A sinogram warping strategy for pre-reconstruction 4D PET optimization.
Gianoli, Chiara; Riboldi, Marco; Fontana, Giulia; Kurz, Christopher; Parodi, Katia; Baroni, Guido
2016-03-01
A novel strategy for 4D PET optimization in the sinogram domain is proposed, aiming at motion model application before image reconstruction ("sinogram warping" strategy). Compared to state-of-the-art 4D-MLEM reconstruction, the proposed strategy is able to optimize the image SNR, avoiding iterative direct and inverse warping procedures, which are typical of the 4D-MLEM algorithm. A full-count statistics sinogram of the motion-compensated 4D PET reference phase is generated by warping the sinograms corresponding to the different PET phases. This is achieved relying on a motion model expressed in the sinogram domain. The strategy was tested on the anthropomorphic 4D PET-CT NCAT phantom in comparison with the 4D-MLEM algorithm, with particular reference to robustness to PET-CT co-registrations artefacts. The MLEM reconstruction of the warped sinogram according to the proposed strategy exhibited better accuracy (up to +40.90 % with respect to the ideal value), whereas images reconstructed according to the 4D-MLEM reconstruction resulted in less noisy (down to -26.90 % with respect to the ideal value) but more blurred. The sinogram warping strategy demonstrates advantages with respect to 4D-MLEM algorithm. These advantages are paid back by introducing approximation of the deformation field, and further efforts are required to mitigate the impact of such an approximation in clinical 4D PET reconstruction. PMID:26126871
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.
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
Fully 4D list-mode reconstruction applied to respiratory-gated PET scans
NASA Astrophysics Data System (ADS)
Grotus, N; Reader, A J; Stute, S; Rosenwald, J C; Giraud, P; Buvat, I
2009-03-01
18F-fluoro-deoxy-glucose (18F-FDG) positron emission tomography (PET) is one of the most sensitive and specific imaging modalities for the diagnosis of non-small cell lung cancer. A drawback of PET is that it requires several minutes of acquisition per bed position, which results in images being affected by respiratory blur. Respiratory gating techniques have been developed to deal with respiratory motion in the PET images. However, these techniques considerably increase the level of noise in the reconstructed images unless the acquisition time is increased. The aim of this paper is to evaluate a four-dimensional (4D) image reconstruction algorithm that combines the acquired events in all the gates whilst preserving the motion deblurring. This algorithm was compared to classic ordered subset expectation maximization (OSEM) reconstruction of gated and non-gated images, and to temporal filtering of gated images reconstructed with OSEM. Two datasets were used for comparing the different reconstruction approaches: one involving the NEMA IEC/2001 body phantom in motion, the other obtained using Monte-Carlo simulations of the NCAT breathing phantom. Results show that 4D reconstruction reaches a similar performance in terms of the signal-to-noise ratio (SNR) as non-gated reconstruction whilst preserving the motion deblurring. In particular, 4D reconstruction improves the SNR compared to respiratory-gated images reconstructed with the OSEM algorithm. Temporal filtering of the OSEM-reconstructed images helps improve the SNR, but does not achieve the same performance as 4D reconstruction. 4D reconstruction of respiratory-gated images thus appears as a promising tool to reach the same performance in terms of the SNR as non-gated acquisitions while reducing the motion blur, without increasing the acquisition time.
NASA Astrophysics Data System (ADS)
Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib
2016-08-01
Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation–maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10–20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were
Karakatsanis, Nicolas A; Casey, Michael E; Lodge, Martin A; Rahmim, Arman; Zaidi, Habib
2016-08-01
Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible (18)F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published (18)F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were
NASA Astrophysics Data System (ADS)
Feng, Tao; Wang, Jizhe; Fung, George; Tsui, Benjamin
2016-01-01
Respiratory motion (RM) and cardiac motion (CM) degrade the quality and resolution in cardiac PET scans. We have developed non-rigid motion estimation methods to estimate both RM and CM based on 4D cardiac gated PET data alone, and compensate the dual respiratory and cardiac (R&C) motions after (MCAR), during (MCDR), and before (MCBR) image reconstruction. In all three R&C motion correction methods, attenuation-activity mismatch effect was modeled by using transformed attenuation maps using the estimated RM. The difference of using activity preserving and non-activity preserving models in R&C correction was also studied. Realistic Monte Carlo simulated 4D cardiac PET data using the 4D XCAT phantom and accurate models of the scanner design parameters and performance characteristics at different noise levels were employed as the known truth and for method development and evaluation. Results from the simulation study suggested that all three dual R&C motion correction methods provide substantial improvement in the quality of 4D cardiac gated PET images as compared with no motion correction. Specifically, the MCDR method yields the best performance for all different noise levels compared with the MCAR and MCBR methods. While MCBR reduces computational time dramatically but the resultant 4D cardiac gated PET images has overall inferior image quality when compared to that from the MCAR and MCDR approaches in the ‘almost’ noise free case. Also, the MCBR method has better noise handling properties when compared with MCAR and provides better quantitative results in high noise cases. When the goal is to reduce scan time or patient radiation dose, MCDR and MCBR provide a good compromise between image quality and computational times.
Kotasidis, F A; Mehranian, A; Zaidi, H
2016-05-01
Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image
NASA Astrophysics Data System (ADS)
Kotasidis, F. A.; Mehranian, A.; Zaidi, H.
