nonnegativity and sparsity of the image density map are imposed using negative-energy and smooth ℓ{sub 1}-norm penalty terms. We propose a two-step scheme for refining the mass attenuation discretization grid by using higher sampling rate over the range with higher photon energy, and eliminating the discretization points that have little effect on accuracy of the forward projection model. This refinement allows us to successfully handle the characteristic lines (Dirac impulses) in the incident energy density spectrum. We compare the proposed method with the standard filtered backprojection, which ignores the polychromatic nature of the measurements and sparsity of the image density map. Numerical simulations using both realistic simulated and real x-ray ct data are presented.

  19. Sparse signal reconstruction from polychromatic X-ray CT measurements via mass attenuation discretization

    NASA Astrophysics Data System (ADS)

    Gu, Renliang; Dogandžić, Aleksandar

    2014-02-01

    We propose a method for reconstructing sparse images from polychromatic x-ray computed tomography (ct) measurements via mass attenuation coefficient discretization. The material of the inspected object and the incident spectrum are assumed to be unknown. We rewrite the Lambert-Beer's law in terms of integral expressions of mass attenuation and discretize the resulting integrals. We then present a penalized constrained least-squares optimization approach for reconstructing the underlying object from log-domain measurements, where an active set approach is employed to estimate incident energy density parameters and the nonnegativity and sparsity of the image density map are imposed using negative-energy and smooth ℓ1-norm penalty terms. We propose a two-step scheme for refining the mass attenuation discretization grid by using higher sampling rate over the range with higher photon energy, and eliminating the discretization points that have little effect on accuracy of the forward projection model. This refinement allows us to successfully handle the characteristic lines (Dirac impulses) in the incident energy density spectrum. We compare the proposed method with the standard filtered backprojection, which ignores the polychromatic nature of the measurements and sparsity of the image density map. Numerical simulations using both realistic simulated and real x-ray ct data are presented.

  20. Optimization of Proton CT Detector System and Image Reconstruction Algorithm for On-Line Proton Therapy.

    PubMed

    Lee, Chae Young; Song, Hankyeol; Park, Chan Woo; Chung, Yong Hyun; Kim, Jin Sung; Park, Justin C

    2016-01-01

    The purposes of this study were to optimize a proton computed tomography system (pCT) for proton range verification and to confirm the pCT image reconstruction algorithm based on projection images generated with optimized parameters. For this purpose, we developed a new pCT scanner using the Geometry and Tracking (GEANT) 4.9.6 simulation toolkit. GEANT4 simulations were performed to optimize the geometric parameters representing the detector thickness and the distance between the detectors for pCT. The system consisted of four silicon strip detectors for particle tracking and a calorimeter to measure the residual energies of the individual protons. The optimized pCT system design was then adjusted to ensure that the solution to a CS-based convex optimization problem would converge to yield the desired pCT images after a reasonable number of iterative corrections. In particular, we used a total variation-based formulation that has been useful in exploiting prior knowledge about the minimal variations of proton attenuation characteristics in the human body. Examinations performed using our CS algorithm showed that high-quality pCT images could be reconstructed using sets of 72 projections within 20 iterations and without any streaks or noise, which can be caused by under-sampling and proton starvation. Moreover, the images yielded by this CS algorithm were found to be of higher quality than those obtained using other reconstruction algorithms. The optimized pCT scanner system demonstrated the potential to perform high-quality pCT during on-line image-guided proton therapy, without increasing the imaging dose, by applying our CS based proton CT reconstruction algorithm. Further, we make our optimized detector system and CS-based proton CT reconstruction algorithm potentially useful in on-line proton therapy.

  1. Optimization of Proton CT Detector System and Image Reconstruction Algorithm for On-Line Proton Therapy.

    PubMed

    Lee, Chae Young; Song, Hankyeol; Park, Chan Woo; Chung, Yong Hyun; Kim, Jin Sung; Park, Justin C

    2016-01-01

    The purposes of this study were to optimize a proton computed tomography system (pCT) for proton range verification and to confirm the pCT image reconstruction algorithm based on projection images generated with optimized parameters. For this purpose, we developed a new pCT scanner using the Geometry and Tracking (GEANT) 4.9.6 simulation toolkit. GEANT4 simulations were performed to optimize the geometric parameters representing the detector thickness and the distance between the detectors for pCT. The system consisted of four silicon strip detectors for particle tracking and a calorimeter to measure the residual energies of the individual protons. The optimized pCT system design was then adjusted to ensure that the solution to a CS-based convex optimization problem would converge to yield the desired pCT images after a reasonable number of iterative corrections. In particular, we used a total variation-based formulation that has been useful in exploiting prior knowledge about the minimal variations of proton attenuation characteristics in the human body. Examinations performed using our CS algorithm showed that high-quality pCT images could be reconstructed using sets of 72 projections within 20 iterations and without any streaks or noise, which can be caused by under-sampling and proton starvation. Moreover, the images yielded by this CS algorithm were found to be of higher quality than those obtained using other reconstruction algorithms. The optimized pCT scanner system demonstrated the potential to perform high-quality pCT during on-line image-guided proton therapy, without increasing the imaging dose, by applying our CS based proton CT reconstruction algorithm. Further, we make our optimized detector system and CS-based proton CT reconstruction algorithm potentially useful in on-line proton therapy. PMID:27243822

  2. Clinically feasible reconstruction of 3D whole-body PET/CT data using blurred anatomical labels

    NASA Astrophysics Data System (ADS)

    Comtat, Claude; Kinahan, Paul E.; Fessler, Jeffrey A.; Beyer, Thomas; Townsend, David W.; Defrise, Michel; Michel, Christian

    2002-01-01

    We present the results of utilizing aligned anatomical information from CT images to locally adjust image smoothness during the reconstruction of three-dimensional (3D) whole-body positron emission tomography (PET) data. The ability of whole-body PET imaging to detect malignant neoplasms is becoming widely recognized. Potentially useful, however, is the role of whole-body PET in quantitative estimation of tracer uptake. The utility of PET in oncology is often limited by the high level of statistical noise in the images. Reduction in noise can be obtained by incorporating a priori image smoothness information from correlated anatomical information during the reconstruction of PET data. A combined PET/CT scanner allows the acquisition of accurately aligned PET and x-ray CT whole-body data. We use the Fourier rebinning algorithm (FORE) to accurately convert the 3D PET data to two-dimensional (2D) data to accelerate the image reconstruction process. The 2D datasets are reconstructed with successive over-relaxation of a penalized weighted least squares (PWLS) objective function to model the statistics of the acquisition, data corrections, and rebinning. A 3D voxel label model is presented that incorporates the anatomical information via the penalty weights of the PWLS objective function. This combination of FORE + PWLS + labels was developed as it allows for both reconstruction of 3D whole-body data sets in clinically feasible times and also the inclusion of anatomical information in such a way that convergence can be guaranteed. Since mismatches between anatomical (CT) and functional (PET) data are unavoidable in practice, the labels are 'blurred' to reflect the uncertainty associated with the anatomical information. Simulated and experimental results show the potential advantage of incorporating anatomical information by using blurred labels to calculate the penalty weights. We conclude that while the effect of this method on detection tasks is complicated and unclear

  3. Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

    SciTech Connect

    Dong, Xue; Niu, Tianye; Zhu, Lei

    2014-05-15

    order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.

  4. MicroCT: Automated Analysis of CT Reconstructed Data of Home Made Explosive Materials Using the Matlab MicroCT Analysis GUI

    SciTech Connect

    Seetho, I M; Brown, W D; Kallman, J S; Martz, H E; White, W T

    2011-09-22

    This Standard Operating Procedure (SOP) provides the specific procedural steps for analyzing reconstructed CT images obtained under the IDD Standard Operating Procedures for data acquisition [1] and MicroCT image reconstruction [2], per the IDD Quality Assurance Plan for MicroCT Scanning [3]. Although intended to apply primarily to MicroCT data acquired in the HEAFCAT Facility at LLNL, these procedures may also be applied to data acquired at Tyndall from the YXLON cabinet and at TSL from the HEXCAT system. This SOP also provides the procedural steps for preparing the tables and graphs to be used in the reporting of analytical results. This SOP applies to production work - for R and D there are two other semi-automated methods as given in [4, 5].

  5. MicroCT: Semi-Automated Analysis of CT Reconstructed Data of Home Made Explosive Materials Using the Matlab MicroCT Analysis GUI

    SciTech Connect

    Seetho, I M; Brown, W D; Kallman, J S; Martz, H E; White, W T

    2011-09-22

    This Standard Operating Procedure (SOP) provides the specific procedural steps for analyzing reconstructed CT images obtained under the IDD Standard Operating Procedures for data acquisition [1] and MicroCT image reconstruction [2], per the IDD Quality Assurance Plan for MicroCT Scanning [3]. Although intended to apply primarily to MicroCT data acquired in the HEAFCAT Facility at LLNL, these procedures may also be applied to data acquired at Tyndall from the YXLON cabinet and at TSL from the HEXCAT system. This SOP also provides the procedural steps for preparing the tables and graphs to be used in the reporting of analytical results. This SOP applies to R and D work - for production applications, use [4].

  6. Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image

    PubMed Central

    Guo, Jingyu; Qi, Hongliang; Xu, Yuan; Chen, Zijia; Li, Shulong; Zhou, Linghong

    2016-01-01

    Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges. PMID:27066107

  7. Cardiac motion correction based on partial angle reconstructed images in x-ray CT

    SciTech Connect

    Kim, Seungeon; Chang, Yongjin; Ra, Jong Beom

    2015-05-15

    Purpose: Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. Methods: A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of

  8. An Approximate Cone Beam Reconstruction Algorithm for Gantry-Tilted CT Using Tangential Filtering.

    PubMed

    Yan, Ming; Zhang, Cishen; Liang, Hongzhu

    2006-01-01

    FDK algorithm is a well-known 3D (three-dimensional) approximate algorithm for CT (computed tomography) image reconstruction and is also known to suffer from considerable artifacts when the scanning cone angle is large. Recently, it has been improved by performing the ramp filtering along the tangential direction of the X-ray source helix for dealing with the large cone angle problem. In this paper, we present an FDK-type approximate reconstruction algorithm for gantry-tilted CT imaging. The proposed method improves the image reconstruction by filtering the projection data along a proper direction which is determined by CT parameters and gantry-tilted angle. As a result, the proposed algorithm for gantry-tilted CT reconstruction can provide more scanning flexibilities in clinical CT scanning and is efficient in computation. The performance of the proposed algorithm is evaluated with turbell clock phantom and thorax phantom and compared with FDK algorithm and a popular 2D (two-dimensional) approximate algorithm. The results show that the proposed algorithm can achieve better image quality for gantry-tilted CT image reconstruction.

  9. Estimation of skull table thickness with clinical CT and validation with microCT.

    PubMed

    Lillie, Elizabeth M; Urban, Jillian E; Weaver, Ashley A; Powers, Alexander K; Stitzel, Joel D

    2015-01-01

    Brain injuries resulting from motor vehicle crashes (MVC) are extremely common yet the details of the mechanism of injury remain to be well characterized. Skull deformation is believed to be a contributing factor to some types of traumatic brain injury (TBI). Understanding biomechanical contributors to skull deformation would provide further insight into the mechanism of head injury resulting from blunt trauma. In particular, skull thickness is thought be a very important factor governing deformation of the skull and its propensity for fracture. Current computed tomography (CT) technology is limited in its ability to accurately measure cortical thickness using standard techniques. A method to evaluate cortical thickness using cortical density measured from CT data has been developed previously. This effort validates this technique for measurement of skull table thickness in clinical head CT scans using two postmortem human specimens. Bone samples were harvested from the skulls of two cadavers and scanned with microCT to evaluate the accuracy of the estimated cortical thickness measured from clinical CT. Clinical scans were collected at 0.488 and 0.625 mm in plane resolution with 0.625 mm thickness. The overall cortical thickness error was determined to be 0.078 ± 0.58 mm for cortical samples thinner than 4 mm. It was determined that 91.3% of these differences fell within the scanner resolution. Color maps of clinical CT thickness estimations are comparable to color maps of microCT thickness measurements, indicating good quantitative agreement. These data confirm that the cortical density algorithm successfully estimates skull table thickness from clinical CT scans. The application of this technique to clinical CT scans enables evaluation of cortical thickness in population-based studies.

  10. [The use of 3-dimensional CT reconstruction in childhood. Technics and dosimetry].

    PubMed

    Taccone, A; Ciccone, M A; Galano, N; Fondelli, M P; Ghiorzi, M; Cama, A; Roberti, M; Klamert, V; Pelizza, A

    1989-03-01

    A new computer method has been developed that allows the reprocessing of standard CT scans to produce 3D surface images. We employed the 3D reconstruction program developed by Hitachi Medical System using an Ansaldo A-TOM XR 1200 scanner. The process requires only standard CT scanner hardware, and reconstruction time is comparable to that of sagittal and coronal reconstructions. The applications of this technique and methodology to pediatric patients are discussed. In order to assess the relationship between image quality and radiation dose, we performed many CT scans with different protocols. A skull was employed for phantom, and plunged into a physiological solution, which helped us to determine the radiation exposure dose from every single CT scan. The measurements were taken with film and thermoluminescent crystal dosimeters (TLD). The results confirm that low-dose techniques allow a significant reduction in the total exposure. The authors discuss the clinical indications and the eventual applications of these techniques.

  11. A new iterative reconstruction algorithm for 2D exterior fan-beam CT.

    PubMed

    Zeng, Li; Liu, Baodong; Liu, Linghui; Xiang, Caibing

    2010-01-01

    The exterior computed tomography (CT) problem is one kind of truncation problem. It is very ill-posed, so that accurate reconstruction of the attenuation function is hardly possible from real data. Based on projection onto convex sets (POCS) algorithm, total variation minimization (TVM) methods, and C-V model, we develop and investigate a new iterative reconstruction algorithm, which is referred to as subregion-averaged-TVM-POCS (SA-TVM-POCS). Numerical simulations are presented to illustrate the efficiency of the algorithm. The results of this paper can be easily applied to other x-ray CT reconstruction problems.

  12. CT scanner x-ray spectrum estimation from transmission measurements

    PubMed Central

    Duan, Xinhui; Wang, Jia; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H.

    2011-01-01

    Purpose: In diagnostic CT imaging, multiple important applications depend on the knowledge of the x-ray spectrum, including Monte Carlo dose calculations and dual-energy material decomposition analysis. Due to the high photon flux involved, it is difficult to directly measure spectra from the x-ray tube of a CT scanner. One potential method for indirect measurement involves estimating the spectrum from transmission measurements. The expectation maximization (EM) method is an accurate and robust method to solve this problem. In this article, this method was evaluated in a commercial CT scanner. Methods: Two step-wedges (polycarbonate and aluminum) were used to produce different attenuation levels. Transmission measurements were performed on the scanner and the measured data from the scanner were exported to an external computer to calculate the spectra. The EM method was applied to solve the equations that represent the attenuation processes of polychromatic x-ray photons. Estimated spectra were compared to the spectra simulated using a software provided by the manufacturer of the scanner. To test the accuracy of the spectra, a verification experiment was performed using a phantom containing different depths of water. The measured transmission data were compared to the transmission values calculated using the estimated spectra. Results: Spectra of 80, 100, 120, and 140 kVp from a dual-source CT scanner were estimated. The estimated and simulated spectra were well matched. The differences of mean energies were less than 1 keV. In the verification experiment, the measured and calculated transmission values were in excellent agreement. Conclusions: Spectrum estimation using transmission data and the EM method is a quantitatively accurate and robust technique to estimate the spectrum of a CT system. This method could benefit studies relying on accurate knowledge of the x-ray spectra from CT scanner. PMID:21452736

  13. High-Dynamic-Range CT Reconstruction Based on Varying Tube-Voltage Imaging

    PubMed Central

    2015-01-01

    For complicated structural components characterized by wide X-ray attenuation ranges, the conventional computed tomography (CT) imaging using a single tube-voltage at each rotation angle cannot obtain all structural information. This limitation results in a shortage of CT information, because the effective thickness of the components along the direction of X-ray penetration exceeds the limitation of the dynamic range of the X-ray imaging system. To address this problem, high-dynamic-range CT (HDR-CT) reconstruction is proposed. For this new method, the tube’s voltage is adjusted several times to match the corresponding effective thickness about the local information from an object. Then, HDR fusion and HDR-CT are applied to obtain the full reconstruction information. An accompanying experiment demonstrates that this new technology can extend the dynamic range of X-ray imaging systems and provide the complete internal structures of complicated structural components. PMID:26544723

  14. Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction

    NASA Technical Reports Server (NTRS)

    Yan, C. H.; Whalen, R. T.; Beaupre, G. S.; Yen, S. Y.; Napel, S.

    2000-01-01

    This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture of two known substances, for example, a mixture of trabecular bone and marrow, or a mixture of fat and flesh. These assumptions are easily satisfied in a quantitative computed tomography (QCT) setting. We have compared our algorithm to three commonly used single-energy correction techniques. Experimental results show that our algorithm is much more robust and accurate. We have also shown that QCT measurements obtained using our algorithm are five times more accurate than that from current QCT systems (using calibration). The dual-energy mode does not require any prior knowledge of the object in the scan field, and can be used to estimate the attenuation coefficient function of unknown materials. We have tested the dual-energy setup to obtain an accurate estimate for the attenuation coefficient function of K2 HPO4 solution.

  15. Image reconstruction for PET/CT scanners: past achievements and future challenges

    PubMed Central

    Tong, Shan; Alessio, Adam M; Kinahan, Paul E

    2011-01-01

    PET is a medical imaging modality with proven clinical value for disease diagnosis and treatment monitoring. The integration of PET and CT on modern scanners provides a synergy of the two imaging modalities. Through different mathematical algorithms, PET data can be reconstructed into the spatial distribution of the injected radiotracer. With dynamic imaging, kinetic parameters of specific biological processes can also be determined. Numerous efforts have been devoted to the development of PET image reconstruction methods over the last four decades, encompassing analytic and iterative reconstruction methods. This article provides an overview of the commonly used methods. Current challenges in PET image reconstruction include more accurate quantitation, TOF imaging, system modeling, motion correction and dynamic reconstruction. Advances in these aspects could enhance the use of PET/CT imaging in patient care and in clinical research studies of pathophysiology and therapeutic interventions. PMID:21339831

  16. Investigation of iterative image reconstruction in low-dose breast CT

    NASA Astrophysics Data System (ADS)

    Bian, Junguo; Yang, Kai; Boone, John M.; Han, Xiao; Sidky, Emil Y.; Pan, Xiaochuan

    2014-06-01

    There is interest in developing computed tomography (CT) dedicated to breast-cancer imaging. Because breast tissues are radiation-sensitive, the total radiation exposure in a breast-CT scan is kept low, often comparable to a typical two-view mammography exam, thus resulting in a challenging low-dose-data-reconstruction problem. In recent years, evidence has been found that suggests that iterative reconstruction may yield images of improved quality from low-dose data. In this work, based upon the constrained image total-variation minimization program and its numerical solver, i.e., the adaptive steepest descent-projection onto the convex set (ASD-POCS), we investigate and evaluate iterative image reconstructions from low-dose breast-CT data of patients, with a focus on identifying and determining key reconstruction parameters, devising surrogate utility metrics for characterizing reconstruction quality, and tailoring the program and ASD-POCS to the specific reconstruction task under consideration. The ASD-POCS reconstructions appear to outperform the corresponding clinical FDK reconstructions, in terms of subjective visualization and surrogate utility metrics.

  17. Model-based PSF and MTF estimation and validation from skeletal clinical CT images

    SciTech Connect

    Pakdel, Amirreza; Mainprize, James G.; Robert, Normand; Fialkov, Jeffery; Whyne, Cari M.

    2014-01-15

    Purpose: A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones. Methods: Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation. Results: The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm). Conclusions: The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the

  18. Accuracy assessment of 3D bone reconstructions using CT: an intro comparison.

    PubMed

    Lalone, Emily A; Willing, Ryan T; Shannon, Hannah L; King, Graham J W; Johnson, James A

    2015-08-01

    Computed tomography provides high contrast imaging of the joint anatomy and is used routinely to reconstruct 3D models of the osseous and cartilage geometry (CT arthrography) for use in the design of orthopedic implants, for computer assisted surgeries and computational dynamic and structural analysis. The objective of this study was to assess the accuracy of bone and cartilage surface model reconstructions by comparing reconstructed geometries with bone digitizations obtained using an optical tracking system. Bone surface digitizations obtained in this study determined the ground truth measure for the underlying geometry. We evaluated the use of a commercially available reconstruction technique using clinical CT scanning protocols using the elbow joint as an example of a surface with complex geometry. To assess the accuracies of the reconstructed models (8 fresh frozen cadaveric specimens) against the ground truth bony digitization-as defined by this study-proximity mapping was used to calculate residual error. The overall mean error was less than 0.4 mm in the cortical region and 0.3 mm in the subchondral region of the bone. Similarly creating 3D cartilage surface models from CT scans using air contrast had a mean error of less than 0.3 mm. Results from this study indicate that clinical CT scanning protocols and commonly used and commercially available reconstruction algorithms can create models which accurately represent the true geometry.

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

    SciTech Connect

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

    2010-04-15

    Purpose: 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. Methods: 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. Results: 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 {approx}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. Conclusions: 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.

  20. Low-dose CT reconstruction via edge-preserving total variation regularization

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

    High radiation dose in computed tomography (CT) scans increases the 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 (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a 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.

  1. Motion estimation and compensation for coronary artery and myocardium in cardiac CT

    NASA Astrophysics Data System (ADS)

    Tang, Qiulin; Matthews, James; Razeto, Marco; Linde, Jesper J.; Nakanishi, Satoru

    2015-03-01

    Motion blurring is still a challenge for cardiac CT imaging. A new motion estimation (ME) and motion compensation method is developed for cardiac CT. The proposed method estimates motion of entire heart, and then applies motion compensation. Therefore, the proposed method reduces motion artifacts not only in coronary artery region as most other methods did, but also reduces motion blurring in myocardium region. In motion compensated reconstruction, we use the Fourier transfer method proposed by Pack et al to obtain a series of partial images, and then warp and sum together to obtain final motion compensated images. The robustness and performance of the proposed method was verified with data from 10 patients and improvements in sharpness of both coronary arteries and myocardium were obtained.

  2. Sparse angular CT reconstruction using non-local means based iterative-correction POCS.

    PubMed

    Huang, Jing; Ma, Jianhua; Liu, Nan; Zhang, Hua; Bian, Zhaoying; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

    2011-04-01

    In divergent-beam computed tomography (CT), sparse angular sampling frequently leads to conspicuous streak artifacts. In this paper, we propose a novel non-local means (NL-means) based iterative-correction projection onto convex sets (POCS) algorithm, named as NLMIC-POCS, for effective and robust sparse angular CT reconstruction. The motivation for using NLMIC-POCS is that NL-means filtered image can produce an acceptable priori solution for sequential POCS iterative reconstruction. The NLMIC-POCS algorithm has been tested on simulated and real phantom data. The experimental results show that the presented NLMIC-POCS algorithm can significantly improve the image quality of the sparse angular CT reconstruction in suppressing streak artifacts and preserving the edges of the image.

  3. Adaptive region of interest method for analytical micro-CT reconstruction.

    PubMed

    Yang, Wanneng; Xu, Xiaochun; Bi, Kun; Zeng, Shaoqun; Liu, Qian; Chen, Shangbin

    2011-01-01

    The real-time imaging is important in automatic successive inspection with micro-computerized tomography (micro-CT). Generally, the size of the detector is chosen according to the most probable size of the measured object to acquire all the projection data. Given enough imaging area and imaging resolution of X-ray detector, the detector is larger than specimen projection area, which results in redundant data in the Sinogram. The process of real-time micro-CT is computation-intensive because of the large amounts of source and destination data. The speed of the reconstruction algorithm can't always meet the requirements of real-time applications. A preprocessing method called adaptive region of interest (AROI), which detects the object's boundaries automatically to focus the active Sinogram regions, is introduced into the analytical reconstruction algorithm in this paper. The AROI method reduces the volume of the reconstructing data and thus directly accelerates the reconstruction process. It has been further shown that image quality is not compromised when applying AROI, while the reconstruction speed is increased as the square of the ratio of the sizes of the detector and the specimen slice. In practice, the conch reconstruction experiment indicated that the process is accelerated by 5.2 times with AROI and the imaging quality is not degraded. Therefore, the AROI method improves the speed of analytical micro-CT reconstruction significantly.

  4. Adaptive region of interest method for analytical micro-CT reconstruction.

    PubMed

    Yang, Wanneng; Xu, Xiaochun; Bi, Kun; Zeng, Shaoqun; Liu, Qian; Chen, Shangbin

    2011-01-01

    The real-time imaging is important in automatic successive inspection with micro-computerized tomography (micro-CT). Generally, the size of the detector is chosen according to the most probable size of the measured object to acquire all the projection data. Given enough imaging area and imaging resolution of X-ray detector, the detector is larger than specimen projection area, which results in redundant data in the Sinogram. The process of real-time micro-CT is computation-intensive because of the large amounts of source and destination data. The speed of the reconstruction algorithm can't always meet the requirements of real-time applications. A preprocessing method called adaptive region of interest (AROI), which detects the object's boundaries automatically to focus the active Sinogram regions, is introduced into the analytical reconstruction algorithm in this paper. The AROI method reduces the volume of the reconstructing data and thus directly accelerates the reconstruction process. It has been further shown that image quality is not compromised when applying AROI, while the reconstruction speed is increased as the square of the ratio of the sizes of the detector and the specimen slice. In practice, the conch reconstruction experiment indicated that the process is accelerated by 5.2 times with AROI and the imaging quality is not degraded. Therefore, the AROI method improves the speed of analytical micro-CT reconstruction significantly. PMID:21422587

  5. Sparse-view x-ray CT reconstruction via total generalized variation regularization

    NASA Astrophysics Data System (ADS)

    Niu, Shanzhou; Gao, Yang; Bian, Zhaoying; Huang, Jing; Chen, Wufan; Yu, Gaohang; Liang, Zhengrong; Ma, Jianhua

    2014-06-01

    Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively on the basis of a noise- and artifact-reducing model, resulting in significant radiation dose reduction while maintaining image quality. However, the piecewise constant assumption of TV minimization often leads to the appearance of noticeable patchy artifacts in reconstructed images. To obviate this drawback, we present a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating the new concept of total generalized variation (TGV) regularization. We refer to the proposed scheme as ‘PWLS-TGV’ for simplicity. Specifically, TGV regularization utilizes higher order derivatives of the objective image, and the weighted least-squares term considers data-dependent variance estimation, which fully contribute to improving the image quality with sparse-view projection measurement. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the PWLS-TGV method, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present PWLS-TGV method can achieve images with several noticeable gains over the original TV-based method in terms of accuracy and resolution properties.

  6. Intramyocardial capillary blood volume estimated by whole-body CT: validation by micro-CT

    NASA Astrophysics Data System (ADS)

    Dong, Yue; Beighley, Patricia E.; Eaker, Diane R.; Zamir, Mair; Ritman, Erik L.

    2008-03-01

    Fast CT has shown that myocardial perfusion (F) is related to myocardial intramuscular blood volume (Bv) as Bv=A*F+B*F 1/2 where A,B are constant coefficients. The goal of this study was to estimate the range of diameters of the vessels that are represented by the A*F term. Pigs were placed in an Electron Beam CT (EBCT) scanner for a perfusion CT scan sequence over 40 seconds after an IV contrast agent injection. Intramyocardial blood volume (Bv) and flow (F) were calculated in a region of the myocardium perfused by the LAD. Coefficients A and B were estimated over the range of F=1-5ml/g/min. After the CT scan, the LAD was injected with Microfil (R) contrast agent following which the myocardium was scanned by micro-CT at 20μm, 4μm and 2.5 μm cubic voxel resolutions. The Bv of the intramyocardial vessels was calculated for diameter ranges d=0-5, 5-10, 10-15, 15-20μm, etc. EBCT-derived data were presented so that it could be directly compared the micro-CT data. The results indicated that the blood in vessels less than 10μm in lumen diameter occupied 0.27-0.42 of total intravascular blood volume, which is in good agreement with EBCT-based values 0.28-0.48 (R2 =0.96). We conclude that whole-body CT image data obtained during the passage of a bolus of IV contrast agent can provide a measure of the intramyocardial intracapillary blood volume.

  7. Single-slice reconstruction method for helical cone-beam differential phase-contrast CT.

    PubMed

    Fu, Jian; Chen, Liyuan

    2014-01-01

    X-ray phase-contrast computed tomography (PC-CT) can provide the internal structure information of biomedical specimens with high-quality cross-section images and has become an invaluable analysis tool. Here a simple and fast reconstruction algorithm is reported for helical cone-beam differential PC-CT (DPC-CT), which is called the DPC-CB-SSRB algorithm. It combines the existing CB-SSRB method of helical cone-beam absorption-contrast CT with the differential nature of DPC imaging. The reconstruction can be performed using 2D fan-beam filtered back projection algorithm with the Hilbert imaginary filter. The quality of the results for large helical pitches is surprisingly good. In particular, with this algorithm comparable quality is obtained using helical cone-beam DPC-CT data with a normalized pitch of 10 to that obtained using the traditional inter-row interpolation reconstruction with a normalized pitch of 2. This method will push the future medical helical cone-beam DPC-CT imaging applications.

  8. The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

    NASA Astrophysics Data System (ADS)

    Rit, S.; Vila Oliva, M.; Brousmiche, S.; Labarbe, R.; Sarrut, D.; Sharp, G. C.

    2014-03-01

    We propose the Reconstruction Toolkit (RTK, http://www.openrtk.org), an open-source toolkit for fast cone-beam CT reconstruction, based on the Insight Toolkit (ITK) and using GPU code extracted from Plastimatch. RTK is developed by an open consortium (see affiliations) under the non-contaminating Apache 2.0 license. The quality of the platform is daily checked with regression tests in partnership with Kitware, the company supporting ITK. Several features are already available: Elekta, Varian and IBA inputs, multi-threaded Feldkamp-David-Kress reconstruction on CPU and GPU, Parker short scan weighting, multi-threaded CPU and GPU forward projectors, etc. Each feature is either accessible through command line tools or C++ classes that can be included in independent software. A MIDAS community has been opened to share CatPhan datasets of several vendors (Elekta, Varian and IBA). RTK will be used in the upcoming cone-beam CT scanner developed by IBA for proton therapy rooms. Many features are under development: new input format support, iterative reconstruction, hybrid Monte Carlo / deterministic CBCT simulation, etc. RTK has been built to freely share tomographic reconstruction developments between researchers and is open for new contributions.

  9. Investigation on viewing direction dependent detectability in a reconstructed 3D volume for a cone beam CT system

    NASA Astrophysics Data System (ADS)

    Park, Junhan; Lee, Changwoo; Baek, Jongduk

    2015-03-01

    In medical imaging systems, several factors (e.g., reconstruction algorithm, noise structures, target size, contrast, etc) affect the detection performance and need to be considered for object detection. In a cone beam CT system, FDK reconstruction produces different noise structures in axial and coronal slices, and thus we analyzed directional dependent detectability of objects using detection SNR of Channelized Hotelling observer. To calculate the detection SNR, difference-of-Gaussian channel model with 10 channels was implemented, and 20 sphere objects with different radius (i.e., 0.25 (mm) to 5 (mm) equally spaced by 0.25 (mm)), reconstructed by FDK algorithm, were used as object templates. Covariance matrix in axial and coronal direction was estimated from 3000 reconstructed noise volumes, and then the SNR ratio between axial and coronal direction was calculated. Corresponding 2D noise power spectrum was also calculated. The results show that as the object size increases, the SNR ratio decreases, especially lower than 1 when the object size is larger than 2.5 mm radius. The reason is because the axial (coronal) noise power is higher in high (low) frequency band, and therefore the detectability of a small (large) object is higher in coronal (axial) images. Our results indicate that it is more beneficial to use coronal slices in order to improve the detectability of a small object in a cone beam CT system.

  10. Analytic image reconstruction from partial data for a single-scan cone-beam CT with scatter correction

    SciTech Connect

    Min, Jonghwan; Pua, Rizza; Cho, Seungryong; Kim, Insoo; Han, Bumsoo

    2015-11-15

    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 a 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

  11. Reconstruction of a time-averaged midposition CT scan for radiotherapy planning of lung cancer patients using deformable registration

    SciTech Connect

    Wolthaus, J. W. H.; Sonke, J.-J.; Herk, M. van; Damen, E. M. F.

    2008-09-15

    for the clearly visible features (e.g., tumor and diaphragm). The shape of the tumor, with respect to that of the BH CT scan, was better represented by the MidP reconstructions than any of the 4D CT frames (including MidV; reduction of 'shape differences' was 66%). The MidP scans contained about one-third the noise of individual 4D CT scan frames. Conclusions: We implemented an accurate method to estimate the motion of structures in a 4D CT scan. Subsequently, a novel method to create a midposition CT scan (time-weighted average of the anatomy) for treatment planning with reduced noise and artifacts was introduced. Tumor shape and position in the MidP CT scan represents that of the BH CT scan better than MidV CT scan and, therefore, was found to be appropriate for treatment planning.

  12. FFT and cone-beam CT reconstruction on graphics hardware

    NASA Astrophysics Data System (ADS)

    Després, Philippe; Sun, Mingshan; Hasegawa, Bruce H.; Prevrhal, Sven

    2007-03-01

    Graphics processing units (GPUs) are increasingly used for general purpose calculations. Their pipelined architecture can be exploited to accelerate various parallelizable algorithms. Medical imaging applications are inherently well suited to benefit from the development of GPU-based computational platforms. We evaluate in this work the potential of GPUs to improve the execution speed of two common medical imaging tasks, namely Fourier transforms and tomographic reconstructions. A two-dimensional fast Fourier transform (FFT) algorithm was GPU-implemented and compared, in terms of execution speed, to two popular CPU-based FFT routines. Similarly, the Feldkamp, David and Kress (FDK) algorithm for cone-beam tomographic reconstruction was implemented on the GPU and its performance compared to a CPU version. Different reconstruction strategies were employed to assess the performance of various GPU memory layouts. For the specific hardware used, GPU implementations of the FFT were up to 20 times faster than their CPU counterparts, but slower than highly optimized CPU versions of the algorithm. Tomographic reconstructions were faster on the GPU by a factor up to 30, allowing 256 3 voxel reconstructions of 256 projections in about 20 seconds. Overall, GPUs are an attractive alternative to other imaging-dedicated computing hardware like application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs) in terms of cost, simplicity and versatility. With the development of simpler language extensions and programming interfaces, GPUs are likely to become essential tools in medical imaging.

  13. Few-view cone-beam CT reconstruction with deformed prior image

    SciTech Connect

    Zhang, Hua; Ouyang, Luo; Wang, Jing E-mail: jing.wang@utsouthwestern.edu; Huang, Jing; Ma, Jianhua E-mail: jing.wang@utsouthwestern.edu; Chen, Wufan

    2014-12-15

    Purpose: Prior images can be incorporated into the image reconstruction process to improve the quality of subsequent cone-beam CT (CBCT) images from sparse-view or low-dose projections. The purpose of this work is to develop a deformed prior image-based reconstruction (DPIR) strategy to mitigate the deformation between the prior image and the target image. Methods: The deformed prior image is obtained by a projection-based registration approach. Specifically, the deformation vector fields used to deform the prior image are estimated through iteratively matching the forward projection of the deformed prior image and the measured on-treatment projections. The deformed prior image is then used as the prior image in the standard prior image constrained compressed sensing (PICCS) algorithm. A simulation study on an XCAT phantom and a clinical study on a head-and-neck cancer patient were conducted to evaluate the performance of the proposed DPIR strategy. Results: The deformed prior image matches the geometry of the on-treatment CBCT more closely as compared to the original prior image. Consequently, the performance of the DPIR strategy from few-view projections is improved in comparison to the standard PICCS algorithm, based on both visual inspection and quantitative measures. In the XCAT phantom study using 20 projections, the average root mean squared error is reduced from 14% in PICCS to 10% in DPIR, and the average universal quality index increases from 0.88 in PICCS to 0.92 in DPIR. Conclusions: The present DPIR approach provides a practical solution to the mismatch problem between the prior image and target image, which improves the performance of the original PICCS algorithm for CBCT reconstruction from few-view or low-dose projections.

  14. Chord-based image reconstruction in cone-beam CT with a curved detector

    SciTech Connect

    Zuo Nianming; Xia Dan; Zou Yu; Jiang Tianzi; Pan Xiaochuan

    2006-10-15

    Modern computed tomography (CT) scanners use cone-beam configurations for increasing volume coverage, improving x-ray-tube utilization, and yielding isotropic spatial resolution. Recently, there have been significant developments in theory and algorithms for exact image reconstruction from cone-beam projections. In particular, algorithms have been proposed for image reconstruction on chords; and advantages over the existing algorithms offered by the chord-based algorithms include the high flexibility of exact image reconstruction for general scanning trajectories and the capability of exact reconstruction of images within a region of interest from truncated data. These chord-based algorithms have been developed only for flat-panel detectors. Many cone-beam CT scanners employ curved detectors for important practical considerations. Therefore, in this work, we have derived chord-based algorithms for a curved detector so that they can be applied to reconstructing images directly from data acquired by use of a CT scanner with a curved detector. We have also conducted preliminary numerical studies to demonstrate and evaluate the reconstruction properties of the derived chord-based algorithms for curved detectors.

  15. 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.

  16. Estimating mineral changes in enamel formation by ashing/BSE and microCT.

    PubMed

    Schmitz, J E; Teepe, J D; Hu, Y; Smith, C E; Fajardo, R J; Chun, Y-H P

    2014-03-01

    Enamel formation produces the most highly mineralized tissue in the human body. The growth of enamel crystallites is assisted by enamel proteins and proteinases. As enamel formation progresses from secretory to maturation stages, the composition of the matrix with its mineral and non-mineral components dynamically changes in an inverse fashion. We hypothesized that appropriately calibrated micro-computed tomography (µCT) technology is suitable to estimate the mineral content (weight and/or density) and volume comparable in accuracy with that for directly weighed and sectioned enamel. Different sets of mouse mandibular incisors of C57BL/6 mice were used for dissections and µCT reconstructions. Calibration phantoms corresponding to the range of enamel mineral densities were used. Secretory-stage enamel contained little mineral and was consequently too poor in contrast for enamel volumes to be accurately estimated by µCT. Maturation-stage enamel, however, showed remarkable correspondence for total mineral content per volume where comparisons were possible between and among the different analytical techniques used. The main advantages of the µCT approach are that it is non-destructive, time-efficient, and can monitor changes in mineral content of the most mature enamel, which is too physically hard to dissect away from the tooth.

  17. Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

    SciTech Connect

    Hofmann, Christian; Sawall, Stefan; Knaup, Michael; Kachelrieß, Marc

    2014-06-15

    factor for contrast-resolution plots. Furthermore, the authors calculate the contrast-to-noise ratio with the low contrast disks and the authors compare the agreement of the reconstructions with the ground truth by calculating the normalized cross-correlation and the root-mean-square deviation. To evaluate the clinical performance of the proposed method, the authors reconstruct patient data acquired with a Somatom Definition Flash dual source CT scanner (Siemens Healthcare, Forchheim, Germany). Results: The results of the simulation study show that among the compared algorithms AIR achieves the highest resolution and the highest agreement with the ground truth. Compared to the reference FBP reconstruction AIR is able to reduce the relative pixel noise by up to 50% and at the same time achieve a higher resolution by maintaining the edge information from the basis images. These results can be confirmed with the patient data. Conclusions: To evaluate the AIR algorithm simulated and measured patient data of a state-of-the-art clinical CT system were processed. It is shown, that generating CT images through the reconstruction of weighting coefficients has the potential to improve the resolution noise trade-off and thus to improve the dose usage in clinical CT.

  18. Scatter correction, intermediate view estimation and dose characterization in megavoltage cone-beam CT imaging

    NASA Astrophysics Data System (ADS)

    Sramek, Benjamin Koerner

    The ability to deliver conformal dose distributions in radiation therapy through intensity modulation and the potential for tumor dose escalation to improve treatment outcome has necessitated an increase in localization accuracy of inter- and intra-fractional patient geometry. Megavoltage cone-beam CT imaging using the treatment beam and onboard electronic portal imaging device is one option currently being studied for implementation in image-guided radiation therapy. However, routine clinical use is predicated upon continued improvements in image quality and patient dose delivered during acquisition. The formal statement of hypothesis for this investigation was that the conformity of planned to delivered dose distributions in image-guided radiation therapy could be further enhanced through the application of kilovoltage scatter correction and intermediate view estimation techniques to megavoltage cone-beam CT imaging, and that normalized dose measurements could be acquired and inter-compared between multiple imaging geometries. The specific aims of this investigation were to: (1) incorporate the Feldkamp, Davis and Kress filtered backprojection algorithm into a program to reconstruct a voxelized linear attenuation coefficient dataset from a set of acquired megavoltage cone-beam CT projections, (2) characterize the effects on megavoltage cone-beam CT image quality resulting from the application of Intermediate View Interpolation and Intermediate View Reprojection techniques to limited-projection datasets, (3) incorporate the Scatter and Primary Estimation from Collimator Shadows (SPECS) algorithm into megavoltage cone-beam CT image reconstruction and determine the set of SPECS parameters which maximize image quality and quantitative accuracy, and (4) evaluate the normalized axial dose distributions received during megavoltage cone-beam CT image acquisition using radiochromic film and thermoluminescent dosimeter measurements in anthropomorphic pelvic and head and

  19. Statistical image reconstruction for low-dose CT using nonlocal means-based regularization.

    PubMed

    Zhang, Hao; Ma, Jianhua; Wang, Jing; Liu, Yan; Lu, Hongbing; Liang, Zhengrong

    2014-09-01

    Low-dose computed tomography (CT) imaging without sacrifice of clinical tasks is desirable due to the growing concerns about excessive radiation exposure to the patients. One common strategy to achieve low-dose CT imaging is to lower the milliampere-second (mAs) setting in data scanning protocol. However, the reconstructed CT images by the conventional filtered back-projection (FBP) method from the low-mAs acquisitions may be severely degraded due to the excessive noise. Statistical image reconstruction (SIR) methods have shown potentials to significantly improve the reconstructed image quality from the low-mAs acquisitions, wherein the regularization plays a critical role and an established family of regularizations is based on the Markov random field (MRF) model. Inspired by the success of nonlocal means (NLM) in image processing applications, in this work, we propose to explore the NLM-based regularization for SIR to reconstruct low-dose CT images from low-mAs acquisitions. Experimental results with both digital and physical phantoms consistently demonstrated that SIR with the NLM-based regularization can achieve more gains than SIR with the well-known Gaussian MRF regularization or the generalized Gaussian MRF regularization and the conventional FBP method, in terms of image noise reduction and resolution preservation.

  20. Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images.

    PubMed

    Zhang, Hao; Han, Hao; Liang, Zhengrong; Hu, Yifan; Liu, Yan; Moore, William; Ma, Jianhua; Lu, Hongbing

    2016-03-01

    Markov random field (MRF) model has been widely employed in edge-preserving regional noise smoothing penalty to reconstruct piece-wise smooth images in the presence of noise, such as in low-dose computed tomography (LdCT). While it preserves edge sharpness, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it may compromise clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodules or colon polyps. This study aims to shift the edge-preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of muscle, fat, bone, lung, etc. from previous full-dose CT (FdCT) scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of the proposed reconstruction framework, experiments using clinical patient scans were conducted. The experimental outcomes showed a dramatic gain by the a priori knowledge for LdCT image reconstruction using the commonly-used Haralick texture measures. Thus, it is conjectured that the texture-preserving LdCT reconstruction has advantages over the edge-preserving regional smoothing paradigm for texture-specific clinical applications.

  1. SU-E-I-86: Ultra-Low Dose Computed Tomography Attenuation Correction for Pediatric PET CT Using Adaptive Statistical Iterative Reconstruction (ASiR™)

    SciTech Connect

    Brady, S; Shulkin, B

    2015-06-15

    Purpose: To develop ultra-low dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT). Methods: A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultra-low doses (10–35 mAs). CT quantitation: noise, low-contrast resolution, and CT numbers for eleven tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% CTDIvol (0.39/3.64; mGy) radiation dose from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET images were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUVbw) of various diameter targets (range 8–37 mm), background uniformity, and spatial resolution. Radiation organ dose, as derived from patient exam size specific dose estimate (SSDE), was converted to effective dose using the standard ICRP report 103 method. Effective dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative patient population dose reduction and noise control. Results: CT numbers were constant to within 10% from the non-dose reduced CTAC image down to 90% dose reduction. No change in SUVbw, background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols reconstructed with ASiR and down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62%–86% (3.2/8.3−0.9/6.2; mSv). Noise magnitude in dose-reduced patient images increased but was not statistically different from pre dose-reduced patient images. Conclusion: Using ASiR allowed for aggressive reduction in CTAC dose with no change in PET reconstructed images while maintaining sufficient image quality for co

  2. Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction

    NASA Astrophysics Data System (ADS)

    Wu, Haibo; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim

    2012-03-01

    Four dimensional computed tomography (4D-CT) is very important for treatment planning in thorax or abdomen area, e.g. for guiding radiation therapy planning. The respiratory motion makes the reconstruction problem illposed. Recently, compressed sensing theory was introduced. It uses sparsity as a prior to solve the problem and improves image quality considerably. However, the images at each phase are reconstructed individually. The correlations between neighboring phases are not considered in the reconstruction process. In this paper, we propose the spatial-temporal total variation regularization (STTVR) method which not only employs the sparsity in the spatial domain but also in the temporal domain. The algorithm is validated with XCAT thorax phantom. The Euclidean norm of the reconstructed image and ground truth is calculated for evaluation. The results indicate that our method improves the reconstruction quality by more than 50% compared to standard ART.

  3. Estimation of cartilaginous region in noncontrast CT of the chest

    NASA Astrophysics Data System (ADS)

    Zhao, Qian; Safdar, Nabile; Yu, Glenna; Myers, Emmarie; Sandler, Anthony; Linguraru, Marius George

    2014-03-01

    Pectus excavatum is a posterior depression of the sternum and adjacent costal cartilages and is the most common congenital deformity of the anterior chest wall. Its surgical repair can be performed via minimally invasive procedures that involve sternum and cartilage relocation and benefit from adequate surgical planning. In this study, we propose a method to estimate the cartilage regions in thoracic CT scans, which is the first step of statistical modeling of the osseous and cartilaginous structures for the rib cage. The ribs and sternum are first segmented by using interactive region growing and removing the vertebral column with morphological operations. The entire chest wall is also segmented to estimate the skin surface. After the segmentation, surface meshes are generated from the volumetric data and the skeleton of the ribs is extracted using surface contraction method. Then the cartilage surface is approximated via contracting the skin surface to the osseous structure. The ribs' skeleton is projected to the cartilage surface and the cartilages are estimated using cubic interpolation given the joints with the sternum. The final cartilage regions are formed by the cartilage surface inside the convex hull of the estimated cartilages. The method was validated with the CT scans of two pectus excavatum patients and three healthy subjects. The average distance between the estimated cartilage surface and the ground truth is 2.89 mm. The promising results indicate the effectiveness of cartilage surface estimation using the skin surface.

  4. Estimation of adipose compartment volumes in CT images of a mastectomy specimen

    NASA Astrophysics Data System (ADS)

    Imran, Abdullah-Al-Zubaer; Pokrajac, David D.; Maidment, Andrew D. A.; Bakic, Predrag R.

    2016-03-01

    Anthropomorphic software breast phantoms have been utilized for preclinical quantitative validation of breast imaging systems. Efficacy of the simulation-based validation depends on the realism of phantom images. Anatomical measurements of the breast tissue, such as the size and distribution of adipose compartments or the thickness of Cooper's ligaments, are essential for the realistic simulation of breast anatomy. Such measurements are, however, not readily available in the literature. In this study, we assessed the statistics of adipose compartments as visualized in CT images of a total mastectomy specimen. The specimen was preserved in formalin, and imaged using a standard body CT protocol and high X-ray dose. A human operator manually segmented adipose compartments in reconstructed CT images using ITK-SNAP software, and calculated the volume of each compartment. In addition, the time needed for the manual segmentation and the operator's confidence were recorded. The average volume, standard deviation, and the probability distribution of compartment volumes were estimated from 205 segmented adipose compartments. We also estimated the potential correlation between the segmentation time, operator's confidence, and compartment volume. The statistical tests indicated that the estimated compartment volumes do not follow the normal distribution. The compartment volumes are found to be correlated with the segmentation time; no significant correlation between the volume and the operator confidence. The performed study is limited by the mastectomy specimen position. The analysis of compartment volumes will better inform development of more realistic breast anatomy simulation.

  5. Low-Dose X-ray CT Reconstruction via Dictionary Learning

    PubMed Central

    Xu, Qiong; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2013-01-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. PMID:22542666

  6. Low-dose X-ray CT reconstruction via dictionary learning.

    PubMed

    Xu, Qiong; Yu, Hengyong; Mou, Xuanqin; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2012-09-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. PMID:22542666

  7. [Examination of Visual Effect in Low-dose Cerebral CT Perfusion Phantom Image Using Iterative Reconstruction].

    PubMed

    Ohmura, Tomomi; Lee, Yongbum; Takahashi, Noriyuki; Sato, Yuichiro; Ishida, Takato; Toyoshima, Hideto

    2015-11-01

    CT perfusion (CTP) is obtained cerebrovascular circulation image for assessment of stroke patients; however, at the expense of increased radiation dose by dynamic scan. Iterative reconstruction (IR) method is possible to decrease image noise, it has the potential to reduce radiation dose. The purpose of this study is to assess the visual effect of IR method by using a digital perfusion phantom. The digital perfusion phantom was created by reconstructed filtered back projection (FBP) method and IR method CT images that had five exposure doses. Various exposure dose cerebral blood flow (CBF) images were derived from deconvolution algorithm. Contrast-to-noise ratio (CNR) and visual assessment were compared among the various exposure dose and each reconstructions. Result of low exposure dose with IR method showed, compared with FBP method, high CNR in severe ischemic area, and visual assessment was significantly improvement. IR method is useful for improving image quality of low-dose CTP. PMID:26596197

  8. Rank-sparsity constrained, spectro-temporal reconstruction for retrospectively gated, dynamic CT

    NASA Astrophysics Data System (ADS)

    Clark, D. P.; Lee, C. L.; Kirsch, D. G.; Badea, C. T.

    2015-03-01

    Relative to prospective projection gating, retrospective projection gating for dynamic CT applications allows fast imaging times, minimizing the potential for physiological and anatomic variability. Preclinically, fast imaging is attractive due to the rapid clearance of low molecular weight contrast agents and the rapid heart rate of rodents. Clinically, retrospective gating is relevant for intraoperative C-arm CT. More generally, retrospective sampling provides an opportunity for significant reduction in x-ray dose within the framework of compressive sensing theory and sparsity-constrained iterative reconstruction. Even so, CT reconstruction from projections with random temporal sampling is a very poorly conditioned inverse problem, requiring high fidelity regularization to minimize variability in the reconstructed results. Here, we introduce a highly novel data acquisition and regularization strategy for spectro-temporal (5D) CT reconstruction from retrospectively gated projections. We show that by taking advantage of the rank-sparse structure and separability of the temporal and spectral reconstruction sub-problems, being able to solve each sub-problem independently effectively guarantees that we can solve both problems together. In this paper, we show 4D simulation results (2D + 2 energies + time) using the proposed technique and compare them with two competing techniques— spatio-temporal total variation minimization and prior image constrained compressed sensing. We also show in vivo, 5D (3D + 2 energies + time) myocardial injury data acquired in a mouse, reconstructing 20 data sets (10 phases, 2 energies) and performing material decomposition from data acquired over a single rotation (360°, dose: ~60 mGy).

  9. Smoothed l0 Norm Regularization for Sparse-View X-Ray CT Reconstruction

    PubMed Central

    Li, Ming; Peng, Chengtao; Guan, Yihui; Xu, Pin

    2016-01-01

    Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artifacts, and improve noise properties. In this work, we present an iterative reconstruction approach using improved smoothed l0 (SL0) norm regularization which is used to approximate l0 norm by a family of continuous functions to fully exploit the sparseness of the image gradient. Due to the excellent sparse representation of the reconstruction signal, the desired tissue details are preserved in the resulting images. To evaluate the performance of the proposed SL0 regularization method, we reconstruct the simulated dataset acquired from the Shepp-Logan phantom and clinical head slice image. Additional experimental verification is also performed with two real datasets from scanned animal experiment. Compared to the referenced FBP reconstruction and the total variation (TV) regularization reconstruction, the results clearly reveal that the presented method has characteristic strengths. In particular, it improves reconstruction quality via reducing noise while preserving anatomical features. PMID:27725935

  10. Performance benchmarking of liver CT image segmentation and volume estimation

    NASA Astrophysics Data System (ADS)

    Xiong, Wei; Zhou, Jiayin; Tian, Qi; Liu, Jimmy J.; Qi, Yingyi; Leow, Wee Kheng; Han, Thazin; Wang, Shih-chang

    2008-03-01

    In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs, including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation algorithms.

  11. An efficient polyenergetic SART (pSART) reconstruction algorithm for quantitative myocardial CT perfusion

    SciTech Connect

    Lin, Yuan Samei, Ehsan

    2014-02-15

    Purpose: In quantitative myocardial CT perfusion imaging, beam hardening effect due to dense bone and high concentration iodinated contrast agent can result in visible artifacts and inaccurate CT numbers. In this paper, an efficient polyenergetic Simultaneous Algebraic Reconstruction Technique (pSART) was presented to eliminate the beam hardening artifacts and to improve the CT quantitative imaging ability. Methods: Our algorithm made threea priori assumptions: (1) the human body is composed of several base materials (e.g., fat, breast, soft tissue, bone, and iodine); (2) images can be coarsely segmented to two types of regions, i.e., nonbone regions and noniodine regions; and (3) each voxel can be decomposed into a mixture of two most suitable base materials according to its attenuation value and its corresponding region type information. Based on the above assumptions, energy-independent accumulated effective lengths of all base materials can be fast computed in the forward ray-tracing process and be used repeatedly to obtain accurate polyenergetic projections, with which a SART-based equation can correctly update each voxel in the backward projecting process to iteratively reconstruct artifact-free images. This approach effectively reduces the influence of polyenergetic x-ray sources and it further enables monoenergetic images to be reconstructed at any arbitrarily preselected target energies. A series of simulation tests were performed on a size-variable cylindrical phantom and a realistic anthropomorphic thorax phantom. In addition, a phantom experiment was also performed on a clinical CT scanner to further quantitatively validate the proposed algorithm. Results: The simulations with the cylindrical phantom and the anthropomorphic thorax phantom showed that the proposed algorithm completely eliminated beam hardening artifacts and enabled quantitative imaging across different materials, phantom sizes, and spectra, as the absolute relative errors were reduced

  12. Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ-based CT scan planning

    SciTech Connect

    Yin, Zhye De Man, Bruno; Yao, Yangyang; Wu, Mingye; Montillo, Albert; Edic, Peter M.; Kalra, Mannudeep

    2015-05-15

    Purpose: Traditionally, 2D radiographic preparatory scan images (scout scans) are used to plan diagnostic CT scans. However, a 3D CT volume with a full 3D organ segmentation map could provide superior information for customized scan planning and other purposes. A practical challenge is to design the volumetric scout acquisition and processing steps to provide good image quality (at least good enough to enable 3D organ segmentation) while delivering a radiation dose similar to that of the conventional 2D scout. Methods: The authors explored various acquisition methods, scan parameters, postprocessing methods, and reconstruction methods through simulation and cadaver data studies to achieve an ultralow dose 3D scout while simultaneously reducing the noise and maintaining the edge strength around the target organ. Results: In a simulation study, the 3D scout with the proposed acquisition, preprocessing, and reconstruction strategy provided a similar level of organ segmentation capability as a traditional 240 mAs diagnostic scan, based on noise and normalized edge strength metrics. At the same time, the proposed approach delivers only 1.25% of the dose of a traditional scan. In a cadaver study, the authors’ pictorial-structures based organ localization algorithm successfully located the major abdominal-thoracic organs from the ultralow dose 3D scout obtained with the proposed strategy. Conclusions: The authors demonstrated that images with a similar degree of segmentation capability (interpretability) as conventional dose CT scans can be achieved with an ultralow dose 3D scout acquisition and suitable postprocessing. Furthermore, the authors applied these techniques to real cadaver CT scans with a CTDI dose level of less than 0.1 mGy and successfully generated a 3D organ localization map.

  13. Normalization of CT scans reconstructed with different kernels to reduce variability in emphysema measurements

    NASA Astrophysics Data System (ADS)

    Gallardo Estrella, L.; van Ginneken, B.; van Rikxoort, E. M.

    2013-03-01

    Chronic Obstructive Pulmonary Disease (COPD) is a lung disease characterized by progressive air flow limitation caused by emphysema and chronic bronchitis. Emphysema is quantified from chest computed tomography (CT) scans as the percentage of attentuation values below a fixed threshold. The emphysema quantification varies substantially between scans reconstructed with different kernels, limiting the possibilities to compare emphysema quantifications obtained from scans with different reconstruction parameters. In this paper we propose a method to normalize scans reconstructed with different kernels to have the same characteristics as scans reconstructed with a reference kernel and investigate if this normalization reduces the variability in emphysema quantification. The proposed normalization splits a CT scan into different frequency bands based on hierarchical unsharp masking. Normalization is performed by changing the energy in each frequency band to the average energy in each band in the reference kernel. A database of 15 subjects with COPD was constructed for this study. All subjects were scanned at total lung capacity and the scans were reconstructed with four different reconstruction kernels. The normalization was applied to all scans. Emphysema quantification was performed before and after normalization. It is shown that the emphysema score varies substantially before normalization but the variation diminishes after normalization.

  14. A motion-compensated scheme for helical cone-beam reconstruction in cardiac CT angiography

    SciTech Connect

    Stevendaal, U. van; Berg, J. von; Lorenz, C.; Grass, M.

    2008-07-15

    Since coronary heart disease is one of the main causes of death all over the world, cardiac computed tomography (CT) imaging is an application of very high interest in order to verify indications timely. Due to the cardiac motion, electrocardiogram (ECG) gating has to be implemented into the reconstruction of the measured projection data. However, the temporal and spatial resolution is limited due to the mechanical movement of the gantry and due to the fact that a finite angular span of projections has to be acquired for the reconstruction of each voxel. In this article, a motion-compensated reconstruction method for cardiac CT is described, which can be used to increase the signal-to-noise ratio or to suppress motion blurring. Alternatively, it can be translated into an improvement of the temporal and spatial resolution. It can be applied to the entire heart in common and to high contrast objects moving with the heart in particular, such as calcified plaques or devices like stents. The method is based on three subsequent steps: As a first step, the projection data acquired in low pitch helical acquisition mode together with the ECG are reconstructed at multiple phase points. As a second step, the motion-vector field is calculated from the reconstructed images in relation to the image in a reference phase. Finally, a motion-compensated reconstruction is carried out for the reference phase using those projections, which cover the cardiac phases for which the motion-vector field has been determined.

  15. List-mode reconstruction for the Biograph mCT with physics modeling and event-by-event motion correction

    NASA Astrophysics Data System (ADS)

    Jin, Xiao; Chan, Chung; Mulnix, Tim; Panin, Vladimir; Casey, Michael E.; Liu, Chi; Carson, Richard E.

    2013-08-01

    Whole-body PET/CT scanners are important clinical and research tools to study tracer distribution throughout the body. In whole-body studies, respiratory motion results in image artifacts. We have previously demonstrated for brain imaging that, when provided with accurate motion data, event-by-event correction has better accuracy than frame-based methods. Therefore, the goal of this work was to develop a list-mode reconstruction with novel physics modeling for the Siemens Biograph mCT with event-by-event motion correction, based on the MOLAR platform (Motion-compensation OSEM List-mode Algorithm for Resolution-Recovery Reconstruction). Application of MOLAR for the mCT required two algorithmic developments. First, in routine studies, the mCT collects list-mode data in 32 bit packets, where averaging of lines-of-response (LORs) by axial span and angular mashing reduced the number of LORs so that 32 bits are sufficient to address all sinogram bins. This degrades spatial resolution. In this work, we proposed a probabilistic LOR (pLOR) position technique that addresses axial and transaxial LOR grouping in 32 bit data. Second, two simplified approaches for 3D time-of-flight (TOF) scatter estimation were developed to accelerate the computationally intensive calculation without compromising accuracy. The proposed list-mode reconstruction algorithm was compared to the manufacturer's point spread function + TOF (PSF+TOF) algorithm. Phantom, animal, and human studies demonstrated that MOLAR with pLOR gives slightly faster contrast recovery than the PSF+TOF algorithm that uses the average 32 bit LOR sinogram positioning. Moving phantom and a whole-body human study suggested that event-by-event motion correction reduces image blurring caused by respiratory motion. We conclude that list-mode reconstruction with pLOR positioning provides a platform to generate high quality images for the mCT, and to recover fine structures in whole-body PET scans through event-by-event motion

  16. Precision of cortical bone reconstruction based on 3D CT scans.

    PubMed

    Wang, Jianping; Ye, Ming; Liu, Zhongtang; Wang, Chengtao

    2009-04-01

    The precision and accuracy of human cortical bone reconstruction using 3D CT scans was evaluated using machined bone segments. Both linear and angular errors were measured. Cadaver adult femoral and tibial cortical bone segments were obtained and machined in six orthogonal planes with a precision milling machine. CT scans were then obtained and the bone segments were reconstructed as digital replicas. Dimensional and angular measurements errors were evaluated for the machined bone segments and the results were compared with known dimensions based on milling machine settings to calculate errors due to scanning and model reconstruction. The model dimensional error in the coronal, sagittal and axial directions had a mean of 0.21 mm, with standard a deviation of 0.12 mm and a maximum error of 0.47 mm. The mean percent error was 0.74% and the maximum percent error was 1.9%. The angular error of models in the coronal, sagittal and axial directions was calculated, yielding a mean of 0.47 degrees with a standard deviation of 0.37 degrees and a maximum of 1.33 degrees. The error in the cross-sectional axial direction had a mean of 0.54 mm with a maximum error of 0.83 mm, depending on the slice interval. The main error source was of the image processing, which was about 70% of the total error. We found that machining cortical bone segments prior to CT scanning is an effective method for accuracy evaluation of CT-based bone reconstruction. This method can provide a reference for assessing the sensitivity, reliability and accuracy of CT-based applications in the study of movement, finite element modeling, and prosthesis construction.

  17. A multi-resolution approach to retrospectively-gated cardiac micro-CT reconstruction

    NASA Astrophysics Data System (ADS)

    Clark, D. P.; Johnson, G. A.; Badea, C. T.

    2014-03-01

    In preclinical research, micro-CT is commonly used to provide anatomical information; however, there is significant interest in using this technology to obtain functional information in cardiac studies. The fastest acquisition in 4D cardiac micro-CT imaging is achieved via retrospective gating, resulting in irregular angular projections after binning the projections into phases of the cardiac cycle. Under these conditions, analytical reconstruction algorithms, such as filtered back projection, suffer from streaking artifacts. Here, we propose a novel, multi-resolution, iterative reconstruction algorithm inspired by robust principal component analysis which prevents the introduction of streaking artifacts, while attempting to recover the highest temporal resolution supported by the projection data. The algorithm achieves these results through a unique combination of the split Bregman method and joint bilateral filtration. We illustrate the algorithm's performance using a contrast-enhanced, 2D slice through the MOBY mouse phantom and realistic projection acquisition and reconstruction parameters. Our results indicate that the algorithm is robust to under sampling levels of only 34 projections per cardiac phase and, therefore, has high potential in reducing both acquisition times and radiation dose. Another potential advantage of the multi-resolution scheme is the natural division of the reconstruction problem into a large number of independent sub-problems which can be solved in parallel. In future work, we will investigate the performance of this algorithm with retrospectively-gated, cardiac micro-CT data.

  18. Effect of low-dose CT and iterative reconstruction on trabecular bone microstructure assessment

    NASA Astrophysics Data System (ADS)

    Kopp, Felix K.; Baum, Thomas; Nasirudin, Radin A.; Mei, Kai; Garcia, Eduardo G.; Burgkart, Rainer; Rummeny, Ernst J.; Bauer, Jan S.; Noël, Peter B.

    2016-03-01

    The trabecular bone microstructure is an important factor in the development of osteoporosis. It is well known that its deterioration is one effect when osteoporosis occurs. Previous research showed that the analysis of trabecular bone microstructure enables more precise diagnoses of osteoporosis compared to a sole measurement of the mineral density. Microstructure parameters are assessed on volumetric images of the bone acquired either with high-resolution magnetic resonance imaging, high-resolution peripheral quantitative computed tomography or high-resolution computed tomography (CT), with only CT being applicable to the spine, which is one of clinically most relevant fracture sites. However, due to the high radiation exposure for imaging the whole spine these measurements are not applicable in current clinical routine. In this work, twelve vertebrae from three different donors were scanned with standard and low radiation dose. Trabecular bone microstructure parameters were assessed for CT images reconstructed with statistical iterative reconstruction (SIR) and analytical filtered backprojection (FBP). The resulting structure parameters were correlated to the biomechanically determined fracture load of each vertebra. Microstructure parameters assessed for low-dose data reconstructed with SIR significantly correlated with fracture loads as well as parameters assessed for standard-dose data reconstructed with FBP. Ideal results were achieved with low to zero regularization strength yielding microstructure parameters not significantly different from those assessed for standard-dose FPB data. Moreover, in comparison to other approaches, superior noise-resolution trade-offs can be found with the proposed methods.

  19. GPU-based iterative reconstruction with total variation minimization for micro-CT

    NASA Astrophysics Data System (ADS)

    Johnston, S. M.; Johnson, G. A.; Badea, C. T.

    2010-04-01

    Dynamic imaging with micro-CT often produces poorly-distributed sets of projections, and reconstructions of this data with filtered backprojection algorithms (FBP) may be affected by artifacts. Iterative reconstruction algorithms and total variation (TV) denoising are promising alternatives to FBP, but may require running times that are frustratingly long. This obstacle can be overcome by implementing reconstruction algorithms on graphics processing units (GPU). This paper presents an implementation of a family of iterative reconstruction algorithms with TV denoising on a GPU, and a series of tests to optimize and compare the ability of different algorithms to reduce artifacts. The mathematical and computational details of the implementation are explored. The performance, measured by the accuracy of the reconstruction versus the running time, is assessed in simulations with a virtual phantom and in an in vivo scan of a mouse. We conclude that the simultaneous algebraic reconstruction technique with TV minimization (SART-TV) is a time-effective reconstruction algorithm for producing reconstructions with fewer artifacts than FBP.

  20. Segmentation of tooth in CT images for the 3D reconstruction of teeth

    NASA Astrophysics Data System (ADS)

    Heo, Hoon; Chae, Ok-Sam

    2004-05-01

    In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

  1. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

    PubMed Central

    Pan, Xiaochuan; Sidky, Emil Y; Vannier, Michael

    2010-01-01

    Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues. PMID:20376330

  2. Evaluation of a 4D cone-beam CT reconstruction approach using a simulation framework.

    PubMed

    Hartl, Alexander; Yaniv, Ziv

    2009-01-01

    Current image-guided navigation systems for thoracic abdominal interventions utilize three dimensional (3D) images acquired at breath-hold. As a result they can only provide guidance at a specific point in the respiratory cycle. The intervention is thus performed in a gated manner, with the physician advancing only when the patient is at the same respiratory cycle in which the 3D image was acquired. To enable a more continuous workflow we propose to use 4D image data. We describe an approach to constructing a set of 4D images from a diagnostic CT acquired at breath-hold and a set of intraoperative cone-beam CT (CBCT) projection images acquired while the patient is freely breathing. Our approach is based on an initial reconstruction of a gated 4D CBCT data set. The 3D CBCT images for each respiratory phase are then non-rigidly registered to the diagnostic CT data. Finally the diagnostic CT is deformed based on the registration results, providing a 4D data set with sufficient quality for navigation purposes. In this work we evaluate the proposed reconstruction approach using a simulation framework. A 3D CBCT dataset of an anthropomorphic phantom is deformed using internal motion data acquired from an animal model to create a ground truth 4D CBCT image. Simulated projection images are then created from the 4D image and the known CBCT scan parameters. Finally, the original 3D CBCT and the simulated X-ray images are used as input to our reconstruction method. The resulting 4D data set is then compared to the known ground truth by normalized cross correlation(NCC). We show that the deformed diagnostic CTs are of better quality than the gated reconstructions with a mean NCC value of 0.94 versus a mean 0.81 for the reconstructions. PMID:19964143

  3. GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

    SciTech Connect

    Matenine, Dmitri; Goussard, Yves

    2015-04-15

    Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it is implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.

  4. Novel iterative reconstruction method with optimal dose usage for partially redundant CT-acquisition

    NASA Astrophysics Data System (ADS)

    Bruder, H.; Raupach, R.; Sunnegardh, J.; Allmendinger, T.; Klotz, E.; Stierstorfer, K.; Flohr, T.

    2015-11-01

    In CT imaging, a variety of applications exist which are strongly SNR limited. However, in some cases redundant data of the same body region provide additional quanta. Examples: in dual energy CT, the spatial resolution has to be compromised to provide good SNR for material decomposition. However, the respective spectral dataset of the same body region provides additional quanta which might be utilized to improve SNR of each spectral component. Perfusion CT is a high dose application, and dose reduction is highly desirable. However, a meaningful evaluation of perfusion parameters might be impaired by noisy time frames. On the other hand, the SNR of the average of all time frames is extremely high. In redundant CT acquisitions, multiple image datasets can be reconstructed and averaged to composite image data. These composite image data, however, might be compromised with respect to contrast resolution and/or spatial resolution and/or temporal resolution. These observations bring us to the idea of transferring high SNR of composite image data to low SNR ‘source’ image data, while maintaining their resolution. It has been shown that the noise characteristics of CT image data can be improved by iterative reconstruction (Popescu et al 2012 Book of Abstracts, 2nd CT Meeting (Salt Lake City, UT) p 148). In case of data dependent Gaussian noise it can be modelled with image-based iterative reconstruction at least in an approximate manner (Bruder et al 2011 Proc. SPIE 7961 79610J). We present a generalized update equation in image space, consisting of a linear combination of the previous update, a correction term which is constrained by the source image data, and a regularization prior, which is initialized by the composite image data. This iterative reconstruction approach we call bimodal reconstruction (BMR). Based on simulation data it is shown that BMR can improve low contrast detectability, substantially reduces the noise power and has the potential to recover

  5. "High-precision, reconstructed 3D model" of skull scanned by conebeam CT: Reproducibility verified using CAD/CAM data.

    PubMed

    Katsumura, Seiko; Sato, Keita; Ikawa, Tomoko; Yamamura, Keiko; Ando, Eriko; Shigeta, Yuko; Ogawa, Takumi

    2016-01-01

    Computed tomography (CT) scanning has recently been introduced into forensic medicine and dentistry. However, the presence of metal restorations in the dentition can adversely affect the quality of three-dimensional reconstruction from CT scans. In this study, we aimed to evaluate the reproducibility of a "high-precision, reconstructed 3D model" obtained from a conebeam CT scan of dentition, a method that might be particularly helpful in forensic medicine. We took conebeam CT and helical CT images of three dry skulls marked with 47 measuring points; reconstructed three-dimensional images; and measured the distances between the points in the 3D images with a computer-aided design/computer-aided manufacturing (CAD/CAM) marker. We found that in comparison with the helical CT, conebeam CT is capable of reproducing measurements closer to those obtained from the actual samples. In conclusion, our study indicated that the image-reproduction from a conebeam CT scan was more accurate than that from a helical CT scan. Furthermore, the "high-precision reconstructed 3D model" facilitates reliable visualization of full-sized oral and maxillofacial regions in both helical and conebeam CT scans. PMID:26832374

  6. Developing milk industry estimates for dose reconstruction projects

    SciTech Connect

    Beck, D.M.; Darwin, R.F. )

    1991-01-01

    One of the most important contributors to radiation doses from hanford during the 1944-1947 period was radioactive iodine. Consumption of milk from cows that ate vegetation contaminated with iodine is likely the dominant pathway of human exposure. To estimate the doses people could have received from this pathway, it is necessary to reconstruct the amount of milk consumed by people living near Hanford, the source of the milk, and the type of feed that the milk cows ate. This task is challenging because the dairy industry has undergone radical changes since the end of World War 2, and records that document the impact of these changes on the study area are scarce. Similar problems are faced by researchers on most dose reconstruction efforts. The purpose of this work is to document and evaluate the methods used on the Hanford Environmental Dose Reconstruction (HEDR) Project to reconstruct the milk industry and to present preliminary results.

  7. Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging

    SciTech Connect

    Lauzier, Pascal Theriault; Tang Jie; Speidel, Michael A.; Chen Guanghong

    2012-07-15

    Purpose: To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI. Methods: Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA. Results: Images reconstructed using FBP showed a highly nonuniform spatial distribution

  8. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  9. Constrain static target kinetic iterative image reconstruction for 4D cardiac CT imaging

    NASA Astrophysics Data System (ADS)

    Alessio, Adam M.; La Riviere, Patrick J.

    2011-03-01

    Iterative image reconstruction offers improved signal to noise properties for CT imaging. A primary challenge with iterative methods is the substantial computation time. This computation time is even more prohibitive in 4D imaging applications, such as cardiac gated or dynamic acquisition sequences. In this work, we propose only updating the time-varying elements of a 4D image sequence while constraining the static elements to be fixed or slowly varying in time. We test the method with simulations of 4D acquisitions based on measured cardiac patient data from a) a retrospective cardiac-gated CT acquisition and b) a dynamic perfusion CT acquisition. We target the kinetic elements with one of two methods: 1) position a circular ROI on the heart, assuming area outside ROI is essentially static throughout imaging time; and 2) select varying elements from the coefficient of variation image formed from fast analytic reconstruction of all time frames. Targeted kinetic elements are updated with each iteration, while static elements remain fixed at initial image values formed from the reconstruction of data from all time frames. Results confirm that the computation time is proportional to the number of targeted elements; our simulations suggest that <30% of elements need to be updated in each frame leading to >3 times reductions in reconstruction time. The images reconstructed with the proposed method have matched mean square error with full 4D reconstruction. The proposed method is amenable to most optimization algorithms and offers the potential for significant computation improvements, which could be traded off for more sophisticated system models or penalty terms.

  10. An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

    PubMed Central

    2016-01-01

    Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method. PMID:27689076

  11. An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

    PubMed Central

    2016-01-01

    Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.

  12. Cardiac-state-driven CT image reconstruction algorithm for cardiac imaging

    NASA Astrophysics Data System (ADS)

    Cesmeli, Erdogan; Edic, Peter M.; Iatrou, Maria; Hsieh, Jiang; Gupta, Rajiv; Pfoh, Armin H.

    2002-05-01

    Multi-slice CT scanners use EKG gating to predict the cardiac phase during slice reconstruction from projection data. Cardiac phase is generally defined with respect to the RR interval. The implicit assumption made is that the duration of events in a RR interval scales linearly when the heart rate changes. Using a more detailed EKG analysis, we evaluate the impact of relaxing this assumption on image quality. We developed a reconstruction algorithm that analyzes the associated EKG waveform to extract the natural cardiac states. A wavelet transform was used to decompose each RR-interval into P, QRS, and T waves. Subsequently, cardiac phase was defined with respect to these waves instead of a percentage or time delay from the beginning or the end of RR intervals. The projection data was then tagged with the cardiac phase and processed using temporal weights that are function of their cardiac phases. Finally, the tagged projection data were combined from multiple cardiac cycles using a multi-sector algorithm to reconstruct images. The new algorithm was applied to clinical data, collected on a 4-slice (GE LightSpeed Qx/i) and 8-slice CT scanner (GE LightSpeed Plus), with heart rates of 40 to 80 bpm. The quality of reconstruction is assessed by the visualization of the major arteries, e.g. RCA, LAD, LC in the reformat 3D images. Preliminary results indicate that Cardiac State Driven reconstruction algorithm offers better image quality than their RR-based counterparts.

  13. Importance of the grayscale in early assessment of image quality gains with iterative CT reconstruction

    NASA Astrophysics Data System (ADS)

    Noo, F.; Hahn, K.; Guo, Z.

    2016-03-01

    Iterative reconstruction methods have become an important research topic in X-ray computed tomography (CT), due to their ability to yield improvements in image quality in comparison with the classical filtered bacprojection method. There are many ways to design an effective iterative reconstruction method. Moreover, for each design, there may be a large number of parameters that can be adjusted. Thus, early assessment of image quality, before clinical deployment, plays a large role in identifying and refining solutions. Currently, there are few publications reporting on early, task-based assessment of image quality achieved with iterative reconstruction methods. We report here on such an assessment, and we illustrate at the same time the importance of the grayscale used for image display when conducting this type of assessment. Our results further support observations made by others that the edge preserving penalty term used in iterative reconstruction is a key ingredient to improving image quality in terms of detection task. Our results also provide a clear demonstration of an implication made in one of our previous publications, namely that the grayscale window plays an important role in image quality comparisons involving iterative CT reconstruction methods.

  14. Dual-resolution image reconstruction for region-of-interest CT scan

    NASA Astrophysics Data System (ADS)

    Jin, S. O.; Shin, K. Y.; Yoo, S. K.; Kim, J. G.; Kim, K. H.; Huh, Y.; Lee, S. Y.; Kwon, O.-K.

    2014-07-01

    In ordinary CT scan, so called full field-of-view (FFOV) scan, in which the x-ray beam span covers the whole section of the body, a large number of projections are necessary to reconstruct high resolution images. However, excessive x-ray dose is a great concern in FFOV scan. Region-of-interest (ROI) scan is a method to visualize the ROI in high resolution while reducing the x-ray dose. But, ROI scan suffers from bright-band artifacts which may hamper CT-number accuracy. In this study, we propose an image reconstruction method to eliminate the band artifacts in the ROI scan. In addition to the ROI scan with high sampling rate in the view direction, we get FFOV projection data with much lower sampling rate. Then, we reconstruct images in the compressed sensing (CS) framework with dual resolutions, that is, high resolution in the ROI and low resolution outside the ROI. For the dual-resolution image reconstruction, we implemented the dual-CS reconstruction algorithm in which data fidelity and total variation (TV) terms were enforced twice in the framework of adaptive steepest descent projection onto convex sets (ASD-POCS). The proposed method has remarkably reduced the bright-band artifacts at around the ROI boundary, and it has also effectively suppressed the streak artifacts over the entire image. We expect the proposed method can be greatly used for dual-resolution imaging with reducing the radiation dose, artifacts and scan time.

  15. Estimation of Radiation Dose in CT Based on Projection Data.

    PubMed

    Tian, Xiaoyu; Yin, Zhye; De Man, Bruno; Samei, Ehsan

    2016-10-01

    Managing and optimizing radiation dose has become a core problem for the CT community. As a fundamental step for dose optimization, accurate and computationally efficient dose estimates are crucial. The purpose of this study was to devise a computationally efficient projection-based dose metric. The absorbed energy and object mass were individually modeled using the projection data. The absorbed energy was estimated using the difference between intensity of the primary photon and the exit photon. The mass was estimated using the volume under the attenuation profile. The feasibility of the approach was evaluated across phantoms with a broad size range, various kVp settings, and two bowtie filters, using a simulation tool, the Computer Assisted Tomography SIMulator (CATSIM) software. The accuracy of projection-based dose estimation was validated against Monte Carlo (MC) simulations. The relationship between projection-based dose metric and MC dose estimate was evaluated using regression models. The projection-based dose metric showed a strong correlation with Monte Carlo dose estimates (R (2) > 0.94). The prediction errors for the projection-based dose metric were all below 15 %. This study demonstrated the feasibility of computationally efficient dose estimation requiring only the projection data.

  16. Oxygen transport properties estimation by DSMC-CT simulations

    SciTech Connect

    Bruno, Domenico; Frezzotti, Aldo; Ghiroldi, Gian Pietro

    2014-12-09

    Coupling DSMC simulations with classical trajectories calculations is emerging as a powerful tool to improve predictive capabilities of computational rarefied gas dynamics. The considerable increase of computational effort outlined in the early application of the method (Koura,1997) can be compensated by running simulations on massively parallel computers. In particular, GPU acceleration has been found quite effective in reducing computing time (Ferrigni,2012; Norman et al.,2013) of DSMC-CT simulations. The aim of the present work is to study rarefied Oxygen flows by modeling binary collisions through an accurate potential energy surface, obtained by molecular beams scattering (Aquilanti, et al.,1999). The accuracy of the method is assessed by calculating molecular Oxygen shear viscosity and heat conductivity following three different DSMC-CT simulation methods. In the first one, transport properties are obtained from DSMC-CT simulations of spontaneous fluctuation of an equilibrium state (Bruno et al, Phys. Fluids, 23, 093104, 2011). In the second method, the collision trajectory calculation is incorporated in a Monte Carlo integration procedure to evaluate the Taxman’s expressions for the transport properties of polyatomic gases (Taxman,1959). In the third, non-equilibrium zero and one-dimensional rarefied gas dynamic simulations are adopted and the transport properties are computed from the non-equilibrium fluxes of momentum and energy. The three methods provide close values of the transport properties, their estimated statistical error not exceeding 3%. The experimental values are slightly underestimated, the percentage deviation being, again, few percent.

  17. Projection domain denoising method based on dictionary learning for low-dose CT image reconstruction.

    PubMed

    Zhang, Haiyan; Zhang, Liyi; Sun, Yunshan; Zhang, Jingyu

    2015-01-01

    Reducing X-ray tube current is one of the widely used methods for decreasing the radiation dose. Unfortunately, the signal-to-noise ratio (SNR) of the projection data degrades simultaneously. To improve the quality of reconstructed images, a dictionary learning based penalized weighted least-squares (PWLS) approach is proposed for sinogram denoising. The weighted least-squares considers the statistical characteristic of noise and the penalty models the sparsity of sinogram based on dictionary learning. Then reconstruct CT image using filtered back projection (FBP) algorithm from the denoised sinogram. The proposed method is particularly suitable for the projection data with low SNR. Experimental results show that the proposed method can get high-quality CT images when the signal to noise ratio of projection data declines sharply.

  18. Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

    PubMed Central

    Fahimian, Benjamin P.; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J.; Osher, Stanley J.; McNitt-Gray, Michael F.; Miao, Jianwei

    2013-01-01

    Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest

  19. Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

    SciTech Connect

    Fahimian, Benjamin P.; Zhao Yunzhe; Huang Zhifeng; Fung, Russell; Zhu Chun; Miao Jianwei; Mao Yu; Khatonabadi, Maryam; DeMarco, John J.; McNitt-Gray, Michael F.; Osher, Stanley J.

    2013-03-15

    Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest

  20. Evaluating low pass filters on SPECT reconstructed cardiac orientation estimation

    NASA Astrophysics Data System (ADS)

    Dwivedi, Shekhar

    2009-02-01

    Low pass filters can affect the quality of clinical SPECT images by smoothing. Appropriate filter and parameter selection leads to optimum smoothing that leads to a better quantification followed by correct diagnosis and accurate interpretation by the physician. This study aims at evaluating the low pass filters on SPECT reconstruction algorithms. Criteria for evaluating the filters are estimating the SPECT reconstructed cardiac azimuth and elevation angle. Low pass filters studied are butterworth, gaussian, hamming, hanning and parzen. Experiments are conducted using three reconstruction algorithms, FBP (filtered back projection), MLEM (maximum likelihood expectation maximization) and OSEM (ordered subsets expectation maximization), on four gated cardiac patient projections (two patients with stress and rest projections). Each filter is applied with varying cutoff and order for each reconstruction algorithm (only butterworth used for MLEM and OSEM). The azimuth and elevation angles are calculated from the reconstructed volume and the variation observed in the angles with varying filter parameters is reported. Our results demonstrate that behavior of hamming, hanning and parzen filter (used with FBP) with varying cutoff is similar for all the datasets. Butterworth filter (cutoff > 0.4) behaves in a similar fashion for all the datasets using all the algorithms whereas with OSEM for a cutoff < 0.4, it fails to generate cardiac orientation due to oversmoothing, and gives an unstable response with FBP and MLEM. This study on evaluating effect of low pass filter cutoff and order on cardiac orientation using three different reconstruction algorithms provides an interesting insight into optimal selection of filter parameters.

  1. Novel iterative reconstruction method for optimal dose usage in redundant CT - acquisitions

    NASA Astrophysics Data System (ADS)

    Bruder, H.; Raupach, R.; Allmendinger, T.; Kappler, S.; Sunnegardh, J.; Stierstorfer, K.; Flohr, T.

    2014-03-01

    In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the measured data provide redundant information, composite image data with high SNR can be computed. Generally, these composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution. This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution) `source' image data. It was shown that the SNR of CT image data can be improved using iterative reconstruction [1] .We present a novel iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region. The generalized update equation is formulated in image space without further referring to raw data after initial reconstruction of source and composite image data. The update equation consists of a linear combination of the previous update, a correction term constrained by the source data, and a regularization prior initialized by the composite data. The efficiency of the method is demonstrated for different applications: (i) Spectral imaging: we have analysed material decomposition data from dual energy data of our photon counting prototype scanner: the material images can be significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution: splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac imaging: the optimal phase image (`best phase') can be improved by transferring all applied over

  2. SU-D-207-04: GPU-Based 4D Cone-Beam CT Reconstruction Using Adaptive Meshing Method

    SciTech Connect

    Zhong, Z; Gu, X; Iyengar, P; Mao, W; Wang, J; Guo, X

    2015-06-15

    Purpose: Due to the limited number of projections at each phase, the image quality of a four-dimensional cone-beam CT (4D-CBCT) is often degraded, which decreases the accuracy of subsequent motion modeling. One of the promising methods is the simultaneous motion estimation and image reconstruction (SMEIR) approach. The objective of this work is to enhance the computational speed of the SMEIR algorithm using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the tetrahedral mesh based on the features of a reference phase 4D-CBCT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. After the mesh generation, the updated motion model and other phases of 4D-CBCT can be obtained by matching the 4D-CBCT projection images at each phase with the corresponding forward projections of the deformed reference phase of 4D-CBCT. The entire process of this 4D-CBCT reconstruction method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its tremendous parallel computing ability. Results: A 4D XCAT digital phantom was used to test the proposed mesh-based image reconstruction algorithm. The image Result shows both bone structures and inside of the lung are well-preserved and the tumor position can be well captured. Compared to the previous voxel-based CPU implementation of SMEIR, the proposed method is about 157 times faster for reconstructing a 10 -phase 4D-CBCT with dimension 256×256×150. Conclusion: The GPU-based parallel 4D CBCT reconstruction method uses the feature-based mesh for estimating motion model and demonstrates equivalent image Result with previous voxel-based SMEIR approach, with significantly improved computational speed.

  3. Patient-specific dose estimation for pediatric chest CT

    SciTech Connect

    Li Xiang; Samei, Ehsan; Segars, W. Paul; Sturgeon, Gregory M.; Colsher, James G.; Frush, Donald P.

    2008-12-15

    Current methods for organ and effective dose estimations in pediatric CT are largely patient generic. Physical phantoms and computer models have only been developed for standard/limited patient sizes at discrete ages (e.g., 0, 1, 5, 10, 15 years old) and do not reflect the variability of patient anatomy and body habitus within the same size/age group. In this investigation, full-body computer models of seven pediatric patients in the same size/protocol group (weight: 11.9-18.2 kg) were created based on the patients' actual multi-detector array CT (MDCT) data. Organs and structures in the scan coverage were individually segmented. Other organs and structures were created by morphing existing adult models (developed from visible human data) to match the framework defined by the segmented organs, referencing the organ volume and anthropometry data in ICRP Publication 89. Organ and effective dose of these patients from a chest MDCT scan protocol (64 slice LightSpeed VCT scanner, 120 kVp, 70 or 75 mA, 0.4 s gantry rotation period, pitch of 1.375, 20 mm beam collimation, and small body scan field-of-view) was calculated using a Monte Carlo program previously developed and validated to simulate radiation transport in the same CT system. The seven patients had normalized effective dose of 3.7-5.3 mSv/100 mAs (coefficient of variation: 10.8%). Normalized lung dose and heart dose were 10.4-12.6 mGy/100 mAs and 11.2-13.3 mGy/100 mAs, respectively. Organ dose variations across the patients were generally small for large organs in the scan coverage (<7%), but large for small organs in the scan coverage (9%-18%) and for partially or indirectly exposed organs (11%-77%). Normalized effective dose correlated weakly with body weight (correlation coefficient: r=-0.80). Normalized lung dose and heart dose correlated strongly with mid-chest equivalent diameter (lung: r=-0.99, heart: r=-0.93); these strong correlation relationships can be used to estimate patient-specific organ dose for

  4. A dual formulation of a penalized maximum likelihood x-ray CT reconstruction problem

    NASA Astrophysics Data System (ADS)

    Xu, Jingyan; Taguchi, Katsuyuki; Gullberg, Grant T.; Tsui, Benjamin M. W.