2016-05-01
Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image
Gravel, Paul; Reader, Andrew J
2015-06-01
This work assesses the one-step late maximum likelihood expectation maximization (OSL-MLEM) 4D PET reconstruction algorithm for direct estimation of parametric images from raw PET data when using the simplified reference tissue model with the basis function method (SRTM-BFM) for the kinetic analysis. To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component. We extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 3D + time data sets (with use of [(11)C]raclopride time-activity curves from real data) and on real data sets acquired with the high resolution research tomograph. The performance of the proposed method is evaluated by comparing voxel-level binding potential (BPND) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation. For the commonly chosen number of iterations used in practice, our results show that for the 3D + time simulation, the direct method delivers results with lower (%)RMSE at the normal count level (decreases of 9-10 percentage points, corresponding to a 38-44% reduction), and also at low count levels (decreases of 17-21 percentage points, corresponding to a 26-36% reduction). As for the real 3D data set, the results obtained follow a similar trend, with the direct reconstruction method offering a 21% decrease in (%)CV compared to the post reconstruction method at low count levels. Thus, based on the results presented herein, using the SRTM-BFM kinetic model in conjunction with the OSL-MLEM direct 4D PET MLEM reconstruction method offers an improvement in performance when compared to conventional post reconstruction methods. PMID:25992999
Gianoli, Chiara; Kurz, Christopher; Riboldi, Marco; Bauer, Julia; Fontana, Giulia; Baroni, Guido; Debus, Jürgen; Parodi, Katia
2016-06-01
A clinical trial named PROMETHEUS is currently ongoing for inoperable hepatocellular carcinoma (HCC) at the Heidelberg Ion Beam Therapy Center (HIT, Germany). In this framework, 4D PET-CT datasets are acquired shortly after the therapeutic treatment to compare the irradiation induced PET image with a Monte Carlo PET prediction resulting from the simulation of treatment delivery. The extremely low count statistics of this measured PET image represents a major limitation of this technique, especially in presence of target motion. The purpose of the study is to investigate two different 4D PET motion compensation strategies towards the recovery of the whole count statistics for improved image quality of the 4D PET-CT datasets for PET-based treatment verification. The well-known 4D-MLEM reconstruction algorithm, embedding the motion compensation in the reconstruction process of 4D PET sinograms, was compared to a recently proposed pre-reconstruction motion compensation strategy, which operates in sinogram domain by applying the motion compensation to the 4D PET sinograms. With reference to phantom and patient datasets, advantages and drawbacks of the two 4D PET motion compensation strategies were identified. The 4D-MLEM algorithm was strongly affected by inverse inconsistency of the motion model but demonstrated the capability to mitigate the noise-break-up effects. Conversely, the pre-reconstruction warping showed less sensitivity to inverse inconsistency but also more noise in the reconstructed images. The comparison was performed by relying on quantification of PET activity and ion range difference, typically yielding similar results. The study demonstrated that treatment verification of moving targets could be accomplished by relying on the whole count statistics image quality, as obtained from the application of 4D PET motion compensation strategies. In particular, the pre-reconstruction warping was shown to represent a promising choice when combined with intra-reconstruction
NASA Astrophysics Data System (ADS)
Gianoli, Chiara; Kurz, Christopher; Riboldi, Marco; Bauer, Julia; Fontana, Giulia; Baroni, Guido; Debus, Jürgen; Parodi, Katia
2016-06-01
A clinical trial named PROMETHEUS is currently ongoing for inoperable hepatocellular carcinoma (HCC) at the Heidelberg Ion Beam Therapy Center (HIT, Germany). In this framework, 4D PET-CT datasets are acquired shortly after the therapeutic treatment to compare the irradiation induced PET image with a Monte Carlo PET prediction resulting from the simulation of treatment delivery. The extremely low count statistics of this measured PET image represents a major limitation of this technique, especially in presence of target motion. The purpose of the study is to investigate two different 4D PET motion compensation strategies towards the recovery of the whole count statistics for improved image quality of the 4D PET-CT datasets for PET-based treatment verification. The well-known 4D-MLEM reconstruction algorithm, embedding the motion compensation in the reconstruction process of 4D PET sinograms, was compared to a recently proposed pre-reconstruction motion compensation strategy, which operates in sinogram domain by applying the motion compensation to the 4D PET sinograms. With reference to phantom and patient datasets, advantages and drawbacks of the two 4D PET motion compensation strategies were identified. The 4D-MLEM algorithm was strongly affected by inverse inconsistency of the motion model but demonstrated the capability to mitigate the noise-break-up effects. Conversely, the pre-reconstruction warping showed less sensitivity to inverse inconsistency but also more noise in the reconstructed images. The comparison was performed by relying on quantification of PET activity and ion range difference, typically yielding similar results. The study demonstrated that treatment verification of moving targets could be accomplished by relying on the whole count statistics image quality, as obtained from the application of 4D PET motion compensation strategies. In particular, the pre-reconstruction warping was shown to represent a promising choice when combined with intra-reconstruction
Constrained reconstructions for 4D intervention guidance
NASA Astrophysics Data System (ADS)
Kuntz, J.; Flach, B.; Kueres, R.; Semmler, W.; Kachelrieß, M.; Bartling, S.
2013-05-01
Image-guided interventions are an increasingly important part of clinical minimally invasive procedures. However, up to now they cannot be performed under 4D (3D + time) guidance due to the exceedingly high x-ray dose. In this work we investigate the applicability of compressed sensing reconstructions for highly undersampled CT datasets combined with the incorporation of prior images in order to yield low dose 4D intervention guidance. We present a new reconstruction scheme prior image dynamic interventional CT (PrIDICT) that accounts for specific image features in intervention guidance and compare it to PICCS and ASD-POCS. The optimal parameters for the dose per projection and the numbers of projections per reconstruction are determined in phantom simulations and measurements. In vivo experiments in six pigs are performed in a cone-beam CT; measured doses are compared to current gold-standard intervention guidance represented by a clinical fluoroscopy system. Phantom studies show maximum image quality for identical overall doses in the range of 14 to 21 projections per reconstruction. In vivo studies reveal that interventional materials can be followed in 4D visualization and that PrIDICT, compared to PICCS and ASD-POCS, shows superior reconstruction results and fewer artifacts in the periphery with dose in the order of biplane fluoroscopy. These results suggest that 4D intervention guidance can be realized with today’s flat detector and gantry systems using the herein presented reconstruction scheme.