    2009-02-01

    This work studies the dual formulation of a penalized maximum likelihood reconstruction problem in x-ray CT. The primal objective function is a Poisson log-likelihood combined with a weighted cross-entropy penalty term. The dual formulation of the primal optimization problem is then derived and the optimization procedure outlined. The dual formulation better exploits the structure of the problem, which translates to faster convergence of iterative reconstruction algorithms. A gradient descent algorithm is implemented for solving the dual problem and its performance is compared with the filtered back-projection algorithm, and with the primal formulation optimized by using surrogate functions. The 3D XCAT phantom and an analytical x-ray CT simulator are used to generate noise-free and noisy CT projection data set with monochromatic and polychromatic x-ray spectrums. The reconstructed images from the dual formulation delineate the internal structures at early iterations better than the primal formulation using surrogate functions. However the body contour is slower to converge in the dual than in the primal formulation. The dual formulation demonstrate better noise-resolution tradeoff near the internal organs than the primal formulation. Since the surrogate functions in general can provide a diagonal approximation of the Hessian matrix of the objective function, further convergence speed up may be achieved by deriving the surrogate function of the dual objective function.

  5. Novel reconstruction algorithm for multiphasic cardiac imaging using multislice helical CT

    NASA Astrophysics Data System (ADS)

    Cesmeli, Erdogan; Edic, Peter M.; Iatrou, Maria; Pfoh, Armin H.

    2001-06-01

    Cardiac imaging is still a challenge to CT reconstruction algorithms due to the dynamic nature of the heart. We have developed a new reconstruction technique, called the Flexible Algorithm, which achieves high temporal resolution while it is robust to heart-rate variations. The Flexible Algorithm, first, retrospectively tags helical CT views with corresponding cardiac phases obtained from associated EKG. Next, it determines a set of views for each slice, a stack of which covers the entire heart. Subsequently, the algorithm selects an optimum subset of views to achieve the highest temporal resolution for the desired cardiac phase. Finally, it spatiotemporally filters the views in the selected subsets to reconstruct slices. We tested the performance of our algorithm using both a dynamic analytical phantom and clinical data. Preliminary results indicate that the Flexible Algorithm obtains improved spatiotemporal resolution for a large range of heart rates and variations than standard algorithms do. By providing improved image quality at any desired cardiac phase, and robustness to heart rate variations, the Flexible Algorithm enables cardiac applications in CT, including those that benefit from multiphase information.

  6. Objective assessment of image quality and dose reduction in CT iterative reconstruction

    SciTech Connect

    Vaishnav, J. Y. Jung, W. C.; Popescu, L. M.; Zeng, R.; Myers, K. J.

    2014-07-15

    Purpose: Iterative reconstruction (IR) algorithms have the potential to reduce radiation dose in CT diagnostic imaging. As these algorithms become available on the market, a standardizable method of quantifying the dose reduction that a particular IR method can achieve would be valuable. Such a method would assist manufacturers in making promotional claims about dose reduction, buyers in comparing different devices, physicists in independently validating the claims, and the United States Food and Drug Administration in regulating the labeling of CT devices. However, the nonlinear nature of commercially available IR algorithms poses challenges to objectively assessing image quality, a necessary step in establishing the amount of dose reduction that a given IR algorithm can achieve without compromising that image quality. This review paper seeks to consolidate information relevant to objectively assessing the quality of CT IR images, and thereby measuring the level of dose reduction that a given IR algorithm can achieve. Methods: The authors discuss task-based methods for assessing the quality of CT IR images and evaluating dose reduction. Results: The authors explain and review recent literature on signal detection and localization tasks in CT IR image quality assessment, the design of an appropriate phantom for these tasks, possible choices of observers (including human and model observers), and methods of evaluating observer performance. Conclusions: Standardizing the measurement of dose reduction is a problem of broad interest to the CT community and to public health. A necessary step in the process is the objective assessment of CT image quality, for which various task-based methods may be suitable. This paper attempts to consolidate recent literature that is relevant to the development and implementation of task-based methods for the assessment of CT IR image quality.

  7. 3D cardiac motion reconstruction from CT data and tagged MRI.

    PubMed

    Wang, Xiaoxu; Mihalef, Viorel; Qian, Zhen; Voros, Szilard; Metaxas, Dimitris

    2012-01-01

    In this paper we present a novel method for left ventricle (LV) endocardium motion reconstruction using high resolution CT data and tagged MRI. High resolution CT data provide anatomic details on the LV endocardial surface, such as the papillary muscle and trabeculae carneae. Tagged MRI provides better time resolution. The combination of these two imaging techniques can give us better understanding on left ventricle motion. The high resolution CT images are segmented with mean shift method and generate the LV endocardium mesh. The meshless deformable model built with high resolution endocardium surface from CT data fit to the tagged MRI of the same phase. 3D deformation of the myocardium is computed with the Lagrangian dynamics and local Laplacian deformation. The segmented inner surface of left ventricle is compared with the heart inner surface picture and show high agreement. The papillary muscles are attached to the inner surface with roots. The free wall of the left ventricle inner surface is covered with trabeculae carneae. The deformation of the heart wall and the papillary muscle in the first half of the cardiac cycle is presented. The motion reconstruction results are very close to the live heart video. PMID:23366825

  8. 3D Cardiac Motion Reconstruction from CT Data and Tagged MRI

    PubMed Central

    Wang, Xiaoxu; Mihalef, Viorel; Qian, Zhen; Voros, Szilard; Metaxas, Dimitris

    2016-01-01

    In this paper we present a novel method for left ventricle (LV) endocardium motion reconstruction using high resolution CT data and tagged MRI. High resolution CT data provide anatomic details on the LV endocardial surface, such as the papillary muscle and trabeculae carneae. Tagged MRI provides better time resolution. The combination of these two imaging techniques can give us better understanding on left ventricle motion. The high resolution CT images are segmented with mean shift method and generate the LV endocardium mesh. The meshless deformable model built with high resolution endocardium surface from CT data fit to the tagged MRI of the same phase. 3D deformation of the myocardium is computed with the Lagrangian dynamics and local Laplacian deformation. The segmented inner surface of left ventricle is compared with the heart inner surface picture and show high agreement. The papillary muscles are attached to the inner surface with roots. The free wall of the left ventricle inner surface is covered with trabeculae carneae. The deformation of the heart wall and the papillary muscle in the first half of the cardiac cycle is presented. The motion reconstruction results are very close to the live heart video. PMID:23366825

  9. Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization

    NASA Astrophysics Data System (ADS)

    Rigie, David S.; La Rivière, Patrick J.

    2015-02-01

    We explore the use of the recently proposed ‘total nuclear variation’ (TVN) as a regularizer for reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension of the total variation (TV) to vector-valued images and has the advantage of encouraging common edge locations and a shared gradient direction among image channels. We show how it can be incorporated into a general, data-constrained reconstruction framework and derive update equations based on the first-order, primal-dual algorithm of Chambolle and Pock. Early simulation studies based on the numerical XCAT phantom indicate that the inter-channel coupling introduced by the TVN leads to better preservation of image features at high levels of regularization, compared to independent, channel-by-channel TV reconstructions.

  10. TU-A-12A-07: CT-Based Biomarkers to Characterize Lung Lesion: Effects of CT Dose, Slice Thickness and Reconstruction Algorithm Based Upon a Phantom Study

    SciTech Connect

    Zhao, B; Tan, Y; Tsai, W; Lu, L; Schwartz, L; So, J; Goldman, J; Lu, Z

    2014-06-15

    Purpose: Radiogenomics promises the ability to study cancer tumor genotype from the phenotype obtained through radiographic imaging. However, little attention has been paid to the sensitivity of image features, the image-based biomarkers, to imaging acquisition techniques. This study explores the impact of CT dose, slice thickness and reconstruction algorithm on measuring image features using a thorax phantom. Methods: Twentyfour phantom lesions of known volume (1 and 2mm), shape (spherical, elliptical, lobular and spicular) and density (-630, -10 and +100 HU) were scanned on a GE VCT at four doses (25, 50, 100, and 200 mAs). For each scan, six image series were reconstructed at three slice thicknesses of 5, 2.5 and 1.25mm with continuous intervals, using the lung and standard reconstruction algorithms. The lesions were segmented with an in-house 3D algorithm. Fifty (50) image features representing lesion size, shape, edge, and density distribution/texture were computed. Regression method was employed to analyze the effect of CT dose, slice of thickness and reconstruction algorithm on these features adjusting 3 confounding factors (size, density and shape of phantom lesions). Results: The coefficients of CT dose, slice thickness and reconstruction algorithm are presented in Table 1 in the supplementary material. No significant difference was found between the image features calculated on low dose CT scans (25mAs and 50mAs). About 50% texture features were found statistically different between low doses and high doses (100 and 200mAs). Significant differences were found for almost all features when calculated on 1.25mm, 2.5mm, and 5mm slice thickness images. Reconstruction algorithms significantly affected all density-based image features, but not morphological features. Conclusions: There is a great need to standardize the CT imaging protocols for radiogenomics study because CT dose, slice thickness and reconstruction algorithm impact quantitative image features to

  11. Handling of long objects in iterative improvement of nonexact reconstruction in helical cone-beam CT.

    PubMed

    Magnusson, Maria; Danielsson, Per-Erik; Sunnegårdh, Johan

    2006-07-01

    In medical helical cone-beam CT, it is common that the region-of-interest (ROI) is contained inside the helix cylinder, while the complete object is long and extends outside the top and the bottom of the cylinder. This is the Long Object Problem. Analytical reconstruction methods for helical cone-beam CT have been designed to handle this problem. It has been shown that a moderate amount of over-scanning is sufficient for reconstruction of a certain ROI. The over-scanning projection rays travel both through the ROI, as well as outside the ROI. This is unfortunate for iterative methods since it seems impossible to compute accurate values for the projection rays which travel partly inside and partly outside the ROI. Therefore, it seems that the useful ROI will diminish for every iteration step. We propose the following solution to the problem. First, we reconstruct volume regions also outside the ROI. These volume regions will certainly be incompletely reconstructed, but our experimental results show that they serve well for projection generation. This is rather counter-intuitive and contradictory to our initial assumptions. Second, we use careful extrapolation and masking of projection data. This is not a general necessity, but needed for the chosen iterative algorithm, which includes rebinning and iterative filtered backprojection. Our idea here was to use an approximate reconstruction method which gives cone-beam artifacts and then improve the reconstructed result by iterative filtered backprojection. The experimental results seem very encouraging. The cone-beam artifacts can indeed be removed. Even voxels close to the boundary of the ROI are as well enhanced by the iterative loop as those in the middle of the ROI.

  12. Low dose dynamic CT myocardial perfusion imaging using a statistical iterative reconstruction method

    SciTech Connect

    Tao, Yinghua; Chen, Guang-Hong; Hacker, Timothy A.; Raval, Amish N.; Van Lysel, Michael S.; Speidel, Michael A.

    2014-07-15

    Purpose: Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition. Methods: Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution. Results: Forin vivo studies, the 500 mA FBP maps gave −88.4%, −96.0%, −76.7%, and −65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring −94.7%, −81.6%, −84.0%, and −72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, −11.8%, and −3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was −9.7%, 8.8%, −3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937

  13. Reconstruction of a cone-beam CT image via forward iterative projection matching

    SciTech Connect

    Brock, R. Scott; Docef, Alen; Murphy, Martin J.

    2010-12-15

    Purpose: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set. Methods: A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections. Results: When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining

  14. Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance

    PubMed Central

    Li, Ke; Tang, Jie; Chen, Guang-Hong

    2014-01-01

    Purpose: To reduce radiation dose in CT imaging, the statistical model based iterative reconstruction (MBIR) method has been introduced for clinical use. Based on the principle of MBIR and its nonlinear nature, the noise performance of MBIR is expected to be different from that of the well-understood filtered backprojection (FBP) reconstruction method. The purpose of this work is to experimentally assess the unique noise characteristics of MBIR using a state-of-the-art clinical CT system. Methods: Three physical phantoms, including a water cylinder and two pediatric head phantoms, were scanned in axial scanning mode using a 64-slice CT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI) at seven different mAs levels (5, 12.5, 25, 50, 100, 200, 300). At each mAs level, each phantom was repeatedly scanned 50 times to generate an image ensemble for noise analysis. Both the FBP method with a standard kernel and the MBIR method (Veo®, GE Healthcare, Waukesha, WI) were used for CT image reconstruction. Three-dimensional (3D) noise power spectrum (NPS), two-dimensional (2D) NPS, and zero-dimensional NPS (noise variance) were assessed both globally and locally. Noise magnitude, noise spatial correlation, noise spatial uniformity and their dose dependence were examined for the two reconstruction methods. Results: (1) At each dose level and at each frequency, the magnitude of the NPS of MBIR was smaller than that of FBP. (2) While the shape of the NPS of FBP was dose-independent, the shape of the NPS of MBIR was strongly dose-dependent; lower dose lead to a “redder” NPS with a lower mean frequency value. (3) The noise standard deviation (σ) of MBIR and dose were found to be related through a power law of σ ∝ (dose)−β with the component β ≈ 0.25, which violated the classical σ ∝ (dose)−0.5 power law in FBP. (4) With MBIR, noise reduction was most prominent for thin image slices. (5) MBIR lead to better noise spatial uniformity when compared

  15. Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance

    SciTech Connect

    Li, Ke; Tang, Jie; Chen, Guang-Hong

    2014-04-15

    Purpose: To reduce radiation dose in CT imaging, the statistical model based iterative reconstruction (MBIR) method has been introduced for clinical use. Based on the principle of MBIR and its nonlinear nature, the noise performance of MBIR is expected to be different from that of the well-understood filtered backprojection (FBP) reconstruction method. The purpose of this work is to experimentally assess the unique noise characteristics of MBIR using a state-of-the-art clinical CT system. Methods: Three physical phantoms, including a water cylinder and two pediatric head phantoms, were scanned in axial scanning mode using a 64-slice CT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI) at seven different mAs levels (5, 12.5, 25, 50, 100, 200, 300). At each mAs level, each phantom was repeatedly scanned 50 times to generate an image ensemble for noise analysis. Both the FBP method with a standard kernel and the MBIR method (Veo{sup ®}, GE Healthcare, Waukesha, WI) were used for CT image reconstruction. Three-dimensional (3D) noise power spectrum (NPS), two-dimensional (2D) NPS, and zero-dimensional NPS (noise variance) were assessed both globally and locally. Noise magnitude, noise spatial correlation, noise spatial uniformity and their dose dependence were examined for the two reconstruction methods. Results: (1) At each dose level and at each frequency, the magnitude of the NPS of MBIR was smaller than that of FBP. (2) While the shape of the NPS of FBP was dose-independent, the shape of the NPS of MBIR was strongly dose-dependent; lower dose lead to a “redder” NPS with a lower mean frequency value. (3) The noise standard deviation (σ) of MBIR and dose were found to be related through a power law of σ ∝ (dose){sup −β} with the component β ≈ 0.25, which violated the classical σ ∝ (dose){sup −0.5} power law in FBP. (4) With MBIR, noise reduction was most prominent for thin image slices. (5) MBIR lead to better noise spatial

  16. Convex-hull mass estimates of the dodo (Raphus cucullatus): application of a CT-based mass estimation technique

    PubMed Central

    O’Mahoney, Thomas G.; Kitchener, Andrew C.; Manning, Phillip L.; Sellers, William I.

    2016-01-01

    The external appearance of the dodo (Raphus cucullatus, Linnaeus, 1758) has been a source of considerable intrigue, as contemporaneous accounts or depictions are rare. The body mass of the dodo has been particularly contentious, with the flightless pigeon alternatively reconstructed as slim or fat depending upon the skeletal metric used as the basis for mass prediction. Resolving this dichotomy and obtaining a reliable estimate for mass is essential before future analyses regarding dodo life history, physiology or biomechanics can be conducted. Previous mass estimates of the dodo have relied upon predictive equations based upon hind limb dimensions of extant pigeons. Yet the hind limb proportions of dodo have been found to differ considerably from those of their modern relatives, particularly with regards to midshaft diameter. Therefore, application of predictive equations to unusually robust fossil skeletal elements may bias mass estimates. We present a whole-body computed tomography (CT) -based mass estimation technique for application to the dodo. We generate 3D volumetric renders of the articulated skeletons of 20 species of extant pigeons, and wrap minimum-fit ‘convex hulls’ around their bony extremities. Convex hull volume is subsequently regressed against mass to generate predictive models based upon whole skeletons. Our best-performing predictive model is characterized by high correlation coefficients and low mean squared error (a = − 2.31, b = 0.90, r2 = 0.97, MSE = 0.0046). When applied to articulated composite skeletons of the dodo (National Museums Scotland, NMS.Z.1993.13; Natural History Museum, NHMUK A.9040 and S/1988.50.1), we estimate eviscerated body masses of 8–10.8 kg. When accounting for missing soft tissues, this may equate to live masses of 10.6–14.3 kg. Mass predictions presented here overlap at the lower end of those previously published, and support recent suggestions of a relatively slim dodo. CT-based reconstructions provide a

  17. Convex-hull mass estimates of the dodo (Raphus cucullatus): application of a CT-based mass estimation technique.

    PubMed

    Brassey, Charlotte A; O'Mahoney, Thomas G; Kitchener, Andrew C; Manning, Phillip L; Sellers, William I

    2016-01-01

    The external appearance of the dodo (Raphus cucullatus, Linnaeus, 1758) has been a source of considerable intrigue, as contemporaneous accounts or depictions are rare. The body mass of the dodo has been particularly contentious, with the flightless pigeon alternatively reconstructed as slim or fat depending upon the skeletal metric used as the basis for mass prediction. Resolving this dichotomy and obtaining a reliable estimate for mass is essential before future analyses regarding dodo life history, physiology or biomechanics can be conducted. Previous mass estimates of the dodo have relied upon predictive equations based upon hind limb dimensions of extant pigeons. Yet the hind limb proportions of dodo have been found to differ considerably from those of their modern relatives, particularly with regards to midshaft diameter. Therefore, application of predictive equations to unusually robust fossil skeletal elements may bias mass estimates. We present a whole-body computed tomography (CT) -based mass estimation technique for application to the dodo. We generate 3D volumetric renders of the articulated skeletons of 20 species of extant pigeons, and wrap minimum-fit 'convex hulls' around their bony extremities. Convex hull volume is subsequently regressed against mass to generate predictive models based upon whole skeletons. Our best-performing predictive model is characterized by high correlation coefficients and low mean squared error (a = - 2.31, b = 0.90, r (2) = 0.97, MSE = 0.0046). When applied to articulated composite skeletons of the dodo (National Museums Scotland, NMS.Z.1993.13; Natural History Museum, NHMUK A.9040 and S/1988.50.1), we estimate eviscerated body masses of 8-10.8 kg. When accounting for missing soft tissues, this may equate to live masses of 10.6-14.3 kg. Mass predictions presented here overlap at the lower end of those previously published, and support recent suggestions of a relatively slim dodo. CT-based reconstructions provide a means of

  18. Convex-hull mass estimates of the dodo (Raphus cucullatus): application of a CT-based mass estimation technique.

    PubMed

    Brassey, Charlotte A; O'Mahoney, Thomas G; Kitchener, Andrew C; Manning, Phillip L; Sellers, William I

    2016-01-01

    The external appearance of the dodo (Raphus cucullatus, Linnaeus, 1758) has been a source of considerable intrigue, as contemporaneous accounts or depictions are rare. The body mass of the dodo has been particularly contentious, with the flightless pigeon alternatively reconstructed as slim or fat depending upon the skeletal metric used as the basis for mass prediction. Resolving this dichotomy and obtaining a reliable estimate for mass is essential before future analyses regarding dodo life history, physiology or biomechanics can be conducted. Previous mass estimates of the dodo have relied upon predictive equations based upon hind limb dimensions of extant pigeons. Yet the hind limb proportions of dodo have been found to differ considerably from those of their modern relatives, particularly with regards to midshaft diameter. Therefore, application of predictive equations to unusually robust fossil skeletal elements may bias mass estimates. We present a whole-body computed tomography (CT) -based mass estimation technique for application to the dodo. We generate 3D volumetric renders of the articulated skeletons of 20 species of extant pigeons, and wrap minimum-fit 'convex hulls' around their bony extremities. Convex hull volume is subsequently regressed against mass to generate predictive models based upon whole skeletons. Our best-performing predictive model is characterized by high correlation coefficients and low mean squared error (a = - 2.31, b = 0.90, r (2) = 0.97, MSE = 0.0046). When applied to articulated composite skeletons of the dodo (National Museums Scotland, NMS.Z.1993.13; Natural History Museum, NHMUK A.9040 and S/1988.50.1), we estimate eviscerated body masses of 8-10.8 kg. When accounting for missing soft tissues, this may equate to live masses of 10.6-14.3 kg. Mass predictions presented here overlap at the lower end of those previously published, and support recent suggestions of a relatively slim dodo. CT-based reconstructions provide a means of

  19. Robust image reconstruction enhancement based on Gaussian mixture model estimation

    NASA Astrophysics Data System (ADS)

    Zhao, Fan; Zhao, Jian; Han, Xizhen; Wang, He; Liu, Bochao

    2016-03-01

    The low quality of an image is often characterized by low contrast and blurred edge details. Gradients have a direct relationship with image edge details. More specifically, the larger the gradients, the clearer the image details become. Robust image reconstruction enhancement based on Gaussian mixture model estimation is proposed here. First, image is transformed to its gradient domain, obtaining the gradient histogram. Second, the gradient histogram is estimated and extended using a Gaussian mixture model, and the predetermined function is constructed. Then, using histogram specification technology, the gradient field is enhanced with the constraint of the predetermined function. Finally, a matrix sine transform-based method is applied to reconstruct the enhanced image from the enhanced gradient field. Experimental results show that the proposed algorithm can effectively enhance different types of images such as medical image, aerial image, and visible image, providing high-quality image information for high-level processing.

  20. Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods

    SciTech Connect

    Chen, Baiyu; Christianson, Olav; Wilson, Joshua M.; Samei, Ehsan

    2014-07-15

    Purpose: For nonlinear iterative image reconstructions (IR), the computed tomography (CT) noise and resolution properties can depend on the specific imaging conditions, such as lesion contrast and image noise level. Therefore, it is imperative to develop a reliable method to measure the noise and resolution properties under clinically relevant conditions. This study aimed to develop a robust methodology to measure the three-dimensional CT noise and resolution properties under such conditions and to provide guidelines to achieve desirable levels of accuracy and precision. Methods: The methodology was developed based on a previously reported CT image quality phantom. In this methodology, CT noise properties are measured in the uniform region of the phantom in terms of a task-based 3D noise-power spectrum (NPS{sub task}). The in-plane resolution properties are measured in terms of the task transfer function (TTF) by applying a radial edge technique to the rod inserts in the phantom. The z-direction resolution properties are measured from a supplemental phantom, also in terms of the TTF. To account for the possible nonlinearity of IR, the NPS{sub task} is measured with respect to the noise magnitude, and the TTF with respect to noise magnitude and edge contrast. To determine the accuracy and precision of the methodology, images of known noise and resolution properties were simulated. The NPS{sub task} and TTF were measured on the simulated images and compared to the truth, with criteria established to achieve NPS{sub task} and TTF measurements with <10% error. To demonstrate the utility of this methodology, measurements were performed on a commercial CT system using five dose levels, two slice thicknesses, and three reconstruction algorithms (filtered backprojection, FBP; iterative reconstruction in imaging space, IRIS; and sinogram affirmed iterative reconstruction with strengths of 5, SAFIRE5). Results: To achieve NPS{sub task} measurements with <10% error, the

  1. Coupling the use of anti-scatter grid with analytical scatter estimation in cone beam CT

    NASA Astrophysics Data System (ADS)

    Rinkel, J.; Gerfault, L.; Estève, F.; Dinten, J.-M.

    2007-03-01

    Cone-Beam Computed Tomography (CBCT) enables three-dimensional imaging with isotropic resolution. X-ray scatter estimation is a big challenge for quantitative CBCT imaging: even in the presence of anti-scatter grid, the scatter level is significantly higher on cone beam systems compared to collimated fan beam systems. The effects of this scattered radiation include cupping artifacts, streaks, and quantification inaccuracies. In this paper, a scatter management process for tomographic projections, without supplementary on-line acquisition, is presented. The scattered radiation is corrected using a method based on scatter calibration through off-line acquisitions. This is combined with on-line analytical transformation based on physical equations, to perform an estimation adapted to the object observed. This approach has been previously applied to a system without anti-scatter grid. The focus of this paper is to show how to combine this approach with an anti-scatter grid. First, the interest of the grid is evaluated in terms of noise to signal ratio and scatter rejection. Then, the method of scatter correction is evaluated by testing it on an anthropomorphic phantom of thorax. The reconstructed volume of the phantom is compared to that obtained with a strongly collimated conventional multi-slice CT scanner. The new method provides results that closely agree with the conventional CT scanner, eliminating cupping artifacts and significantly improving quantification.

  2. A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

    SciTech Connect

    Yan, Hao; Folkerts, Michael; Jiang, Steve B. E-mail: steve.jiang@UTSouthwestern.edu; Jia, Xun E-mail: steve.jiang@UTSouthwestern.edu; Zhen, Xin; Li, Yongbao; Pan, Tinsu; Cervino, Laura

    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 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. 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

  3. Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction

    NASA Astrophysics Data System (ADS)

    Benson, T. M.; Gregor, J.

    2006-09-01

    Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method.

  4. 3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

    PubMed Central

    Cai, Ailong; Zhang, Hanming; Li, Lei; Xi, Xiaoqi; Guan, Min; Li, Jianxin

    2014-01-01

    Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256 × 256 × 256 and 36 projections of the size of 512 × 512. PMID:25045400

  5. ECG-gated HYPR reconstruction for undersampled CT myocardial perfusion imaging

    NASA Astrophysics Data System (ADS)

    Speidel, Michael A.; Van Lysel, Michael S.; Reeder, Scott B.; Supanich, Mark; Nett, Brian E.; Zambelli, Joseph; Chang, Su Min; Hsieh, Jiang; Chen, Guang-Hong; Mistretta, Charles A.

    2007-03-01

    In this study we develop a novel ECG-gated method of HYPR (HighlY constrained backPRojection) CT reconstruction for low-dose myocardial perfusion imaging and present its first application in a porcine model. HYPR is a method of reconstructing time-resolved images from view-undersampled projection data. Scanning and reconstruction techniques were explored using x-ray projections from a 50 sec contrast-enhanced axial scan of a 47 kg swine on a 64-slice MDCT system. Scans were generated with view undersampling factors from 2 to 10. A HYPR reconstruction algorithm was developed in which a fully-sampled composite image is generated from views collected from multiple cardiac cycles within a diastolic window. A time frame image for a heartbeat was produced by modifying the composite with projections from the cycle of interest. Heart rate variations were handled by automatically selecting cardiac window size and number of cycles per composite within defined limits. Cardiac window size averaged 35% of the R-R interval for 2x undersampling and increased to 64% R-R using 10x undersampling. The selected window size and cycles per composite was sensitive to synchrony between heart rate, gantry rate, and the view undersampling pattern. Temporal dynamics and perfusion metrics measured in conventional short-scan (FBP) images were well-reproduced in the undersampled HYPR time series. Mean transit times determined from HYPR myocardial time-density curves agreed to within 8% with the FBP results. The results indicate potential for an order of magnitude reduction in dose requirement per image in cardiac perfusion CT via undersampled scanning and ECG-gated HYPR reconstruction.

  6. The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique

    PubMed Central

    Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

    2015-01-01

    Objective: To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. Methods: 27 consecutive patients (mean body mass index: 23.55 kg m−2 underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. Results: At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19–49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Conclusion: Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. Advances in knowledge: This study represents the first

  7. The PRIMA collaboration: Preliminary results in FBP reconstruction of pCT data

    NASA Astrophysics Data System (ADS)

    Vanzi, Eleonora; Bruzzi, Mara; Bucciolini, Marta; Cirrone, G. A. Pablo; Civinini, Carlo; Cuttone, Giacomo; Lo Presti, Domenico; Pallotta, Stefania; Pugliatti, Cristina; Randazzo, Nunzio; Romano, Francesco; Scaringella, Monica; Sipala, Valeria; Stancampiano, Concetta; Talamonti, Cinzia; Zani, Margherita

    2013-12-01

    A first prototype of proton Computed Tomography (pCT) scanner, made of four planes and a calorimeter, has been developed by the PRIMA (PRoton IMAging) Italian collaboration and first results concerning tomographic image reconstruction of experimentally acquired data are discussed in this paper. The Filtered Back-Projection (FBP) algorithm was used to reconstruct projections of a phantom acquired with a 62 MeV proton beam. Image noise and spatial resolution were assessed for different parameters of the filter used, with and without selection strategies on proton directions. A satisfactory image quality (0.88 mm resolution and 2.5% noise) was achieved even when the backprojection line was defined using only the line connecting the impact points on the second and third planes and all the data were used, irrespective of the proton direction and residual energy. Probably due to the specific detector-phantom arrangement used in this experiment and due to the substantial reduction of the number of useful events, cuts on proton directions did not increase the image resolution significantly. The results confirm the good performances of the PRIMA scanner prototype. They also demonstrate that FBP can produce images of sufficient quality to be used for patient positioning and to initialize iterative pCT reconstruction methods.

  8. Acceleration of EM-Based 3D CT Reconstruction Using FPGA.

    PubMed

    Choi, Young-Kyu; Cong, Jason

    2016-06-01

    Reducing radiation doses is one of the key concerns in computed tomography (CT) based 3D reconstruction. Although iterative methods such as the expectation maximization (EM) algorithm can be used to address this issue, applying this algorithm to practice is difficult due to the long execution time. Our goal is to decrease this long execution time to an order of a few minutes, so that low-dose 3D reconstruction can be performed even in time-critical events. In this paper we introduce a novel parallel scheme that takes advantage of numerous block RAMs on field-programmable gate arrays (FPGAs). Also, an external memory bandwidth reduction strategy is presented to reuse both the sinogram and the voxel intensity. Moreover, a customized processing engine based on the FPGA is presented to increase overall throughput while reducing the logic consumption. Finally, a hardware and software flow is proposed to quickly construct a design for various CT machines. The complete reconstruction system is implemented on an FPGA-based server-class node. Experiments on actual patient data show that a 26.9 × speedup can be achieved over a 16-thread multicore CPU implementation.

  9. Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.

    PubMed

    Liu, Yi; Shangguan, Hong; Zhang, Quan; Zhu, Hongqing; Shu, Huazhong; Gui, Zhiguo

    2015-05-01

    It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method.

  10. Acceleration of EM-Based 3D CT Reconstruction Using FPGA.

    PubMed

    Choi, Young-Kyu; Cong, Jason

    2016-06-01

    Reducing radiation doses is one of the key concerns in computed tomography (CT) based 3D reconstruction. Although iterative methods such as the expectation maximization (EM) algorithm can be used to address this issue, applying this algorithm to practice is difficult due to the long execution time. Our goal is to decrease this long execution time to an order of a few minutes, so that low-dose 3D reconstruction can be performed even in time-critical events. In this paper we introduce a novel parallel scheme that takes advantage of numerous block RAMs on field-programmable gate arrays (FPGAs). Also, an external memory bandwidth reduction strategy is presented to reuse both the sinogram and the voxel intensity. Moreover, a customized processing engine based on the FPGA is presented to increase overall throughput while reducing the logic consumption. Finally, a hardware and software flow is proposed to quickly construct a design for various CT machines. The complete reconstruction system is implemented on an FPGA-based server-class node. Experiments on actual patient data show that a 26.9 × speedup can be achieved over a 16-thread multicore CPU implementation. PMID:26462240

  11. Implementation and evaluation of two helical CT reconstruction algorithms in CIVA

    NASA Astrophysics Data System (ADS)

    Banjak, H.; Costin, M.; Vienne, C.; Kaftandjian, V.

    2016-02-01

    The large majority of industrial CT systems reconstruct the 3D volume by using an acquisition on a circular trajec-tory. However, when inspecting long objects which are highly anisotropic, this scanning geometry creates severe artifacts in the reconstruction. For this reason, the use of an advanced CT scanning method like helical data acquisition is an efficient way to address this aspect known as the long-object problem. Recently, several analytically exact and quasi-exact inversion formulas for helical cone-beam reconstruction have been proposed. Among them, we identified two algorithms of interest for our case. These algorithms are exact and of filtered back-projection structure. In this work we implemented the filtered-backprojection (FBP) and backprojection-filtration (BPF) algorithms of Zou and Pan (2004). For performance evaluation, we present a numerical compari-son of the two selected algorithms with the helical FDK algorithm using both complete (noiseless and noisy) and truncated data generated by CIVA (the simulation platform for non-destructive testing techniques developed at CEA).

  12. A fast CT reconstruction scheme for a general multi-core PC.

    PubMed

    Zeng, Kai; Bai, Erwei; Wang, Ge

    2007-01-01

    Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors. PMID:18256731

  13. Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

    NASA Astrophysics Data System (ADS)

    Gu, Renliang; Dogandžić, Aleksandar

    2015-03-01

    We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov's proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme.

  14. Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

    SciTech Connect

    Gu, Renliang E-mail: ald@iastate.edu; Dogandžić, Aleksandar E-mail: ald@iastate.edu

    2015-03-31

    We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov’s proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme.

  15. Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: A pediatric oncology perspective

    SciTech Connect

    Brady, S. L.; Yee, B. S.; Kaufman, R. A.

    2012-09-15

    Purpose: This study demonstrates a means of implementing an adaptive statistical iterative reconstruction (ASiR Trade-Mark-Sign ) technique for dose reduction in computed tomography (CT) while maintaining similar noise levels in the reconstructed image. The effects of image quality and noise texture were assessed at all implementation levels of ASiR Trade-Mark-Sign . Empirically derived dose reduction limits were established for ASiR Trade-Mark-Sign for imaging of the trunk for a pediatric oncology population ranging from 1 yr old through adolescence/adulthood. Methods: Image quality was assessed using metrics established by the American College of Radiology (ACR) CT accreditation program. Each image quality metric was tested using the ACR CT phantom with 0%-100% ASiR Trade-Mark-Sign blended with filtered back projection (FBP) reconstructed images. Additionally, the noise power spectrum (NPS) was calculated for three common reconstruction filters of the trunk. The empirically derived limitations on ASiR Trade-Mark-Sign implementation for dose reduction were assessed using (1, 5, 10) yr old and adolescent/adult anthropomorphic phantoms. To assess dose reduction limits, the phantoms were scanned in increments of increased noise index (decrementing mA using automatic tube current modulation) balanced with ASiR Trade-Mark-Sign reconstruction to maintain noise equivalence of the 0% ASiR Trade-Mark-Sign image. Results: The ASiR Trade-Mark-Sign algorithm did not produce any unfavorable effects on image quality as assessed by ACR criteria. Conversely, low-contrast resolution was found to improve due to the reduction of noise in the reconstructed images. NPS calculations demonstrated that images with lower frequency noise had lower noise variance and coarser graininess at progressively higher percentages of ASiR Trade-Mark-Sign reconstruction; and in spite of the similar magnitudes of noise, the image reconstructed with 50% or more ASiR Trade-Mark-Sign presented a more

  16. Estimation of food consumption. Hanford Environmental Dose Reconstruction Project

    SciTech Connect

    Callaway, J.M. Jr.

    1992-04-01

    The research reported in this document was conducted as a part of the Hanford Environmental Dose Reconstruction (HEDR) Project. The objective of the HEDR Project is to estimate the radiation doses that people could have received from operations at the Hanford Site. Information required to estimate these doses includes estimates of the amounts of potentially contaminated foods that individuals in the region consumed during the study period. In that general framework, the objective of the Food Consumption Task was to develop a capability to provide information about the parameters of the distribution(s) of daily food consumption for representative groups in the population for selected years during the study period. This report describes the methods and data used to estimate food consumption and presents the results developed for Phase I of the HEDR Project.

  17. A simple, direct method for x-ray scatter estimation and correction in digital radiography and cone-beam CT

    SciTech Connect

    Siewerdsen, J.H.; Daly, M.J.; Bakhtiar, B.

    2006-01-15

    X-ray scatter poses a significant limitation to image quality in cone-beam CT (CBCT), resulting in contrast reduction, image artifacts, and lack of CT number accuracy. We report the performance of a simple scatter correction method in which scatter fluence is estimated directly in each projection from pixel values near the edge of the detector behind the collimator leaves. The algorithm operates on the simple assumption that signal in the collimator shadow is attributable to x-ray scatter, and the 2D scatter fluence is estimated by interpolating between pixel values measured along the top and bottom edges of the detector behind the collimator leaves. The resulting scatter fluence estimate is subtracted from each projection to yield an estimate of the primary-only images for CBCT reconstruction. Performance was investigated in phantom experiments on an experimental CBCT benchtop, and the effect on image quality was demonstrated in patient images (head, abdomen, and pelvis sites) obtained on a preclinical system for CBCT-guided radiation therapy. The algorithm provides significant reduction in scatter artifacts without compromise in contrast-to-noise ratio (CNR). For example, in a head phantom, cupping artifact was essentially eliminated, CT number accuracy was restored to within 3%, and CNR (breast-to-water) was improved by up to 50%. Similarly in a body phantom, cupping artifact was reduced by at least a factor of 2 without loss in CNR. Patient images demonstrate significantly increased uniformity, accuracy, and contrast, with an overall improvement in image quality in all sites investigated. Qualitative evaluation illustrates that soft-tissue structures that are otherwise undetectable are clearly delineated in scatter-corrected reconstructions. Since scatter is estimated directly in each projection, the algorithm is robust with respect to system geometry, patient size and heterogeneity, patient motion, etc. Operating without prior information, analytical modeling

  18. Compressive sampling based interior reconstruction for dynamic carbon nanotube micro-CT.

    PubMed

    Yu, Hengyong; Cao, Guohua; Burk, Laurel; Lee, Yueh; Lu, Jianping; Santago, Pete; Zhou, Otto; Wang, Ge

    2009-01-01

    In the computed tomography (CT) field, one recent invention is the so-called carbon nanotube (CNT) based field emission x-ray technology. On the other hand, compressive sampling (CS) based interior tomography is a new innovation. Combining the strengths of these two novel subjects, we apply the interior tomography technique to local mouse cardiac imaging using respiration and cardiac gating with a CNT based micro-CT scanner. The major features of our method are: (1) it does not need exact prior knowledge inside an ROI; and (2) two orthogonal scout projections are employed to regularize the reconstruction. Both numerical simulations and in vivo mouse studies are performed to demonstrate the feasibility of our methodology. PMID:19923686

  19. CT scans and 3D reconstructions of Florida manatee (Trichechus manatus latirostris) heads and ear bones.

    PubMed

    Chapla, Marie E; Nowacek, Douglas P; Rommel, Sentiel A; Sadler, Valerie M

    2007-06-01

    The auditory anatomy of the Florida manatee (Trichechus manatus latirostris) was investigated using computerized tomography (CT), three-dimensional reconstructions, and traditional dissection of heads removed during necropsy. The densities (kg/m3) of the soft tissues of the head were measured directly using the displacement method and those of the soft tissues and bone were calculated from CT measurements (Hounsfield units). The manatee's fatty tissue was significantly less dense than the other soft tissues within the head (p<0.05). The squamosal bone was significantly less dense than the other bones of the head (p<0.05). Measurements of the ear bones (tympanic, periotic, malleus, incus, and stapes) collected during dissection revealed that the ossicular chain was overly massive for the mass of the tympanoperiotic complex.

  20. CT scans and 3D reconstructions of Florida manatee (Trichechus manatus latirostris) heads and ear bones.

    PubMed

    Chapla, Marie E; Nowacek, Douglas P; Rommel, Sentiel A; Sadler, Valerie M

    2007-06-01

    The auditory anatomy of the Florida manatee (Trichechus manatus latirostris) was investigated using computerized tomography (CT), three-dimensional reconstructions, and traditional dissection of heads removed during necropsy. The densities (kg/m3) of the soft tissues of the head were measured directly using the displacement method and those of the soft tissues and bone were calculated from CT measurements (Hounsfield units). The manatee's fatty tissue was significantly less dense than the other soft tissues within the head (p<0.05). The squamosal bone was significantly less dense than the other bones of the head (p<0.05). Measurements of the ear bones (tympanic, periotic, malleus, incus, and stapes) collected during dissection revealed that the ossicular chain was overly massive for the mass of the tympanoperiotic complex. PMID:17420106

  1. Applications of the Medipix3-CT in combination with iterative reconstruction techniques

    NASA Astrophysics Data System (ADS)

    Fischer, F.; Procz, S.; Fauler, A.; Fiederle, M.

    2016-02-01

    The pixelated semiconductor detectors of the Medipix family with their photon-counting abilities offer the possibility of high quality X-ray radiography as well as computed tomography. The generated signal from each photon is amplified and shaped before it is compared to an energy threshold. For a photon with an energy above the threshold the counter is incremented by one count. Photons below the operator-defined threshold do not increment the counter and therefore do not participate in the image formation. Furthermore, compared to other detectors like scintillators, an additional conversion step is dispensed due to the direct converting nature of photon-counting detectors, leading to a higher signal-to-noise-ratio. Additionally, the photon processing capabilities of photon-counting detectors allow photons to be weighted equally and not proportional to their energy as it is the case for charge integrating devices, where high energy photons are weighted stronger than low energy photons. Compared to integrating devices, this leads to an increase in contrast for images of both high and low contrast objects, hence improve object information. The use of photon-counting detectors in combination with iterative reconstruction techniques based on OSEM (ordered subset expectation maximization) algorithms is the basis of our computed tomography scans for material analysis. Due to its ability to operate with highly undersampled data sets, iterative reconstruction offers the possibility to decrease dose in CT scans. In order to identify the limits of the data set reduction, a first series of scans was performed to test, under real conditions, the CT-image quality when a strongly reduced amount of projections is used for reconstruction. In addition, the effect of a total variation minimization tool on these undersampled data sets was evaluated. Furthermore, this paper includes a number of recent CT-results with scans performed at two different setups within our facility.

  2. Spatial estimates of snow water equivalent from reconstruction

    NASA Astrophysics Data System (ADS)

    Rittger, Karl; Bair, Edward H.; Kahl, Annelen; Dozier, Jeff

    2016-08-01

    Operational ground-based measurements of snow water equivalent (SWE) do not adequately explain spatial variability in mountainous terrain. To address this problem, we combine satellite-based retrievals of fractional snow cover for the period 2000 to 2011 with spatially distributed energy balance calculations to reconstruct SWE values throughout each melt season in the Sierra Nevada of California. Modeled solar radiation, longwave radiation, and air temperature from NLDAS drive the snowmelt model. The modeled solar radiation compares well to ground observations, but modeled longwave radiation is slightly lower than observations. Validation of reconstructed SWE with snow courses and our own snow surveys shows that the model can accurately estimate SWE at the sampled locations in a variety of topographic settings for a range of wet to dry years. The relationships of SWE with elevation and latitude are significantly different for wet, mean and dry years as well as between drainages. In all the basins studied, the relationship between remaining SWE and snow-covered area (SCA) becomes increasingly correlated from March to July as expected because SCA is an important model input. Though the SWE is calculated retrospectively SCA observations are available in near-real time and combined with historical reconstructions may be sufficient for estimating SWE with more confidence as the melt season progresses.

  3. Towards local progression estimation of pulmonary emphysema using CT

    SciTech Connect

    Staring, M. Bakker, M. E.; Shamonin, D. P.; Reiber, J. H. C.; Stoel, B. C.; Stolk, J.

    2014-02-15

    Purpose: Whole lung densitometry on chest CT images is an accepted method for measuring tissue destruction in patients with pulmonary emphysema in clinical trials. Progression measurement is required for evaluation of change in health condition and the effect of drug treatment. Information about the location of emphysema progression within the lung may be important for the correct interpretation of drug efficacy, or for determining a treatment plan. The purpose of this study is therefore to develop and validate methods that enable the local measurement of lung density changes, which requires proper modeling of the effect of respiration on density. Methods: Four methods, all based on registration of baseline and follow-up chest CT scans, are compared. The first naïve method subtracts registered images. The second employs the so-called dry sponge model, where volume correction is performed using the determinant of the Jacobian of the transformation. The third and the fourth introduce a novel adaptation of the dry sponge model that circumvents its constant-mass assumption, which is shown to be invalid. The latter two methods require a third CT scan at a different inspiration level to estimate the patient-specific density-volume slope, where one method employs a global and the other a local slope. The methods were validated on CT scans of a phantom mimicking the lung, where mass and volume could be controlled. In addition, validation was performed on data of 21 patients with pulmonary emphysema. Results: The image registration method was optimized leaving a registration error below half the slice increment (median 1.0 mm). The phantom study showed that the locally adapted slope model most accurately measured local progression. The systematic error in estimating progression, as measured on the phantom data, was below 2 gr/l for a 70 ml (6%) volume difference, and 5 gr/l for a 210 ml (19%) difference, if volume correction was applied. On the patient data an underlying

  4. Multiplanar CT reconstruction of the jaw: a new way for implant diagnostics

    NASA Astrophysics Data System (ADS)

    Gursky, S.; Wittek, Werner; Wilke, Walter; Schulz, H.; Lieberenz, S.

    1990-11-01

    For preoperative planning of dental implantations pictorial representations are required that permit to evaluate the size of teeth holes, size and structure of jaw bones, position of the mandibular channel and of the alveolar nerve. Since normal transaxial. CT images do not meet these requirements special secondary reconstructions adapted to jaw anatomy are necessary: -panoramic secondary cuts The cut line follows jaw curvature and represents a similar view as orthopantomographic pictures. (see Fig.1) -oblique secondary cuts That are plane cuts perpendicular to the panoramic cut line. (see Fig.2)

  5. Model-based cone-beam CT reconstruction for image-guided minimally invasive treatment of hip osteolysis

    NASA Astrophysics Data System (ADS)

    Otake, Yoshito; Stayman, J. W.; Zbijewski, W.; Murphy, R. J.; Kutzer, M. D.; Taylor, R. H.; Siewerdsen, J. H.; Armand, M.

    2013-03-01

    Purpose: Accurate assessment of the size and location of osteolytic regions is essential in minimally invasive hip revision surgery. Moreover, image-guided robotic intervention for osteolysis treatment requires precise localization of implant components. However, high density metallic implants in proximity to the hip make assessment by either 2D or 3D x-ray imaging difficult. This paper details the initial implementation and evaluation of an advanced model-based conebeam CT (CBCT) reconstruction algorithm to improve guidance and assessment of hip osteolysis treatment. Method: A model-based reconstruction approach called Known Component Reconstruction (KCR) was employed to obtain high-quality reconstruction of regions neighboring metallic implants. KCR incorporates knowledge about the implant shape and material to precisely reconstruct surrounding anatomy while simultaneously estimating implant position. A simulation study involving a phantom generated from a CBCT scan of a cadaveric hip was performed. Registration accuracy in KCR iterations was evaluated as translational and rotational error from the true registration. Improvement in image quality was evaluated using normalized cross correlation (NCC) in two regions of interest (ROIs) about the femoral and acetabular components. Result: The study showed significant improvement in image quality over conventional filtered backprojection (FBP) and penalized-likelihood (PL) reconstruction. The NCC in the two ROIs improved from 0.74 and 0.81 (FBP) to 0.98 and 0.86 (PL) and >0.99 for KCR. The registration error was 0.01 mm in translation (0.02° in rotation) for the acetabular component and 0.01 mm (0.01° rotation) for the femoral component. Conclusions: Application of KCR to imaging hip osteolysis in the presence of the implant offers a promising step toward quantitative assessment in minimally invasive image-guided osteolysis treatment. The method

  6. Computational methods estimating uncertainties for profile reconstruction in scatterometry

    NASA Astrophysics Data System (ADS)

    Gross, H.; Rathsfeld, A.; Scholze, F.; Model, R.; Bär, M.

    2008-04-01

    The solution of the inverse problem in scatterometry, i.e. the determination of periodic surface structures from light diffraction patterns, is incomplete without knowledge of the uncertainties associated with the reconstructed surface parameters. With decreasing feature sizes of lithography masks, increasing demands on metrology techniques arise. Scatterometry as a non-imaging indirect optical method is applied to periodic line-space structures in order to determine geometric parameters like side-wall angles, heights, top and bottom widths and to evaluate the quality of the manufacturing process. The numerical simulation of the diffraction process is based on the finite element solution of the Helmholtz equation. The inverse problem seeks to reconstruct the grating geometry from measured diffraction patterns. Restricting the class of gratings and the set of measurements, this inverse problem can be reformulated as a non-linear operator equation in Euclidean spaces. The operator maps the grating parameters to the efficiencies of diffracted plane wave modes. We employ a Gauss-Newton type iterative method to solve this operator equation and end up minimizing the deviation of the measured efficiency or phase shift values from the simulated ones. The reconstruction properties and the convergence of the algorithm, however, is controlled by the local conditioning of the non-linear mapping and the uncertainties of the measured efficiencies or phase shifts. In particular, the uncertainties of the reconstructed geometric parameters essentially depend on the uncertainties of the input data and can be estimated by various methods. We compare the results obtained from a Monte Carlo procedure to the estimations gained from the approximative covariance matrix of the profile parameters close to the optimal solution and apply them to EUV masks illuminated by plane waves with wavelengths in the range of 13 nm.

  7. Methodology for reconstruction of historical food consumption estimates

    SciTech Connect

    Anderson, D.M.

    1992-05-01

    This report was written to provide the food consumption methodology to be used in the Hanford Environmental Dose Reconstruction (HDER) Project beyond Phase I (which ended in July 1990). In Phase I (Callaway 1992), baseline food consumption estimates (grams per day) for 10 primary food types in the original 10-county study region were derived from the 1977--1978 National Food Consumption Survey (USDA 1983). The baseline estimates were multiplied by the 1945:1977 ratios to produce consumption estimates for 1945. This ratio backcasting method used in Phase I to project consumption estimates from 1977 back to 1945 will be refined using additional USDA data to improve and document the acceptability of the ratios for deriving backcast consumption estimates. The number of food types and population groups will be expanded to provide more disaggregated estimates of food consumption. Food consumption estimates will be developed for 1945, 1951, and 1957. A database of individual diets will be created from which daily diets will be randomly selected for use in the dose model to calculate doses for reference individuals.

  8. Observer Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction

    PubMed Central

    Yu, Lifeng; Li, Zhoubo; Manduca, Armando; Blezek, Daniel J.; Hough, David M.; Venkatesh, Sudhakar K.; Brickner, Gregory C.; Cernigliaro, Joseph C.; Hara, Amy K.; Fidler, Jeff L.; Lake, David S.; Shiung, Maria; Lewis, David; Leng, Shuai; Augustine, Kurt E.; Carter, Rickey E.; Holmes, David R.; McCollough, Cynthia H.

    2015-01-01

    Purpose To determine if lower-dose computed tomographic (CT) scans obtained with adaptive image-based noise reduction (adaptive nonlocal means [ANLM]) or iterative reconstruction (sinogram-affirmed iterative reconstruction [SAFIRE]) result in reduced observer performance in the detection of malignant hepatic nodules and masses compared with routine-dose scans obtained with filtered back projection (FBP). Materials and Methods This study was approved by the institutional review board and was compliant with HIPAA. Informed consent was obtained from patients for the retrospective use of medical records for research purposes. CT projection data from 33 abdominal and 27 liver or pancreas CT examinations were collected (median volume CT dose index, 13.8 and 24.0 mGy, respectively). Hepatic malignancy was defined by progression or regression or with histopathologic findings. Lower-dose data were created by using a validated noise insertion method (10.4 mGy for abdominal CT and 14.6 mGy for liver or pancreas CT) and images reconstructed with FBP, ANLM, and SAFIRE. Four readers evaluated routine-dose FBP images and all lower-dose images, circumscribing liver lesions and selecting diagnosis. The jackknife free-response receiver operating characteristic figure of merit (FOM) was calculated on a per–malignant nodule or per-mass basis. Noninferiority was defined by the lower limit of the 95% confidence interval (CI) of the difference between lower-dose and routine-dose FOMs being less than −0.10. Results Twenty-nine patients had 62 malignant hepatic nodules and masses. Estimated FOM differences between lower-dose FBP and lower-dose ANLM versus routine-dose FBP were noninferior (difference: −0.041 [95% CI: −0.090, 0.009] and −0.003 [95% CI: −0.052, 0.047], respectively). In patients with dedicated liver scans, lower-dose ANLM images were noninferior (difference: +0.015 [95% CI: −0.077, 0.106]), whereas lower-dose FBP images were not (difference −0.049 [95% CI:

  9. Regularization Designs for Uniform Spatial Resolution and Noise Properties in Statistical Image Reconstruction for 3D X-ray CT

    PubMed Central

    Cho, Jang Hwan; Fessler, Jeffrey A.

    2014-01-01

    Statistical image reconstruction methods for X-ray computed tomography (CT) provide improved spatial resolution and noise properties over conventional filtered back-projection (FBP) reconstruction, along with other potential advantages such as reduced patient dose and artifacts. Conventional regularized image reconstruction leads to spatially variant spatial resolution and noise characteristics because of interactions between the system models and the regularization. Previous regularization design methods aiming to solve such issues mostly rely on circulant approximations of the Fisher information matrix that are very inaccurate for undersampled geometries like short-scan cone-beam CT. This paper extends the regularization method proposed in [1] to 3D cone-beam CT by introducing a hypothetical scanning geometry that helps address the sampling properties. The proposed regularization designs were compared with the original method in [1] with both phantom simulation and clinical reconstruction in 3D axial X-ray CT. The proposed regularization methods yield improved spatial resolution or noise uniformity in statistical image reconstruction for short-scan axial cone-beam CT. PMID:25361500

  10. Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction

    NASA Astrophysics Data System (ADS)

    Kim, Hojin; Chen, Josephine; Wang, Adam; Chuang, Cynthia; Held, Mareike; Pouliot, Jean

    2016-09-01

    The compressed sensing (CS) technique has been employed to reconstruct CT/CBCT images from fewer projections as it is designed to recover a sparse signal from highly under-sampled measurements. Since the CT image itself cannot be sparse, a variety of transforms were developed to make the image sufficiently sparse. The total-variation (TV) transform with local image gradient in L1-norm was adopted in most cases. This approach, however, which utilizes very local information and penalizes the weight at a constant rate regardless of different degrees of spatial gradient, may not produce qualified reconstructed images from noise-contaminated CT projection data. This work presents a new non-local operator of total-variation (NLTV) to overcome the deficits stated above by utilizing a more global search and non-uniform weight penalization in reconstruction. To further improve the reconstructed results, a reweighted L1-norm that approximates the ideal sparse signal recovery of the L0-norm is incorporated into the NLTV reconstruction with additional iterates. This study tested the proposed reconstruction method (reweighted NLTV) from under-sampled projections of 4 objects and 5 experiments (1 digital phantom with low and high noise scenarios, 1 pelvic CT, and 2 CBCT images). We assessed its performance against the conventional TV, NLTV and reweighted TV transforms in the tissue contrast, reconstruction accuracy, and imaging resolution by comparing contrast-noise-ratio (CNR), normalized root-mean square error (nRMSE), and profiles of the reconstructed images. Relative to the conventional NLTV, combining the reweighted L1-norm with NLTV further enhanced the CNRs by 2-4 times and improved reconstruction accuracy. Overall, except for the digital phantom with low noise simulation, our proposed algorithm produced the reconstructed image with the lowest nRMSEs and the highest CNRs for each experiment.

  11. SU-E-I-33: Initial Evaluation of Model-Based Iterative CT Reconstruction Using Standard Image Quality Phantoms

    SciTech Connect

    Gingold, E; Dave, J

    2014-06-01

    Purpose: The purpose of this study was to compare a new model-based iterative reconstruction with existing reconstruction methods (filtered backprojection and basic iterative reconstruction) using quantitative analysis of standard image quality phantom images. Methods: An ACR accreditation phantom (Gammex 464) and a CATPHAN600 phantom were scanned using 3 routine clinical acquisition protocols (adult axial brain, adult abdomen, and pediatric abdomen) on a Philips iCT system. Each scan was acquired using default conditions and 75%, 50% and 25% dose levels. Images were reconstructed using standard filtered backprojection (FBP), conventional iterative reconstruction (iDose4) and a prototype model-based iterative reconstruction (IMR). Phantom measurements included CT number accuracy, contrast to noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD), and noise power spectrum (NPS). Results: The choice of reconstruction method had no effect on CT number accuracy, or MTF (p<0.01). The CNR of a 6 HU contrast target was improved by 1–67% with iDose4 relative to FBP, while IMR improved CNR by 145–367% across all protocols and dose levels. Within each scan protocol, the CNR improvement from IMR vs FBP showed a general trend of greater improvement at lower dose levels. NPS magnitude was greatest for FBP and lowest for IMR. The NPS of the IMR reconstruction showed a pronounced decrease with increasing spatial frequency, consistent with the unusual noise texture seen in IMR images. Conclusion: Iterative Model Reconstruction reduces noise and improves contrast-to-noise ratio without sacrificing spatial resolution in CT phantom images. This offers the possibility of radiation dose reduction and improved low contrast detectability compared with filtered backprojection or conventional iterative reconstruction.

  12. Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction

    NASA Astrophysics Data System (ADS)

    Kim, Hojin; Chen, Josephine; Wang, Adam; Chuang, Cynthia; Held, Mareike; Pouliot, Jean

    2016-09-01

    The compressed sensing (CS) technique has been employed to reconstruct CT/CBCT images from fewer projections as it is designed to recover a sparse signal from highly under-sampled measurements. Since the CT image itself cannot be sparse, a variety of transforms were developed to make the image sufficiently sparse. The total-variation (TV) transform with local image gradient in L1-norm was adopted in most cases. This approach, however, which utilizes very local information and penalizes the weight at a constant rate regardless of different degrees of spatial gradient, may not produce qualified reconstructed images from noise-contaminated CT projection data. This work presents a new non-local operator of total-variation (NLTV) to overcome the deficits stated above by utilizing a more global search and non-uniform weight penalization in reconstruction. To further improve the reconstructed results, a reweighted L1-norm that approximates the ideal sparse signal recovery of the L0-norm is incorporated into the NLTV reconstruction with additional iterates. This study tested the proposed reconstruction method (reweighted NLTV) from under-sampled projections of 4 objects and 5 experiments (1 digital phantom with low and high noise scenarios, 1 pelvic CT, and 2 CBCT images). We assessed its performance against the conventional TV, NLTV and reweighted TV transforms in the tissue contrast, reconstruction accuracy, and imaging resolution by comparing contrast-noise-ratio (CNR), normalized root-mean square error (nRMSE), and profiles of the reconstructed images. Relative to the conventional NLTV, combining the reweighted L1-norm with NLTV further enhanced the CNRs by 2–4 times and improved reconstruction accuracy. Overall, except for the digital phantom with low noise simulation, our proposed algorithm produced the reconstructed image with the lowest nRMSEs and the highest CNRs for each experiment.

  13. Estimating selfing rates from reconstructed pedigrees using multilocus genotype data.

    PubMed

    Wang, Jinliang; El-Kassaby, Yousry A; Ritland, Kermit

    2012-01-01

    Several methods have been developed to estimate the selfing rate of a population from a sample of individuals genotyped for several marker loci. These methods can be based on homozygosity excess (or inbreeding), identity disequilibrium, progeny array (PA) segregation or population assignment incorporating partial selfing. Progeny array-based method is generally the best because it is not subject to some assumptions made by other methods (such as lack of misgenotyping, absence of biparental inbreeding and presence of inbreeding equilibrium), and it can reveal other facets of a mixed-mating system such as patterns of shared paternity. However, in practice, it is often difficult to obtain PAs, especially for animal species. In this study, we propose a method to reconstruct the pedigree of a sample of individuals taken from a monoecious diploid population practicing mixed mating, using multilocus genotypic data. Selfing and outcrossing events are then detected when an individual derives from identical parents and from two distinct parents, respectively. Selfing rate is estimated by the proportion of selfed offspring in the reconstructed pedigree of a sample of individuals. The method enjoys many advantages of the PA method, but without the need of a priori family structure, although such information, if available, can be utilized to improve the inference. Furthermore, the new method accommodates genotyping errors, estimates allele frequencies jointly and is robust to the presence of biparental inbreeding and inbreeding disequilibrium. Both simulated and empirical data were analysed by the new and previous methods to compare their statistical properties and accuracies.

  14. Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT

    PubMed Central

    Dunet, Vincent; Hachulla, Anne-Lise; Grimm, Jochen; Beigelman-Aubry, Catherine

    2016-01-01

    Background Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. Purpose To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. Material and Methods Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. Results The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. Conclusion The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.

  15. Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT

    PubMed Central

    Dunet, Vincent; Hachulla, Anne-Lise; Grimm, Jochen; Beigelman-Aubry, Catherine

    2016-01-01

    Background Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. Purpose To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. Material and Methods Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. Results The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. Conclusion The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique. PMID:27635253

  16. Multi-material decomposition using statistical image reconstruction for spectral CT.

    PubMed

    Long, Yong; Fessler, Jeffrey A

    2014-08-01

    Spectral computed tomography (CT) provides information on material characterization and quantification because of its ability to separate different basis materials. Dual-energy (DE) CT provides two sets of measurements at two different source energies. In principle, two materials can be accurately decomposed from DECT measurements. However, many clinical and industrial applications require three or more material images. For triple-material decomposition, a third constraint, such as volume conservation, mass conservation or both, is required to solve three sets of unknowns from two sets of measurements. The recently proposed flexible image-domain (ID) multi-material decomposition) method assumes each pixel contains at most three materials out of several possible materials and decomposes a mixture pixel by pixel. We propose a penalized-likelihood (PL) method with edge-preserving regularizers for each material to reconstruct multi-material images using a similar constraint from sinogram data. We develop an optimization transfer method with a series of pixel-wise separable quadratic surrogate (PWSQS) functions to monotonically decrease the complicated PL cost function. The PWSQS algorithm separates pixels to allow simultaneous update of all pixels, but keeps the basis materials coupled to allow faster convergence rate than our previous proposed material- and pixel-wise SQS algorithms. Comparing with the ID method using 2-D fan-beam simulations, the PL method greatly reduced noise, streak and cross-talk artifacts in the reconstructed basis component images, and achieved much smaller root mean square errors.

  17. CT reconstruction from few-views with anisotropic edge-guided total variance

    NASA Astrophysics Data System (ADS)

    Rong, Junyan; Liu, Wenlei; Gao, Peng; Liao, Qimei; Jiao, Chun; Ma, Jianhua; Lu, Hongbing

    2016-06-01

    To overcome the oversmoothing drawback in the edge areas when reconstructing few-view CT with total variation (TV) minimization, in this paper, we propose an anisotropic edge-guided TV minimization framework for few-view CT reconstruction. In the framework, anisotropic TV is summed with pre-weighted image gradient and then used as the object function for minimizing. It includes edge-guided TV minimization (EGTV) and edge-guided adaptive-weighted TV minimization (EGAwTV) algorithms. For EGTV algorithm, the weights of the TV discretization term are updated by anisotropic edge information detected from the image, whereas the weights for EGAwTV are determined based on edge information and local image-intensity gradients. To solve the minimization problem of the proposed algorithm, a similar TV-based minimization implementation is developed to address the raw data fidelity and other constraints. The evaluation results using both computer simulations with the Shepp-Logan phantom and experimental data from a physical phantom demonstrate that the proposed algorithms exhibit noticeable gains in the merits of spatial resolution compared with the conventional TV and other modified TV algorithms.

  18. Accelerating statistical image reconstruction algorithms for fan-beam x-ray CT using cloud computing

    NASA Astrophysics Data System (ADS)

    Srivastava, Somesh; Rao, A. Ravishankar; Sheinin, Vadim

    2011-03-01

    Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g. lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An alternative solution is to access the required computation power over the internet from a cloud computing service, which is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce implementation might become necessary in the future. All the experiments for this paper, including development and testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.

  19. Quantifying the Importance of the Statistical Assumption in Statistical X-ray CT Image Reconstruction.

    PubMed

    Xu, Jingyan; Tsui, Benjamin M W

    2014-01-01

    Statistical image reconstruction (SIR) is a promising approach to reducing radiation dose in clinical computerized tomography (CT) scans. Clinical CT scanners use energy-integrating detectors. The CT signal follows a compound Poisson distribution, its probability density function (PDF) does not have an analytical form hence cannot be used in an SIR method. The goal of this work is to quantify the effects of using an approximate statistical assumption in SIR methods for clinical CT applications. We apply a pseudo-Ideal Observer (pIO) to simulated CT projection data of the fanbeam geometry at different dose levels. The simulation models the polychromatic X-ray tube spectrum, the effects of the bowtie filter, and the energy-integrating detectors. The pIO uses a pseudo likelihood function (pLF) to calculate the pseudo likelihood ratio, which is the decision variable used by the pIO in a binary detection task. The pLF is an approximation to the true LF of the underlying data. The pIO has inferior performance than the IO unless the pLF coincides with the LF; this performance difference quantifies the closeness between the pseudo likelihood and the exact one. Using lesion detectability in a signal known exactly, background known exactly binary detection task as a figure-of-merit, our results show that at down to 0.1% of a reference tube current level I0, the pIO that uses a Poisson approximation, or a matched variance Gaussian approximation in either the transmission or the line integral domain, achieves 99% the performance of the IO. The constant variance Gaussian approximation has only 70%-80% of the IO performance. At tube currents lower than 0.1% I0, the performance difference is more substantial. We conclude that at current clinical dose levels, it is important to account for the mean-dependent variance in CT projection data in SIR problem formulation, the exact PDF of the CT signal is not as important.

  20. Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability

    PubMed Central

    Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

    2016-01-01

    Purpose To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Methods Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Results Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Conclusions Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features. PMID:27741289

  1. Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization.

    PubMed

    Yu, Zhou; Thibault, Jean-Baptiste; Bouman, Charles A; Sauer, Ken D; Hsieh, Jiang

    2011-01-01

    Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

  2. Direct reconstruction in CT-analogous pharmacokinetic diffuse fluorescence tomography: two-dimensional simulative and experimental validations

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Zhang, Yanqi; Zhang, Limin; Li, Jiao; Zhou, Zhongxing; Zhao, Huijuan; Gao, Feng

    2016-04-01

    We present a generalized strategy for direct reconstruction in pharmacokinetic diffuse fluorescence tomography (DFT) with CT-analogous scanning mode, which can accomplish one-step reconstruction of the indocyanine-green pharmacokinetic-rate images within in vivo small animals by incorporating the compartmental kinetic model into an adaptive extended Kalman filtering scheme and using an instantaneous sampling dataset. This scheme, compared with the established indirect and direct methods, eliminates the interim error of the DFT inversion and relaxes the expensive requirement of the instrument for obtaining highly time-resolved date-sets of complete 360 deg projections. The scheme is validated by two-dimensional simulations for the two-compartment model and pilot phantom experiments for the one-compartment model, suggesting that the proposed method can estimate the compartmental concentrations and the pharmacokinetic-rates simultaneously with a fair quantitative and localization accuracy, and is well suitable for cost-effective and dense-sampling instrumentation based on the highly-sensitive photon counting technique.

  3. Direct reconstruction in CT-analogous pharmacokinetic diffuse fluorescence tomography: two-dimensional simulative and experimental validations

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Zhang, Yanqi; Zhang, Limin; Li, Jiao; Zhou, Zhongxing; Zhao, Huijuan; Gao, Feng

    2016-04-01

    We present a generalized strategy for direct reconstruction in pharmacokinetic diffuse fluorescence tomography (DFT) with CT-analogous scanning mode, which can accomplish one-step reconstruction of the indocyanine-green pharmacokinetic-rate images within in vivo small animals by incorporating the compartmental kinetic model into an adaptive extended Kalman filtering scheme and using an instantaneous sampling dataset. This scheme, compared with the established indirect and direct methods, eliminates the interim error of the DFT inversion and relaxes the expensive requirement of the instrument for obtaining highly time-resolved date-sets of complete 360 deg projections. The scheme is validated by two-dimensional simulations for the two-compartment model and pilot phantom experiments for the one-compartment model, suggesting that the proposed method can estimate the compartmental concentrations and the pharmacokinetic-rates simultaneously with a fair quantitative and localization accuracy, and is well suitable for cost-effective and dense-sampling instrumentation based on the highly-sensitive photon counting technique.

  4. Direct reconstruction in CT-analogous pharmacokinetic diffuse fluorescence tomography: two-dimensional simulative and experimental validations.

    PubMed

    Wang, Xin; Zhang, Yanqi; Zhang, Limin; Li, Jiao; Zhou, Zhongxing; Zhao, Huijuan; Gao, Feng

    2016-04-30

    We present a generalized strategy for direct reconstruction in pharmacokinetic diffuse fluorescence tomography (DFT) with CT-analogous scanning mode, which can accomplish one-step reconstruction of the indocyanine-green pharmacokinetic-rate images within in vivo small animals by incorporating the compartmental kinetic model into an adaptive extended Kalman filtering scheme and using an instantaneous sampling dataset. This scheme, compared with the established indirect and direct methods, eliminates the interim error of the DFT inversion and relaxes the expensive requirement of the instrument for obtaining highly time-resolved date-sets of complete 360 deg projections. The scheme is validated by two-dimensional simulations for the two-compartment model and pilot phantom experiments for the one-compartment model, suggesting that the proposed method can estimate the compartmental concentrations and the pharmacokinetic-rates simultaneously with a fair quantitative and localization accuracy, and is well suitable for cost-effective and dense-sampling instrumentation based on the highly-sensitive photon counting technique. PMID:27093958

  5. Statistical model based iterative reconstruction in myocardial CT perfusion: exploitation of the low dimensionality of the spatial-temporal image matrix

    NASA Astrophysics Data System (ADS)

    Li, Yinsheng; Niu, Kai; Chen, Guang-Hong

    2015-03-01

    Time-resolved CT imaging methods play an increasingly important role in clinical practice, particularly, in the diagnosis and treatment of vascular diseases. In a time-resolved CT imaging protocol, it is often necessary to irradiate the patients for an extended period of time. As a result, the cumulative radiation dose in these CT applications is often higher than that of the static CT imaging protocols. Therefore, it is important to develop new means of reducing radiation dose for time-resolved CT imaging. In this paper, we present a novel statistical model based iterative reconstruction method that enables the reconstruction of low noise time-resolved CT images at low radiation exposure levels. Unlike other well known statistical reconstruction methods, this new method primarily exploits the intrinsic low dimensionality of time-resolved CT images to regularize the reconstruction. Numerical simulations were used to validate the proposed method.

  6. Reconstruction-plane-dependent weighted FDK algorithm for cone beam volumetric CT

    NASA Astrophysics Data System (ADS)

    Tang, Xiangyang; Hsieh, Jiang

    2005-04-01

    The original FDK algorithm has been extensively employed in medical and industrial imaging applications. With an increased cone angle, cone beam (CB) artifacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few "circular plus" trajectories have been proposed in the past to reduce CB artifacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artifacts of the original FDK algorithm is the inconsistency between conjugate rays that are 180° apart in view angle. The inconsistence between conjugate rays is pixel dependent, i.e., it varies dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artifacts that can be avoided if appropriate view weighting strategy is exercised. In this paper, a modified FDK algorithm is proposed, along with an experimental evaluation and verification, in which the helical body phantom and a humanoid head phantom scanned by a volumetric CT (64 x 0.625 mm) are utilized. Without extra trajectories supplemental to the circular trajectory, the modified FDK algorithm applies reconstruction-plane-dependent view weighting on projection data before 3D backprojection, which reduces the inconsistency between conjugate rays by suppressing the contribution of one of the conjugate rays with a larger cone angle. Both computer-simulated and real phantom studies show that, up to a moderate cone angle, the CB artifacts can be substantially suppressed by the modified FDK algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering, and data manipulation efficiency, can be

  7. Tilted helical Feldkamp cone-beam reconstruction algorithm for multislice CT

    NASA Astrophysics Data System (ADS)

    Hein, Ilmar A.; Taguchi, Katsuyuki; Mori, Issei; Kazama, Masahiro; Silver, Michael D.

    2003-05-01

    In many clinical applications, it is necessary to tilt the gantry of an X-ray CT system with respect to the patient. Tilting the gantry introduces no complications for single-slice fan-beam systems; however, most systems today are helical multislice systems with up to 16 slices (and this number is sure to increase in the future). The image reconstruction algorithms used in multislice helical CT systems must be modified to compensate for the tilt. If they are not, the quality of reconstructed images will be poor with the presence of significant artifacts produced by the tilt. Practical helical multislice algorithms currently incorporated in today"s systems include helical fan-beam, ASSR (Advanced single-slice rebinning), and Feldkamp algorithms. This paper presents the modifications necessary to compensate for gantry tilt for the helical cone-beam Feldkamp algorithm implemented by Toshiba (referred to as TCOT for true cone-beam tomography). Unlike some of the other algorithms, gantry tilt compensation is simple and straightforward to implement with no significant increase in computational complexity. It will be shown that the effect of the gantry tilt is to introduce a lateral shift in the isocenter of the reconstructed slice of interest, which is a function of the tilt, couch speed, and view angle. This lateral shift is easily calculated and incorporated into the backprojection algorithm. The tilt-compensated algorithm is called T-TCOT. Experimental tilted-gantry data has been obtained with 8- and 16 slice Toshiba Aquilion systems, and examples of uncompensated and tilt compensated images are presented.

  8. Mapping the nasal airways: using histology to enhance CT-based three-dimensional reconstruction in Nycticebus.

    PubMed

    Deleon, Valerie Burke; Smith, Timothy D

    2014-11-01

    Three-dimensional reconstructions of imaging data are an increasingly common approach for studying anatomical structure. However, certain aspects of anatomy, including microscopic structure and differentiating tissue types, continue to benefit from traditional histological analyses. We present here a detailed methodology for combining data from microCT and histological imaging to create 3D virtual reconstructions for visualization and further analyses. We used this approach to study the distribution of olfactory mucosa on ethmoturbinal I of an adult pygmy slow loris, Nycticebus pygmaeus. MicroCT imaging of the specimen was followed by processing, embedding, and sectioning for histological analysis. We identified corresponding features in the CT and histological data, and used these to reconstruct the plane of section in the CT volume. The CT volume was then digitally re-sliced, such that orthogonal sections of the CT image corresponded to histological sections. Histological images were annotated for the features of interest (in this case, the contour of soft tissue on ethmoturbinal I and the extent of olfactory mucosa), and annotations were transferred to binary masks in the CT volume. These masks were combined with density-based surface reconstructions of the skull to create an enhanced 3D virtual reconstruction, in which the bony surfaces are coded for mucosal function. We identified a series of issues that may be raised in this approach, for example, deformation related to histological processing, and we make recommendations for addressing these issues. This method provides an evidence-based approach to 3D visualization and analysis of microscopic features in an anatomic context.

  9. Effect of reconstruction algorithms on the accuracy of (99m)Tc sestamibi SPECT/CT parathyroid imaging.

    PubMed

    Nichols, Kenneth J; Tronco, Gene G; Palestro, Christopher J

    2015-01-01

    The superiority of SPECT/CT over SPECT for (99m)Tc-sestamibi parathyroid imaging often is assumed to be due to improved lesion localization provided by the anatomic component (computed tomography) of the examination. It also is possible that this superiority may be related to the algorithms used for SPECT data reconstruction. The objective of this investigation was to determine the effect of SPECT reconstruction algorithms on the accuracy of MIBI SPECT/CT parathyroid imaging. We retrospectively analyzed preoperative MIBI SPECT/CT parathyroid imaging studies performed on 106 patients. SPECT data were reconstructed by filtered back projection (FBP) and by iterative reconstruction with corrections for collimator resolution recovery and attenuation (IRC). Two experienced readers independently graded lesion detection certainty on a 5-point scale without knowledge of each other's readings, reconstruction methods, other test results or final diagnoses. All patients had surgical confirmation of the final diagnosis, including disease limited to the neck, and location and weight of excised lesion(s). There were 135 parathyroid lesions among the 106 patients. For FBP SPECT/CT and IRC SPECT/CT sensitivity was 76% and 90% (p = 0.003), specificity was 87% and 87% (p = 0.90), and accuracy was 83% and 88% (p = 0.04), respectively. Inter-rater agreement was significantly higher for IRC than for FBP (kappa = 0.76, "good agreement", versus kappa = 0.58, "moderate agreement", p < 0.0001). We conclude that the improved accuracy of MIBI SPECT/CT compared to MIBI SPECT for preoperative parathyroid lesion localization is due in part to the use of IRC for SPECT data reconstruction.

  10. An angle-dependent estimation of CT x-ray spectrum from rotational transmission measurements

    SciTech Connect

    Lin, Yuan Samei, Ehsan; Ramirez-Giraldo, Juan Carlos; Gauthier, Daniel J.; Stierstorfer, Karl

    2014-06-15

    Purpose: Computed tomography (CT) performance as well as dose and image quality is directly affected by the x-ray spectrum. However, the current assessment approaches of the CT x-ray spectrum require costly measurement equipment and complicated operational procedures, and are often limited to the spectrum corresponding to the center of rotation. In order to address these limitations, the authors propose an angle-dependent estimation technique, where the incident spectra across a wide range of angular trajectories can be estimated accurately with only a single phantom and a single axial scan in the absence of the knowledge of the bowtie filter. Methods: The proposed technique uses a uniform cylindrical phantom, made of ultra-high-molecular-weight polyethylene and positioned in an off-centered geometry. The projection data acquired with an axial scan have a twofold purpose. First, they serve as a reflection of the transmission measurements across different angular trajectories. Second, they are used to reconstruct the cross sectional image of the phantom, which is then utilized to compute the intersection length of each transmission measurement. With each CT detector element recording a range of transmission measurements for a single angular trajectory, the spectrum is estimated for that trajectory. A data conditioning procedure is used to combine information from hundreds of collected transmission measurements to accelerate the estimation speed, to reduce noise, and to improve estimation stability. The proposed spectral estimation technique was validated experimentally using a clinical scanner (Somatom Definition Flash, Siemens Healthcare, Germany) with spectra provided by the manufacturer serving as the comparison standard. Results obtained with the proposed technique were compared against those obtained from a second conventional transmission measurement technique with two materials (i.e., Cu and Al). After validation, the proposed technique was applied to measure

  11. Task-driven image acquisition and reconstruction in cone-beam CT.

    PubMed

    Gang, Grace J; Stayman, J Webster; Ehtiati, Tina; Siewerdsen, Jeffrey H

    2015-04-21

    This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d') is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ± 30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d' for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d' by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt

  12. Fast hybrid CPU- and GPU-based CT reconstruction algorithm using air skipping technique.

    PubMed

    Lee, Byeonghun; Lee, Ho; Shin, Yeong Gil

    2010-01-01

    This paper presents a fast hybrid CPU- and GPU-based CT reconstruction algorithm to reduce the amount of back-projection operation using air skipping involving polygon clipping. The algorithm easily and rapidly selects air areas that have significantly higher contrast in each projection image by applying K-means clustering method on CPU, and then generates boundary tables for verifying valid region using segmented air areas. Based on these boundary tables of each projection image, clipped polygon that indicates active region when back-projection operation is performed on GPU is determined on each volume slice. This polygon clipping process makes it possible to use smaller number of voxels to be back-projected, which leads to a faster GPU-based reconstruction method. This approach has been applied to a clinical data set and Shepp-Logan phantom data sets having various ratio of air region for quantitative and qualitative comparison and analysis of our and conventional GPU-based reconstruction methods. The algorithm has been proved to reduce computational time to half without losing any diagnostic information, compared to conventional GPU-based approaches.

  13. [Motion-compensated compressed sensing four-dimensional cone-beam CT reconstruction].

    PubMed

    Yang, Xuan; Zhang, Hua; He, Ji; Zeng, Dong; Zhang, Xin-Yu; Bian, Zhao-Ying; Zhang, Jing; Ma, Jian-Hua

    2016-06-20

    Restriction by hardware caused the very low projection number at a single phase for 4-dimensional cone beam (4D-CBCT) CT imaging, and reconstruction using conventional reconstruction algorithms is thus constrained by serious streak artifacts and noises. To address this problem, we propose an approach to reconstructing 4D-CBCT images with multi-phase projections based on the assumption that the image at one phase can be viewed as the motion-compensated image at another phase. Specifically, we formulated a cost function using multi-phase projections to construct the fidelity term and the TV regularization method. For fidelity term construction, the projection data of the current phase and those at other phases were jointly used by reformulating the imaging model. The Gradient-Projection-Barzilai-Line search (GPBL) method was used to optimize the complex cost function. Physical phantom and patient data results showed that the proposed approach could effectively reduce the noise and artifacts, and the introduction of additional temporal correlation did not introduce new artifacts or motion blur. PMID:27435778

  14. Potential of combining iterative reconstruction with noise efficient detector design: aggressive dose reduction in head CT

    PubMed Central

    Bender, B; Schabel, C; Fenchel, M; Ernemann, U; Korn, A

    2015-01-01

    Objective: With further increase of CT numbers and their dominant contribution to medical exposure, there is a recent quest for more effective dose control. While reintroduction of iterative reconstruction (IR) has proved its potential in many applications, a novel focus is placed on more noise efficient detectors. Our purpose was to assess the potential of IR in combination with an integrated circuit detector (ICD) for aggressive dose reduction in head CT. Methods: Non-contrast low-dose head CT [190 mAs; weighted volume CT dose index (CTDIvol), 33.2 mGy] was performed in 50 consecutive patients, using a new noise efficient detector and IR. Images were assessed in terms of quantitative and qualitative image quality and compared with standard dose acquisitions (320 mAs; CTDIvol, 59.7 mGy) using a conventional detector and filtered back projection. Results: By combining ICD and IR in low-dose examinations, the signal to noise was improved by about 13% above the baseline level in the standard-dose control group. Both, contrast-to-noise ratio (2.02 ± 0.6 vs 1.88 ± 0.4; p = 0.18) and objective measurements of image sharpness (695 ± 84 vs 705 ± 151 change in Hounsfield units per pixel; p = 0.79) were fully preserved in the low-dose group. Likewise, there was no significant difference in the grading of several subjective image quality parameters when both noise-reducing strategies were used in low-dose examinations. Conclusion: Combination of noise efficient detector with IR allows for meaningful dose reduction in head CT without compromise of standard image quality. Advances in knowledge: Our study demonstrates the feasibility of almost 50% dose reduction in head CT dose (1.1 mSv per scan) through combination of novel dose-reducing strategies. PMID:25827204

  15. Assessment of dedicated low-dose cardiac micro-CT reconstruction algorithms using the left ventricular volume of small rodents as a performance measure

    SciTech Connect

    Maier, Joscha; Sawall, Stefan; Kachelrieß, Marc

    2014-05-15

    Purpose: Phase-correlated microcomputed tomography (micro-CT) imaging plays an important role in the assessment of mouse models of cardiovascular diseases and the determination of functional parameters as the left ventricular volume. As the current gold standard, the phase-correlated Feldkamp reconstruction (PCF), shows poor performance in case of low dose scans, more sophisticated reconstruction algorithms have been proposed to enable low-dose imaging. In this study, the authors focus on the McKinnon-Bates (MKB) algorithm, the low dose phase-correlated (LDPC) reconstruction, and the high-dimensional total variation minimization reconstruction (HDTV) and investigate their potential to accurately determine the left ventricular volume at different dose levels from 50 to 500 mGy. The results were verified in phantom studies of a five-dimensional (5D) mathematical mouse phantom. Methods: Micro-CT data of eight mice, each administered with an x-ray dose of 500 mGy, were acquired, retrospectively gated for cardiac and respiratory motion and reconstructed using PCF, MKB, LDPC, and HDTV. Dose levels down to 50 mGy were simulated by using only a fraction of the projections. Contrast-to-noise ratio (CNR) was evaluated as a measure of image quality. Left ventricular volume was determined using different segmentation algorithms (Otsu, level sets, region growing). Forward projections of the 5D mouse phantom were performed to simulate a micro-CT scan. The simulated data were processed the same way as the real mouse data sets. Results: Compared to the conventional PCF reconstruction, the MKB, LDPC, and HDTV algorithm yield images of increased quality in terms of CNR. While the MKB reconstruction only provides small improvements, a significant increase of the CNR is observed in LDPC and HDTV reconstructions. The phantom studies demonstrate that left ventricular volumes can be determined accurately at 500 mGy. For lower dose levels which were simulated for real mouse data sets, the

  16. CT radiation dose optimization and estimation: an update for radiologists.

    PubMed

    Goo, Hyun Woo

    2012-01-01

    In keeping with the increasing utilization of CT examinations, the greater concern about radiation hazards from examinations has been addressed. In this regard, CT radiation dose optimization has been given a great deal of attention by radiologists, referring physicians, technologists, and physicists. Dose-saving strategies are continuously evolving in terms of imaging techniques as well as dose management. Consequently, regular updates of this issue are necessary especially for radiologists who play a pivotal role in this activity. This review article will provide an update on how we can optimize CT dose in order to maximize the benefit-to-risk ratio of this clinically useful diagnostic imaging method. PMID:22247630

  17. Practical considerations for noise power spectra estimation for clinical CT scanners.

    PubMed

    Dolly, Steven; Chen, Hsin-Chen; Anastasio, Mark; Mutic, Sasa; Li, Hua

    2016-01-01

    Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ~ 0.15 mm-1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low-frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel

  18. Practical considerations for noise power spectra estimation for clinical CT scanners.

    PubMed

    Dolly, Steven; Chen, Hsin-Chen; Anastasio, Mark; Mutic, Sasa; Li, Hua

    2016-01-01

    Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ~ 0.15 mm-1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low-frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel

  19. Scattered radiation in flat-detector based cone-beam CT: propagation of signal, contrast, and noise into reconstructed volumes

    NASA Astrophysics Data System (ADS)

    Wiegert, Jens; Hohmann, Steffen; Bertram, Matthias

    2007-03-01

    This paper presents a novel framework for the systematic assessment of the impact of scattered radiation in .at-detector based cone-beam CT. While it is well known that scattered radiation causes three di.erent types of artifacts in reconstructed images (inhomogeneity artifacts such as cupping and streaks, degradation of contrast, and enhancement of noise), investigations in the literature quantify the impact of scatter mostly only in terms of inhomogeneity artifacts, giving little insight, e.g., into the visibility of low contrast lesions. Therefore, for this study a novel framework has been developed that in addition to normal reconstruction of the CT (HU) number allows for reconstruction of voxelized expectation values of three additional important characteristics of image quality: signal degradation, contrast reduction, and noise variances. The new framework has been applied to projection data obtained with voxelized Monte-Carlo simulations of clinical CT data sets of high spatial resolution. Using these data, the impact of scattered radiation was thoroughly studied for realistic and clinically relevant patient geometries of the head, thorax, and pelvis region. By means of spatially resolved reconstructions of contrast and noise propagation, the image quality of a scenario with using standard antiscatter grids could be evaluated with great detail. Results show the spatially resolved contrast degradation and the spatially resolved expected standard deviation of the noise at any position in the reconstructed object. The new framework represents a general tool for analyzing image quality in reconstructed images.

  20. Contrast adaptive total p-norm variation minimization approach to CT reconstruction for artifact reduction in reduced-view brain perfusion CT

    NASA Astrophysics Data System (ADS)

    Kim, Chang-Won; Kim, Jong-Hyo

    2011-03-01

    Perfusion CT (PCT) examinations are getting more frequently used for diagnosis of acute brain diseases such as hemorrhage and infarction, because the functional map images it produces such as regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), and mean transit time (MTT) may provide critical information in the emergency work-up of patient care. However, a typical PCT scans the same slices several tens of times after injection of contrast agent, which leads to much increased radiation dose and is inevitability of growing concern for radiation-induced cancer risk. Reducing the number of views in projection in combination of TV minimization reconstruction technique is being regarded as an option for radiation reduction. However, reconstruction artifacts due to insufficient number of X-ray projections become problematic especially when high contrast enhancement signals are present or patient's motion occurred. In this study, we present a novel reconstruction technique using contrast-adaptive TpV minimization that can reduce reconstruction artifacts effectively by using different p-norms in high contrast and low contrast objects. In the proposed method, high contrast components are first reconstructed using thresholded projection data and low p-norm total variation to reflect sparseness in both projection and reconstruction spaces. Next, projection data are modified to contain only low contrast objects by creating projection data of reconstructed high contrast components and subtracting them from original projection data. Then, the low contrast projection data are reconstructed by using relatively high p-norm TV minimization technique, and are combined with the reconstructed high contrast component images to produce final reconstructed images. The proposed algorithm was applied to numerical phantom and a clinical data set of brain PCT exam, and the resultant images were compared with those using filtered back projection (FBP) and conventional TV

  1. Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT

    NASA Astrophysics Data System (ADS)

    Cao, Qian; Zbijewski, Wojciech; Sisniega, Alejandro; Yorkston, John; Siewerdsen, Jeffrey H.; Webster Stayman, J.