4D image reconstruction for emission tomography
NASA Astrophysics Data System (ADS)
Reader, Andrew J.; Verhaeghe, Jeroen
2014-11-01
An overview of the theory of 4D image reconstruction for emission tomography is given along with a review of the current state of the art, covering both positron emission tomography and single photon emission computed tomography (SPECT). By viewing 4D image reconstruction as a matter of either linear or non-linear parameter estimation for a set of spatiotemporal functions chosen to approximately represent the radiotracer distribution, the areas of so-called ‘fully 4D’ image reconstruction and ‘direct kinetic parameter estimation’ are unified within a common framework. Many choices of linear and non-linear parameterization of these functions are considered (including the important case where the parameters have direct biological meaning), along with a review of the algorithms which are able to estimate these often non-linear parameters from emission tomography data. The other crucial components to image reconstruction (the objective function, the system model and the raw data format) are also covered, but in less detail due to the relatively straightforward extension from their corresponding components in conventional 3D image reconstruction. The key unifying concept is that maximum likelihood or maximum a posteriori (MAP) estimation of either linear or non-linear model parameters can be achieved in image space after carrying out a conventional expectation maximization (EM) update of the dynamic image series, using a Kullback-Leibler distance metric (comparing the modeled image values with the EM image values), to optimize the desired parameters. For MAP, an image-space penalty for regularization purposes is required. The benefits of 4D and direct reconstruction reported in the literature are reviewed, and furthermore demonstrated with simple simulation examples. It is clear that the future of reconstructing dynamic or functional emission tomography images, which often exhibit high levels of spatially correlated noise, should ideally exploit these 4D
Experimental investigation of irregular motion impact on 4D PET-based particle therapy monitoring.
Tian, Y; Stützer, K; Enghardt, W; Priegnitz, M; Helmbrecht, S; Bert, C; Fiedler, F
2016-01-21
Particle therapy positron emission tomography (PT-PET) is an in vivo and non-invasive imaging technique to monitor treatment delivery in particle therapy. The inevitable patient respiratory motion during irradiation causes artefacts and inaccurate activity distribution in PET images. Four-dimensional (4D) maximum likelihood expectation maximisation (4D MLEM) allows for a compensation of these effects, but has up to now been restricted to regular motion for PT-PET investigations. However, intra-fractional motion during treatment might differ from that during acquisition of the 4D-planning CT (e.g. amplitude variation, baseline drift) and therefore might induce inaccurate 4D PET reconstruction results. This study investigates the impact of different irregular analytical one-dimensional (1D) motion patterns on PT-PET imaging by means of experiments with a radioactive source and irradiated moving phantoms. Three sorting methods, namely phase sorting, equal amplitude sorting and event-based amplitude sorting, were applied to manage the PET list-mode data. The influence of these sorting methods on the motion compensating algorithm has been analysed. The event-based amplitude sorting showed a superior performance and it is applicable for irregular motions with ⩽ 4 mm amplitude elongation and drift. For motion with 10 mm baseline drift, the normalised root mean square error was as high as 10.5% and a 10 mm range deviation was observed. PMID:26733104
Experimental investigation of irregular motion impact on 4D PET-based particle therapy monitoring
NASA Astrophysics Data System (ADS)
Tian, Y.; Stützer, K.; Enghardt, W.; Priegnitz, M.; Helmbrecht, S.; Bert, C.; Fiedler, F.
2016-01-01
Particle therapy positron emission tomography (PT-PET) is an in vivo and non-invasive imaging technique to monitor treatment delivery in particle therapy. The inevitable patient respiratory motion during irradiation causes artefacts and inaccurate activity distribution in PET images. Four-dimensional (4D) maximum likelihood expectation maximisation (4D MLEM) allows for a compensation of these effects, but has up to now been restricted to regular motion for PT-PET investigations. However, intra-fractional motion during treatment might differ from that during acquisition of the 4D-planning CT (e.g. amplitude variation, baseline drift) and therefore might induce inaccurate 4D PET reconstruction results. This study investigates the impact of different irregular analytical one-dimensional (1D) motion patterns on PT-PET imaging by means of experiments with a radioactive source and irradiated moving phantoms. Three sorting methods, namely phase sorting, equal amplitude sorting and event-based amplitude sorting, were applied to manage the PET list-mode data. The influence of these sorting methods on the motion compensating algorithm has been analysed. The event-based amplitude sorting showed a superior performance and it is applicable for irregular motions with ⩽4 mm amplitude elongation and drift. For motion with 10 mm baseline drift, the normalised root mean square error was as high as 10.5% and a 10 mm range deviation was observed.
SU-C-9A-06: The Impact of CT Image Used for Attenuation Correction in 4D-PET
Cui, Y; Bowsher, J; Yan, S; Cai, J; Das, S; Yin, F
2014-06-01
Purpose: To evaluate the appropriateness of using 3D non-gated CT image for attenuation correction (AC) in a 4D-PET (gated PET) imaging protocol used in radiotherapy treatment planning simulation. Methods: The 4D-PET imaging protocol in a Siemens PET/CT simulator (Biograph mCT, Siemens Medical Solutions, Hoffman Estates, IL) was evaluated. CIRS Dynamic Thorax Phantom (CIRS Inc., Norfolk, VA) with a moving glass sphere (8 mL) in the middle of its thorax portion was used in the experiments. The glass was filled with {sup 18}F-FDG and was in a longitudinal motion derived from a real patient breathing pattern. Varian RPM system (Varian Medical Systems, Palo Alto, CA) was used for respiratory gating. Both phase-gating and amplitude-gating methods were tested. The clinical imaging protocol was modified to use three different CT images for AC in 4D-PET reconstruction: first is to use a single-phase CT image to mimic actual clinical protocol (single-CT-PET); second is to use the average intensity projection CT (AveIP-CT) derived from 4D-CT scanning (AveIP-CT-PET); third is to use 4D-CT image to do the phase-matched AC (phase-matching- PET). Maximum SUV (SUVmax) and volume of the moving target (glass sphere) with threshold of 40% SUVmax were calculated for comparison between 4D-PET images derived with different AC methods. Results: The SUVmax varied 7.3%±6.9% over the breathing cycle in single-CT-PET, compared to 2.5%±2.8% in AveIP-CT-PET and 1.3%±1.2% in phasematching PET. The SUVmax in single-CT-PET differed by up to 15% from those in phase-matching-PET. The target volumes measured from single- CT-PET images also presented variations up to 10% among different phases of 4D PET in both phase-gating and amplitude-gating experiments. Conclusion: Attenuation correction using non-gated CT in 4D-PET imaging is not optimal process for quantitative analysis. Clinical 4D-PET imaging protocols should consider phase-matched 4D-CT image if available to achieve better accuracy.
NASA Astrophysics Data System (ADS)
Doulamis, Anastasios; Ioannides, Marinos; Doulamis, Nikolaos; Hadjiprocopis, Andreas; Fritsch, Dieter; Balet, Olivier; Julien, Martine; Protopapadakis, Eftychios; Makantasis, Kostas; Weinlinger, Guenther; Johnsons, Paul S.; Klein, Michael; Fellner, Dieter; Stork, Andre; Santos, Pedro
2013-08-01
One of the main characteristics of the Internet era we are living in, is the free and online availability of a huge amount of data. This data is of varied reliability and accuracy and exists in various forms and formats. Often, it is cross-referenced and linked to other data, forming a nexus of text, images, animation and audio enabled by hypertext and, recently, by the Web3.0 standard. Search engines can search text for keywords using algorithms of varied intelligence and with limited success. Searching images is a much more complex and computationally intensive task but some initial steps have already been made in this direction, mainly in face recognition. This paper aims to describe our proposed pipeline for integrating data available on Internet repositories and social media, such as photographs, animation and text to produce 3D models of archaeological monuments as well as enriching multimedia of cultural / archaeological interest with metadata and harvesting the end products to EUROPEANA. Our main goal is to enable historians, architects, archaeologists, urban planners and affiliated professionals to reconstruct views of historical monuments from thousands of images floating around the web.
PET Image Reconstruction Using Kernel Method
Wang, Guobao; Qi, Jinyi
2014-01-01
Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results. PMID:25095249
PET image reconstruction using kernel method.
Wang, Guobao; Qi, Jinyi
2015-01-01
Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results. PMID:25095249
Mancosu, Pietro; Danna, Massimo; Bettinardi, Valentino; Aquilina, Mark Anthony; Lobefalo, Francesca; Cozzi, Luca; Fogliata, Antonella; Scorsetti, Marta
2011-01-15
Purpose: Delineating tumor motion by four-dimensional positron emission tomography/computed tomography (4D-PET/CT) is a crucial step for gated radiotherapy (RT). This article quantitatively evaluates semiautomatic algorithms for tumor shift estimation in the lung region due to patient respiration by 4D-PET/CT, in order to support the selection of the best phases for gated RT, by considering the most stable phases of the breathing cycle. Methods: Three mobile spheres and ten selected lesions were included in this study. 4D-PET/CT data were reconstructed and classified into six/ten phases. The semiautomatic algorithms required the generation of single sets of images representative of the full target motion, used as masks for segmenting the phases. For 4D-CT, a pre-established HU range was used, whereas three thresholds (100%, 80%, and 40%) were evaluated for 4D-PET. By using these segmentations, the authors estimated the lesion motion from the shifting centroids, and the phases with the least motion were also deduced including the phases with a curve slope less than 2 mm/{Delta}phase. The proposed algorithms were validated by comparing the results to those generated entirely by manual contouring. Results: In the phantom study, the mean difference between the manual contour and the semiautomatic technique was 0.1{+-}0.1 mm for 4D-CT and 0.2{+-}0.1 mm for the 4D-PET based on 40% threshold. In the patients' series, the mean difference was 0.9{+-}0.6 mm for 4D-CT and 0.8{+-}0.2 mm for the 4D-PET based on 40% threshold. Conclusions: Estimation of lesion motion by the proposed semiautomatic algorithm can be used to evaluate tumor motion due to breathing.
Didierlaurent, David Ribes, Sophie; Caselles, Olivier; Jaudet, Cyril; Dierickx, Lawrence O.; Zerdoud, Slimane; Brillouet, Severine; Weits, Kathleen; Batatia, Hadj; Courbon, Frédéric
2014-11-01
than three bins were necessary for a more accurate measurement of the maximum amplitude of the tumor motion. However, the current 4D-CT technology limits the increase of the number of bins in 4D PET/CT because of missing CT slices. One can reconstruct 4D PET images with more bins but without attenuation/scatter correction.
An innovative detector concept for hybrid 4D-PET/MRI imaging
NASA Astrophysics Data System (ADS)
Cerello, P.; Pennazio, F.; Bisogni, M. G.; Marino, N.; Marzocca, C.; Peroni, C.; Wheadon, R.; Del Guerra, A.