    2016-10-01

    Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size  <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution penalized-weighted least squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10×  can be used without introducing artifacts, yielding a ~50×  speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of

  2. SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

    SciTech Connect

    Niemkiewicz, J; Palmiotti, A; Miner, M; Stunja, L; Bergene, J

    2014-06-01

    Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU values were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation

  3. First 3D reconstruction of the rhizocephalan root system using MicroCT

    NASA Astrophysics Data System (ADS)

    Noever, Christoph; Keiler, Jonas; Glenner, Henrik

    2016-07-01

    Parasitic barnacles (Cirripedia: Rhizocephala) are highly specialized parasites of crustaceans. Instead of an alimentary tract for feeding they utilize a system of roots, which infiltrates the body of their hosts to absorb nutrients. Using X-ray micro computer tomography (MicroCT) and computer-aided 3D-reconstruction, we document the spatial organization of this root system, the interna, inside the intact host and also demonstrate its use for morphological examinations of the parasites reproductive part, the externa. This is the first 3D visualization of the unique root system of the Rhizocephala in situ, showing how it is related to the inner organs of the host. We investigated the interna from different parasitic barnacles of the family Peltogastridae, which are parasitic on anomuran crustaceans. Rhizocephalan parasites of pagurid hermit crabs and lithodid crabs were analysed in this study.

  4. Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction

    NASA Astrophysics Data System (ADS)

    Wang, Adam S.; Webster Stayman, J.; Otake, Yoshito; Kleinszig, Gerhard; Vogt, Sebastian; Gallia, Gary L.; Khanna, A. Jay; Siewerdsen, Jeffrey H.

    2014-02-01

    The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (˜40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2× over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ˜1.7 mGy and benefits from 50% sparsity at dose below ˜1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.

  5. Regularization design for high-quality cone-beam CT of intracranial hemorrhage using statistical reconstruction

    NASA Astrophysics Data System (ADS)

    Dang, H.; Stayman, J. W.; Xu, J.; Sisniega, A.; Zbijewski, W.; Wang, X.; Foos, D. H.; Aygun, N.; Koliatsos, V. E.; Siewerdsen, J. H.

    2016-03-01

    Intracranial hemorrhage (ICH) is associated with pathologies such as hemorrhagic stroke and traumatic brain injury. Multi-detector CT is the current front-line imaging modality for detecting ICH (fresh blood contrast 40-80 HU, down to 1 mm). Flat-panel detector (FPD) cone-beam CT (CBCT) offers a potential alternative with a smaller scanner footprint, greater portability, and lower cost potentially well suited to deployment at the point of care outside standard diagnostic radiology and emergency room settings. Previous studies have suggested reliable detection of ICH down to 3 mm in CBCT using high-fidelity artifact correction and penalized weighted least-squared (PWLS) image reconstruction with a post-artifact-correction noise model. However, ICH reconstructed by traditional image regularization exhibits nonuniform spatial resolution and noise due to interaction between the statistical weights and regularization, which potentially degrades the detectability of ICH. In this work, we propose three regularization methods designed to overcome these challenges. The first two compute spatially varying certainty for uniform spatial resolution and noise, respectively. The third computes spatially varying regularization strength to achieve uniform "detectability," combining both spatial resolution and noise in a manner analogous to a delta-function detection task. Experiments were conducted on a CBCT test-bench, and image quality was evaluated for simulated ICH in different regions of an anthropomorphic head. The first two methods improved the uniformity in spatial resolution and noise compared to traditional regularization. The third exhibited the highest uniformity in detectability among all methods and best overall image quality. The proposed regularization provides a valuable means to achieve uniform image quality in CBCT of ICH and is being incorporated in a CBCT prototype for ICH imaging.

  6. Reduction in radiation dose with reconstruction technique in the brain perfusion CT

    NASA Astrophysics Data System (ADS)

    Kim, H. J.; Lee, H. K.; Song, H.; Ju, M. S.; Dong, K. R.; Chung, W. K.; Cho, M. S.; Cho, J. H.

    2011-12-01

    The principal objective of this study was to verify the utility of the reconstruction imaging technique in the brain perfusion computed tomography (PCT) scan by assessing reductions in the radiation dose and analyzing the generated images. The setting used for image acquisition had a detector coverage of 40 mm, a helical thickness of 0.625 mm, a helical shuttle mode scan type and a rotation time of 0.5 s as the image parameters used for the brain PCT scan. Additionally, a phantom experiment and an animal experiment were carried out. In the phantom and animal experiments, noise was measured in the scanning with the tube voltage fixed at 80 kVp (kilovolt peak) and the level of the adaptive statistical iterative reconstruction (ASIR) was changed from 0% to 100% at 10% intervals. The standard deviation of the CT coefficient was measured three times to calculate the mean value. In the phantom and animal experiments, the absorbed dose was measured 10 times under the same conditions as the ones for noise measurement before the mean value was calculated. In the animal experiment, pencil-type and CT-dedicated ionization chambers were inserted into the central portion of pig heads for measurement. In the phantom study, as the level of the ASIR changed from 0% to 100% under identical scanning conditions, the noise value and dose were proportionally reduced. In our animal experiment, the noise value was lowest when the ASIR level was 50%, unlike in the phantom study. The dose was reduced as in the phantom study.

  7. Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem.

    PubMed

    Chun, Il Yong; Adcock, Ben; Talavage, Thomas M

    2014-01-01

    X-ray computed tomography (CT) scanners provide clinical value through high resolution and fast imaging. However, achievement of higher signal-to-noise ratios generally requires emission of more X-rays, resulting in greater dose delivered to the body of the patient. This is of concern, as higher dose leads to greater risk of cancer, particularly for those exposed at a younger age. Therefore, it is desirable to achieve comparable scan quality while limiting X-ray dose. One means to achieve this compound goal is the use of compressed sensing (CS). A novel framework is presented to combine CS theory with X-ray CT. According to the tensor discrete Fourier slice theorem, the 1-D DFT of discrete Radon transform data is exactly mapped on a Cartesian 2-D DFT grid. The nonuniform random density sampling of Fourier coefficients is made feasible by uniformly sampling projection angles at random. Application of the non-convex CS model further reduces the sufficient number of measurements by enhancing sparsity. The numerical results show that, with limited projection data, the non-convex CS model significantly improves reconstruction performance over the convex model.

  8. Acoustic Property Reconstruction of a Neonate Yangtze Finless Porpoise's (Neophocaena asiaeorientalis) Head Based on CT Imaging

    PubMed Central

    Wei, Chong; Wang, Zhitao; Song, Zhongchang; Wang, Kexiong; Wang, Ding; Au, Whitlow W. L.; Zhang, Yu

    2015-01-01

    The reconstruction of the acoustic properties of a neonate finless porpoise’s head was performed using X-ray computed tomography (CT). The head of the deceased neonate porpoise was also segmented across the body axis and cut into slices. The averaged sound velocity and density were measured, and the Hounsfield units (HU) of the corresponding slices were obtained from computed tomography scanning. A regression analysis was employed to show the linear relationships between the Hounsfield unit and both sound velocity and density of samples. Furthermore, the CT imaging data were used to compare the HU value, sound velocity, density and acoustic characteristic impedance of the main tissues in the porpoise’s head. The results showed that the linear relationships between HU and both sound velocity and density were qualitatively consistent with previous studies on Indo-pacific humpback dolphins and Cuvier’s beaked whales. However, there was no significant increase of the sound velocity and acoustic impedance from the inner core to the outer layer in this neonate finless porpoise’s melon. PMID:25856588

  9. Acoustic property reconstruction of a neonate Yangtze finless porpoise's (Neophocaena asiaeorientalis) head based on CT imaging.

    PubMed

    Wei, Chong; Wang, Zhitao; Song, Zhongchang; Wang, Kexiong; Wang, Ding; Au, Whitlow W L; Zhang, Yu

    2015-01-01

    The reconstruction of the acoustic properties of a neonate finless porpoise's head was performed using X-ray computed tomography (CT). The head of the deceased neonate porpoise was also segmented across the body axis and cut into slices. The averaged sound velocity and density were measured, and the Hounsfield units (HU) of the corresponding slices were obtained from computed tomography scanning. A regression analysis was employed to show the linear relationships between the Hounsfield unit and both sound velocity and density of samples. Furthermore, the CT imaging data were used to compare the HU value, sound velocity, density and acoustic characteristic impedance of the main tissues in the porpoise's head. The results showed that the linear relationships between HU and both sound velocity and density were qualitatively consistent with previous studies on Indo-pacific humpback dolphins and Cuvier's beaked whales. However, there was no significant increase of the sound velocity and acoustic impedance from the inner core to the outer layer in this neonate finless porpoise's melon.

  10. Alternating dual updates algorithm for X-ray CT reconstruction on the GPU

    PubMed Central

    McGaffin, Madison G.; Fessler, Jeffrey A.

    2015-01-01

    Model-based image reconstruction (MBIR) for X-ray computed tomography (CT) offers improved image quality and potential low-dose operation, but has yet to reach ubiquity in the clinic. MBIR methods form an image by solving a large statistically motivated optimization problem, and the long time it takes to numerically solve this problem has hampered MBIR’s adoption. We present a new optimization algorithm for X-ray CT MBIR based on duality and group coordinate ascent that may converge even with approximate updates and can handle a wide range of regularizers, including total variation (TV). The algorithm iteratively updates groups of dual variables corresponding to terms in the cost function; these updates are highly parallel and map well onto the GPU. Although the algorithm stores a large number of variables, the “working size” for each of the algorithm’s steps is small and can be efficiently streamed to the GPU while other calculations are being performed. The proposed algorithm converges rapidly on both real and simulated data and shows promising parallelization over multiple devices. PMID:26878031

  11. An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction

    PubMed Central

    Li, Jiaojiao; Niu, Shanzhou; Huang, Jing; Bian, Zhaoying; Feng, Qianjin; Yu, Gaohang; Liang, Zhengrong; Chen, Wufan; Ma, Jianhua

    2015-01-01

    Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as “ALM-ANAD”. The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics. PMID:26495975

  12. Acceleration of fluoro-CT reconstruction for a mobile C-Arm on GPU and FPGA hardware: a simulation study

    NASA Astrophysics Data System (ADS)

    Xue, Xinwei; Cheryauka, Arvi; Tubbs, David

    2006-03-01

    CT imaging in interventional and minimally-invasive surgery requires high-performance computing solutions that meet operational room demands, healthcare business requirements, and the constraints of a mobile C-arm system. The computational requirements of clinical procedures using CT-like data are increasing rapidly, mainly due to the need for rapid access to medical imagery during critical surgical procedures. The highly parallel nature of Radon transform and CT algorithms enables embedded computing solutions utilizing a parallel processing architecture to realize a significant gain of computational intensity with comparable hardware and program coding/testing expenses. In this paper, using a sample 2D and 3D CT problem, we explore the programming challenges and the potential benefits of embedded computing using commodity hardware components. The accuracy and performance results obtained on three computational platforms: a single CPU, a single GPU, and a solution based on FPGA technology have been analyzed. We have shown that hardware-accelerated CT image reconstruction can be achieved with similar levels of noise and clarity of feature when compared to program execution on a CPU, but gaining a performance increase at one or more orders of magnitude faster. 3D cone-beam or helical CT reconstruction and a variety of volumetric image processing applications will benefit from similar accelerations.

  13. Comparison of extended field-of-view reconstructions in C-arm flat-detector CT using patient size, shape or attenuation information

    NASA Astrophysics Data System (ADS)

    Kolditz, Daniel; Meyer, Michael; Kyriakou, Yiannis; Kalender, Willi A.

    2011-01-01

    In C-arm-based flat-detector computed tomography (FDCT) it frequently happens that the patient exceeds the scan field of view (SFOV) in the transaxial direction because of the limited detector size. This results in data truncation and CT image artefacts. In this work three truncation correction approaches for extended field-of-view (EFOV) reconstructions have been implemented and evaluated. An FDCT-based method estimates the patient size and shape from the truncated projections by fitting an elliptical model to the raw data in order to apply an extrapolation. In a camera-based approach the patient is sampled with an optical tracking system and this information is used to apply an extrapolation. In a CT-based method the projections are completed by artificial projection data obtained from the CT data acquired in an earlier exam. For all methods the extended projections are filtered and backprojected with a standard Feldkamp-type algorithm. Quantitative evaluations have been performed by simulations of voxelized phantoms on the basis of the root mean square deviation and a quality factor Q (Q = 1 represents the ideal correction). Measurements with a C-arm FDCT system have been used to validate the simulations and to investigate the practical applicability using anthropomorphic phantoms which caused truncation in all projections. The proposed approaches enlarged the FOV to cover wider patient cross-sections. Thus, image quality inside and outside the SFOV has been improved. Best results have been obtained using the CT-based method, followed by the camera-based and the FDCT-based truncation correction. For simulations, quality factors up to 0.98 have been achieved. Truncation-induced cupping artefacts have been reduced, e.g., from 218% to less than 1% for the measurements. The proposed truncation correction approaches for EFOV reconstructions are an effective way to ensure accurate CT values inside the SFOV and to recover peripheral information outside the SFOV.

  14. Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

    NASA Astrophysics Data System (ADS)

    Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena

    2012-04-01

    Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an

  15. Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

    NASA Astrophysics Data System (ADS)

    Stsepankou, D.; Arns, A.; Ng, S. K.; Zygmanski, P.; Hesser, J.

    2012-10-01

    The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone-beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system.

  16. Bone tunnel enlargement after ACL reconstruction using autologous hamstring tendons: a CT study

    PubMed Central

    Iorio, Raffaele; Vadalà, Antonio; Argento, Giuseppe; Di Sanzo, Vincenzo

    2006-01-01

    Purpose: To evaluate prospectively the increase in the size of the tibial and femoral bone tunnel following arthroscopic anterior cruciate ligament (ACL) reconstruction with quadrupled-hamstring autograft. Methods: Twenty-five consecutive patients underwent arthroscopic ACL reconstruction with quadrupled-hamstring autograft. Preoperative clinical evaluation was performed using the Lysholm knee score, Tegner activity level, and International Knee Documentation Committee forms and a KT-1000 arthrometer (side to side). Computed tomography (CT) of the femoral and tibial tunnel was performed on the day after operation in all cases and at mean follow-up of 10 months (range 9–11 months).Results: All of the clinical evaluation scales performed showed an overall improvement. The postoperative anterior laxity difference was <3 mm in 16 patients (70%) and 3–5 mm in seven patients (30%). The mean average femoral tunnel diameter increased significantly (3%) from 9.04±0.05 mm postoperatively to 9.3±0.8 mm at 10 months; tibial tunnel increased significantly (11%) from 9.03±0.04 mm to 10±0.8 mm. There were no statistically significant differences between tunnel enlargement, clinical results, and arthrometer evaluation. Conclusions: The rate of tunnel widening observed in this study seems to be lower than that reported in previous studies that used different techniques. We conclude that an anatomical surgical technique and a less aggressive rehabilitation process influenced the amount of tunnel enlargement after ACL reconstruction with doubled hamstrings. PMID:16683112

  17. Diagnostic Accuracy of CT Enterography for Active Inflammatory Terminal Ileal Crohn Disease: Comparison of Full-Dose and Half-Dose Images Reconstructed with FBP and Half-Dose Images with SAFIRE.

    PubMed

    Gandhi, Namita S; Baker, Mark E; Goenka, Ajit H; Bullen, Jennifer A; Obuchowski, Nancy A; Remer, Erick M; Coppa, Christopher P; Einstein, David; Feldman, Myra K; Kanmaniraja, Devaraju; Purysko, Andrei S; Vahdat, Noushin; Primak, Andrew N; Karim, Wadih; Herts, Brian R

    2016-08-01

    Purpose To compare the diagnostic accuracy and image quality of computed tomographic (CT) enterographic images obtained at half dose and reconstructed with filtered back projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) with those of full-dose CT enterographic images reconstructed with FBP for active inflammatory terminal or neoterminal ileal Crohn disease. Materials and Methods This retrospective study was compliant with HIPAA and approved by the institutional review board. The requirement to obtain informed consent was waived. Ninety subjects (45 with active terminal ileal Crohn disease and 45 without Crohn disease) underwent CT enterography with a dual-source CT unit. The reference standard for confirmation of active Crohn disease was active terminal ileal Crohn disease based on ileocolonoscopy or established Crohn disease and imaging features of active terminal ileal Crohn disease. Data from both tubes were reconstructed with FBP (100% exposure); data from the primary tube (50% exposure) were reconstructed with FBP and SAFIRE strengths 3 and 4, yielding four datasets per CT enterographic examination. The mean volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE) at full dose were 13.1 mGy (median, 7.36 mGy) and 15.9 mGy (median, 13.06 mGy), respectively, and those at half dose were 6.55 mGy (median, 3.68 mGy) and 7.95 mGy (median, 6.5 mGy). Images were subjectively evaluated by eight radiologists for quality and diagnostic confidence for Crohn disease. Areas under the receiver operating characteristic curves (AUCs) were estimated, and the multireader, multicase analysis of variance method was used to compare reconstruction methods on the basis of a noninferiority margin of 0.05. Results The mean AUCs with half-dose scans (FBP, 0.908; SAFIRE 3, 0.935; SAFIRE 4, 0.924) were noninferior to the mean AUC with full-dose FBP scans (0.908; P < .003). The proportion of images with inferior quality was significantly higher with all

  18. Total variation-stokes strategy for sparse-view X-ray CT image reconstruction.

    PubMed

    Liu, Yan; Liang, Zhengrong; Ma, Jianhua; Lu, Hongbing; Wang, Ke; Zhang, Hao; Moore, William

    2014-03-01

    Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and/or other constraints, a piecewise-smooth X-ray computed tomography image can be reconstructed from sparse-view projection data. However, due to the piecewise constant assumption for the TV model, the reconstructed images are frequently reported to suffer from the blocky or patchy artifacts. To eliminate this drawback, we present a total variation-stokes-projection onto convex sets (TVS-POCS) reconstruction method in this paper. The TVS model is derived by introducing isophote directions for the purpose of recovering possible missing information in the sparse-view data situation. Thus the desired consistencies along both the normal and the tangent directions are preserved in the resulting images. Compared to the previous TV-based image reconstruction algorithms, the preserved consistencies by the TVS-POCS method are expected to generate noticeable gains in terms of eliminating the patchy artifacts and preserving subtle structures. To evaluate the presented TVS-POCS method, both qualitative and quantitative studies were performed using digital phantom, physical phantom and clinical data experiments. The results reveal that the presented method can yield images with several noticeable gains, measured by the universal quality index and the full-width-at-half-maximum merit, as compared to its corresponding TV-based algorithms. In addition, the results further indicate that the TVS-POCS method approaches to the gold standard result of the filtered back-projection reconstruction in the full-view data case as theoretically expected, while most previous iterative methods may fail in the full-view case because of their artificial textures in the results.

  19. A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT.

    PubMed

    Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim

    2011-06-21

    Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

  20. A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT

    NASA Astrophysics Data System (ADS)

    Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim

    2011-06-01

    Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

  1. Estimating Radiation Dose Metrics for Patients Undergoing Tube Current Modulation CT Scans

    NASA Astrophysics Data System (ADS)

    McMillan, Kyle Lorin

    Computed tomography (CT) has long been a powerful tool in the diagnosis of disease, identification of tumors and guidance of interventional procedures. With CT examinations comes the concern of radiation exposure and the associated risks. In order to properly understand those risks on a patient-specific level, organ dose must be quantified for each CT scan. Some of the most widely used organ dose estimates are derived from fixed tube current (FTC) scans of a standard sized idealized patient model. However, in current clinical practice, patient size varies from neonates weighing just a few kg to morbidly obese patients weighing over 200 kg, and nearly all CT exams are performed with tube current modulation (TCM), a scanning technique that adjusts scanner output according to changes in patient attenuation. Methods to account for TCM in CT organ dose estimates have been previously demonstrated, but these methods are limited in scope and/or restricted to idealized TCM profiles that are not based on physical observations and not scanner specific (e.g. don't account for tube limits, scanner-specific effects, etc.). The goal of this work was to develop methods to estimate organ doses to patients undergoing CT scans that take into account both the patient size as well as the effects of TCM. This work started with the development and validation of methods to estimate scanner-specific TCM schemes for any voxelized patient model. An approach was developed to generate estimated TCM schemes that match actual TCM schemes that would have been acquired on the scanner for any patient model. Using this approach, TCM schemes were then generated for a variety of body CT protocols for a set of reference voxelized phantoms for which TCM information does not currently exist. These are whole body patient models representing a variety of sizes, ages and genders that have all radiosensitive organs identified. TCM schemes for these models facilitated Monte Carlo-based estimates of fully

  2. SU-E-I-45: Reconstruction of CT Images From Sparsely-Sampled Data Using the Logarithmic Barrier Method

    SciTech Connect

    Xu, H

    2014-06-01

    Purpose: To develop and investigate whether the logarithmic barrier (LB) method can result in high-quality reconstructed CT images using sparsely-sampled noisy projection data Methods: The objective function is typically formulated as the sum of the total variation (TV) and a data fidelity (DF) term with a parameter λ that governs the relative weight between them. Finding the optimized value of λ is a critical step for this approach to give satisfactory results. The proposed LB method avoid using λ by constructing the objective function as the sum of the TV and a log function whose augment is the DF term. Newton's method was used to solve the optimization problem. The algorithm was coded in MatLab2013b. Both Shepp-Logan phantom and a patient lung CT image were used for demonstration of the algorithm. Measured data were simulated by calculating the projection data using radon transform. A Poisson noise model was used to account for the simulated detector noise. The iteration stopped when the difference of the current TV and the previous one was less than 1%. Results: Shepp-Logan phantom reconstruction study shows that filtered back-projection (FBP) gives high streak artifacts for 30 and 40 projections. Although visually the streak artifacts are less pronounced for 64 and 90 projections in FBP, the 1D pixel profiles indicate that FBP gives noisier reconstructed pixel values than LB does. A lung image reconstruction is presented. It shows that use of 64 projections gives satisfactory reconstructed image quality with regard to noise suppression and sharp edge preservation. Conclusion: This study demonstrates that the logarithmic barrier method can be used to reconstruct CT images from sparsely-amped data. The number of projections around 64 gives a balance between the over-smoothing of the sharp demarcation and noise suppression. Future study may extend to CBCT reconstruction and improvement on computation speed.

  3. Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction - a phantom study.

    PubMed

    Dodge, Cristina T; Tamm, Eric P; Cody, Dianna D; Liu, Xinming; Jensen, Corey T; Wei, Wei; Kundra, Vikas; Rong, X John

    2016-01-01

    The purpose of this study was to characterize image quality and dose performance with GE CT iterative reconstruction techniques, adaptive statistical iterative recontruction (ASiR), and model-based iterative reconstruction (MBIR), over a range of typical to low-dose intervals using the Catphan 600 and the anthropomorphic Kyoto Kagaku abdomen phantoms. The scope of the project was to quantitatively describe the advantages and limitations of these approaches. The Catphan 600 phantom, supplemented with a fat-equivalent oval ring, was scanned using a GE Discovery HD750 scanner at 120 kVp, 0.8 s rotation time, and pitch factors of 0.516, 0.984, and 1.375. The mA was selected for each pitch factor to achieve CTDIvol values of 24, 18, 12, 6, 3, 2, and 1 mGy. Images were reconstructed at 2.5 mm thickness with filtered back-projection (FBP); 20%, 40%, and 70% ASiR; and MBIR. The potential for dose reduction and low-contrast detectability were evaluated from noise and contrast-to-noise ratio (CNR) measurements in the CTP 404 module of the Catphan. Hounsfield units (HUs) of several materials were evaluated from the cylinder inserts in the CTP 404 module, and the modulation transfer function (MTF) was calculated from the air insert. The results were con-firmed in the anthropomorphic Kyoto Kagaku abdomen phantom at 6, 3, 2, and 1mGy. MBIR reduced noise levels five-fold and increased CNR by a factor of five compared to FBP below 6mGy CTDIvol, resulting in a substantial improvement in image quality. Compared to ASiR and FBP, HU in images reconstructed with MBIR were consistently lower, and this discrepancy was reversed by higher pitch factors in some materials. MBIR improved the conspicuity of the high-contrast spatial resolution bar pattern, and MTF quantification confirmed the superior spatial resolution performance of MBIR versus FBP and ASiR at higher dose levels. While ASiR and FBP were relatively insensitive to changes in dose and pitch, the spatial resolution for MBIR

  4. A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods

    NASA Astrophysics Data System (ADS)

    Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei

    2014-06-01

    As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate {O}(1/k^2). In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.

  5. Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

    NASA Astrophysics Data System (ADS)

    Han, Hao; Zhang, Hao; Wei, Xinzhou; Moore, William; Liang, Zhengrong

    2016-03-01

    In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

  6. Novel ultrahigh resolution data acquisition and image reconstruction for multi-detector row CT

    SciTech Connect

    Flohr, T. G.; Stierstorfer, K.; Suess, C.; Schmidt, B.; Primak, A. N.; McCollough, C. H.

    2007-05-15

    We present and evaluate a special ultrahigh resolution mode providing considerably enhanced spatial resolution both in the scan plane and in the z-axis direction for a routine medical multi-detector row computed tomography (CT) system. Data acquisition is performed by using a flying focal spot both in the scan plane and in the z-axis direction in combination with tantalum grids that are inserted in front of the multi-row detector to reduce the aperture of the detector elements both in-plane and in the z-axis direction. The dose utilization of the system for standard applications is not affected, since the grids are moved into place only when needed and are removed for standard scanning. By means of this technique, image slices with a nominal section width of 0.4 mm (measured full width at half maximum=0.45 mm) can be reconstructed in spiral mode on a CT system with a detector configuration of 32x0.6 mm. The measured 2% value of the in-plane modulation transfer function (MTF) is 20.4 lp/cm, the measured 2% value of the longitudinal (z axis) MTF is 21.5 lp/cm. In a resolution phantom with metal line pair test patterns, spatial resolution of 20 lp/cm can be demonstrated both in the scan plane and along the z axis. This corresponds to an object size of 0.25 mm that can be resolved. The new mode is intended for ultrahigh resolution bone imaging, in particular for wrists, joints, and inner ear studies, where a higher level of image noise due to the reduced aperture is an acceptable trade-off for the clinical benefit brought about by the improved spatial resolution.

  7. Patient dose estimation from CT scans at the Mexican National Neurology and Neurosurgery Institute

    NASA Astrophysics Data System (ADS)

    Alva-Sánchez, Héctor; Reynoso-Mejía, Alberto; Casares-Cruz, Katiuzka; Taboada-Barajas, Jesús

    2014-11-01

    In the radiology department of the Mexican National Institute of Neurology and Neurosurgery, a dedicated institute in Mexico City, on average 19.3 computed tomography (CT) examinations are performed daily on hospitalized patients for neurological disease diagnosis, control scans and follow-up imaging. The purpose of this work was to estimate the effective dose received by hospitalized patients who underwent a diagnostic CT scan using typical effective dose values for all CT types and to obtain the estimated effective dose distributions received by surgical and non-surgical patients. Effective patient doses were estimated from values per study type reported in the applications guide provided by the scanner manufacturer. This retrospective study included all hospitalized patients who underwent a diagnostic CT scan between 1 January 2011 and 31 December 2012. A total of 8777 CT scans were performed in this two-year period. Simple brain scan was the CT type performed the most (74.3%) followed by contrasted brain scan (6.1%) and head angiotomography (5.7%). The average number of CT scans per patient was 2.83; the average effective dose per patient was 7.9 mSv; the mean estimated radiation dose was significantly higher for surgical (9.1 mSv) than non-surgical patients (6.0 mSv). Three percent of the patients had 10 or more brain CT scans and exceeded the organ radiation dose threshold set by the International Commission on Radiological Protection for deterministic effects of the eye-lens. Although radiation patient doses from CT scans were in general relatively low, 187 patients received a high effective dose (>20 mSv) and 3% might develop cataract from cumulative doses to the eye lens.

  8. Patient dose estimation from CT scans at the Mexican National Neurology and Neurosurgery Institute

    SciTech Connect

    Alva-Sánchez, Héctor

    2014-11-07

    In the radiology department of the Mexican National Institute of Neurology and Neurosurgery, a dedicated institute in Mexico City, on average 19.3 computed tomography (CT) examinations are performed daily on hospitalized patients for neurological disease diagnosis, control scans and follow-up imaging. The purpose of this work was to estimate the effective dose received by hospitalized patients who underwent a diagnostic CT scan using typical effective dose values for all CT types and to obtain the estimated effective dose distributions received by surgical and non-surgical patients. Effective patient doses were estimated from values per study type reported in the applications guide provided by the scanner manufacturer. This retrospective study included all hospitalized patients who underwent a diagnostic CT scan between 1 January 2011 and 31 December 2012. A total of 8777 CT scans were performed in this two-year period. Simple brain scan was the CT type performed the most (74.3%) followed by contrasted brain scan (6.1%) and head angiotomography (5.7%). The average number of CT scans per patient was 2.83; the average effective dose per patient was 7.9 mSv; the mean estimated radiation dose was significantly higher for surgical (9.1 mSv) than non-surgical patients (6.0 mSv). Three percent of the patients had 10 or more brain CT scans and exceeded the organ radiation dose threshold set by the International Commission on Radiological Protection for deterministic effects of the eye-lens. Although radiation patient doses from CT scans were in general relatively low, 187 patients received a high effective dose (>20 mSv) and 3% might develop cataract from cumulative doses to the eye lens.

  9. A decomposition-based CT reconstruction formulation for reducing blooming artifacts.

    PubMed

    Do, Synho; Karl, W Clem; Liang, Zhuangli; Kalra, Mannudeep; Brady, Thomas J; Pien, Homer H

    2011-11-21

    Cardiac computed tomography represents an important advancement in the ability to assess coronary vessels. The accuracy of these non-invasive imaging studies is limited, however, by the presence of calcium, since calcium blooming artifacts lead to an over-estimation of the degree of luminal narrowing. To address this problem, we have developed a unified decomposition-based iterative reconstruction formulation, where different penalty functions are imposed on dense objects (i.e. calcium) and soft tissue. The result is a quantifiable reduction in blooming artifacts without the introduction of new distortions away from the blooming observed in other methods. Results are shown for simulations, phantoms, ex vivo, and in vivo studies.

  10. Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)

    NASA Astrophysics Data System (ADS)

    Wang, Tonghe; Zhu, Lei

    2016-09-01

    Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and prior-image-constrained-compressed-sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an

  11. Noise suppression in reconstruction of low-Z target megavoltage cone-beam CT images

    SciTech Connect

    Wang Jing; Robar, James; Guan Huaiqun

    2012-08-15

    Purpose: To improve the image contrast-to-noise (CNR) ratio for low-Z target megavoltage cone-beam CT (MV CBCT) using a statistical projection noise suppression algorithm based on the penalized weighted least-squares (PWLS) criterion. Methods: Projection images of a contrast phantom, a CatPhan{sup Registered-Sign} 600 phantom and a head phantom were acquired by a Varian 2100EX LINAC with a low-Z (Al) target and low energy x-ray beam (2.5 MeV) at a low-dose level and at a high-dose level. The projections were then processed by minimizing the PWLS objective function. The weighted least square (WLS) term models the noise of measured projection and the penalty term enforces the smoothing constraints of the projection image. The variance of projection data was chosen as the weight for the PWLS objective function and it determined the contribution of each measurement. An anisotropic quadratic form penalty that incorporates the gradient information of projection image was used to preserve edges during noise reduction. Low-Z target MV CBCT images were reconstructed by the FDK algorithm after each projection was processed by the PWLS smoothing. Results: Noise in low-Z target MV CBCT images were greatly suppressed after the PWLS projection smoothing, without noticeable sacrifice of the spatial resolution. Depending on the choice of smoothing parameter, the CNR of selected regions of interest in the PWLS processed low-dose low-Z target MV CBCT image can be higher than the corresponding high-dose image.Conclusion: The CNR of low-Z target MV CBCT images was substantially improved by using PWLS projection smoothing. The PWLS projection smoothing algorithm allows the reconstruction of high contrast low-Z target MV CBCT image with a total dose of as low as 2.3 cGy.

  12. SU-D-12A-05: Iterative Reconstruction Techniques to Enable Intrinsic Respiratory Gated CT in Mice

    SciTech Connect

    Sun, T; Sun, N; Tan, S; Liu, Y; Mistry, N

    2014-06-01

    Purpose: Longitudinal studies of lung function in mice need the ability to image different phases of ventilation in free-breathing mice using retrospective gating. However, retrospective gating often produces under-sampled and uneven angular samples, resulting in severe reconstruction artifacts when using traditional FDK based reconstruction algorithms. We wanted to demonstrate the utility of iterative reconstruction method to enable intrinsic respiratory gating in small-animal CT. Methods: Free-breathing mice were imaged using a Siemens Inveon PET/micro-CT system. Evenly distributed projection images were acquired at 360 angles. Retrospective respiratory gating was performed using an intrinsic marker based on the average intensity in a region covering the diaphragm. Projections were classified into 4 and 6 phases (finer temporal resolution) resulting in 138 and 67 projections respectively. Reconstruction was carried out using 3 Methods: conventional FDK, iterative penalized least-square (PWLS) with total variation (TV), and PWLS with edge-preserving penalty. The performance of the methods was compared using contrast-to-noise (CNR) in a region of interest (ROI). Line profile through a specific region was plotted to evaluate the preserving of edges. Results: In both the cases with 4 and 6 phases, inadequate and non-uniform angular sampling results in artifacts using conventional FDK. However, such artifacts are minimized using both the iterative methods. Using both 4 and 6 phases, the iterative techniques outperformed FDK in terms of CNR and maintaining sharp edges. This is further evidenced especially with increased artifacts using FDK for 6 phases. Conclusion: This work indicates fewer artifacts and better image details can be achieved with iterative reconstruction methods in non-uniform under-sampled reconstruction. Using iterative methods can enable free-breathing intrinsic respiratory gating in small-animal CT. Further studies are needed to compare the

  13. DQS advisor: a visual interface and knowledge-based system to balance dose, quality, and reconstruction speed in iterative CT reconstruction with application to NLM-regularization

    NASA Astrophysics Data System (ADS)

    Zheng, Z.; Papenhausen, E.; Mueller, K.

    2013-11-01

    Motivated by growing concerns with regards to the x-ray dose delivered to the patient, low-dose computed tomography (CT) has gained substantial interest in recent years. However, achieving high-quality CT reconstructions from the limited projection data collected at reduced x-ray radiation is challenging, and iterative algorithms have been shown to perform much better than conventional analytical schemes in these cases. A problem with iterative methods in general is that they require users to set many parameters, and if set incorrectly high reconstruction time and/or low image quality are likely consequences. Since the interactions among parameters can be complex and thus effective settings can be difficult to identify for a given scanning scenario, these choices are often left to a highly-experienced human expert. To help alleviate this problem, we devise a computer-based assistant for this purpose, called dose, quality and speed (DQS)-advisor. It allows users to balance the three most important CT metrics--DQS--by ways of an intuitive visual interface. Using a known gold-standard, the system uses the ant-colony optimization algorithm to generate the most effective parameter settings for a comprehensive set of DQS configurations. A visual interface then presents the numerical outcome of this optimization, while a matrix display allows users to compare the corresponding images. The interface allows users to intuitively trade-off GPU-enabled reconstruction speed with quality and dose, while the system picks the associated parameter settings automatically. Further, once the knowledge has been generated, it can be used to correctly set the parameters for any new CT scan taken at similar scenarios.

  14. A projection-driven pre-correction technique for iterative reconstruction of helical cone-beam cardiac CT images

    NASA Astrophysics Data System (ADS)

    Do, Synho; Liang, Zhuangli; Karl, William Clem; Brady, Thomas; Pien, Homer

    2008-03-01

    Modern CT systems have advanced at a dramatic rate. Algebraic iterative reconstruction techniques have shown promising and desirable image characteristics, but are seldom used due to their high computational cost for complete reconstruction of large volumetric datasets. In many cases, however, interest in high resolution reconstructions is restricted to smaller regions of interest within the complete volume. In this paper we present an implementation of a simple and practical method to produce iterative reconstructions of reduced-sized ROI from 3D helical tomographic data. We use the observation that the conventional filtered back-projection reconstruction is generally of high quality throughout the entire volume to predict the contributions to ROI-related projections arising from volumes outside the ROI. These predictions are then used to pre-correct the data to produce a tomographic inversion problem of substantially reduced size and memory demands. Our work expands on those of other researchers who have observed similar potential computational gains by exploiting FBP results. We demonstrate our approach using cardiac CT cone-beam imaging, illustrating our results with both ex vivo and in vivo multi-cycle EKG-gated examples.

  15. A user-friendly nano-CT image alignment and 3D reconstruction platform based on LabVIEW

    NASA Astrophysics Data System (ADS)

    Wang, Sheng-Hao; Zhang, Kai; Wang, Zhi-Li; Gao, Kun; Wu, Zhao; Zhu, Pei-Ping; Wu, Zi-Yu

    2015-01-01

    X-ray computed tomography at the nanometer scale (nano-CT) offers a wide range of applications in scientific and industrial areas. Here we describe a reliable, user-friendly, and fast software package based on LabVIEW that may allow us to perform all procedures after the acquisition of raw projection images in order to obtain the inner structure of the investigated sample. A suitable image alignment process to address misalignment problems among image series due to mechanical manufacturing errors, thermal expansion, and other external factors has been considered, together with a novel fast parallel beam 3D reconstruction procedure that was developed ad hoc to perform the tomographic reconstruction. We have obtained remarkably improved reconstruction results at the Beijing Synchrotron Radiation Facility after the image calibration, the fundamental role of this image alignment procedure was confirmed, which minimizes the unwanted blurs and additional streaking artifacts that are always present in reconstructed slices. Moreover, this nano-CT image alignment and its associated 3D reconstruction procedure are fully based on LabVIEW routines, significantly reducing the data post-processing cycle, thus making the activity of the users faster and easier during experimental runs.

  16. NCICT: a computational solution to estimate organ doses for pediatric and adult patients undergoing CT scans.

    PubMed

    Lee, Choonsik; Kim, Kwang Pyo; Bolch, Wesley E; Moroz, Brian E; Folio, Les

    2015-12-01

    We developed computational methods and tools to assess organ doses for pediatric and adult patients undergoing computed tomography (CT) examinations. We used the International Commission on Radiological Protection (ICRP) reference pediatric and adult phantoms combined with the Monte Carlo simulation of a reference CT scanner to establish comprehensive organ dose coefficients (DC), organ absorbed dose per unit volumetric CT Dose Index (CTDIvol) (mGy/mGy). We also developed methods to estimate organ doses with tube current modulation techniques and size specific dose estimates. A graphical user interface was designed to obtain user input of patient- and scan-specific parameters, and to calculate and display organ doses. A batch calculation routine was also integrated into the program to automatically calculate organ doses for a large number of patients. We entitled the computer program, National Cancer Institute dosimetry system for CT(NCICT). We compared our dose coefficients with those from CT-Expo, and evaluated the performance of our program using CT patient data. Our pediatric DCs show good agreements of organ dose estimation with those from CT-Expo except for thyroid. Our results support that the adult phantom in CT-Expo seems to represent a pediatric individual between 10 and 15 years rather than an adult. The comparison of CTDIvol values between NCICT and dose pages from 10 selected CT scans shows good agreements less than 12% except for two cases (up to 20%). The organ dose comparison between mean and modulated mAs shows that mean mAs-based calculation significantly overestimates dose (up to 2.4-fold) to the organs in close proximity to lungs in chest and chest-abdomen-pelvis scans. Our program provides more realistic anatomy based on the ICRP reference phantoms, higher age resolution, the most up-to-date bone marrow dosimetry, and several convenient features compared to previous tools. The NCICT will be available for research purpose in the near future.

  17. Chest CT with iterative reconstruction algorithms for airway stent evaluation in patients with malignant obstructive tracheobronchial diseases.

    PubMed

    Li, Tingting; Zhang, Yonggao; Wang, Yadong; Gao, Jianbo; Jiang, Yan

    2016-09-01

    The aim of the study was to investigate the image quality of low-dose CT images with different reconstruction algorithms including filtered back projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) algorithms by comparison of routine dose images with FBP reconstruction, in patients with malignant obstructive tracheobronchial diseases.In total, 60 patients (59 ± 9.3 years, 37 males) with airway stent who are randomly assigned into 2 groups (routine-dose [RD] and low-dose [LD] group, 30 for each) underwent chest CT on a 256-slice CT (RD-group 120 kV, 250 mAs, LD-group 120 kV, 120 mAs). Images were reconstructed with filtered back projection (FBP) algorithm in the RD group, whereas with FBP, HIR and IMR algorithms in the LD group. Effective radiation dose of both groups was recorded. Image-quality assessment was performed by 2 radiologists according to structure demarcation near stents, artifacts, noise, and diagnostic confidence using a 5-point scale (1 [poor] to 5 [excellent]). Image noise and CNR were measured.The effective radiation dose of LD group was reduced 52.7% compared with the RD group (10.8 mSv ± 0.58 vs 5.1 mSv ± 0.26, P = 0.00). LD-IMR images enabled lowest image noise and best subjective image quality scores of all 4 indices, when compared with RD images reconstructed with FBP (RD-FBP) images (all P < 0.05). LD images reconstructed with and with HIR (LD-HIR) images enabled higher score in subjective image quality of artifacts (P < 0.05), whereas it showed no difference in the other subjective image-quality indices and image noise. Significant higher image noise and lower score of subjective image quality were observed in LD-FBP images (all P < 0.05).Both IMR and HIR improved image quality of low-dose chest CT by comparison of routine dose images reconstructed with FBP. Meanwhile, IMR allows further image quality improvement than HIR. PMID:27684818

  18. Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data.

    PubMed

    Wang, Tingting; Cao, Lei; Yang, Wei; Feng, Qianjin; Chen, Wufan; Zhang, Yu

    2015-08-01

    Image enhancement of lung four-dimensional computed tomography (4D-CT) data is highly important because image resolution remains a crucial point in lung cancer radiotherapy. In this paper, we proposed a method for lung 4D-CT super resolution (SR) by using an adaptive-patch-based projection onto convex sets (POCS) approach, which is in contrast with the global POCS SR algorithm, to recover fine details with lesser artifacts in images. The main contribution of this patch-based approach is that the interfering local structure from other phases can be rejected by employing a similar patch adaptive selection strategy. The effectiveness of our approach is demonstrated through experiments on simulated images and real lung 4D-CT datasets. A comparison with previously published SR reconstruction methods highlights the favorable characteristics of the proposed method.

  19. Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data

    NASA Astrophysics Data System (ADS)

    Wang, Tingting; Cao, Lei; Yang, Wei; Feng, Qianjin; Chen, Wufan; Zhang, Yu

    2015-08-01

    Image enhancement of lung four-dimensional computed tomography (4D-CT) data is highly important because image resolution remains a crucial point in lung cancer radiotherapy. In this paper, we proposed a method for lung 4D-CT super resolution (SR) by using an adaptive-patch-based projection onto convex sets (POCS) approach, which is in contrast with the global POCS SR algorithm, to recover fine details with lesser artifacts in images. The main contribution of this patch-based approach is that the interfering local structure from other phases can be rejected by employing a similar patch adaptive selection strategy. The effectiveness of our approach is demonstrated through experiments on simulated images and real lung 4D-CT datasets. A comparison with previously published SR reconstruction methods highlights the favorable characteristics of the proposed method.

  20. Regularized ML reconstruction for time/dose reduction in 18F-fluoride PET/CT studies

    NASA Astrophysics Data System (ADS)

    De Bernardi, Elisabetta; Magnani, Patrizia; Gianolli, Luigi; Carla Gilardi, Maria; Bettinardi, Valentino

    2015-01-01

    We are proposing a regularized reconstruction strategy for the detection of bone lesions in 18F-fluoride whole body PET images obtained with 1 min/bed using the anatomical information provided by co-registered CT images. Bones are recognized on CT images and then transposed into the PET volume framework. During PET reconstruction, two different priors are used for bone and non-bone voxels: the relative difference prior in bone and the P-Gaussian prior in non-bone. After a tuning of the priors’ parameters, the reconstruction strategy has been tested on 6 18F-fluoride PET/CT studies, on a total of 67 lesions. Regularized images provided results comparable to the standard 3 min/bed images, in terms image quality, lesion activity, noise level and noise correlation. The proposed strategy therefore appears to be a useful tool to reduce the acquisition time or the injected dose in 18F-fluoride PET studies.