2013-02-01
The importance of a high-quality hybrid imaging, providing morphological and functional information with only one acquisition session, is widely acknowledged by the scientific community. The historical limitations to the quality of PET images are related to the unsatisfactory measurement of the depth of interaction (DOI) in the crystals and of the time of flight (TOF), that cause a parallax error and an unfavorable signal to background condition in the image reconstruction process, respectively. The 4DMPET project is developing a high performance PET block-detector featuring 4D image reconstruction capabilities. The detector module is based on a fast scintillating continuous crystal coupled on both sides to arrays of Silicon PhotoMultipliers (SiPM). The SiPMs collect the scintillation light and provide the trigger signal, the time and the energy released in the crystal at the pixel level. The photon depth of interaction (DOI) is reconstructed by measuring the cluster size asymmetry on the two faces of the crystal, thus obtaining a comparable spatial resolution in the three coordinates and removing the parallax error. The event position along the line of response can be measured with high precision by means of TOF techniques. We discuss the module design concept and the results of the detailed Monte Carlo detector simulation, which inspire the architectural solutions selected for the layout and the front-end The expected resolution for 3D spatial coordinates of the interaction point in the crystal (1 mm) and the TOF (about 110 ps) would provide a substantial improvement of the image quality. 4DMPET aims at building a prototype block detector demonstrating that the proposed layout meets the expected performance and is suitable for designing a detector focused on a specific application.
4D numerical observer for lesion detection in respiratory-gated PET
Lorsakul, Auranuch; Li, Quanzheng; Ouyang, Jinsong; El Fakhri, Georges; Trott, Cathryn M.; Hoog, Christopher; Petibon, Yoann; Laine, Andrew F.
2014-10-15
Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. Methods: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic {sup 18}F-fluorodeoxyglucose activity distributions were simulated using a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using GEANT4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6–31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was
4D numerical observer for lesion detection in respiratory-gated PET
Lorsakul, Auranuch; Li, Quanzheng; Trott, Cathryn M.; Hoog, Christopher; Petibon, Yoann; Ouyang, Jinsong; Laine, Andrew F.; El Fakhri, Georges
2014-01-01
Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. Methods: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic 18F-fluorodeoxyglucose activity distributions were simulated using a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using geant4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6–31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was
NASA Astrophysics Data System (ADS)
Kotasidis, F. A.; Matthews, J. C.; Reader, A. J.; Angelis, G. I.; Zaidi, H.
2014-10-01
Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [15O]H2O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially propagating
Kotasidis, F A; Matthews, J C; Reader, A J; Angelis, G I; Zaidi, H
2014-10-21
Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [(15)O]H2O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially propagating
Validation of a 4D-PET Maximum Intensity Projection for Delineation of an Internal Target Volume
Callahan, Jason; Kron, Tomas; Schneider-Kolsky, Michal; Dunn, Leon; Thompson, Mick; Siva, Shankar; Aarons, Yolanda; Binns, David; Hicks, Rodney J.
2013-07-15
Purpose: The delineation of internal target volumes (ITVs) in radiation therapy of lung tumors is currently performed by use of either free-breathing (FB) {sup 18}F-fluorodeoxyglucose-positron emission tomography-computed tomography (FDG-PET/CT) or 4-dimensional (4D)-CT maximum intensity projection (MIP). In this report we validate the use of 4D-PET-MIP for the delineation of target volumes in both a phantom and in patients. Methods and Materials: A phantom with 3 hollow spheres was prepared surrounded by air then water. The spheres and water background were filled with a mixture of {sup 18}F and radiographic contrast medium. A 4D-PET/CT scan was performed of the phantom while moving in 4 different breathing patterns using a programmable motion device. Nine patients with an FDG-avid lung tumor who underwent FB and 4D-PET/CT and >5 mm of tumor motion were included for analysis. The 3 spheres and patient lesions were contoured by 2 contouring methods (40% of maximum and PET edge) on the FB-PET, FB-CT, 4D-PET, 4D-PET-MIP, and 4D-CT-MIP. The concordance between the different contoured volumes was calculated using a Dice coefficient (DC). The difference in lung tumor volumes between FB-PET and 4D-PET volumes was also measured. Results: The average DC in the phantom using 40% and PET edge, respectively, was lowest for FB-PET/CT (DCAir = 0.72/0.67, DCBackground 0.63/0.62) and highest for 4D-PET/CT-MIP (DCAir = 0.84/0.83, DCBackground = 0.78/0.73). The average DC in the 9 patients using 40% and PET edge, respectively, was also lowest for FB-PET/CT (DC = 0.45/0.44) and highest for 4D-PET/CT-MIP (DC = 0.72/0.73). In the 9 lesions, the target volumes of the FB-PET using 40% and PET edge, respectively, were on average 40% and 45% smaller than the 4D-PET-MIP. Conclusion: A 4D-PET-MIP produces volumes with the highest concordance with 4D-CT-MIP across multiple breathing patterns and lesion sizes in both a phantom and among patients. Freebreathing PET/CT consistently
TU-F-12A-05: Sensitivity of Textural Features to 3D Vs. 4D FDG-PET/CT Imaging in NSCLC Patients
Yang, F; Nyflot, M; Bowen, S; Kinahan, P; Sandison, G
2014-06-15
Purpose: Neighborhood Gray-level difference matrices (NGLDM) based texture parameters extracted from conventional (3D) 18F-FDG PET scans in patients with NSCLC have been previously shown to associate with response to chemoradiation and poorer patient outcome. However, the change in these parameters when utilizing respiratory-correlated (4D) FDG-PET scans has not yet been characterized for NSCLC. The Objectives: of this study was to assess the extent to which NGLDM-based texture parameters on 4D PET images vary with reference to values derived from 3D scans in NSCLC. Methods: Eight patients with newly diagnosed NSCLC treated with concomitant chemoradiotherapy were included in this study. 4D PET scans were reconstructed with OSEM-IR in 5 respiratory phase-binned images and corresponding CT data of each phase were employed for attenuation correction. NGLDM-based texture features, consisting of coarseness, contrast, busyness, complexity and strength, were evaluated for gross tumor volumes defined on 3D/4D PET scans by radiation oncologists. Variation of the obtained texture parameters over the respiratory cycle were examined with respect to values extracted from 3D scans. Results: Differences between texture parameters derived from 4D scans at different respiratory phases and those extracted from 3D scans ranged from −30% to 13% for coarseness, −12% to 40% for contrast, −5% to 50% for busyness, −7% to 38% for complexity, and −43% to 20% for strength. Furthermore, no evident correlations were observed between respiratory phase and 4D scan texture parameters. Conclusion: Results of the current study showed that NGLDM-based texture parameters varied considerably based on choice of 3D PET and 4D PET reconstruction of NSCLC patient images, indicating that standardized image acquisition and analysis protocols need to be established for clinical studies, especially multicenter clinical trials, intending to validate prognostic values of texture features for NSCLC.