  1. Determination of the Optimal Dose Reduction Level via Iterative Reconstruction Using 640-Slice Volume Chest CT in a Pig Model

    PubMed Central

    Liu, Xingli; Wang, Jingshi; Liu, Qin; Zhao, Pengfei; Hou, Yang; Ma, Yue; Guo, Qiyong

    2015-01-01

    Aim To determine the optimal dose reduction level of iterative reconstruction technique for paediatric chest CT in pig models. Materials and Methods 27 infant pigs underwent 640-slice volume chest CT with 80kVp and different mAs. Automatic exposure control technique was used, and the index of noise was set to SD10 (Group A, routine dose), SD12.5, SD15, SD17.5, SD20 (Groups from B to E) to reduce dose respectively. Group A was reconstructed with filtered back projection (FBP), and Groups from B to E were reconstructed using iterative reconstruction (IR). Objective and subjective image quality (IQ) among groups were compared to determine an optimal radiation reduction level. Results The noise and signal-to-noise ratio (SNR) in Group D had no significant statistical difference from that in Group A (P = 1.0). The scores of subjective IQ in Group A were not significantly different from those in Group D (P>0.05). There were no obvious statistical differences in the objective and subjective index values among the subgroups (small, medium and large subgroups) of Group D. The effective dose (ED) of Group D was 58.9% lower than that of Group A (0.20±0.05mSv vs 0.48±0.10mSv, p <0.001). Conclusions In infant pig chest CT, using iterative reconstruction can provide diagnostic image quality; furthermore, it can reduce the dosage by 58.9%. PMID:25764485

  2. A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy.

    PubMed

    Li, Hua; Dolly, Steven; Chen, Hsin-Chen; Anastasio, Mark A; Low, Daniel A; Li, Harold H; Michalski, Jeff M; Thorstad, Wade L; Gay, Hiram; Mutic, Sasa

    2016-07-08

    CT image reconstruction is typically evaluated based on the ability to reduce the radiation dose to as-low-as-reasonably-achievable (ALARA) while maintaining acceptable image quality. However, the determination of common image quality metrics, such as noise, contrast, and contrast-to-noise ratio, is often insufficient for describing clinical radiotherapy task performance. In this study we designed and implemented a new comparative analysis method associating image quality, radiation dose, and patient size with radiotherapy task performance, with the purpose of guiding the clinical radiotherapy usage of CT reconstruction algorithms. The iDose4 iterative reconstruction algorithm was selected as the target for comparison, wherein filtered back-projection (FBP) reconstruction was regarded as the baseline. Both phantom and patient images were analyzed. A layer-adjustable anthropomorphic pelvis phantom capable of mimicking 38-58 cm lateral diameter-sized patients was imaged and reconstructed by the FBP and iDose4 algorithms with varying noise-reduction-levels, respectively. The resulting image sets were quantitatively assessed by two image quality indices, noise and contrast-to-noise ratio, and two clinical task-based indices, target CT Hounsfield number (for electron density determination) and structure contouring accuracy (for dose-volume calculations). Additionally, CT images of 34 patients reconstructed with iDose4 with six noise reduction levels were qualitatively evaluated by two radiation oncologists using a five-point scoring mechanism. For the phantom experiments, iDose4 achieved noise reduction up to 66.1% and CNR improvement up to 53.2%, compared to FBP without considering the changes of spatial resolution among images and the clinical acceptance of reconstructed images. Such improvements consistently appeared across different iDose4 noise reduction levels, exhibiting limited interlevel noise (< 5 HU) and target CT number variations (< 1 HU). The radiation

  3. Efficient and robust 3D CT image reconstruction based on total generalized variation regularization using the alternating direction method.

    PubMed

    Chen, Jianlin; Wang, Linyuan; Yan, Bin; Zhang, Hanming; Cheng, Genyang

    2015-01-01

    Iterative reconstruction algorithms for computed tomography (CT) through total variation regularization based on piecewise constant assumption can produce accurate, robust, and stable results. Nonetheless, this approach is often subject to staircase artefacts and the loss of fine details. To overcome these shortcomings, we introduce a family of novel image regularization penalties called total generalized variation (TGV) for the effective production of high-quality images from incomplete or noisy projection data for 3D reconstruction. We propose a new, fast alternating direction minimization algorithm to solve CT image reconstruction problems through TGV regularization. Based on the theory of sparse-view image reconstruction and the framework of augmented Lagrange function method, the TGV regularization term has been introduced in the computed tomography and is transformed into three independent variables of the optimization problem by introducing auxiliary variables. This new algorithm applies a local linearization and proximity technique to make the FFT-based calculation of the analytical solutions in the frequency domain feasible, thereby significantly reducing the complexity of the algorithm. Experiments with various 3D datasets corresponding to incomplete projection data demonstrate the advantage of our proposed algorithm in terms of preserving fine details and overcoming the staircase effect. The computation cost also suggests that the proposed algorithm is applicable to and is effective for CBCT imaging. Theoretical and technical optimization should be investigated carefully in terms of both computation efficiency and high resolution of this algorithm in application-oriented research.

  4. Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method

    NASA Astrophysics Data System (ADS)

    Schullcke, Benjamin; Gong, Bo; Krueger-Ziolek, Sabine; Soleimani, Manuchehr; Mueller-Lisse, Ullrich; Moeller, Knut

    2016-05-01

    Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.

  5. Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method

    PubMed Central

    Schullcke, Benjamin; Gong, Bo; Krueger-Ziolek, Sabine; Soleimani, Manuchehr; Mueller-Lisse, Ullrich; Moeller, Knut

    2016-01-01

    Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications. PMID:27181695

  6. Estimation of aortic valve leaflets from 3D CT images using local shape dictionaries and linear coding

    NASA Astrophysics Data System (ADS)

    Liang, Liang; Martin, Caitlin; Wang, Qian; Sun, Wei; Duncan, James

    2016-03-01

    Aortic valve (AV) disease is a significant cause of morbidity and mortality. The preferred treatment modality for severe AV disease is surgical resection and replacement of the native valve with either a mechanical or tissue prosthetic. In order to develop effective and long-lasting treatment methods, computational analyses, e.g., structural finite element (FE) and computational fluid dynamic simulations, are very effective for studying valve biomechanics. These computational analyses are based on mesh models of the aortic valve, which are usually constructed from 3D CT images though many hours of manual annotation, and therefore an automatic valve shape reconstruction method is desired. In this paper, we present a method for estimating the aortic valve shape from 3D cardiac CT images, which is represented by triangle meshes. We propose a pipeline for aortic valve shape estimation which includes novel algorithms for building local shape dictionaries and for building landmark detectors and curve detectors using local shape dictionaries. The method is evaluated on real patient image dataset using a leave-one-out approach and achieves an average accuracy of 0.69 mm. The work will facilitate automatic patient-specific computational modeling of the aortic valve.

  7. The effect of CT dose on glenohumeral joint congruency measurements using 3D reconstructed patient-specific bone models

    NASA Astrophysics Data System (ADS)

    Lalone, Emily A.; Fox, Anne-Marie V.; Kedgley, Angela E.; Jenkyn, Thomas R.; King, Graham J. W.; Athwal, George S.; Johnson, James A.; Peters, Terry M.

    2011-10-01

    The study of joint congruency at the glenohumeral joint of the shoulder using computed tomography (CT) and three-dimensional (3D) reconstructions of joint surfaces is an area of significant clinical interest. However, ionizing radiation delivered to patients during CT examinations is much higher than other types of radiological imaging. The shoulder represents a significant challenge for this modality as it is adjacent to the thyroid gland and breast tissue. The objective of this study was to determine the optimal CT scanning techniques that would minimize radiation dose while accurately quantifying joint congruency of the shoulder. The results suggest that only one-tenth of the standard applied total current (mA) and a pitch ratio of 1.375:1 was necessary to produce joint congruency values consistent with that of the higher dose scans. Using the CT scanning techniques examined in this study, the effective dose applied to the shoulder to quantify joint congruency was reduced by 88.9% compared to standard clinical CT imaging techniques.

  8. Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method

    NASA Astrophysics Data System (ADS)

    Guerrero, Thomas; Zhang, Geoffrey; Huang, Tzung-Chi; Lin, Kang-Ping

    2004-09-01

    The purpose of this work was to develop and validate an automated method for intrathoracic tumour motion estimation from breath-hold computed tomography (BH CT) imaging using the three-dimensional optical flow method (3D OFM). A modified 3D OFM algorithm provided 3D displacement vectors for each voxel which were used to map tumour voxels on expiration BH CT onto inspiration BH CT images. A thoracic phantom and simulated expiration/inspiration BH CT pairs were used for validation. The 3D OFM was applied to the measured inspiration and expiration BH CT images from one lung cancer and one oesophageal cancer patient. The resulting displacements were plotted in histogram format and analysed to provide insight regarding the tumour motion. The phantom tumour displacement was measured as 1.20 and 2.40 cm with full-width at tenth maximum (FWTM) for the distribution of displacement estimates of 0.008 and 0.006 cm, respectively. The maximum error of any single voxel's motion estimate was 1.1 mm along the z-dimension or approximately one-third of the z-dimension voxel size. The simulated BH CT pairs revealed an rms error of less than 0.25 mm. The displacement of the oesophageal tumours was nonuniform and up to 1.4 cm, this was a new finding. A lung tumour maximum displacement of 2.4 cm was found in the case evaluated. In conclusion, 3D OFM provided an accurate estimation of intrathoracic tumour motion, with estimated errors less than the voxel dimension in a simulated motion phantom study. Surprisingly, oesophageal tumour motion was large and nonuniform, with greatest motion occurring at the gastro-oesophageal junction. Presented at The IASTED Second International Conference on Biomedical Engineering (BioMED 2004), Innsbruck, Austria, 16-18 February 2004.

  9. Preliminary study of region-of-interest image reconstruction with intensity weighting in cone-beam CT using iterative algorithm

    NASA Astrophysics Data System (ADS)

    Son, Kihong; Lee, Jiseoc; Lee, Younjeong; Kim, Jin Sung; Cho, Seungryong

    2014-03-01

    In computed tomography (CT) imaging, radiat ion dose delivered to the patient is one of the major concerns. Many CT developers and researchers have been making efforts to reduce radiat ion dose. Spars e-view CT takes project ions at sparser view-angles and provides a viable option to reducing radiation dose. Sparse-view CT inspired by a compressive sensing (CS) theory, which acquires sparsely sampled data in projection angles to reconstruct volumetric images of the scanned object, is under active research for low-dose imaging. Also, region of interest (ROI) imaging method is one of the reasonable approaches to reducing the integral dose to the patient and the risk of overdose. In this study, we combined the two approaches together to achieve an ultra-low-dose imaging: a sparse-view imaging and the intensityweighted region-of-interest (IWROI) imaging. IWROI imaging technique is particularly interesting because it can reduce the imaging radiation dose substantially to the structures away from the imaging target, while allowing a stable solution of the reconstruction problem in comparison with the interior problem. We used a total-variation (TV) minimization algorithm that exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view data. In this study, we implemented an imaging mode that combines a sparse-view imaging and an ROI imaging. We obtained promising results and believe that the proposed scanning approach can help reduce radiation dose to the patients while preserving good quality images for applications such as image-guided radiation therapy. We are in progress of applying the method to the real data.

  10. SU-E-J-153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments

    SciTech Connect

    Beaudry, J; Bergman, A; Cropp, R

    2015-06-15

    Purpose: To verify that the planned Primary Target Volume (PTV) and Internal Gross Tumor Volume (IGTV) fully enclose a moving lung tumor volume as visualized on a pre-SABR treatment verification 4D Cone Beam CT. Methods: Daily 3DCBCT image sets were acquired immediately prior to treatment for 10 SABR lung patients using the on-board imaging system integrated into a Varian TrueBeam (v1.6: no 4DCBCT module available). Respiratory information was acquired during the scan using the Varian RPM system. The CBCT projections were sorted into 8 bins offline, both by breathing phase and amplitude, using in-house software. An iterative algorithm based on total variation minimization, implemented in the open source reconstruction toolkit (RTK), was used to reconstruct the binned projections into 4DCBCT images. The relative tumor motion was quantified by tracking the centroid of the tumor volume from each 4DCBCT image. Following CT-CBCT registration, the planning CT volumes were compared to the location of the CBCT tumor volume as it moves along its breathing trajectory. An overlap metric quantified the ability of the planned PTV and IGTV to contain the tumor volume at treatment. Results: The 4DCBCT reconstructed images visibly show the tumor motion. The mean overlap between the planned PTV (IGTV) and the 4DCBCT tumor volumes was 100% (94%), with an uncertainty of 5% from the 4DCBCT tumor volume contours. Examination of the tumor motion and overlap metric verify that the IGTV drawn at the planning stage is a good representation of the tumor location at treatment. Conclusion: It is difficult to compare GTV volumes from a 4DCBCT and a planning CT due to image quality differences. However, it was possible to conclude the GTV remained within the PTV 100% of the time thus giving the treatment staff confidence that SABR lung treatements are being delivered accurately.

  11. Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT.

    PubMed

    Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R; La Riviere, Patrick J; Alessio, Adam M

    2014-04-01

    Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This

  12. Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

    NASA Astrophysics Data System (ADS)

    Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R.; La Riviere, Patrick J.; Alessio, Adam M.

    2014-04-01

    Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)-1, cardiac output = 3, 5, 8 L min-1). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that

  13. Task-based image quality evaluation of iterative reconstruction methods for low dose CT using computer simulations

    NASA Astrophysics Data System (ADS)

    Xu, Jingyan; Fuld, Matthew K.; Fung, George S. K.; Tsui, Benjamin M. W.

    2015-04-01

    Iterative reconstruction (IR) methods for x-ray CT is a promising approach to improve image quality or reduce radiation dose to patients. The goal of this work was to use task based image quality measures and the channelized Hotelling observer (CHO) to evaluate both analytic and IR methods for clinical x-ray CT applications. We performed realistic computer simulations at five radiation dose levels, from a clinical reference low dose D0 to 25% D0. A fixed size and contrast lesion was inserted at different locations into the liver of the XCAT phantom to simulate a weak signal. The simulated data were reconstructed on a commercial CT scanner (SOMATOM Definition Flash; Siemens, Forchheim, Germany) using the vendor-provided analytic (WFBP) and IR (SAFIRE) methods. The reconstructed images were analyzed by CHOs with both rotationally symmetric (RS) and rotationally oriented (RO) channels, and with different numbers of lesion locations (5, 10, and 20) in a signal known exactly (SKE), background known exactly but variable (BKEV) detection task. The area under the receiver operating characteristic curve (AUC) was used as a summary measure to compare the IR and analytic methods; the AUC was also used as the equal performance criterion to derive the potential dose reduction factor of IR. In general, there was a good agreement in the relative AUC values of different reconstruction methods using CHOs with RS and RO channels, although the CHO with RO channels achieved higher AUCs than RS channels. The improvement of IR over analytic methods depends on the dose level. The reference dose level D0 was based on a clinical low dose protocol, lower than the standard dose due to the use of IR methods. At 75% D0, the performance improvement was statistically significant (p < 0.05). The potential dose reduction factor also depended on the detection task. For the SKE/BKEV task involving 10 lesion locations, a dose reduction of at least 25% from D0 was achieved.

  14. Noninvasive Vascular Displacement Estimation for Relative Elastic Modulus Reconstruction in Transversal Imaging Planes

    PubMed Central

    Hansen, Hendrik H.G.; Richards, Michael S.; Doyley, Marvin M.; de Korte, Chris L.

    2013-01-01

    Atherosclerotic plaque rupture can initiate stroke or myocardial infarction. Lipid-rich plaques with thin fibrous caps have a higher risk to rupture than fibrotic plaques. Elastic moduli differ for lipid-rich and fibrous tissue and can be reconstructed using tissue displacements estimated from intravascular ultrasound radiofrequency (RF) data acquisitions. This study investigated if modulus reconstruction is possible for noninvasive RF acquisitions of vessels in transverse imaging planes using an iterative 2D cross-correlation based displacement estimation algorithm. Furthermore, since it is known that displacements can be improved by compounding of displacements estimated at various beam steering angles, we compared the performance of the modulus reconstruction with and without compounding. For the comparison, simulated and experimental RF data were generated of various vessel-mimicking phantoms. Reconstruction errors were less than 10%, which seems adequate for distinguishing lipid-rich from fibrous tissue. Compounding outperformed single-angle reconstruction: the interquartile range of the reconstructed moduli for the various homogeneous phantom layers was approximately two times smaller. Additionally, the estimated lateral displacements were a factor of 2–3 better matched to the displacements corresponding to the reconstructed modulus distribution. Thus, noninvasive elastic modulus reconstruction is possible for transverse vessel cross sections using this cross-correlation method and is more accurate with compounding. PMID:23478602

  15. Noninvasive vascular displacement estimation for relative elastic modulus reconstruction in transversal imaging planes.

    PubMed

    Hansen, Hendrik H G; Richards, Michael S; Doyley, Marvin M; de Korte, Chris L

    2013-01-01

    Atherosclerotic plaque rupture can initiate stroke or myocardial infarction. Lipid-rich plaques with thin fibrous caps have a higher risk to rupture than fibrotic plaques. Elastic moduli differ for lipid-rich and fibrous tissue and can be reconstructed using tissue displacements estimated from intravascular ultrasound radiofrequency (RF) data acquisitions. This study investigated if modulus reconstruction is possible for noninvasive RF acquisitions of vessels in transverse imaging planes using an iterative 2D cross-correlation based displacement estimation algorithm. Furthermore, since it is known that displacements can be improved by compounding of displacements estimated at various beam steering angles, we compared the performance of the modulus reconstruction with and without compounding. For the comparison, simulated and experimental RF data were generated of various vessel-mimicking phantoms. Reconstruction errors were less than 10%, which seems adequate for distinguishing lipid-rich from fibrous tissue. Compounding outperformed single-angle reconstruction: the interquartile range of the reconstructed moduli for the various homogeneous phantom layers was approximately two times smaller. Additionally, the estimated lateral displacements were a factor of 2-3 better matched to the displacements corresponding to the reconstructed modulus distribution. Thus, noninvasive elastic modulus reconstruction is possible for transverse vessel cross sections using this cross-correlation method and is more accurate with compounding.

  16. Patient-specific radiation dose and cancer risk estimation in CT: Part II. Application to patients

    SciTech Connect

    Li Xiang; Samei, Ehsan; Segars, W. Paul; Sturgeon, Gregory M.; Colsher, James G.; Toncheva, Greta; Yoshizumi, Terry T.; Frush, Donald P.

    2011-01-15

    Purpose: Current methods for estimating and reporting radiation dose from CT examinations are largely patient-generic; the body size and hence dose variation from patient to patient is not reflected. Furthermore, the current protocol designs rely on dose as a surrogate for the risk of cancer incidence, neglecting the strong dependence of risk on age and gender. The purpose of this study was to develop a method for estimating patient-specific radiation dose and cancer risk from CT examinations. Methods: The study included two patients (a 5-week-old female patient and a 12-year-old male patient), who underwent 64-slice CT examinations (LightSpeed VCT, GE Healthcare) of the chest, abdomen, and pelvis at our institution in 2006. For each patient, a nonuniform rational B-spine (NURBS) based full-body computer model was created based on the patient's clinical CT data. Large organs and structures inside the image volume were individually segmented and modeled. Other organs were created by transforming an existing adult male or female full-body computer model (developed from visible human data) to match the framework defined by the segmented organs, referencing the organ volume and anthropometry data in ICRP Publication 89. A Monte Carlo program previously developed and validated for dose simulation on the LightSpeed VCT scanner was used to estimate patient-specific organ dose, from which effective dose and risks of cancer incidence were derived. Patient-specific organ dose and effective dose were compared with patient-generic CT dose quantities in current clinical use: the volume-weighted CT dose index (CTDI{sub vol}) and the effective dose derived from the dose-length product (DLP). Results: The effective dose for the CT examination of the newborn patient (5.7 mSv) was higher but comparable to that for the CT examination of the teenager patient (4.9 mSv) due to the size-based clinical CT protocols at our institution, which employ lower scan techniques for smaller

  17. Geometric Parameters Estimation and Calibration in Cone-Beam Micro-CT

    PubMed Central

    Zhao, Jintao; Hu, Xiaodong; Zou, Jing; Hu, Xiaotang

    2015-01-01

    The quality of Computed Tomography (CT) images crucially depends on the precise knowledge of the scanner geometry. Therefore, it is necessary to estimate and calibrate the misalignments before image acquisition. In this paper, a Two-Piece-Ball (TPB) phantom is used to estimate a set of parameters that describe the geometry of a cone-beam CT system. Only multiple projections of the TPB phantom at one position are required, which can avoid the rotation errors when acquiring multi-angle projections. Also, a corresponding algorithm is derived. The performance of the method is evaluated through simulation and experimental data. The results demonstrated that the proposed method is valid and easy to implement. Furthermore, the experimental results from the Micro-CT system demonstrate the ability to reduce artifacts and improve image quality through geometric parameter calibration. PMID:26371008

  18. AB-OSEM reconstruction for improved Patlak kinetic parameter estimation: a simulation study

    NASA Astrophysics Data System (ADS)

    Verhaeghe, Jeroen; Reader, Andrew J.

    2010-11-01

    The non-negativity constraint inherently present in OSEM reconstruction successfully reduces the standard deviation in cold regions but at the cost of introducing a positive bias, especially at low iteration numbers. For low-count data, as often encountered in short-duration frames in dynamic imaging protocols, it has been shown that it can be advantageous (in terms of bias in the reconstructed image) to remove the non-negativity constraint. In this work two competing algorithms that do not impose non-negativity in the reconstructed image are investigated: NEG-ML and AB-OSEM. It was found that the AB-OSEM reconstruction outperformed the NEG-ML reconstruction. The AB-OSEM algorithm was then further developed to allow a forward model that includes randoms and scatter background terms. In addition to static reconstruction the current analysis was extended to consider the important case of kinetic parameter estimation from dynamic PET data. Simulation studies (comparing OSEM, FBP and AB-OSEM) showed that the positive bias obtained with OSEM reconstruction can be avoided in both static and parametric imaging through use of a negative lower bound in AB-OSEM reconstruction (i.e. by lifting the implicit non-negativity constraint of OSEM). When quantification tasks are considered, the overall error in the estimates (composed of both bias and standard deviation) is often of primary concern. An important finding of this work is that in most cases the activity concentration and the kinetic parameters obtained from images reconstructed using AB-OSEM showed a lower overall root mean squared error compared to the popular choices of either OSEM or FBP reconstruction for both cold and warm regions. As such, AB-OSEM should be preferred instead of the standard OSEM and FBP reconstructions when kinetic parameter estimation is considered. Finally, this work shows example parametric images from the high-resolution research tomograph obtained using the different reconstruction methods.

  19. AB-OSEM reconstruction for improved Patlak kinetic parameter estimation: a simulation study.

    PubMed

    Verhaeghe, Jeroen; Reader, Andrew J

    2010-11-21

    The non-negativity constraint inherently present in OSEM reconstruction successfully reduces the standard deviation in cold regions but at the cost of introducing a positive bias, especially at low iteration numbers. For low-count data, as often encountered in short-duration frames in dynamic imaging protocols, it has been shown that it can be advantageous (in terms of bias in the reconstructed image) to remove the non-negativity constraint. In this work two competing algorithms that do not impose non-negativity in the reconstructed image are investigated: NEG-ML and AB-OSEM. It was found that the AB-OSEM reconstruction outperformed the NEG-ML reconstruction. The AB-OSEM algorithm was then further developed to allow a forward model that includes randoms and scatter background terms. In addition to static reconstruction the current analysis was extended to consider the important case of kinetic parameter estimation from dynamic PET data. Simulation studies (comparing OSEM, FBP and AB-OSEM) showed that the positive bias obtained with OSEM reconstruction can be avoided in both static and parametric imaging through use of a negative lower bound in AB-OSEM reconstruction (i.e. by lifting the implicit non-negativity constraint of OSEM). When quantification tasks are considered, the overall error in the estimates (composed of both bias and standard deviation) is often of primary concern. An important finding of this work is that in most cases the activity concentration and the kinetic parameters obtained from images reconstructed using AB-OSEM showed a lower overall root mean squared error compared to the popular choices of either OSEM or FBP reconstruction for both cold and warm regions. As such, AB-OSEM should be preferred instead of the standard OSEM and FBP reconstructions when kinetic parameter estimation is considered. Finally, this work shows example parametric images from the high-resolution research tomograph obtained using the different reconstruction methods.

  20. L1 and total variation regularized C-arm cardiac cone beam CT reconstruction

    NASA Astrophysics Data System (ADS)

    Liu, Bo; Zhou, Fugen

    2013-10-01

    A new iterative reconstruction method based on joint L1 and total variation regularization is proposed for ECG-gated tomographic reconstruction of coronary vessels. The reconstruction problem is formulated as a constrained optimization model and solved using linearized Bregman and forward-backward splitting methods. Experiments were conducted to evaluate its performance using an anthropomorphic phantom, and the results shown that the proposed method could reconstruct accurate vascular morphology from 5-10 angiograms.

  1. Radiation Dose Reduction in Pediatric Body CT Using Iterative Reconstruction and a Novel Image-Based Denoising Method

    PubMed Central

    Yu, Lifeng; Fletcher, Joel G.; Shiung, Maria; Thomas, Kristen B.; Matsumoto, Jane M.; Zingula, Shannon N.; McCollough, Cynthia H.

    2016-01-01

    OBJECTIVE The objective of this study was to evaluate the radiation dose reduction potential of a novel image-based denoising technique in pediatric abdominopelvic and chest CT examinations and compare it with a commercial iterative reconstruction method. MATERIALS AND METHODS Data were retrospectively collected from 50 (25 abdominopelvic and 25 chest) clinically indicated pediatric CT examinations. For each examination, a validated noise-insertion tool was used to simulate half-dose data, which were reconstructed using filtered back-projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) methods. A newly developed denoising technique, adaptive nonlocal means (aNLM), was also applied. For each of the 50 patients, three pediatric radiologists evaluated four datasets: full dose plus FBP, half dose plus FBP, half dose plus SAFIRE, and half dose plus aNLM. For each examination, the order of preference for the four datasets was ranked. The organ-specific diagnosis and diagnostic confidence for five primary organs were recorded. RESULTS The mean (± SD) volume CT dose index for the full-dose scan was 5.3 ± 2.1 mGy for abdominopelvic examinations and 2.4 ± 1.1 mGy for chest examinations. For abdominopelvic examinations, there was no statistically significant difference between the half dose plus aNLM dataset and the full dose plus FBP dataset (3.6 ± 1.0 vs 3.6 ± 0.9, respectively; p = 0.52), and aNLM performed better than SAFIRE. For chest examinations, there was no statistically significant difference between the half dose plus SAFIRE and the full dose plus FBP (4.1 ± 0.6 vs 4.2 ± 0.6, respectively; p = 0.67), and SAFIRE performed better than aNLM. For all organs, there was more than 85% agreement in organ-specific diagnosis among the three half-dose configurations and the full dose plus FBP configuration. CONCLUSION Although a novel image-based denoising technique performed better than a commercial iterative reconstruction method in pediatric

  2. Marker-free lung tumor trajectory estimation from a cone beam CT sinogram

    NASA Astrophysics Data System (ADS)

    Hugo, Geoffrey D.; Liang, Jian; Yan, Di

    2010-05-01

    An algorithm was developed to estimate the 3D lung tumor position using the projection data forming a cone beam CT sinogram and a template registration method. A pre-existing respiration-correlated CT image was used to generate templates of the target, which were then registered to the individual cone beam CT projections, and estimates of the target position were made for each projection. The registration search region was constrained based on knowledge of the mean tumor position during the cone beam CT scan acquisition. Several template registration algorithms were compared, including correlation coefficient and robust methods such as block correlation, robust correlation coefficient and robust gradient correlation. Robust registration metrics were found to be less sensitive to occlusions such as overlying tissue and the treatment couch. The mean accuracy of the position estimation was 1.4 mm in phantom with a robust registration algorithm. In two research subjects with peripheral tumors, the mean position and mean target excursion were estimated to within 2.0 mm compared to the results obtained with a '4D' registration of 4D image volumes.

  3. Estimation of myocardial volume at risk from CT angiography

    NASA Astrophysics Data System (ADS)

    Zhu, Liangjia; Gao, Yi; Mohan, Vandana; Stillman, Arthur; Faber, Tracy; Tannenbaum, Allen

    2011-03-01

    The determination of myocardial volume at risk distal to coronary stenosis provides important information for prognosis and treatment of coronary artery disease. In this paper, we present a novel computational framework for estimating the myocardial volume at risk in computed tomography angiography (CTA) imagery. Initially, epicardial and endocardial surfaces, and coronary arteries are extracted using an active contour method. Then, the extracted coronary arteries are projected onto the epicardial surface, and each point on this surface is associated with its closest coronary artery using the geodesic distance measurement. The likely myocardial region at risk on the epicardial surface caused by a stenosis is approximated by the region in which all its inner points are associated with the sub-branches distal to the stenosis on the coronary artery tree. Finally, the likely myocardial volume at risk is approximated by the volume in between the region at risk on the epicardial surface and its projection on the endocardial surface, which is expected to yield computational savings over risk volume estimation using the entire image volume. Furthermore, we expect increased accuracy since, as compared to prior work using the Euclidean distance, we employ the geodesic distance in this work. The experimental results demonstrate the effectiveness of the proposed approach on pig heart CTA datasets.

  4. Analysis of bite marks in foodstuffs by computer tomography (cone beam CT)--3D reconstruction.

    PubMed

    Marques, Jeidson; Musse, Jamilly; Caetano, Catarina; Corte-Real, Francisco; Corte-Real, Ana Teresa

    2013-12-01

    The use of three-dimensional (3D) analysis of forensic evidence is highlighted in comparison with traditional methods. This three-dimensional analysis is based on the registration of the surface from a bitten object. The authors propose to use Cone Beam Computed Tomography (CBCT), which is used in dental practice, in order to study the surface and interior of bitten objects and dental casts of suspects. In this study, CBCT is applied to the analysis of bite marks in foodstuffs, which may be found in a forensic case scenario. 6 different types of foodstuffs were used: chocolate, cheese, apple, chewing gum, pizza and tart (flaky pastry and custard). The food was bitten into and dental casts of the possible suspects were made. The dental casts and bitten objects were registered using an x-ray source and the CBCT equipment iCAT® (Pennsylvania, EUA). The software InVivo5® (Anatomage Inc, EUA) was used to visualize and analyze the tomographic slices and 3D reconstructions of the objects. For each material an estimate of its density was assessed by two methods: HU values and specific gravity. All the used materials were successfully reconstructed as good quality 3D images. The relative densities of the materials in study were compared. Amongst the foodstuffs, the chocolate had the highest density (median value 100.5 HU and 1,36 g/cm(3)), while the pizza showed to have the lowest (median value -775 HU and 0,39 g/cm(3)), on both scales. Through tomographic slices and three-dimensional reconstructions it was possible to perform the metric analysis of the bite marks in all the foodstuffs, except for the pizza. These measurements could also be obtained from the dental casts. The depth of the bite mark was also successfully determined in all the foodstuffs except for the pizza. Cone Beam Computed Tomography has the potential to become an important tool for forensic sciences, namely for the registration and analysis of bite marks in foodstuffs that may be found in a crime

  5. Analysis of bite marks in foodstuffs by computer tomography (cone beam CT)--3D reconstruction.

    PubMed

    Marques, Jeidson; Musse, Jamilly; Caetano, Catarina; Corte-Real, Francisco; Corte-Real, Ana Teresa

    2013-12-01

    The use of three-dimensional (3D) analysis of forensic evidence is highlighted in comparison with traditional methods. This three-dimensional analysis is based on the registration of the surface from a bitten object. The authors propose to use Cone Beam Computed Tomography (CBCT), which is used in dental practice, in order to study the surface and interior of bitten objects and dental casts of suspects. In this study, CBCT is applied to the analysis of bite marks in foodstuffs, which may be found in a forensic case scenario. 6 different types of foodstuffs were used: chocolate, cheese, apple, chewing gum, pizza and tart (flaky pastry and custard). The food was bitten into and dental casts of the possible suspects were made. The dental casts and bitten objects were registered using an x-ray source and the CBCT equipment iCAT® (Pennsylvania, EUA). The software InVivo5® (Anatomage Inc, EUA) was used to visualize and analyze the tomographic slices and 3D reconstructions of the objects. For each material an estimate of its density was assessed by two methods: HU values and specific gravity. All the used materials were successfully reconstructed as good quality 3D images. The relative densities of the materials in study were compared. Amongst the foodstuffs, the chocolate had the highest density (median value 100.5 HU and 1,36 g/cm(3)), while the pizza showed to have the lowest (median value -775 HU and 0,39 g/cm(3)), on both scales. Through tomographic slices and three-dimensional reconstructions it was possible to perform the metric analysis of the bite marks in all the foodstuffs, except for the pizza. These measurements could also be obtained from the dental casts. The depth of the bite mark was also successfully determined in all the foodstuffs except for the pizza. Cone Beam Computed Tomography has the potential to become an important tool for forensic sciences, namely for the registration and analysis of bite marks in foodstuffs that may be found in a crime

  6. Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT

    NASA Astrophysics Data System (ADS)

    Zhang, Hao; Ma, Jianhua; Wang, Jing; Liu, Yan; Han, Hao; Li, Lihong; Moore, William; Liang, Zhengrong

    2015-03-01

    To reduce radiation dose in X-ray computed tomography (CT) imaging, one of the common strategies is to lower the milliampere-second (mAs) setting during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The edge-preserving nonlocal means (NLM) filtering can help to reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate them, especially under very low-dose circumstance when the image is severely degraded. To deal with this situation, we proposed a statistical image reconstruction scheme using a NLM-based regularization, which can suppress the noise and streak artifacts more effectively. However, we noticed that using uniform filtering parameter in the NLM-based regularization was rarely optimal for the entire image. Therefore, in this study, we further developed a novel approach for designing adaptive filtering parameters by considering local characteristics of the image, and the resulting regularization is referred to as adaptive NLM-based regularization. Experimental results with physical phantom and clinical patient data validated the superiority of using the proposed adaptive NLM-regularized statistical image reconstruction method for low-dose X-ray CT, in terms of noise/streak artifacts suppression and edge/detail/contrast/texture preservation.

  7. Improved least squares MR image reconstruction using estimates of k-space data consistency.

    PubMed

    Johnson, Kevin M; Block, Walter F; Reeder, Scott B; Samsonov, Alexey

    2012-06-01

    This study describes a new approach to reconstruct data that has been corrupted by unfavorable magnetization evolution. In this new framework, images are reconstructed in a weighted least squares fashion using all available data and a measure of consistency determined from the data itself. The reconstruction scheme optimally balances uncertainties from noise error with those from data inconsistency, is compatible with methods that model signal corruption, and may be advantageous for more accurate and precise reconstruction with many least squares-based image estimation techniques including parallel imaging and constrained reconstruction/compressed sensing applications. Performance of the several variants of the algorithm tailored for fast spin echo and self-gated respiratory gating applications was evaluated in simulations, phantom experiments, and in vivo scans. The data consistency weighting technique substantially improved image quality and reduced noise as compared to traditional reconstruction approaches.

  8. Improved Least Squares MR Image Reconstruction Using Estimates of k-Space Data Consistency

    PubMed Central

    Johnson, Kevin M.; Block, Walter F.; Reeder, Scott. B.; Samsonov, Alexey

    2011-01-01

    This work describes a new approach to reconstruct data that has been corrupted by unfavorable magnetization evolution. In this new framework, images are reconstructed in a weighted least squares fashion using all available data and a measure of consistency determined from the data itself. The reconstruction scheme optimally balances uncertainties from noise error with those from data inconsistency, is compatible with methods that model signal corruption, and may be advantageous for more accurate and precise reconstruction with many least-squares based image estimation techniques including parallel imaging and constrained reconstruction/compressed sensing applications. Performance of the several variants of the algorithm tailored for fast spin echo (FSE) and self gated respiratory gating applications was evaluated in simulations, phantom experiments, and in-vivo scans. The data consistency weighting technique substantially improved image quality and reduced noise as compared to traditional reconstruction approaches. PMID:22135155

  9. 1975 Memorial Award Paper. Image generation and display techniques for CT scan data. Thin transverse and reconstructed coronal and sagittal planes.

    PubMed

    Glenn, W V; Johnston, R J; Morton, P E; Dwyer, S J

    1975-01-01

    The various limitations to computerized axial tomographic (CT) interpretation are due in part to the 8-13 mm standard tissue plane thickness and in part to the absence of alternative planes of view, such as coronal or sagittal images. This paper describes a method for gathering multiple overlapped 8 mm transverse sections, subjecting these data to a deconvolution process, and then displaying thin (1 mm) transverse as well as reconstructed coronal and sagittal CT images. Verification of the deconvolution technique with phantom experiments is described. Application of the phantom results to human post mortem CT scan data illustrates this method's faithful reconstruction of coronal and sagittal tissue densities when correlated with actual specimen photographs of a sectioned brain. A special CT procedure, limited basal overlap scanning, is proposed for use on current first generation CT scanners without hardware modification.

  10. Spectral CT modeling and reconstruction with hybrid detectors in dynamic-threshold-based counting and integrating modes.

    PubMed

    Li, Liang; Chen, Zhiqiang; Cong, Wenxiang; Wang, Ge

    2015-03-01

    Spectral CT with photon counting detectors can significantly improve CT performance by reducing image noise and dose, increasing contrast resolution and material specificity, as well as enabling functional and molecular imaging with existing and emerging probes. However, the current photon counting detector architecture is difficult to balance the number of energy bins and the statistical noise in each energy bin. Moreover, the hardware support for multi-energy bins demands a complex circuit which is expensive. In this paper, we promote a new scheme known as hybrid detectors that combine the dynamic-threshold-based counting and integrating modes. In this scheme, an energy threshold can be dynamically changed during a spectral CT scan, which can be considered as compressive sensing along the spectral dimension. By doing so, the number of energy bins can be retrospectively specified, even in a spatially varying fashion. To establish the feasibility and merits of such hybrid detectors, we develop a tensor-based PRISM algorithm to reconstruct a spectral CT image from dynamic dual-energy data, and perform experiments with simulated and real data, producing very promising results.

  11. Evidence of dose saving in routine CT practice using iterative reconstruction derived from a national diagnostic reference level survey

    PubMed Central

    Hayton, A; Beveridge, T; Marks, P; Wallace, A

    2015-01-01

    Objective: To assess the influence and significance of the use of iterative reconstruction (IR) algorithms on patient dose in CT in Australia. Methods: We examined survey data submitted to the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) National Diagnostic Reference Level Service (NDRLS) during 2013 and 2014. We compared median survey dose metrics with categorization by scan region and use of IR. Results: The use of IR results in a reduction in volume CT dose index of between 17% and 44% and a reduction in dose–length product of between 14% and 34% depending on the specific scan region. The reduction was highly significant (p < 0.001, Wilcoxon rank-sum test) for all six scan regions included in the NDRLS. Overall, 69% (806/1167) of surveys included in the analysis used IR. Conclusion: The use of IR in CT is achieving dose savings of 20–30% in routine practice in Australia. IR appears to be widely used by participants in the ARPANSA NDRLS with approximately 70% of surveys submitted employing this technique. Advances in knowledge: This study examines the impact of the use of IR on patient dose in CT on a national scale. PMID:26133224

  12. Convolution-based estimation of organ dose in tube current modulated CT

    PubMed Central

    Tian, Xiaoyu; Segars, W Paul; Dixon, Robert L; Samei, Ehsan

    2016-01-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients (hOrgan) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate (CTDIvol)organ, convolution values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying (CTDIvol)organ, convolution with the organ dose coefficients (hOrgan). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The discrepancy between the estimated organ dose and dose simulated using TCM Monte Carlo program was quantified. We further compared the

  13. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled

  14. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The

  15. Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model

    NASA Astrophysics Data System (ADS)

    Dang, H.; Stayman, J. W.; Sisniega, A.; Xu, J.; Zbijewski, W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

    2015-03-01

    Traumatic brain injury (TBI) is a major cause of death and disability. The current front-line imaging modality for TBI detection is CT, which reliably detects intracranial hemorrhage (fresh blood contrast 30-50 HU, size down to 1 mm) in non-contrast-enhanced exams. Compared to CT, flat-panel detector (FPD) cone-beam CT (CBCT) systems offer lower cost, greater portability, and smaller footprint suitable for point-of-care deployment. We are developing FPD-CBCT to facilitate TBI detection at the point-of-care such as in emergent, ambulance, sports, and military applications. However, current FPD-CBCT systems generally face challenges in low-contrast, soft-tissue imaging. Model-based reconstruction can improve image quality in soft-tissue imaging compared to conventional filtered back-projection (FBP) by leveraging high-fidelity forward model and sophisticated regularization. In FPD-CBCT TBI imaging, measurement noise characteristics undergo substantial change following artifact correction, resulting in non-negligible noise amplification. In this work, we extend the penalized weighted least-squares (PWLS) image reconstruction to include the two dominant artifact corrections (scatter and beam hardening) in FPD-CBCT TBI imaging by correctly modeling the variance change following each correction. Experiments were performed on a CBCT test-bench using an anthropomorphic phantom emulating intra-parenchymal hemorrhage in acute TBI, and the proposed method demonstrated an improvement in blood-brain contrast-to-noise ratio (CNR = 14.2) compared to FBP (CNR = 9.6) and PWLS using conventional weights (CNR = 11.6) at fixed spatial resolution (1 mm edge-spread width at the target contrast). The results support the hypothesis that FPD-CBCT can fulfill the image quality requirements for reliable TBI detection, using high-fidelity artifact correction and statistical reconstruction with accurate post-artifact-correction noise models.

  16. Variability of dental cone beam CT grey values for density estimations

    PubMed Central

    Pauwels, R; Nackaerts, O; Bellaiche, N; Stamatakis, H; Tsiklakis, K; Walker, A; Bosmans, H; Bogaerts, R; Jacobs, R; Horner, K

    2013-01-01

    Objective The aim of this study was to investigate the use of dental cone beam CT (CBCT) grey values for density estimations by calculating the correlation with multislice CT (MSCT) values and the grey value error after recalibration. Methods A polymethyl methacrylate (PMMA) phantom was developed containing inserts of different density: air, PMMA, hydroxyapatite (HA) 50 mg cm−3, HA 100, HA 200 and aluminium. The phantom was scanned on 13 CBCT devices and 1 MSCT device. Correlation between CBCT grey values and CT numbers was calculated, and the average error of the CBCT values was estimated in the medium-density range after recalibration. Results Pearson correlation coefficients ranged between 0.7014 and 0.9996 in the full-density range and between 0.5620 and 0.9991 in the medium-density range. The average error of CBCT voxel values in the medium-density range was between 35 and 1562. Conclusion Even though most CBCT devices showed a good overall correlation with CT numbers, large errors can be seen when using the grey values in a quantitative way. Although it could be possible to obtain pseudo-Hounsfield units from certain CBCTs, alternative methods of assessing bone tissue should be further investigated. Advances in knowledge The suitability of dental CBCT for density estimations was assessed, involving a large number of devices and protocols. The possibility for grey value calibration was thoroughly investigated. PMID:23255537

  17. Interferometric synthetic aperture radar detection and estimation based 3D image reconstruction

    NASA Astrophysics Data System (ADS)

    Austin, Christian D.; Moses, Randolph L.

    2006-05-01

    This paper explores three-dimensional (3D) interferometric synthetic aperture radar (IFSAR) image reconstruction when multiple scattering centers and noise are present in a radar resolution cell. We introduce an IFSAR scattering model that accounts for both multiple scattering centers and noise. The problem of 3D image reconstruction is then posed as a multiple hypothesis detection and estimation problem; resolution cells containing a single scattering center are detected and the 3D location of these cells' pixels are estimated; all other pixels are rejected from the image. Detection and estimation statistics are derived using the multiple scattering center IFSAR model. A 3D image reconstruction algorithm using these statistics is then presented, and its performance is evaluated for a 3D reconstruction of a backhoe from noisy IFSAR data.