4D offline PET-based treatment verification in scanned ion beam therapy: a phantom study
NASA Astrophysics Data System (ADS)
Kurz, Christopher; Bauer, Julia; Unholtz, Daniel; Richter, Daniel; Stützer, Kristin; Bert, Christoph; Parodi, Katia
2015-08-01
At the Heidelberg Ion-Beam Therapy Center, patient irradiation with scanned proton and carbon ion beams is verified by offline positron emission tomography (PET) imaging: the {β+} -activity measured within the patient is compared to a prediction calculated on the basis of the treatment planning data in order to identify potential delivery errors. Currently, this monitoring technique is limited to the treatment of static target structures. However, intra-fractional organ motion imposes considerable additional challenges to scanned ion beam radiotherapy. In this work, the feasibility and potential of time-resolved (4D) offline PET-based treatment verification with a commercial full-ring PET/CT (x-ray computed tomography) device are investigated for the first time, based on an experimental campaign with moving phantoms. Motion was monitored during the gated beam delivery as well as the subsequent PET acquisition and was taken into account in the corresponding 4D Monte-Carlo simulations and data evaluation. Under the given experimental conditions, millimeter agreement between the prediction and measurement was found. Dosimetric consequences due to the phantom motion could be reliably identified. The agreement between PET measurement and prediction in the presence of motion was found to be similar as in static reference measurements, thus demonstrating the potential of 4D PET-based treatment verification for future clinical applications.
Clinical Utility of 4D FDG-PET/CT Scans in Radiation Treatment Planning
Aristophanous, Michalis; Sher, David J.; Allen, Aaron M.; Larson, Elysia; Chen, Aileen B.
2012-01-01
Purpose: The potential role of four-dimensional (4D) positron emission tomography (PET)/computed tomography (CT) in radiation treatment planning, relative to standard three-dimensional (3D) PET/CT, was examined. Methods and Materials: Ten patients with non-small-cell lung cancer had sequential 3D and 4D [{sup 18}F]fluorodeoxyglucose PET/CT scans in the treatment position prior to radiation therapy. The gross tumor volume and involved lymph nodes were contoured on the PET scan by use of three different techniques: manual contouring by an experienced radiation oncologist using a predetermined protocol; a technique with a constant threshold of standardized uptake value (SUV) greater than 2.5; and an automatic segmentation technique. For each technique, the tumor volume was defined on the 3D scan (VOL3D) and on the 4D scan (VOL4D) by combining the volume defined on each of the five breathing phases individually. The range of tumor motion and the location of each lesion were also recorded, and their influence on the differences observed between VOL3D and VOL4D was investigated. Results: We identified and analyzed 22 distinct lesions, including 9 primary tumors and 13 mediastinal lymph nodes. Mean VOL4D was larger than mean VOL3D with all three techniques, and the difference was statistically significant (p < 0.01). The range of tumor motion and the location of the tumor affected the magnitude of the difference. For one case, all three tumor definition techniques identified volume of moderate uptake of approximately 1 mL in the hilar region on the 4D scan (SUV maximum, 3.3) but not on the 3D scan (SUV maximum, 2.3). Conclusions: In comparison to 3D PET, 4D PET may better define the full physiologic extent of moving tumors and improve radiation treatment planning for lung tumors. In addition, reduction of blurring from free-breathing images may reveal additional information regarding regional disease.
Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT
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
Nyflot, Matthew J.; Lee, Tzu-Cheng; Alessio, Adam M.; Kinahan, Paul E.; Wollenweber, Scott D.; Stearns, Charles W.; Bowen, Stephen R.
2015-01-15
Purpose: Respiratory-correlated positron emission tomography (PET/CT) 4D PET/CT is used to mitigate errors from respiratory motion; however, the optimal CT attenuation correction (CTAC) method for 4D PET/CT is unknown. The authors performed a phantom study to evaluate the quantitative performance of CTAC methods for 4D PET/CT in the ground truth setting. Methods: A programmable respiratory motion phantom with a custom movable insert designed to emulate a lung lesion and lung tissue was used for this study. The insert was driven by one of five waveforms: two sinusoidal waveforms or three patient-specific respiratory waveforms. 3DPET and 4DPET images of the phantom under motion were acquired and reconstructed with six CTAC methods: helical breath-hold (3DHEL), helical free-breathing (3DMOT), 4D phase-averaged (4DAVG), 4D maximum intensity projection (4DMIP), 4D phase-matched (4DMATCH), and 4D end-exhale (4DEXH) CTAC. Recovery of SUV{sub max}, SUV{sub mean}, SUV{sub peak}, and segmented tumor volume was evaluated as RC{sub max}, RC{sub mean}, RC{sub peak}, and RC{sub vol}, representing percent difference relative to the static ground truth case. Paired Wilcoxon tests and Kruskal–Wallis ANOVA were used to test for significant differences. Results: For 4DPET imaging, the maximum intensity projection CTAC produced significantly more accurate recovery coefficients than all other CTAC methods (p < 0.0001 over all metrics). Over all motion waveforms, ratios of 4DMIP CTAC recovery were 0.2 ± 5.4, −1.8 ± 6.5, −3.2 ± 5.0, and 3.0 ± 5.9 for RC{sub max}, RC{sub peak}, RC{sub mean}, and RC{sub vol}. In comparison, recovery coefficients for phase-matched CTAC were −8.4 ± 5.3, −10.5 ± 6.2, −7.6 ± 5.0, and −13.0 ± 7.7 for RC{sub max}, RC{sub peak}, RC{sub mean}, and RC{sub vol}. When testing differences between phases over all CTAC methods and waveforms, end-exhale phases were significantly more accurate (p = 0.005). However, these differences were driven by
Reilhac, Anthonin; Charil, Arnaud; Wimberley, Catriona; Angelis, Georgios; Hamze, Hasar; Callaghan, Paul; Garcia, Marie-Paule; Boisson, Frederic; Ryder, Will; Meikle, Steven R; Gregoire, Marie-Claude
2015-09-01
Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. PMID:26080302
Region of interest motion compensation for PET image reconstruction.
Qiao, Feng; Pan, Tinsu; Clark, John W; Mawlawi, Osama R
2007-05-21
A motion-incorporated reconstruction (MIR) method for gated PET imaging has recently been developed by several authors to correct for respiratory motion artifacts in PET imaging. This method however relies on a motion map derived from images (4D PET or 4D CT) of the entire field of view (FOV). In this study we present a region of interest (ROI)-based extension to this method, whereby only the motion map of a user-defined ROI is required and motion incorporation during image reconstruction is solely performed within the ROI. A phantom study and an NCAT computer simulation study were performed to test the feasibility of this method. The phantom study showed that the ROI-based MIR produced results that are within 1.26% of those obtained by the full image-based MIR approach when using the same accurate motion information. The NCAT phantom study on the other hand, further verified that motion of features of interest in an image can be estimated more efficiently and potentially more accurately using the ROI-based approach. A reduction of motion estimation time from 450 s to 30 and 73 s was achieved for two different ROIs respectively. In addition, the ROI-based approach showed a reduction in registration error of 43% for one ROI, which effectively reduced quantification bias by 44% and 32% using mean and maximum voxel values, respectively. PMID:17473344
Schwaab, Julia; Kurz, Christopher; Sarti, Cristina; Bongers, André; Schoenahl, Frédéric; Bert, Christoph; Debus, Jürgen; Parodi, Katia; Jenne, Jürgen Walter
2015-01-01
Target motion, particularly in the abdomen, due to respiration or patient movement is still a challenge in many diagnostic and therapeutic processes. Hence, methods to detect and compensate this motion are required. Diagnostic ultrasound (US) represents a non-invasive and dose-free alternative to fluoroscopy, providing more information about internal target motion than respiration belt or optical tracking. The goal of this project is to develop an US-based motion tracking for real-time motion correction in radiation therapy and diagnostic imaging, notably in 4D positron emission tomography (PET). In this work, a workflow is established to enable the transformation of US tracking data to the coordinates of the treatment delivery or imaging system – even if the US probe is moving due to respiration. It is shown that the US tracking signal is equally adequate for 4D PET image reconstruction as the clinically used respiration belt and provides additional opportunities in this concern. Furthermore, it is demonstrated that the US probe being within the PET field of view generally has no relevant influence on the image quality. The accuracy and precision of all the steps in the calibration workflow for US tracking-based 4D PET imaging are found to be in an acceptable range for clinical implementation. Eventually, we show in vitro that an US-based motion tracking in absolute room coordinates with a moving US transducer is feasible. PMID:26649277
Evaluation of a 4D cone-beam CT reconstruction approach using a simulation framework.
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
4-D reconstruction of fluorescence molecular tomography using re-assembled measurement data
Liu, Xin; He, Xiaowe; Yan, Zhuangzhi; Lu, Hongbing
2015-01-01
Challenges remain in the reconstruction of dynamic (4-D) fluorescence molecular tomography (FMT). In our previous work, we implemented a fully 4-D FMT reconstruction approach using Karhunen-Loève (KL) transformation. However, in the reconstruction processes, the input data were scan-by-scan fluorescence projections. As a result, the reconstruction interval is limited by the data acquisition time for scanning one circle projections, leading to a long time (typically >1 min). In this paper, we propose a new method to reduce the reconstruction interval of dynamic FMT imaging, which is achieved by re-assembling the acquired fluorescence projection sequence. Further, to eliminate the temporal correlations within measurement data, the re-assembled projection sequence is reconstructed by the KL-based method. The numerical simulation and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that after re-assembling measurement data, the reconstruction interval can be greatly reduced (~2.5 sec/frame). In addition, the proposed re-assembling method is helpful for improving reconstruction quality of the KL-based method. PMID:26114022
Uniform distribution of projection data for improved reconstruction quality of 4D EPR imaging
Ahmad, Rizwan; Vikram, Deepti S.; Clymer, Bradley; Potter, Lee C.; Deng, Yuanmu; Srinivasan, Parthasarathy; Zweier, Jay L.; Kuppusamy, Periannan
2008-01-01
In continuous wave (CW) electron paramagnetic resonance imaging (EPRI), high quality of reconstruction in a limited acquisition time is a high priority. It has been shown for the case of 3D EPRI, that a uniform distribution of the projection data generally enhances reconstruction quality. In this work, we have suggested two data acquisition techniques for which the gradient orientations are more evenly distributed over the 4D acquisition space as compared to the existing methods. The first sampling technique is based on equal solid angle partitioning of 4D space, while the second technique is based on Fekete points estimation in 4D to generate a more uniform distribution of data. After acquisition, filtered backprojection (FBP) is applied to carryout the reconstruction in a single stage. The single-stage reconstruction improves the spatial resolution by eliminating the necessity of data interpolation in multi-stage reconstructions. For the proposed data distributions, the simulations and experimental results indicate a higher fidelity to the true object configuration. Using the uniform distribution, we expect about 50% reduction in the acquisition time over the traditional method of equal linear angle acquisition. PMID:17562375
A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaginga)
Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cervino, Laura; Jiang, Steve B.; Jia, Xun
2014-01-01
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
A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging
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
4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling.