  18. A method for extracting multi-organ from four-phase contrasted CT images based on CT value distribution estimation using EM-algorithm

    NASA Astrophysics Data System (ADS)

    Sakashita, Makiko; Kitasaka, Takayuki; Mori, Kensaku; Suenaga, Yasuhito; Nawano, Shigeru

    2007-03-01

    This paper presents a method for extracting multi-organs from four-phase contrasted CT images taken at different contrast timings (non-contrast, early, portal, and late phases). First, we apply a median filter to each CT image and align four-phase CT images by performing non-rigid volumetric image registration. Then, a three-dimensional joint histogram of CT values is computed from three-phase (early-, portal-, and late-) CT images. We assume that this histogram is a mixture of normal distributions corresponding to the liver, spleen, kidney, vein, artery, muscle, and bone regions. The EM algorithm is employed to estimate each normal distribution. Organ labels are assigned to each voxel using the mahalanobis distance measure. Connected component analysis is applied to correct the shape of each organ region. After that, the pancreas region is extracted from non-contrasted CT images in which other extracted organs and vessel regions are excluded. The EM algorithm is also employed for estimating the distribution of CT values inside the pancreas. We applied this method to seven cases of four-phase CT images. Extraction results show that the proposed method extracted multi-organs satisfactorily.

  19. Factors affecting uncertainty in lung nodule volume estimation with CT: comparisons of findings from two estimation methods in a phantom study

    NASA Astrophysics Data System (ADS)

    Li, Qin; Gavrielides, Marios A.; Zeng, Rongping; Myers, Kyle J.; Sahiner, Berkman; Petrick, Nicholas

    2015-03-01

    This work aimed to compare two different types of volume estimation methods (a model-based and a segmentationbased method) in terms of identifying factors affecting measurement uncertainty. Twenty-nine synthetic nodules with varying size, radiodensity, and shape were placed in an anthropomorphic thoracic phantom and scanned with a 16- detector row CT scanner. Ten repeat scans were acquired using three exposures and two slice collimations, and were reconstructed with varying slice thicknesses. Nodule volumes were estimated from the reconstructed data using a matched-filter and a segmentation approach. Log transformed volumes were used to obtain measurement error with truth obtained through micro-CT. ANOVA and multiple linear regression were applied to measurement error to identify significant factors affecting volume estimation for each method. Root mean square of measurement errors (RMSE) for meaningful subgroups, repeatability coefficients (RC) for different imaging protocols, and reproducibility coefficients (RDC) for thin and thick collimation conditions were evaluated. Results showed that for both methods, nodule size, shape and slice thickness were significant factors. Collimation was significant for the matched-filter method. RMSEs for matched-filter measurements were in general smaller than segmentation. To achieve RMSE on the order of 15% or less for {5, 8, 9, 10mm} nodules, the corresponding maximum allowable slice thicknesses were {3, 5, 5, 5mm} for the matched-filter and {0.8, 3, 3, 3mm} for the segmentation method. RCs showed similar patterns for both methods, increasing with slice thickness. For 8-10mm nodules, the measurements were highly repeatable provided the slice thickness was ≤3mm, regardless of method and across varying acquisition conditions. RDCs were lower for thin collimation than thick collimation protocols. While RDC of matched filter volume estimation results was always lower than segmentation results, for 8-10mm nodules with thin

  20. Image quality of CT angiography with model-based iterative reconstruction in young children with congenital heart disease: comparison with filtered back projection and adaptive statistical iterative reconstruction.

    PubMed

    Son, Sung Sil; Choo, Ki Seok; Jeon, Ung Bae; Jeon, Gye Rok; Nam, Kyung Jin; Kim, Tae Un; Yeom, Jeong A; Hwang, Jae Yeon; Jeong, Dong Wook; Lim, Soo Jin

    2015-06-01

    To retrospectively evaluate the image quality of CT angiography (CTA) reconstructed by model-based iterative reconstruction (MBIR) and to compare this with images obtained by filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) in newborns and infants with congenital heart disease (CHD). Thirty-seven children (age 4.8 ± 3.7 months; weight 4.79 ± 0.47 kg) with suspected CHD underwent CTA on a 64detector MDCT without ECG gating (80 kVp, 40 mA using tube current modulation). Total dose length product was recorded in all patients. Images were reconstructed using FBP, ASIR, and MBIR. Objective image qualities (density, noise) were measured in the great vessels and heart chambers. The contrast-to-noise ratio (CNR) was calculated by measuring the density and noise of myocardial walls. Two radiologists evaluated images for subjective noise, diagnostic confidence, and sharpness at the level prior to the first branch of the main pulmonary artery. Images were compared with respect to reconstruction method, and reconstruction times were measured. Images from all patients were diagnostic, and the effective dose was 0.22 mSv. The objective image noise of MBIR was significantly lower than those of FBP and ASIR in the great vessels and heart chambers (P < 0.05); however, with respect to attenuations in the four chambers, ascending aorta, descending aorta, and pulmonary trunk, no statistically significant difference was observed among the three methods (P > 0.05). Mean CNR values were 8.73 for FBP, 14.54 for ASIR, and 22.95 for MBIR. In addition, the subjective image noise of MBIR was significantly lower than those of the others (P < 0.01). Furthermore, while FBP had the highest score for image sharpness, ASIR had the highest score for diagnostic confidence (P < 0.05), and mean reconstruction times were 5.1 ± 2.3 s for FBP and ASIR and 15.1 ± 2.4 min for MBIR. While CTA with MBIR in newborns and infants with CHD can reduce image noise and

  1. Application of a non-convex smooth hard threshold regularizer to sparse-view CT image reconstruction

    NASA Astrophysics Data System (ADS)

    Rose, Sean; Sidky, Emil Y.; Pan, Xioachuan

    2015-03-01

    In this work, we apply non-convex, sparsity exploiting regularization techniques to image reconstruction in computed tomography (CT).We modify the well-known total variation (TV) penalty to use a non-convex smooth hard threshold (SHT) penalty as opposed to the typical l1 norm. The SHT penalty is different from the p <1 norms in that it is bounded above and has bounded gradient as its argument approaches the zero vector. We propose a re-weighting scheme utilizing the Chambolle-Pock (CP) algorithm in an attempt to solve a data-error constrained optimization problem utilizing the SHT penalty and call the resulting algorithm SHTCP. We then demonstrate the algorithm on sparse-view reconstruction of a simulated breast phantom with noiseless and noisy data and compare the converged images to those generated by a CP algorithm solving the analogous data-error constrained problem utilizing the TV. We demonstrate that SHTCP allows for more accurate reconstruction in the case of sparse-view noisy data and, in the case of noiseless data, allows for accurate reconstruction from fewer views than its TV counterpart.

  2. Reconstruction algorithm for diffuse optical tomography using x-ray CT anatomical information and application to bioluminescence tomography

    NASA Astrophysics Data System (ADS)

    Naser, Mohamed A.; Pekar, Julius; Patterson, Michael S.

    2011-02-01

    An algorithm to solve the diffuse optical tomography (DOT) problem is described which uses the anatomical information from x-ray CT images. These provide a priori information about the distribution of the optical properties hence reducing the number of variables and permitting a unique solution to the ill-posed problem. The light fluence rate at the boundary is written as a Taylor series expansion around an initial guess corresponding to an optically homogenous object. The second order approximation is considered and the derivatives are calculated by direct methods. These are used in an iterative algorithm to reconstruct the tissue optical properties. The reconstructed optical properties are then used for bioluminescence tomography where a minimization problem is formed based on the L1 norm objective function which uses normalized values for the light fluence rates and the corresponding Green's functions. Then an iterative minimization solution shrinks the permissible regions where the sources are allowed by selecting points with higher probability to contribute to the source distribution. Throughout this process the permissible region shrinks from the entire object to just a few points. The optimum reconstructed bioluminescence distributions are chosen to be the results of the iteration corresponding to the permissible region where the objective function has its global minimum. This provides efficient BLT reconstruction algorithms without the need for a priori information about the bioluminescence sources.

  3. RADIANCE: An automated, enterprise-wide solution for archiving and reporting CT radiation dose estimates.

    PubMed

    Cook, Tessa S; Zimmerman, Stefan L; Steingall, Scott R; Maidment, Andrew D A; Kim, Woojin; Boonn, William W

    2011-01-01

    There is growing interest in the ability to monitor, track, and report exposure to radiation from medical imaging. Historically, however, dose information has been stored on an image-based dose sheet, an arrangement that precludes widespread indexing. Although scanner manufacturers are beginning to include dose-related parameters in the Digital Imaging and Communications in Medicine (DICOM) headers of imaging studies, there remains a vast repository of retrospective computed tomographic (CT) data with image-based dose sheets. Consequently, it is difficult for imaging centers to monitor their dose estimates or participate in the American College of Radiology (ACR) Dose Index Registry. An automated extraction software pipeline known as Radiation Dose Intelligent Analytics for CT Examinations (RADIANCE) has been designed that quickly and accurately parses CT dose sheets to extract and archive dose-related parameters. Optical character recognition of information in the dose sheet leads to creation of a text file, which along with the DICOM study header is parsed to extract dose-related data. The data are then stored in a relational database that can be queried for dose monitoring and report creation. RADIANCE allows efficient dose analysis of CT examinations and more effective education of technologists, radiologists, and referring physicians regarding patient exposure to radiation at CT. RADIANCE also allows compliance with the ACR's dose reporting guidelines and greater awareness of patient radiation dose, ultimately resulting in improved patient care and treatment.

  4. RADIANCE: An automated, enterprise-wide solution for archiving and reporting CT radiation dose estimates.

    PubMed

    Cook, Tessa S; Zimmerman, Stefan L; Steingall, Scott R; Maidment, Andrew D A; Kim, Woojin; Boonn, William W

    2011-01-01

    There is growing interest in the ability to monitor, track, and report exposure to radiation from medical imaging. Historically, however, dose information has been stored on an image-based dose sheet, an arrangement that precludes widespread indexing. Although scanner manufacturers are beginning to include dose-related parameters in the Digital Imaging and Communications in Medicine (DICOM) headers of imaging studies, there remains a vast repository of retrospective computed tomographic (CT) data with image-based dose sheets. Consequently, it is difficult for imaging centers to monitor their dose estimates or participate in the American College of Radiology (ACR) Dose Index Registry. An automated extraction software pipeline known as Radiation Dose Intelligent Analytics for CT Examinations (RADIANCE) has been designed that quickly and accurately parses CT dose sheets to extract and archive dose-related parameters. Optical character recognition of information in the dose sheet leads to creation of a text file, which along with the DICOM study header is parsed to extract dose-related data. The data are then stored in a relational database that can be queried for dose monitoring and report creation. RADIANCE allows efficient dose analysis of CT examinations and more effective education of technologists, radiologists, and referring physicians regarding patient exposure to radiation at CT. RADIANCE also allows compliance with the ACR's dose reporting guidelines and greater awareness of patient radiation dose, ultimately resulting in improved patient care and treatment. PMID:21969661

  5. SU-E-I-04: Improving CT Quality for Radiation Therapy of Patients with High Body Mass Index Using Iterative Reconstruction Algorithms

    SciTech Connect

    Noid, G; Tai, A; Li, X

    2015-06-15

    Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. The CT IQ for patients with a high Body Mass Index (BMI) can suffer from increased noise due to photon starvation. The purpose of this study is to investigate and to quantify the IQ enhancement for high BMI patients through the application of IR algorithms. Methods: CT raw data collected for 6 radiotherapy (RT) patients with BMI, greater than or equal to 30 were retrospectively analyzed. All CT data were acquired using a CT scanner (Somaton Definition AS Open, Siemens) installed in a linac room (CT-on-rails) using standard imaging protocols. The CT data were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared and correlated with patient depth and BMI. The patient depth was defined as the largest distance from anterior to posterior along the bilateral symmetry axis. Results: IR techniques are demonstrated to preserve contrast and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is roughly doubled by adopting the highest SAFIRE strength. A significant correlation was observed between patient depth and IR noise reduction through Pearson’s correlation test (R = 0.9429/P = 0.0167). The mean patient depth was 30.4 cm and the average relative noise reduction for the strongest iterative reconstruction was 55%. Conclusion: The IR techniques produce a measureable enhancement to CT IQ by reducing the noise. Dramatic noise reduction is evident for the high BMI patients. The improved CT IQ enables more accurate delineation of tumors and organs at risk and more accuarte dose calculations for RT planning and delivery guidance. Supported by Siemens.

  6. Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation.

    PubMed

    Naumovich, S S; Naumovich, S A; Goncharenko, V G

    2015-01-01

    The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks.

  7. Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation

    PubMed Central

    Goncharenko, V G

    2015-01-01

    The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks. PMID:25564886

  8. Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation.

    PubMed

    Naumovich, S S; Naumovich, S A; Goncharenko, V G

    2015-01-01

    The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks. PMID:25564886

  9. Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging

    SciTech Connect

    Yu, Lifeng Vrieze, Thomas J.; Leng, Shuai; Fletcher, Joel G.; McCollough, Cynthia H.

    2015-05-15

    Purpose: The spatial resolution of iterative reconstruction (IR) in computed tomography (CT) is contrast- and noise-dependent because of the nonlinear regularization. Due to the severe noise contamination, it is challenging to perform precise spatial-resolution measurements at very low-contrast levels. The purpose of this study was to measure the spatial resolution of a commercially available IR method using ensemble-averaged images acquired from repeated scans. Methods: A low-contrast phantom containing three rods (7, 14, and 21 HU below background) was scanned on a 128-slice CT scanner at three dose levels (CTDI{sub vol} = 16, 8, and 4 mGy). Images were reconstructed using two filtered-backprojection (FBP) kernels (B40 and B20) and a commercial IR method (sinogram affirmed iterative reconstruction, SAFIRE, Siemens Healthcare) with two strength settings (I40-3 and I40-5). The same scan was repeated 100 times at each dose level. The modulation transfer function (MTF) was calculated based on the edge profile measured on the ensemble-averaged images. Results: The spatial resolution of the two FBP kernels, B40 and B20, remained relatively constant across contrast and dose levels. However, the spatial resolution of the two IR kernels degraded relative to FBP as contrast or dose level decreased. For a given dose level at 16 mGy, the MTF{sub 50%} value normalized to the B40 kernel decreased from 98.4% at 21 HU to 88.5% at 7 HU for I40-3 and from 97.6% to 82.1% for I40-5. At 21 HU, the relative MTF{sub 50%} value decreased from 98.4% at 16 mGy to 90.7% at 4 mGy for I40-3 and from 97.6% to 85.6% for I40-5. Conclusions: A simple technique using ensemble averaging from repeated CT scans can be used to measure the spatial resolution of IR techniques in CT at very low contrast levels. The evaluated IR method degraded the spatial resolution at low contrast and high noise levels.

  10. Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features

    PubMed Central

    Lo, P.; Young, S.; Kim, H. J.; Brown, M. S.

    2016-01-01

    Purpose: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. Methods: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. Results: The

  11. Combining Automatic Tube Current Modulation with Adaptive Statistical Iterative Reconstruction for Low-Dose Chest CT Screening

    PubMed Central

    Chen, Jiang-Hong; Jin, Er-Hu; He, Wen; Zhao, Li-Qin

    2014-01-01

    Objective To reduce radiation dose while maintaining image quality in low-dose chest computed tomography (CT) by combining adaptive statistical iterative reconstruction (ASIR) and automatic tube current modulation (ATCM). Methods Patients undergoing cancer screening (n = 200) were subjected to 64-slice multidetector chest CT scanning with ASIR and ATCM. Patients were divided into groups 1, 2, 3, and 4 (n = 50 each), with a noise index (NI) of 15, 20, 30, and 40, respectively. Each image set was reconstructed with 4 ASIR levels (0% ASIR, 30% ASIR, 50% ASIR, and 80% ASIR) in each group. Two radiologists assessed subjective image noise, image artifacts, and visibility of the anatomical structures. Objective image noise and signal-to-noise ratio (SNR) were measured, and effective dose (ED) was recorded. Results Increased NI was associated with increased subjective and objective image noise results (P<0.001), and SNR decreased with increasing NI (P<0.001). These values improved with increased ASIR levels (P<0.001). Images from all 4 groups were clinically diagnosable. Images with NI = 30 and 50% ASIR had average subjective image noise scores and nearly average anatomical structure visibility scores, with a mean objective image noise of 23.42 HU. The EDs for groups 1, 2, 3 and 4 were 2.79±1.17, 1.69±0.59, 0.74±0.29, and 0.37±0.22 mSv, respectively. Compared to group 1 (NI = 15), the ED reductions were 39.43%, 73.48%, and 86.74% for groups 2, 3, and 4, respectively. Conclusions Using NI = 30 with 50% ASIR in the chest CT protocol, we obtained average or above-average image quality but a reduced ED. PMID:24691208

  12. Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control

    NASA Astrophysics Data System (ADS)

    D'Amato, Anthony M.

    Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate

  13. SU-E-J-99: Reconstruction of Cone Beam CT Image Using Volumetric Modulated Arc Therapy Exit Beams

    SciTech Connect

    Jeong, K; Goddard, L; Savacool, M; Mynampati, D; Godoy Scripes, P; Tome', W; Kuo, H; Basavatia, A; Hong, L; Yaparpalvi, R; Kalnicki, S

    2014-06-01

    Purpose: To test the possibility of obtaining an image of the treated volume during volumetric modulated arc therapy (VMAT) with exit beams. Method: Using a Varian Clinac 21EX and MVCT detector the following three sets of detector projection data were obtained for cone beam CT reconstruction with and without a Catphan 504 phantom. 1) 72 projection images from 20 × 16 cm{sup 2} open beam with 3 MUs, 2) 72 projection images from 20 × 16 cm{sup 2} MLC closed beam with 14 MUs. 3) 137 projection images from a test RapicArc QA plan. All projection images were obtained in ‘integrated image’ mode. We used OSCaR code to reconstruct the cone beam CT images. No attempts were made to reduce scatter or artifacts. Results: With projection set 1) we obtained a good quality MV CBCT image by optimizing the reconstruction parameters. Using projection set 2) we were not able to obtain a CBCT image of the phantom, which was determined to be due to the variation of interleaf leakage with gantry angle. From projection set 3), we were able to obtain a weak but meaningful signal in the image, especially in the target area where open beam signals were dominant. This finding suggests that one might be able to acquire CBCT images with rough body shape and some details inside the irradiated target area. Conclusion: Obtaining patient images using the VMAT exit beam is challenging but possible. We were able to determine sources of image degradation such as gantry angle dependent interleaf leakage and beams with a large scatter component. We are actively working on improving image quality.

  14. A clinical evaluation of total variation-Stokes image reconstruction strategy for low-dose CT imaging of the chest

    NASA Astrophysics Data System (ADS)

    Liu, Yan; Zhang, Hao; Moore, William; Bhattacharji, Priya; Liang, Zhengrong

    2015-03-01

    One hundred "normal-dose" computed tomography (CT) studies of the chest (i.e., 1,160 projection views, 120kVp, 100mAs) data sets were acquired from the patients who were scheduled for lung biopsy at Stony Brook University Hospital under informed consent approved by our Institutional Review Board. To mimic low-dose CT imaging scenario (i.e., sparse-view scan), sparse projection views were evenly extracted from the total 1,160 projections of each patient and the total radiation dose was reduced according to how many sparse views were selected. A standard filtered backprojection (FBP) algorithm was applied to the 1160 projections to produce reference images for comparison purpose. In the low-dose scenario, both the FBP and total variation-stokes (TVS) algorithms were applied to reconstruct the corresponding low-dose images. The reconstructed images were evaluated by an experienced thoracic radiologist against the reference images. Both the low-dose reconstructions and the reference images were displayed on a 4- megapixel monitor in soft tissue and lung windows. The images were graded by a five-point scale from 0 to 4 (0, nondiagnostic; 1, severe artifact with low confidence; 2, moderate artifact or moderate diagnostic confidences; 3, mild artifact or high confidence; 4, well depicted without artifacts). Quantitative evaluation measurements such as standard deviations for different tissue types and universal quality index were also studied and reported for the results. The evaluation concluded that the TVS can reduce the view number from 1,160 to 580 with slightly lower scores as the reference, resulting in a dose reduction to close 50%.

  15. Electrical CT image reconstruction technique for powder flow in petroleum refinery process

    NASA Astrophysics Data System (ADS)

    Takei, Masahiro; Doh, Deog-Hee; Ochi, Mitsuaki

    2008-03-01

    A new reconstruction method called sampled pattern matching (SPM) was applied to the image reconstruction of an electrical capacitance computed tomography in powder flow in a vertical pipe for petroleum refinery process. This new method is able to achieve stable convergence without the use of an empirical value. Experiments were carried out using fluid catalytic cracking (FCC) catalysts as powder with two air volume flow rates and four powder volume flow rates to measure the capacitance of a pipe cross section. The SPM method is compared with conventional methods in terms of volume fraction, residual capacitance, and correlation capacitance. Overall, the SPM method proved superior to conventional methods without any empirical value because SPM achieves accurate reconstruction by using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance.

  16. Three-dimensional visualization and characterization of bone structure using reconstructed in-vitro μCT images: A pilot study for bone microarchitecture analysis

    SciTech Connect

    Latief, Fourier Dzar Eljabbar; Dewi, Dyah Ekashanti Octorina; Shari, Mohd Aliff Bin Mohd

    2014-03-24

    Micro Computed Tomography (μCT) has been largely used to perform micrometer scale imaging of specimens, bone biopsies and small animals for the study of porous or cavity-containing objects. One of its favored applications is for assessing structural properties of bone. In this research, we perform a pilot study to visualize and characterize bone structure of a chicken bone thigh, as well as to delineate its cortical and trabecular bone regions. We utilize an In-Vitro μCT scanner Skyscan 1173 to acquire a three dimensional image data of a chicken bone thigh. The thigh was scanned using X-ray voltage of 45 kV and current of 150 μA. The reconstructed images have spatial resolution of 142.50 μm/pixel. Using image processing and analysis e.i segmentation by thresholding the gray values (which represent the pseudo density) and binarizing the images, we were able to visualize each part of the bone, i.e., the cortical and trabecular regions. Total volume of the bone is 4663.63 mm{sup 3}, and the surface area of the bone is 7913.42 mm{sup 2}. The volume of the cortical is approximately 1988.62 mm{sup 3} which is nearly 42.64% of the total bone volume. This pilot study has confirmed that the μCT is capable of quantifying 3D bone structural properties and defining its regions separately. For further development, these results can be improved for understanding the pathophysiology of bone abnormality, testing the efficacy of pharmaceutical intervention, or estimating bone biomechanical properties.

  17. SU-E-J-147: Monte Carlo Study of the Precision and Accuracy of Proton CT Reconstructed Relative Stopping Power Maps

    SciTech Connect

    Dedes, G; Asano, Y; Parodi, K; Arbor, N; Dauvergne, D; Testa, E; Letang, J; Rit, S

    2015-06-15

    Purpose: The quantification of the intrinsic performances of proton computed tomography (pCT) as a modality for treatment planning in proton therapy. The performance of an ideal pCT scanner is studied as a function of various parameters. Methods: Using GATE/Geant4, we simulated an ideal pCT scanner and scans of several cylindrical phantoms with various tissue equivalent inserts of different sizes. Insert materials were selected in order to be of clinical relevance. Tomographic images were reconstructed using a filtered backprojection algorithm taking into account the scattering of protons into the phantom. To quantify the performance of the ideal pCT scanner, we study the precision and the accuracy with respect to the theoretical relative stopping power ratios (RSP) values for different beam energies, imaging doses, insert sizes and detector positions. The planning range uncertainty resulting from the reconstructed RSP is also assessed by comparison with the range of the protons in the analytically simulated phantoms. Results: The results indicate that pCT can intrinsically achieve RSP resolution below 1%, for most examined tissues at beam energies below 300 MeV and for imaging doses around 1 mGy. RSP maps accuracy of less than 0.5 % is observed for most tissue types within the studied dose range (0.2–1.5 mGy). Finally, the uncertainty in the proton range due to the accuracy of the reconstructed RSP map is well below 1%. Conclusion: This work explores the intrinsic performance of pCT as an imaging modality for proton treatment planning. The obtained results show that under ideal conditions, 3D RSP maps can be reconstructed with an accuracy better than 1%. Hence, pCT is a promising candidate for reducing the range uncertainties introduced by the use of X-ray CT alongside with a semiempirical calibration to RSP.Supported by the DFG Cluster of Excellence Munich-Centre for Advanced Photonics (MAP)

  18. Voice estimation in patients after reconstructive subtotal laryngectomy

    PubMed Central

    2011-01-01

    Background Treatment of laryngeal cancers, may include surgery, radiotherapy, chemotherapy, or a combination. Total laryngectomy (TL) has been the standard surgical treatment. Partial laryngectomy procedures were performed, their advantage over TL is preservation of laryngeal functions. Methods The investigation was carried out on a group of 20 patients (3 female and 17 male), who underwent surgery according the techniques mentioned above. The methods of investigation were based on perceptual voice estimation (GRBAS), videolaryngostroboscopy, acoustic voice analysis, aerodynamic measure maximum phonation time, voice self-assessment (VHI). Results and Conclusions The perceptual voice estimation revealed a good phonation result in only 3 cases after using surgery with the Calearo method as well as the best results of MPT. The VHI reflected severe voice handicap in 2 patients (26 to 40 points). No statistically significant differences were observed between the values of the acoustic parameters in MDVP analysis after following operation -CHEP, Calearo, Sedlacek. PMID:22029703

  19. Volume-of-interest reconstruction from severely truncated data in dental cone-beam CT

    NASA Astrophysics Data System (ADS)

    Zhang, Zheng; Kusnoto, Budi; Han, Xiao; Sidky, E. Y.; Pan, Xiaochuan

    2015-03-01

    As cone-beam computed tomography (CBCT) has gained popularity rapidly in dental imaging applications in the past two decades, radiation dose in CBCT imaging remains a potential, health concern to the patients. It is a common practice in dental CBCT imaging that only a small volume of interest (VOI) containing the teeth of interest is illuminated, thus substantially lowering imaging radiation dose. However, this would yield data with severe truncations along both transverse and longitudinal directions. Although images within the VOI reconstructed from truncated data can be of some practical utility, they often are compromised significantly by truncation artifacts. In this work, we investigate optimization-based reconstruction algorithms for VOI image reconstruction from CBCT data of dental patients containing severe truncations. In an attempt to further reduce imaging dose, we also investigate optimization-based image reconstruction from severely truncated data collected at projection views substantially fewer than those used in clinical dental applications. Results of our study show that appropriately designed optimization-based reconstruction can yield VOI images with reduced truncation artifacts, and that, when reconstructing from only one half, or even one quarter, of clinical data, it can also produce VOI images comparable to that of clinical images.

  20. Comparison of measured and estimated maximum skin doses during CT fluoroscopy lung biopsies

    SciTech Connect

    Zanca, F.; Jacobs, A.; Crijns, W.; De Wever, W.

    2014-07-15

    Purpose: To measure patient-specific maximum skin dose (MSD) associated with CT fluoroscopy (CTF) lung biopsies and to compare measured MSD with the MSD estimated from phantom measurements, as well as with the CTDIvol of patient examinations. Methods: Data from 50 patients with lung lesions who underwent a CT fluoroscopy-guided biopsy were collected. The CT protocol consisted of a low-kilovoltage (80 kV) protocol used in combination with an algorithm for dose reduction to the radiology staff during the interventional procedure, HandCare (HC). MSD was assessed during each intervention using EBT2 gafchromic films positioned on patient skin. Lesion size, position, total fluoroscopy time, and patient-effective diameter were registered for each patient. Dose rates were also estimated at the surface of a normal-size anthropomorphic thorax phantom using a 10 cm pencil ionization chamber placed at every 30°, for a full rotation, with and without HC. Measured MSD was compared with MSD values estimated from the phantom measurements and with the cumulative CTDIvol of the procedure. Results: The median measured MSD was 141 mGy (range 38–410 mGy) while the median cumulative CTDIvol was 72 mGy (range 24–262 mGy). The ratio between the MSD estimated from phantom measurements and the measured MSD was 0.87 (range 0.12–4.1) on average. In 72% of cases the estimated MSD underestimated the measured MSD, while in 28% of the cases it overestimated it. The same trend was observed for the ratio of cumulative CTDIvol and measured MSD. No trend was observed as a function of patient size. Conclusions: On average, estimated MSD from dose rate measurements on phantom as well as from CTDIvol of patient examinations underestimates the measured value of MSD. This can be attributed to deviations of the patient's body habitus from the standard phantom size and to patient positioning in the gantry during the procedure.

  1. Respiratory-gated segment reconstruction for radiation treatment planning using 256-slice CT-scanner during free breathing

    NASA Astrophysics Data System (ADS)

    Mori, Shinichiro; Endo, Masahiro; Kohno, Ryosuke; Minohara, Shinichi; Kohno, Kazutoshi; Asakura, Hiroshi; Fujiwara, Hideaki; Murase, Kenya

    2005-04-01

    The conventional respiratory-gated CT scan technique includes anatomic motion induced artifacts due to the low temporal resolution. They are a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Temporal resolution and image quality are important factors to minimize planning target volume margin due to the respiratory motion. To achieve high temporal resolution and high signal-to-noise ratio, we developed a respiratory gated segment reconstruction algorithm and adapted it to Feldkamp-Davis-Kress algorithm (FDK) with a 256-detector row CT. The 256-detector row CT could scan approximately 100 mm in the cranio-caudal direction with 0.5 mm slice thickness in one rotation. Data acquisition for the RS-FDK relies on the assistance of the respiratory sensing system by a cine scan mode (table remains stationary). We evaluated RS-FDK in phantom study with the 256-detector row CT and compared it with full scan (FS-FDK) and HS-FDK results with regard to volume accuracy and image noise, and finally adapted the RS-FDK to an animal study. The RS-FDK gave a more accurate volume than the others and it had the same signal-to-noise ratio as the FS-FDK. In the animal study, the RS-FDK visualized the clearest edges of the liver and pulmonary vessels of all the algorithms. In conclusion, the RS-FDK algorithm has a capability of high temporal resolution and high signal-to-noise ratio. Therefore it will be useful when combined with new radiotherapy techniques including image guided radiation therapy (IGRT) and 4D radiation therapy.

  2. Non-uniform noise spatial distribution in CT myocardial perfusion and a potential solution: statistical image reconstruction

    NASA Astrophysics Data System (ADS)

    Thériault Lauzier, Pascal; Tang, Jie; Chen, Guang-Hong

    2012-03-01

    Myocardial perfusion scans are an important tool in the assessment of myocardial viability following an infarction. Cardiac perfusion analysis using CT datasets is limited by the presence of so-called partial scan artifacts. These artifacts are due to variations in beam hardening and scatter between different short-scan angular ranges. In this research, another angular range dependent effect is investigated: non-uniform noise spatial distribution. Images reconstructed using filtered backprojection (FBP) are subject to this effect. Statistical image reconstruction (SIR) is proposed as a potential solution. A numerical phantom with added Poisson noise was simulated and two swines were scanned in vivo to study the effect of FBP and SIR on the spatial uniformity of the noise distribution. It was demonstrated that images reconstructed using FBP often show variations in noise on the order of 50% between different time frames. This variation is mitigated to about 10% using SIR. The noise level is also reduced by a factor of 2 in SIR images. Finally, it is demonstrated that the measurement of quantitative perfusion metrics are generally more accurate when SIR is used instead of FBP.

  3. Effect of light source instability on uniformity of 3D reconstructions from a cone beam optical CT scanner.

    PubMed

    Begg, J; Taylor, M L; Holloway, L; Kron, T; Franich, R D

    2014-12-01

    Temporally varying light intensity during acquisition of projection images in an optical CT scanner can potentially be misinterpreted as physical properties of the sample. This work investigated the impact of LED light source intensity instability on measured attenuation coefficients. Different scenarios were investigated by conducting one or both of the reference and data scans in a 'cold' scanner, where the light source intensity had not yet stabilised. Uniform samples were scanned to assess the impact on measured uniformity. The orange (590 nm) light source decreased in intensity by 29 % over the first 2 h, while the red (633 nm) decreased by 9 %. The rates of change of intensity at 2 h were 0.1 and 0.03 % respectively over a 5 min period-corresponding to the scan duration. The normalisation function of the reconstruction software does not fully account for the intensity differences and discrepancies remain. Attenuation coefficient inaccuracies of up to 8 % were observed for data reconstructed from projection images acquired with a cold scanner. Increased noise was observed for most cases where one or both of the scans was acquired without sufficient warm-up. The decrease in accuracy and increase in noise were most apparent for data reconstructed from reference and data scans acquired with a cold scanner on different days. PMID:25262165

  4. Image Quality of 3rd Generation Spiral Cranial Dual-Source CT in Combination with an Advanced Model Iterative Reconstruction Technique: A Prospective Intra-Individual Comparison Study to Standard Sequential Cranial CT Using Identical Radiation Dose

    PubMed Central

    Wenz, Holger; Maros, Máté E.; Meyer, Mathias; Förster, Alex; Haubenreisser, Holger; Kurth, Stefan; Schoenberg, Stefan O.; Flohr, Thomas; Leidecker, Christianne; Groden, Christoph; Scharf, Johann; Henzler, Thomas

    2015-01-01

    Objectives To prospectively intra-individually compare image quality of a 3rd generation Dual-Source-CT (DSCT) spiral cranial CT (cCT) to a sequential 4-slice Multi-Slice-CT (MSCT) while maintaining identical intra-individual radiation dose levels. Methods 35 patients, who had a non-contrast enhanced sequential cCT examination on a 4-slice MDCT within the past 12 months, underwent a spiral cCT scan on a 3rd generation DSCT. CTDIvol identical to initial 4-slice MDCT was applied. Data was reconstructed using filtered backward projection (FBP) and 3rd-generation iterative reconstruction (IR) algorithm at 5 different IR strength levels. Two neuroradiologists independently evaluated subjective image quality using a 4-point Likert-scale and objective image quality was assessed in white matter and nucleus caudatus with signal-to-noise ratios (SNR) being subsequently calculated. Results Subjective image quality of all spiral cCT datasets was rated significantly higher compared to the 4-slice MDCT sequential acquisitions (p<0.05). Mean SNR was significantly higher in all spiral compared to sequential cCT datasets with mean SNR improvement of 61.65% (p*Bonferroni0.05<0.0024). Subjective image quality improved with increasing IR levels. Conclusion Combination of 3rd-generation DSCT spiral cCT with an advanced model IR technique significantly improves subjective and objective image quality compared to a standard sequential cCT acquisition acquired at identical dose levels. PMID:26288186

  5. Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

    SciTech Connect

    Li, Ke; Chen, Guang-Hong; Garrett, John; Ge, Yongshuai

    2014-07-15

    Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI{sub vol} =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo{sup ®}, GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d′. Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than

  6. Estimation of 3D reconstruction errors in a stereo-vision system

    NASA Astrophysics Data System (ADS)

    Belhaoua, A.; Kohler, S.; Hirsch, E.

    2009-06-01

    The paper presents an approach for error estimation for the various steps of an automated 3D vision-based reconstruction procedure of manufactured workpieces. The process is based on a priori planning of the task and built around a cognitive intelligent sensory system using so-called Situation Graph Trees (SGT) as a planning tool. Such an automated quality control system requires the coordination of a set of complex processes performing sequentially data acquisition, its quantitative evaluation and the comparison with a reference model (e.g., CAD object model) in order to evaluate quantitatively the object. To ensure efficient quality control, the aim is to be able to state if reconstruction results fulfill tolerance rules or not. Thus, the goal is to evaluate independently the error for each step of the stereo-vision based 3D reconstruction (e.g., for calibration, contour segmentation, matching and reconstruction) and then to estimate the error for the whole system. In this contribution, we analyze particularly the segmentation error due to localization errors for extracted edge points supposed to belong to lines and curves composing the outline of the workpiece under evaluation. The fitting parameters describing these geometric features are used as quality measure to determine confidence intervals and finally to estimate the segmentation errors. These errors are then propagated through the whole reconstruction procedure, enabling to evaluate their effect on the final 3D reconstruction result, specifically on position uncertainties. Lastly, analysis of these error estimates enables to evaluate the quality of the 3D reconstruction, as illustrated by the shown experimental results.

  7. Towards a comprehensive CT image segmentation for thoracic organ radiation dose estimation and reporting

    NASA Astrophysics Data System (ADS)

    Lorenz, Cristian; Ruppertshofen, Heike; Vik, Torbjörn; Prinsen, Peter; Wiegert, Jens

    2014-03-01

    Administered dose of ionizing radiation during medical imaging is an issue of increasing concern for the patient, for the clinical community, and for respective regulatory bodies. CT radiation dose is currently estimated based on a set of very simplifying assumptions which do not take the actual body geometry and organ specific doses into account. This makes it very difficult to accurately report imaging related administered dose and to track it for different organs over the life of the patient. In this paper this deficit is addressed in a two-fold way. In a first step, the absorbed radiation dose in each image voxel is estimated based on a Monte-Carlo simulation of X-ray absorption and scattering. In a second step, the image is segmented into tissue types with different radio sensitivity. In combination this allows to calculate the effective dose as a weighted sum of the individual organ doses. The main purpose of this paper is to assess the feasibility of automatic organ specific dose estimation. With respect to a commercially applicable solution and respective robustness and efficiency requirements, we investigated the effect of dose sampling rather than integration over the organ volume. We focused on the thoracic anatomy as the exemplary body region, imaged frequently by CT. For image segmentation we applied a set of available approaches which allowed us to cover the main thoracic radio-sensitive tissue types. We applied the dose estimation approach to 10 thoracic CT datasets and evaluated segmentation accuracy and administered dose and could show that organ specific dose estimation can be achieved.

  8. Experimental study on the application of a compressed-sensing (CS) algorithm to dental cone-beam CT (CBCT) for accurate, low-dose image reconstruction

    NASA Astrophysics Data System (ADS)

    Oh, Jieun; Cho, Hyosung; Je, Uikyu; Lee, Minsik; Kim, Hyojeong; Hong, Daeki; Park, Yeonok; Lee, Seonhwa; Cho, Heemoon; Choi, Sungil; Koo, Yangseo

    2013-03-01

    In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient data. In computed tomography (CT); for example, image reconstruction from few views would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction method based on a compressed-sensing (CS) algorithm, which exploits the sparseness of the gradient image with substantially high accuracy, for accurate, low-dose dental cone-beam CT (CBCT) reconstruction. We applied the algorithm to a commercially-available dental CBCT system (Expert7™, Vatech Co., Korea) and performed experimental works to demonstrate the algorithm for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images from several undersampled data and evaluated the reconstruction quality in terms of the universal-quality index (UQI). Experimental demonstrations of the CS-based reconstruction algorithm appear to show that it can be applied to current dental CBCT systems for reducing imaging doses and improving the image quality.

  9. Myocardial strain estimation from CT: towards computer-aided diagnosis on infarction identification

    NASA Astrophysics Data System (ADS)

    Wong, Ken C. L.; Tee, Michael; Chen, Marcus; Bluemke, David A.; Summers, Ronald M.; Yao, Jianhua

    2015-03-01

    Regional myocardial strains have the potential for early quantification and detection of cardiac dysfunctions. Although image modalities such as tagged and strain-encoded MRI can provide motion information of the myocardium, they are uncommon in clinical routine. In contrary, cardiac CT images are usually available, but they only provide motion information at salient features such as the cardiac boundaries. To estimate myocardial strains from a CT image sequence, we adopted a cardiac biomechanical model with hyperelastic material properties to relate the motion on the cardiac boundaries to the myocardial deformation. The frame-to-frame displacements of the cardiac boundaries are obtained using B-spline deformable image registration based on mutual information, which are enforced as boundary conditions to the biomechanical model. The system equation is solved by the finite element method to provide the dense displacement field of the myocardium, and the regional values of the three principal strains and the six strains in cylindrical coordinates are computed in terms of the American Heart Association nomenclature. To study the potential of the estimated regional strains on identifying myocardial infarction, experiments were performed on cardiac CT image sequences of ten canines with artificially induced myocardial infarctions. The leave-one-subject-out cross validations show that, by using the optimal strain magnitude thresholds computed from ROC curves, the radial strain and the first principal strain have the best performance.

  10. An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.

    PubMed

    Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang

    2015-01-01

    Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

  11. An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain

    PubMed Central

    Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang

    2014-01-01

    Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398

  12. Iterative CT reconstruction using coordinate descent with ordered subsets of data

    NASA Astrophysics Data System (ADS)

    Noo, F.; Hahn, K.; Schöndube, H.; Stierstorfer, K.

    2016-04-01

    Image reconstruction based on iterative minimization of a penalized weighted least-square criteria has become an important topic of research in X-ray computed tomography. This topic is motivated by increasing evidence that such a formalism may enable a significant reduction in dose imparted to the patient while maintaining or improving image quality. One important issue associated with this iterative image reconstruction concept is slow convergence and the associated computational effort. For this reason, there is interest in finding methods that produce approximate versions of the targeted image with a small number of iterations and an acceptable level of discrepancy. We introduce here a novel method to produce such approximations: ordered subsets in combination with iterative coordinate descent. Preliminary results demonstrate that this method can produce, within 10 iterations and using only a constant image as initial condition, satisfactory reconstructions that retain the noise properties of the targeted image.

  13. Myocardial blood flow measurement with a conventional dual-head SPECT/CT with spatiotemporal iterative reconstructions - a clinical feasibility study

    PubMed Central

    Alhassen, Fares; Nguyen, Nhan; Bains, Sukhkarn; Gould, Robert G; Seo, Youngho; Bacharach, Stephen L; Song, Xiyun; Shao, Lingxiong; Gullberg, Grant T; Aparici, Carina Mari

    2014-01-01

    Cardiac single photon emission computed tomography (SPECT) cameras typically rotate too slowly around a patient to capture changes in the blood pool activity distribution and provide accurate kinetic parameters. A spatiotemporal iterative reconstruction method to overcome these limitations was investigated. Dynamic rest/stress 99mTc-methoxyisobutylisonitrile (99mTc-MIBI) SPECT/CT was performed along with reference standard rest/stress dynamic positron emission tomography (PET/CT) 13N-NH3 in five patients. The SPECT data were reconstructed using conventional and spatiotemporal iterative reconstruction methods. The spatiotemporal reconstruction yielded improved image quality, defined here as a statistically significant (p<0.01) 50% contrast enhancement. We did not observe a statistically significant difference between the correlations of the conventional and spatiotemporal SPECT myocardial uptake K 1 values with PET K 1 values (r=0.25, 0.88, respectively) (p<0.17). These results indicate the clinical feasibility of quantitative, dynamic SPECT/CT using 99mTc-MIBI and warrant further investigation. Spatiotemporal reconstruction clearly provides an advantage over a conventional reconstruction in computing K 1. PMID:24380045

  14. GPU-based iterative cone-beam CT reconstruction using tight frame regularization

    NASA Astrophysics Data System (ADS)

    Jia, Xun; Dong, Bin; Lou, Yifei; Jiang, Steve B.

    2011-07-01

    The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight-frame (TF)-based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512 × 512 × 70 can be reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstruct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of the modulation-transfer function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.

  15. ANL CT Image Reconstruction Algorithm for Utilizing Digital X-ray Detector Array

    2004-08-05

    Reconstructs X-ray computed tomographic images from large data sets known as 16-bit binary sinograms. The algorithm uses the concept of generation of an image from carefully obtained multiple l-D or 2-0 X-ray projections. The individual projections are filtered using a digital Fast Fourier Transform. The literature refers to this as filtered back projection. The software is capable of processing a large file for reconstructing single images or volumetnc (3-D) images from large area high resolutionmore » digital X-ray detectors.« less

  16. Improvement of the size estimation of 3D tracked droplets using digital in-line holography with joint estimation reconstruction

    NASA Astrophysics Data System (ADS)

    Verrier, N.; Grosjean, N.; Dib, E.; Méès, L.; Fournier, C.; Marié, J.-L.

    2016-04-01

    Digital holography is a valuable tool for three-dimensional information extraction. Among existing configurations, the originally proposed set-up (i.e. Gabor, or in-line holography), is reasonably immune to variations in the experimental environment making it a method of choice for studies of fluid dynamics. Nevertheless, standard hologram reconstruction techniques, based on numerical light back-propagation are prone to artifacts such as twin images or aliases that limit both the quality and quantity of information extracted from the acquired holograms. To get round this issue, the hologram reconstruction as a parametric inverse problem has been shown to accurately estimate 3D positions and the size of seeding particles directly from the hologram. To push the bounds of accuracy on size estimation still further, we propose to fully exploit the information redundancy of a hologram video sequence using joint estimation reconstruction. Applying this approach in a bench-top experiment, we show that it led to a relative precision of 0.13% (for a 60 μm diameter droplet) for droplet size estimation, and a tracking precision of {σx}× {σy}× {σz}=0.15× 0.15× 1~\\text{pixels} .

  17. A fast nonlinear regression method for estimating permeability in CT perfusion imaging

    PubMed Central

    Bennink, Edwin; Riordan, Alan J; Horsch, Alexander D; Dankbaar, Jan Willem; Velthuis, Birgitta K; de Jong, Hugo W

    2013-01-01

    Blood–brain barrier damage, which can be quantified by measuring vascular permeability, is a potential predictor for hemorrhagic transformation in acute ischemic stroke. Permeability is commonly estimated by applying Patlak analysis to computed tomography (CT) perfusion data, but this method lacks precision. Applying more elaborate kinetic models by means of nonlinear regression (NLR) may improve precision, but is more time consuming and therefore less appropriate in an acute stroke setting. We propose a simplified NLR method that may be faster and still precise enough for clinical use. The aim of this study is to evaluate the reliability of in total 12 variations of Patlak analysis and NLR methods, including the simplified NLR method. Confidence intervals for the permeability estimates were evaluated using simulated CT attenuation–time curves with realistic noise, and clinical data from 20 patients. Although fixating the blood volume improved Patlak analysis, the NLR methods yielded significantly more reliable estimates, but took up to 12 × longer to calculate. The simplified NLR method was ∼4 × faster than other NLR methods, while maintaining the same confidence intervals (CIs). In conclusion, the simplified NLR method is a new, reliable way to estimate permeability in stroke, fast enough for clinical application in an acute stroke setting. PMID:23881247

  18. Pose estimation of known objects during transmission tomographic image reconstruction.

    PubMed

    Murphy, Ryan J; Yan, Shenyu; O'Sullivan, Joseph A; Snyder, Donald L; Whiting, Bruce R; Politte, David G; Lasio, Giovanni; Williamson, Jeffrey F

    2006-10-01

    We address the problem of image formation in transmission tomography when metal objects of known composition and shape, but unknown pose, are present in the scan subject. Using an alternating minimization (AM) algorithm, derived from a model in which the detected data are viewed as Poisson-distributed photon counts, we seek to eliminate the streaking artifacts commonly seen in filtered back projection images containing high-contrast objects. We show that this algorithm, which minimizes the I-divergence (or equivalently, maximizes the log-likelihood) between the measured data and model-based estimates of the means of the data, converges much faster when knowledge of the high-density materials (such as brachytherapy applicators or prosthetic implants) is exploited. The algorithm incorporates a steepest descent-based method to find the position and orientation (collectively called the pose) of the known objects. This pose is then used to constrain the image pixels to their known attenuation values, or, for example, to form a mask on the "missing" projection data in the shadow of the objects. Results from two-dimensional simulations are shown in this paper. The extension of the model and methods used to three dimensions is outlined.

  19. A CT-ultrasound-coregistered augmented reality enhanced image-guided surgery system and its preliminary study on brain-shift estimation

    NASA Astrophysics Data System (ADS)

    Huang, C. H.; Hsieh, C. H.; Lee, J. D.; Huang, W. C.; Lee, S. T.; Wu, C. T.; Sun, Y. N.; Wu, Y. T.

    2012-08-01

    With the combined view on the physical space and the medical imaging data, augmented reality (AR) visualization can provide perceptive advantages during image-guided surgery (IGS). However, the imaging data are usually captured before surgery and might be different from the up-to-date one due to natural shift of soft tissues. This study presents an AR-enhanced IGS system which is capable to correct the movement of soft tissues from the pre-operative CT images by using intra-operative ultrasound images. First, with reconstructing 2-D free-hand ultrasound images to 3-D volume data, the system applies a Mutual-Information based registration algorithm to estimate the deformation between pre-operative and intra-operative ultrasound images. The estimated deformation transform describes the movement of soft tissues and is then applied to the pre-operative CT images which provide high-resolution anatomical information. As a result, the system thus displays the fusion of the corrected CT images or the real-time 2-D ultrasound images with the patient in the physical space through a head mounted display device, providing an immersive augmented-reality environment. For the performance validation of the proposed system, a brain phantom was utilized to simulate brain-shift scenario. Experimental results reveal that when the shift of an artificial tumor is from 5mm ~ 12mm, the correction rates can be improved from 32% ~ 45% to 87% ~ 95% by using the proposed system.

  20. Statistical iterative reconstruction algorithm for X-ray phase-contrast CT

    PubMed Central

    Hahn, Dieter; Thibault, Pierre; Fehringer, Andreas; Bech, Martin; Koehler, Thomas; Pfeiffer, Franz; Noël, Peter B.

    2015-01-01

    Grating-based phase-contrast computed tomography (PCCT) is a promising imaging tool on the horizon for pre-clinical and clinical applications. Until now PCCT has been plagued by strong artifacts when dense materials like bones are present. In this paper, we present a new statistical iterative reconstruction algorithm which overcomes this limitation. It makes use of the fact that an X-ray interferometer provides a conventional absorption as well as a dark-field signal in addition to the phase-contrast signal. The method is based on a statistical iterative reconstruction algorithm utilizing maximum-a-posteriori principles and integrating the statistical properties of the raw data as well as information of dense objects gained from the absorption signal. Reconstruction of a pre-clinical mouse scan illustrates that artifacts caused by bones are significantly reduced and image quality is improved when employing our approach. Especially small structures, which are usually lost because of streaks, are recovered in our results. In comparison with the current state-of-the-art algorithms our approach provides significantly improved image quality with respect to quantitative and qualitative results. In summary, we expect that our new statistical iterative reconstruction method to increase the general usability of PCCT imaging for medical diagnosis apart from applications focused solely on soft tissue visualization. PMID:26067714

  1. Statistical iterative reconstruction algorithm for X-ray phase-contrast CT.

    PubMed

    Hahn, Dieter; Thibault, Pierre; Fehringer, Andreas; Bech, Martin; Koehler, Thomas; Pfeiffer, Franz; Noël, Peter B

    2015-01-01

    Grating-based phase-contrast computed tomography (PCCT) is a promising imaging tool on the horizon for pre-clinical and clinical applications. Until now PCCT has been plagued by strong artifacts when dense materials like bones are present. In this paper, we present a new statistical iterative reconstruction algorithm which overcomes this limitation. It makes use of the fact that an X-ray interferometer provides a conventional absorption as well as a dark-field signal in addition to the phase-contrast signal. The method is based on a statistical iterative reconstruction algorithm utilizing maximum-a-posteriori principles and integrating the statistical properties of the raw data as well as information of dense objects gained from the absorption signal. Reconstruction of a pre-clinical mouse scan illustrates that artifacts caused by bones are significantly reduced and image quality is improved when employing our approach. Especially small structures, which are usually lost because of streaks, are recovered in our results. In comparison with the current state-of-the-art algorithms our approach provides significantly improved image quality with respect to quantitative and qualitative results. In summary, we expect that our new statistical iterative reconstruction method to increase the general usability of PCCT imaging for medical diagnosis apart from applications focused solely on soft tissue visualization.

  2. Uncertainty estimation and reconstruction of historical streamflow records

    NASA Astrophysics Data System (ADS)

    Kuentz, A.; Mathevet, T.; Perret, C.; Andréassian, V.

    2012-04-01

    Long historical series of streamflow are a precious source of information in the context of hydrological studies, such as research of trends or breaks due to climate variability or anthropogenic influences. For this kind of studies, it could be very important to go back as far as possible in the past, in order to highlight information content of historical observations. During our research we concentrate on the Durance watershed (14000 km2) in order to understand last century (1900-2010) hydrological variability due to climate changes and/or anthropogenic influences. This watershed, situated in the Alps, is characterized by variable hydrological processes (from snowy to Mediterranean regimes) and a wide range of anthropogenic influences (hydropower generation, irrigation, industries, drinking water, etc.). We are convinced that this research is necessary before any climate and hydrological projection. Documentary researches lead in collaboration with a historian allowed to find about ten long streamflow series from the beginnings of the 20th century on the Durance watershed. The analysis of theses series is necessary to better understand the natural hydrological behavior of the watershed, before the development of most of the anthropogenic influences. If the usefulness of such long streamflow series is obvious, they have some limitations, one of them being their heterogeneity, which can have many origins: shift of the gauging station, changes in the anthropogenic influences, or evolution in the methods used to build the series. Before their interpretation in terms of climate or land use changes, uncertainty estimation of historical streamflow records is therefore very important to assess data quality and homogeneity over time. This paper focuses on the estimation of the historical streamflow records uncertainty due to the evolution of their construction methods. Since the beginnings of the 20th century, we have listed three main methods of construction of daily

  3. Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions.

    PubMed

    Rathnayaka, Kanchana; Sahama, Tony; Schuetz, Michael A; Schmutz, Beat

    2011-03-01

    An accurate and accessible image segmentation method is in high demand for generating 3D bone models from CT scan data, as such models are required in many areas of medical research. Even though numerous sophisticated segmentation methods have been published over the years, most of them are not readily available to the general research community. Therefore, this study aimed to quantify the accuracy of three popular image segmentation methods, two implementations of intensity thresholding and Canny edge detection, for generating 3D models of long bones. In order to reduce user dependent errors associated with visually selecting a threshold value, we present a new approach of selecting an appropriate threshold value based on the Canny filter. A mechanical contact scanner in conjunction with a microCT scanner was utilised to generate the reference models for validating the 3D bone models generated from CT data of five intact ovine hind limbs. When the overall accuracy of the bone model is considered, the three investigated segmentation methods generated comparable results with mean errors in the range of 0.18-0.24 mm. However, for the bone diaphysis, Canny edge detection and Canny filter based thresholding generated 3D models with a significantly higher accuracy compared to those generated through visually selected thresholds. This study demonstrates that 3D models with sub-voxel accuracy can be generated utilising relatively simple segmentation methods that are available to the general research community.

  4. Prospective estimation of organ dose in CT under tube current modulation

    SciTech Connect

    Tian, Xiaoyu; Li, Xiang; Segars, W. Paul; Frush, Donald P.; Samei, Ehsan

    2015-04-15

    Purpose: Computed tomography (CT) has been widely used worldwide as a tool for medical diagnosis and imaging. However, despite its significant clinical benefits, CT radiation dose at the population level has become a subject of public attention and concern. In this light, optimizing radiation dose has become a core responsibility for the CT community. As a fundamental step to manage and optimize dose, it may be beneficial to have accurate and prospective knowledge about the radiation dose for an individual patient. In this study, the authors developed a framework to prospectively estimate organ dose for chest and abdominopelvic CT exams under tube current modulation (TCM). Methods: The organ dose is mainly dependent on two key factors: patient anatomy and irradiation field. A prediction process was developed to accurately model both factors. To model the anatomical diversity and complexity in the patient population, the authors used a previously developed library of computational phantoms with broad distributions of sizes, ages, and genders. A selected clinical patient, represented by a computational phantom in the study, was optimally matched with another computational phantom in the library to obtain a representation of the patient’s anatomy. To model the irradiation field, a previously validated Monte Carlo program was used to model CT scanner systems. The tube current profiles were modeled using a ray-tracing program as previously reported that theoretically emulated the variability of modulation profiles from major CT machine manufacturers Li et al., [Phys. Med. Biol. 59, 4525–4548 (2014)]. The prediction of organ dose was achieved using the following process: (1) CTDI{sub vol}-normalized-organ dose coefficients (h{sub organ}) for fixed tube current were first estimated as the prediction basis for the computational phantoms; (2) each computation phantom, regarded as a clinical patient, was optimally matched with one computational phantom in the library; (3

  5. Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: Feasibility study in lung cancer screening

    SciTech Connect

    Ohkubo, Masaki; Wada, Shinichi; Kayugawa, Akihiro; Matsumoto, Toru; Murao, Kohei

    2011-07-15

    Purpose: While the acquisition of projection data in a computed tomography (CT) scanner is generally carried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening. Methods: The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects. Results: Filtered images for all combinations of three types of reconstruction kernels (''smooth,''''standard,'' and ''sharp'' kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for

  6. Modeling and Reconstruction of Micro-structured 3D Chitosan/Gelatin Porous Scaffolds Using Micro-CT

    NASA Astrophysics Data System (ADS)

    Gong, Haibo; Li, Dichen; He, Jiankang; Liu, Yaxiong; Lian, Qin; Zhao, Jinna

    2008-09-01

    Three dimensional (3D) channel networks are the key to promise the uniform distribution of nutrients inside 3D hepatic tissue engineering scaffolds and prompt elimination of metabolic products out of the scaffolds. 3D chitosan/gelatin porous scaffolds with predefined internal channels were fabricated and a combination of light microscope, laser confocal microscopy and micro-CT were employed to characterize the structure of porous scaffolds. In order to evaluate the flow field distribution inside the micro-structured 3D scaffolds, a computer reconstructing method based on Micro-CT was proposed. According to this evaluating method, a contrast between 3D porous scaffolds with and without predefined internal channels was also performed to assess scaffolds' fluid characters. Results showed that the internal channel of the 3D scaffolds formed the 3D fluid channel network; the uniformity of flow field distribution of the scaffolds fabricated in this paper was better than the simple porous scaffold without micro-fluid channels.

  7. An automated technique for estimating patient-specific regional imparted energy and dose in TCM CT exams

    NASA Astrophysics Data System (ADS)

    Sanders, Jeremiah W.; Tian, Xiaoyu; Segars, W. Paul; Boone, John; Samei, Ehsan

    2016-03-01

    Currently computed tomography (CT) dosimetry relies on CT dose index (CTDI) and size specific dose estimates (SSDE). Organ dose is a better metric of radiation burden. However, organ dose estimation requires precise knowledge of organ locations. Regional imparted energy and dose can also be used to quantify radiation burden. Estimating the imparted energy from CT exams is beneficial in that it does not require precise estimates of the organ size or location. This work investigated an automated technique for retrospectively estimating the imparted energy from chest and abdominopelvic tube current modulated (TCM) CT exams. Monte Carlo simulations of chest and abdominopelvic TCM CT examinations across various tube potentials and TCM strengths were performed on 58 adult computational extended cardiac-torso (XCAT) phantoms to develop relationships between scanned mass and imparted energy normalized by dose length product (DLP). An automated algorithm for calculating the scanned patient volume was further developed using an open source mesh generation toolbox. The scanned patient volume was then used to estimate the scanned mass accounting for diverse density within the scan region. The scanned mass and DLP from the exam were used to estimate the imparted energy to the patient using the knowledgebase developed from the Monte Carlo simulations. Patientspecific imparted energy estimates were made from 20 chest and 20 abdominopelvic clinical CT exams. The average imparted energy was 274 +/- 141 mJ and 681 +/- 376 mJ for the chest and abdominopelvic exams, respectively. This method can be used to estimate the regional imparted energy and/or regional dose in chest and abdominopelvic TCM CT exams across clinical operations.

  8. Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method

    SciTech Connect

    Jia Xun; Tian Zhen; Lou Yifei; Sonke, Jan-Jakob; Jiang, Steve B.

    2012-09-15

    Purpose: Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. Methods: The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on

  9. TV-regularized iterative image reconstruction on a mobile C-ARM CT

    NASA Astrophysics Data System (ADS)

    Pan, Yongsheng; Whitaker, Ross; Cheryauka, Arvi; Ferguson, Dave

    2010-04-01

    3D computed tomography has been extensively studied and widely used in modern society. Although most manufacturers choose the filtered backprojection algorithm (FBP) for its accuracy and efficiency, iterative reconstruction methods have a significant potential to provide superior performance for incomplete, noisy projection data. However, iterative methods have a high computational cost, which hinders their practical use. Furthermore, regularization is usually required to reduce the effects of noise. In this paper, we analyze the use of the Simultaneous Algebraic Reconstruction Technique (SART) with total variation (TV) regularization. Additionally, graphics hardware is utilized to increase the speed of SART. NVIDIA's GPU and Compute Unified Device Architecture (CUDA) comprise the core of our computational platform. GPU implementation details, including ray-based forward projection and voxel-based backprojection are illustrated. Experimental results for high-resolution synthetic and real data are provided to demonstrate the accuracy and efficiency of the proposed framework.

  10. Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT

    NASA Astrophysics Data System (ADS)

    Makeev, Andrey; Ikejimba, Lynda; Lo, Joseph Y.; Glick, Stephen J.

    2016-03-01

    Although digital mammography has reduced breast cancer mortality by approximately 30%, sensitivity and specificity are still far from perfect. In particular, the performance of mammography is especially limited for women with dense breast tissue. Two out of every three biopsies performed in the U.S. are unnecessary, thereby resulting in increased patient anxiety, pain, and possible complications. One promising tomographic breast imaging method that has recently been approved by the FDA is dedicated breast computed tomography (BCT). However, visualizing lesions with BCT can still be challenging for women with dense breast tissue due to the minimal contrast for lesions surrounded by fibroglandular tissue. In recent years there has been renewed interest in improving lesion conspicuity in x-ray breast imaging by administration of an iodinated contrast agent. Due to the fully 3-D imaging nature of BCT, as well as sub-optimal contrast enhancement while the breast is under compression with mammography and breast tomosynthesis, dedicated BCT of the uncompressed breast is likely to offer the best solution for injected contrast-enhanced x-ray breast imaging. It is well known that use of statistically-based iterative reconstruction in CT results in improved image quality at lower radiation dose. Here we investigate possible improvements in image reconstruction for BCT, by optimizing free regularization parameter in method of maximum likelihood and comparing its performance with clinical cone-beam filtered backprojection (FBP) algorithm.

  11. Evaluation of the resolving potency of a novel reconstruction filter on periodontal ligament space with dental cone-beam CT: a quantitative phantom study

    NASA Astrophysics Data System (ADS)

    Houno, Yuuki; Hishikawa, Toshimitsu; Gotoh, Ken-ichi; Naitoh, Munetaka; Ariji, Eiichiro; Kodera, Yoshie

    2014-03-01

    Diagnosis of the alveolar bone condition is important for the treatment planning of periodontal disease. Especially the determination of periodontal ligament space is the most important remark because it represents the periodontal tissue support for tooth retention. However, owing to the image blur of the current cone-beam CT (CBCT) imaging technique, the periodontal ligament space is difficult to visualize. In this study, we developed an original periodontal ligament phantom (PLP) and evaluated the image quality of simulated periodontal ligament space using a novel reconstruction filter for CBCT that emphasized high frequency component. PLP was composed from two resin blocks of different materials, the bone equivalent block and the dentine equivalent block. They were assembled to make continuously changing space from 0.0 to 1.0 millimeter that mimics periodontal ligament space. PLP was placed in water and the image was obtained by using Alphard-3030 dental cone-beam CT (Asahi Roentgen Industry Co., Ltd.). Then we reconstructed the projection data with a novel reconstruction filter. The axial images were compared with conventional reconstructed images. In novel filter reconstruction images, 0.4 millimeter of the space width was steadily detected by calculation of pixel value, on the other hand 0.6 millimeter was in conventional images. With our method, the resolving potency of conebeam CT images was improved.

  12. Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty.

    PubMed

    Kim, Kyungsang; Ye, Jong Chul; Worstell, William; Ouyang, Jinsong; Rakvongthai, Yothin; El Fakhri, Georges; Li, Quanzheng

    2015-03-01

    Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.

  13. Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study

    NASA Astrophysics Data System (ADS)

    Lu, Wenting; Yan, Hao; Gu, Xuejun; Tian, Zhen; Ouyang, Luo; Yang, Liu; Zhou, Linghong; Cervino, Laura; Wang, Jing; Jiang, Steve; Jia, Xun

    2014-10-01

    With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSEs) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3-6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1-3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.

  14. SU-E-I-82: Improving CT Image Quality for Radiation Therapy Using Iterative Reconstruction Algorithms and Slightly Increasing Imaging Doses

    SciTech Connect

    Noid, G; Chen, G; Tai, A; Li, X

    2014-06-01

    Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. For CT in radiation therapy (RT), slightly increasing imaging dose to improve IQ may be justified if it can substantially enhance structure delineation. The purpose of this study is to investigate and to quantify the IQ enhancement as a result of increasing imaging doses and using IR algorithms. Methods: CT images were acquired for phantoms, built to evaluate IQ metrics including spatial resolution, contrast and noise, with a variety of imaging protocols using a CT scanner (Definition AS Open, Siemens) installed inside a Linac room. Representative patients were scanned once the protocols were optimized. Both phantom and patient scans were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and the Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared. Results: IR techniques are demonstrated to preserve spatial resolution as measured by the point spread function and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is doubled by adopting the highest SAFIRE strength. As expected, increasing imaging dose reduces noise for both SAFIRE and FBP reconstructions. The contrast to noise increases from 3 to 5 by increasing the dose by a factor of 4. Similar IQ improvement was observed on the CTs for selected patients with pancreas and prostrate cancers. Conclusion: The IR techniques produce a measurable enhancement to CT IQ by reducing the noise. Increasing imaging dose further reduces noise independent of the IR techniques. The improved CT enables more accurate delineation of tumors and/or organs at risk during RT planning and delivery guidance.

  15. SU-E-I-27: Estimating KERMA Area Product for CT Localizer Images

    SciTech Connect

    Ogden, K; Greene-Donnelly, K; Bennett, R; Thorpe, M

    2015-06-15

    Purpose: To estimate the free-in-air KERMA-Area Product (KAP) incident on patients due to CT localizer scans for common CT exams. Methods: In-plane beam intensity profiles were measured in localizer acquisition mode using OSLs for a 64 slice MDCT scanner (Lightspeed VCT, GE Medical Systems, Waukesha WI). The z-axis beam width was measured as a function of distance from isocenter. The beam profile and width were used to calculate a weighted average air KERMA per unit mAs as a function of intercepted x-axis beam width for objects symmetric about the localizer centerline.Patient areas were measured using manually drawn regions and divided by localizer length to determine average width. Data were collected for 50 head exams (lateral localizer only), 15 head/neck exams, 50 chest exams, and 50 abdomen/pelvis exams. Mean patient widths and acquisition techniques were used to calculate the weighted average free-in-air KERMA, which was multiplied by the patient area to estimate KAP. Results: Scan technique was 120 kV tube voltage, 10 mA current, and table speed of 10 cm/s. The mean ± standard deviation values of KAP were 120 ± 11.6, 469 ± 62.6, 518 ± 45, and 763 ± 93 mGycm{sup 2} for head, head/neck, chest, and abdomen/pelvis exams, respectively. For studies with AP and lateral localizers, the AP/lateral area ratio was 1.20, 1.33, and 1.24 for the head/neck, chest, and abdomen/pelvis exams, respectively. However, the AP/lateral KAP ratios were 1.12, 1.08, and 1.07, respectively. Conclusion: Calculation of KAP in CT localizers is complicated by the non-uniform intensity profile and z-axis beam width. KAP values are similar to those for simple radiographic exams such as a chest radiograph and represent a small fraction of the x-ray exposure at CT. However, as CT doses are reduced the localizer contribution will be a more significant fraction of the total exposure.

  16. The Value of SPECT/CT in Monitoring Prefabricated Tissue-Engineered Bone and Orthotopic rhBMP-2 Implants for Mandibular Reconstruction

    PubMed Central

    Zhou, Miao; Peng, Xin; Mao, Chi; Tian, Jia-he; Zhang, Shu-wen; Xu, Fang; Tu, Jing-jing; Liu, Sheng; Hu, Min; Yu, Guang-yan

    2015-01-01

    Bone tissue engineering shows good prospects for mandibular reconstruction. In recent studies, prefabricated tissue-engineered bone (PTEB) by recombinant human bone morphogenetic proteins (rhBMPs) applied in vivo has found to be an effective alternative for autologous bone grafts. However, the optimal time to transfer PTEB for mandibular reconstruction is still not elucidated. Thus, here in an animal experiment of rhesus monkey, the suitable transferring time for PTEB to reconstruct mandibular defects was evaluated by 99mTc-MDP SPECT/CT, and its value in monitoring orthotopic rhBMP-2 implants for mandibular reconstruction was also evaluated. The result of SPECT/CT showed higher 99mTc-MDP uptake, indicating osteoinductivity, in rhBMP-2 incorporated demineralized freeze-dried bone allograft (DFDBA) and coralline hydroxyapatite (CHA) implants than those without BMP stimulation. 99mTc-MDP uptake of rhBMP-2 implant peaked at 8 weeks following implantation while CT showed the density of these implants increased after 13 weeks’ prefabrication. Histology confirmed that mandibular defects were repaired successfully with PTEB or orthotopically rhBMP-2 incorporated CHA implants, in accordance with SPECT/CT findings. Collectively, data shows 99mTc-MDP SPECT/CT is a sensitive and noninvasive tool to monitor osteoinductivity and bone regeneration of PTEB and orthotopic implants. The PTEB achieved peak osteoinductivity and bone density at 8 to 13 weeks following ectopic implantation, which would serve as a recommendable time frame for its transfer to mandibular reconstruction. PMID:26340447

  17. High-resolution image reconstruction for PET using estimated detector response functions

    NASA Astrophysics Data System (ADS)

    Tohme, Michel S.; Qi, Jinyi

    2007-02-01

    The accuracy of the system model in an iterative reconstruction algorithm greatly affects the quality of reconstructed PET images. For efficient computation in reconstruction, the system model in PET can be factored into a product of geometric projection matrix and detector blurring matrix, where the former is often computed based on analytical calculation, and the latter is estimated using Monte Carlo simulations. In this work, we propose a method to estimate the 2D detector blurring matrix from experimental measurements. Point source data were acquired with high-count statistics in the microPET II scanner using a computer-controlled 2-D motion stage. A monotonically convergent iterative algorithm has been derived to estimate the detector blurring matrix from the point source measurements. The algorithm takes advantage of the rotational symmetry of the PET scanner with the modeling of the detector block structure. Since the resulting blurring matrix stems from actual measurements, it can take into account the physical effects in the photon detection process that are difficult or impossible to model in a Monte Carlo simulation. Reconstructed images of a line source phantom show improved resolution with the new detector blurring matrix compared to the original one from the Monte Carlo simulation. This method can be applied to other small-animal and clinical scanners.

  18. Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

    PubMed

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-01-01

    Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

  19. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

    PubMed Central

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-01-01

    Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

  20. Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

    PubMed

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-12-12

    Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  1. Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT

    PubMed Central

    Chen, Guang-Hong; Li, Yinsheng

    2015-01-01

    Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods: In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity

  2. Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.

    PubMed

    Lavoie, Benjamin R; Okoniewski, Michal; Fear, Elise C

    2016-01-01

    We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range.

  3. Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images

    PubMed Central

    Lavoie, Benjamin R.; Okoniewski, Michal; Fear, Elise C.

    2016-01-01

    We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785

  4. Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.

    PubMed

    Lavoie, Benjamin R; Okoniewski, Michal; Fear, Elise C

    2016-01-01

    We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785

  5. A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT-helical scanning.

    PubMed

    Tang, Xiangyang; Hsieh, Jiang; Nilsen, Roy A; Dutta, Sandeep; Samsonov, Dmitry; Hagiwara, Akira

    2006-02-21

    Based on the structure of the original helical FDK algorithm, a three-dimensional (3D)-weighted cone beam filtered backprojection (CB-FBP) algorithm is proposed for image reconstruction in volumetric CT under helical source trajectory. In addition to its dependence on view and fan angles, the 3D weighting utilizes the cone angle dependency of a ray to improve reconstruction accuracy. The 3D weighting is ray-dependent and the underlying mechanism is to give a favourable weight to the ray with the smaller cone angle out of a pair of conjugate rays but an unfavourable weight to the ray with the larger cone angle out of the conjugate ray pair. The proposed 3D-weighted helical CB-FBP reconstruction algorithm is implemented in the cone-parallel geometry that can improve noise uniformity and image generation speed significantly. Under the cone-parallel geometry, the filtering is naturally carried out along the tangential direction of the helical source trajectory. By exploring the 3D weighting's dependence on cone angle, the proposed helical 3D-weighted CB-FBP reconstruction algorithm can provide significantly improved reconstruction accuracy at moderate cone angle and high helical pitches. The 3D-weighted CB-FBP algorithm is experimentally evaluated by computer-simulated phantoms and phantoms scanned by a diagnostic volumetric CT system with a detector dimension of 64 x 0.625 mm over various helical pitches. The computer simulation study shows that the 3D weighting enables the proposed algorithm to reach reconstruction accuracy comparable to that of exact CB reconstruction algorithms, such as the Katsevich algorithm, under a moderate cone angle (4 degrees) and various helical pitches. Meanwhile, the experimental evaluation using the phantoms scanned by a volumetric CT system shows that the spatial resolution along the z-direction and noise characteristics of the proposed 3D-weighted helical CB-FBP reconstruction algorithm are maintained very well in comparison to the FDK

  6. Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR).

    PubMed

    Kalantari, Faraz; Li, Tianfang; Jin, Mingwu; Wang, Jing

    2016-08-01

    In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10-40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET. PMID:27385378

  7. Estimation of radionuclide ingestion: Lessons from dose reconstruction for fallout from the Nevada Test Site

    SciTech Connect

    Breshears, D.D.; Whicker, F.W.; Kirchner, T.B.; Anspaugh, L.R.

    1994-09-01

    The United States conducted atmospheric testing of nuclear devices at the Nevada Test Site from 1951 through 1963. In 1979 the U.S. Department of Energy established the Off-Site Radiation Exposure Review Project to compile a data base related to health effects from nuclear testing and to reconstruct doses to public residing off of the Nevada Test Site. This project is the most comprehensive dose reconstruction project to date, and, since similar assessments are currently underway at several other locations within and outside the U.S., lessons from ORERP can be valuable. A major component of dose reconstruction is estimation of dose from radionuclide ingestion. The PATHWAY food-chain model was developed to estimate the amount of radionuclides ingested. For agricultural components of the human diet, PATHWAY predicts radionuclide concentrations and quantities ingested. To improve accuracy and model credibility, four components of model analysis were conducted: estimation of uncertainty in model predictions, estimation of sensitivity of model predictions to input parameters, and testing of model predictions against independent data (validation), and comparing predictions from PATHWAY with those from other models. These results identified strengths and weaknesses in the model and aided in establishing the confidence associated with model prediction, which is a critical component risk assessment and dose reconstruction. For fallout from the Nevada Test Site, by far, the largest internal doses were received by the thyroid. However, the predicted number of fatal cancers from ingestion dose was generally much smaller than the number predicted from external dose. The number of fatal cancers predicted from ingestion dose was also orders of magnitude below the normal projected cancer rate. Several lessons were learned during the study that are relevant to other dose reconstruction efforts.

  8. Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR)

    NASA Astrophysics Data System (ADS)

    Kalantari, Faraz; Li, Tianfang; Jin, Mingwu; Wang, Jing

    2016-08-01

    In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10-40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.

  9. Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR).

    PubMed

    Kalantari, Faraz; Li, Tianfang; Jin, Mingwu; Wang, Jing

    2016-08-01

    In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10-40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.

  10. Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR)

    NASA Astrophysics Data System (ADS)

    Kalantari, Faraz; Li, Tianfang; Jin, Mingwu; Wang, Jing

    2016-08-01

    In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10–40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.

  11. A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

    DOE PAGES

    Ray, J.; Lee, J.; Yadav, V.; Lefantzi, S.; Michalak, A. M.; van Bloemen Waanders, B.

    2015-04-29

    Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting.more » Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also

  12. Patient-specific dose estimation for pediatric abdomen-pelvis CT

    NASA Astrophysics Data System (ADS)

    Li, Xiang; Samei, Ehsan; Segars, W. Paul; Sturgeon, Gregory M.; Colsher, James G.; Frush, Donald P.

    2009-02-01

    The purpose of this study is to develop a method for estimating patient-specific dose from abdomen-pelvis CT examinations and to investigate dose variation across patients in the same weight group. Our study consisted of seven pediatric patients in the same weight/protocol group, for whom full-body computer models were previously created based on the patients' CT data obtained for clinical indications. Organ and effective dose of these patients from an abdomen-pelvis scan protocol (LightSpeed VCT scanner, 120-kVp, 85-90 mA, 0.4-s gantry rotation period, 1.375-pitch, 40-mm beam collimation, and small body scan field-of-view) was calculated using a Monte Carlo program previously developed and validated for the same CT system. The seven patients had effective dose of 2.4-2.8 mSv, corresponding to normalized effective dose of 6.6-8.3 mSv/100mAs (coefficient of variation: 7.6%). Dose variations across the patients were small for large organs in the scan coverage (mean: 6.6%; range: 4.9%-9.2%), larger for small organs in the scan coverage (mean: 10.3%; range: 1.4%-15.6%), and the largest for organs partially or completely outside the scan coverage (mean: 14.8%; range: 5.7%-27.7%). Normalized effective dose correlated strongly with body weight (correlation coefficient: r = -0.94). Normalized dose to the kidney and the adrenal gland correlated strongly with mid-liver equivalent diameter (kidney: r = -0.97; adrenal glands: r = -0.98). Normalized dose to the small intestine correlated strongly with mid-intestine equivalent diameter (r = -0.97). These strong correlations suggest that patient-specific dose may be estimated for any other child in the same size group who undergoes the abdomen-pelvis scan.

  13. Estimation of vocal fold plane in 3D CT images for diagnosis of vocal fold abnormalities.

    PubMed

    Hewavitharanage, Sajini; Gubbi, Jayavardhana; Thyagarajan, Dominic; Lau, Ken; Palaniswami, Marimuthu

    2015-01-01

    Vocal folds are the key body structures that are responsible for phonation and regulating air movement into and out of lungs. Various vocal fold disorders may seriously impact the quality of life. When diagnosing vocal fold disorders, CT of the neck is the commonly used imaging method. However, vocal folds do not align with the normal axial plane of a neck and the plane containing vocal cords and arytenoids does vary during phonation. It is therefore important to generate an algorithm for detecting the actual plane containing vocal folds. In this paper, we propose a method to automatically estimate the vocal fold plane using vertebral column and anterior commissure localization. Gray-level thresholding, connected component analysis, rule based segmentation and unsupervised k-means clustering were used in the proposed algorithm. The anterior commissure segmentation method achieved an accuracy of 85%, a good estimate of the expert assessment. PMID:26736949

  14. Using pedigree reconstruction to estimate population size: genotypes are more than individually unique marks

    PubMed Central

    Creel, Scott; Rosenblatt, Elias

    2013-01-01

    Estimates of population size are critical for conservation and management, but accurate estimates are difficult to obtain for many species. Noninvasive genetic methods are increasingly used to estimate population size, particularly in elusive species such as large carnivores, which are difficult to count by most other methods. In most such studies, genotypes are treated simply as unique individual identifiers. Here, we develop a new estimator of population size based on pedigree reconstruction. The estimator accounts for individuals that were directly sampled, individuals that were not sampled but whose genotype could be inferred by pedigree reconstruction, and individuals that were not detected by either of these methods. Monte Carlo simulations show that the population estimate is unbiased and precise if sampling is of sufficient intensity and duration. Simulations also identified sampling conditions that can cause the method to overestimate or underestimate true population size; we present and discuss methods to correct these potential biases. The method detected 2–21% more individuals than were directly sampled across a broad range of simulated sampling schemes. Genotypes are more than unique identifiers, and the information about relationships in a set of genotypes can improve estimates of population size. PMID:23762516

  15. BrachyView: multiple seed position reconstruction and comparison with CT post-implant dosimetry

    NASA Astrophysics Data System (ADS)

    Alnaghy, S.; Loo, K. J.; Cutajar, D. L.; Jalayer, M.; Tenconi, C.; Favoino, M.; Rietti, R.; Tartaglia, M.; Carriero, F.; Safavi-Naeini, M.; Bucci, J.; Jakubek, J.; Pospisil, S.; Zaider, M.; Lerch, M. L. F.; Rosenfeld, A. B.; Petasecca, M.

    2016-05-01

    BrachyView is a novel in-body imaging system utilising high-resolution pixelated silicon detectors (Timepix) and a pinhole collimator for brachytherapy source localisation. Recent studies have investigated various options for real-time intraoperative dynamic dose treatment planning to increase the quality of implants. In a previous proof-of-concept study, the justification of the pinhole concept was shown, allowing for the next step whereby multiple active seeds are implanted into a PMMA phantom to simulate a more realistic clinical scenario. In this study, 20 seeds were implanted and imaged using a lead pinhole of 400 μ m diameter. BrachyView was able to resolve the seed positions within 1-2 mm of expected positions, which was verified by co-registering with a full clinical post-implant CT scan.

  16. Estimating radiation dose to organs of patients undergoing conventional and novel multidetector CT exams using Monte Carlo simulations

    NASA Astrophysics Data System (ADS)

    Angel, Erin

    Advances in Computed Tomography (CT) technology have led to an increase in the modality's diagnostic capabilities and therefore its utilization, which has in turn led to an increase in radiation exposure to the patient population. As a result, CT imaging currently constitutes approximately half of the collective exposure to ionizing radiation from medical procedures. In order to understand the radiation risk, it is necessary to estimate the radiation doses absorbed by patients undergoing CT imaging. The most widely accepted risk models are based on radiosensitive organ dose as opposed to whole body dose. In this research, radiosensitive organ dose was estimated using Monte Carlo based simulations incorporating detailed multidetector CT (MDCT) scanner models, specific scan protocols, and using patient models based on accurate patient anatomy and representing a range of patient sizes. Organ dose estimates were estimated for clinical MDCT exam protocols which pose a specific concern for radiosensitive organs or regions. These dose estimates include estimation of fetal dose for pregnant patients undergoing abdomen pelvis CT exams or undergoing exams to diagnose pulmonary embolism and venous thromboembolism. Breast and lung dose were estimated for patients undergoing coronary CTA imaging, conventional fixed tube current chest CT, and conventional tube current modulated (TCM) chest CT exams. The correlation of organ dose with patient size was quantified for pregnant patients undergoing abdomen/pelvis exams and for all breast and lung dose estimates presented. Novel dose reduction techniques were developed that incorporate organ location and are specifically designed to reduce close to radiosensitive organs during CT acquisition. A generalizable model was created for simulating conventional and novel attenuation-based TCM algorithms which can be used in simulations estimating organ dose for any patient model. The generalizable model is a significant contribution of this

  17. A correction method for dual energy liquid CT image reconstruction with metallic containers.

    PubMed

    Xue, Hui; Zhang, Li; Chen, Zhiqiang; Li, Liang

    2012-01-01

    With its capability of material discrimination, dual energy computed tomography (DECT) is widely used in security inspection for the purpose of detecting contraband. DECT provides effective atomic number image and electron density image in addition to traditional attenuation images. In dual energy liquid inspection system, the presence of metallic containers will cause partial volume effect (PVE) that leads to severe deviation in effective atomic number image. Usually, the deviation is too large for a reliable material discrimination and may cause false results. In this paper, a projection splitting method is proposed to combat the PVE. This method is based on the assumption that a prior projection of the empty container is available and photoelectric and Compton coefficient integrals can be calculated via dual energy decomposition. Each integral is split into two parts by subtracting the integral of the empty container from the total integral. The subtraction removes the integral part contributed by the container, thus discarding the error source created by PVE that appears on the boundary of the sinogram. Images are reconstructed in which only the interior liquid area remains. Experiments are performed in a real liquid inspection system to demonstrate the effectiveness of this method. Accuracy of the reconstructed effective atomic number is greatly improved with this method, which helps a lot in determining the type of the object.

  18. Estimation of signal and noise for a whole-body photon counting research CT system

    NASA Astrophysics Data System (ADS)

    Li, Zhoubo; Leng, Shuai; Yu, Zhicong; Kappler, Steffen; McCollough, Cynthia H.

    2016-03-01

    Photon-counting CT (PCCT) may yield potential value for many clinical applications due to its relative immunity to electronic noise, increased geometric efficiency relative to current scintillating detectors, and the ability to resolve energy information about the detected photons. However, there are a large number of parameters that require optimization, particularly the energy thresholds configuration. Fast and accurate estimation of signal and noise in PCCT can benefit the optimization of acquisition parameters for specific diagnostic tasks. Based on the acquisition parameters and detector response of our research PCCT system, we derived mathematical models for both signal and noise. The signal model took the tube spectrum, beam filtration, object attenuation, water beam hardening, and detector response into account. The noise model considered the relationship between noise and radiation dose, as well as the propagation of noise as threshold data are subtracted to yield energy bin data. To determine the absolute noise value, a noise look-up table (LUT) was acquired using a limited number of calibration scans. The noise estimation algorithm then used the noise LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuation. Validation of the estimation algorithms was performed on our whole-body research PCCT system using semianthropomorphic water phantoms and solutions of calcium and iodine. The algorithms achieved accurate estimation of signal and noise for a variety of scanning parameter combinations. The proposed method can be used to optimize energy thresholds configuration for many clinical applications of PCCT.

  19. Attenuation-based estimation of patient size for the purpose of size specific dose estimation in CT. Part II. Implementation on abdomen and thorax phantoms using cross sectional CT images and scanned projection radiograph images

    SciTech Connect

    Wang Jia; Christner, Jodie A.; Duan Xinhui; Leng Shuai; Yu Lifeng; McCollough, Cynthia H.

    2012-11-15

    Purpose: To estimate attenuation using cross sectional CT images and scanned projection radiograph (SPR) images in a series of thorax and abdomen phantoms. Methods: Attenuation was quantified in terms of a water cylinder with cross sectional area of A{sub w} from both the CT and SPR images of abdomen and thorax phantoms, where A{sub w} is the area of a water cylinder that would absorb the same dose as the specified phantom. SPR and axial CT images were acquired using a dual-source CT scanner operated at 120 kV in single-source mode. To use the SPR image for estimating A{sub w}, the pixel values of a SPR image were calibrated to physical water attenuation using a series of water phantoms. A{sub w} and the corresponding diameter D{sub w} were calculated using the derived attenuation-based methods (from either CT or SPR image). A{sub w} was also calculated using only geometrical dimensions of the phantoms (anterior-posterior and lateral dimensions or cross sectional area). Results: For abdomen phantoms, the geometry-based and attenuation-based methods gave similar results for D{sub w}. Using only geometric parameters, an overestimation of D{sub w} ranging from 4.3% to 21.5% was found for thorax phantoms. Results for D{sub w} using the CT image and SPR based methods agreed with each other within 4% on average in both thorax and abdomen phantoms. Conclusions: Either the cross sectional CT or SPR images can be used to estimate patient attenuation in CT. Both are more accurate than use of only geometrical information for the task of quantifying patient attenuation. The SPR based method requires calibration of SPR pixel values to physical water attenuation and this calibration would be best performed by the scanner manufacturer.

  20. Band-Restricted Estimation of Noise Variance in Filtered Backprojection Reconstructions Using Repeated Scans

    PubMed Central

    Wunderlich, Adam; Noo, Frédéric

    2010-01-01

    We introduce a new estimator for noise variance in tomographic images reconstructed using algorithms of the filtered backprojection type. The new estimator operates on data acquired from repeated scans of the object under examination, is unbiased, and is shown to have significantly lower variance than the conventional unbiased estimator for many scenarios of practical interest. We provide an extensive theoretical analysis of this estimator, highlighting the circumstances under which it is most effective. This analysis includes both general and specific data correlation patterns. Moreover, we have applied our estimator to real x-ray computed tomography data and present preliminary results that support the theory and provide experimental evidence of the new estimator’s efficacy. PMID:20236879

  1. High Fidelity System Modeling for High Quality Image Reconstruction in Clinical CT

    PubMed Central

    Do, Synho; Karl, William Clem; Singh, Sarabjeet; Kalra, Mannudeep; Brady, Tom; Shin, Ellie; Pien, Homer

    2014-01-01

    Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging. PMID:25390888

  2. Estimation of 1945 to 1957 food consumption. Hanford Environmental Dose Reconstruction Project

    SciTech Connect

    Anderson, D.M.; Bates, D.J.; Marsh, T.L.

    1993-07-01

    This report details the methods used and the results of the study on the estimated historic levels of food consumption by individuals in the Hanford Environmental Dose Reconstruction (HEDR) study area from 1945--1957. This period includes the time of highest releases from Hanford and is the period for which data are being collected in the Hanford Thyroid Disease Study. These estimates provide the food-consumption inputs for the HEDR database of individual diets. This database will be an input file in the Hanford Environmental Dose Reconstruction Integrated Code (HEDRIC) computer model that will be used to calculate the radiation dose. The report focuses on fresh milk, eggs, lettuce, and spinach. These foods were chosen because they have been found to be significant contributors to radiation dose based on the Techni