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. PMID:26758496
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.
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.
VMAT QA: Measurement-guided 4D dose reconstruction on a patient
Nelms, Benjamin E.; Opp, Daniel; Robinson, Joshua; Wolf, Theresa K.; Zhang, Geoffrey; Moros, Eduardo; Feygelman, Vladimir
2012-07-15
Purpose: To develop and validate a volume-modulated arc therapy (VMAT) quality assurance (QA) tool that takes as input a time-resolved, low-density ({approx}10 mm) cylindrical surface dose map from a commercial helical diode array, and outputs a high density, volumetric, time-resolved dose matrix on an arbitrary patient dataset. This first validation study is limited to a homogeneous 'patient.'Methods: A VMAT treatment is delivered to a diode array phantom (ARCCHECK, Sun Nuclear Corp., Melbourne, FL). 3DVH software (Sun Nuclear) derives the high-density volumetric dose using measurement-guided dose reconstruction (MGDR). MGDR cylindrical phantom results are then used to perturb the three-dimensional (3D) treatment planning dose on the patient dataset, producing a semiempirical volumetric dose grid. Four-dimensional (4D) dose reconstruction on the patient is also possible by morphing individual sub-beam doses instead of the composite. For conventional (3D) dose comparison two methods were developed, using the four plans (Multi-Target, C-shape, Mock Prostate, and Head and Neck), including their structures and objectives, from the AAPM TG-119 report. First, 3DVH and treatment planning system (TPS) cumulative point doses were compared to ion chamber in a cube water-equivalent phantom ('patient'). The shape of the phantom is different from the ARCCHECK and furthermore the targets were placed asymmetrically. Second, coronal and sagittal absolute film dose distributions in the cube were compared with 3DVH and TPS. For time-resolved (4D) comparisons, three tests were performed. First, volumetric dose differences were calculated between the 3D MGDR and cumulative time-resolved patient (4D MGDR) dose at the end of delivery, where they ideally should be identical. Second, time-resolved (10 Hz sampling rate) ion chamber doses were compared to cumulative point dose vs time curves from 4D MGDR. Finally, accelerator output was varied to assess the linearity of the 4D MGDR with
Registration based super-resolution reconstruction for lung 4D-CT.
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
Advanced image reconstruction strategies for 4D prostate DCE-MRI: steps toward clinical practicality
NASA Astrophysics Data System (ADS)
Stinson, Eric G.; Borisch, Eric A.; Froemming, Adam T.; Kawashima, Akira; Young, Phillip M.; Warndahl, Brent A.; Grimm, Roger C.; Manduca, Armando; Riederer, Stephen J.; Trzasko, Joshua D.
2015-09-01
Dynamic contrast-enhanced (DCE) MRI is an important tool for the detection and characterization of primary and recurring prostate cancer. Advanced reconstruction strategies (e.g., sparse or low-rank regression) provide improved depiction of contrast dynamics and pharmacokinetic parameters; however, the high computation cost of reconstructing 4D (3D+time, 50+ frames) datasets typically inhibits their routine clinical use. Here, a novel alternating direction method-of-multipliers (ADMM) optimization strategy is described that enables these methods to be executed in ∠5 minutes, and thus within the standard clinical workflow. After overviewing the mechanics of this approach, high-performance implementation strategies will be discussed and demonstrated through clinical cases.
Towards 4d Virtual City Reconstruction from LIDAR Point Cloud Sequences
NASA Astrophysics Data System (ADS)
Józsa, O.; Börcs, A.; Benedek, C.
2013-05-01
In this paper we propose a joint approach on virtual city reconstruction and dynamic scene analysis based on point cloud sequences of a single car-mounted Rotating Multi-Beam (RMB) Lidar sensor. The aim of the addressed work is to create 4D spatio-temporal models of large dynamic urban scenes containing various moving and static objects. Standalone RMB Lidar devices have been frequently applied in robot navigation tasks and proved to be efficient in moving object detection and recognition. However, they have not been widely exploited yet for geometric approximation of ground surfaces and building facades due to the sparseness and inhomogeneous density of the individual point cloud scans. In our approach we propose an automatic registration method of the consecutive scans without any additional sensor information such as IMU, and introduce a process for simultaneously extracting reconstructed surfaces, motion information and objects from the registered dense point cloud completed with point time stamp information.
NASA Astrophysics Data System (ADS)
Rodrigues, Pedro L.; Rodrigues, Nuno F.; Fonseca, Jaime C.; Vilaça, João. L.
2015-03-01
An accurate percutaneous puncture is essential for disintegration and removal of renal stones. Although this procedure has proven to be safe, some organs surrounding the renal target might be accidentally perforated. This work describes a new intraoperative framework where tracked surgical tools are superimposed within 4D ultrasound imaging for security assessment of the percutaneous puncture trajectory (PPT). A PPT is first generated from the skin puncture site towards an anatomical target, using the information retrieved by electromagnetic motion tracking sensors coupled to surgical tools. Then, 2D ultrasound images acquired with a tracked probe are used to reconstruct a 4D ultrasound around the PPT under GPU processing. Volume hole-filling was performed in different processing time intervals by a tri-linear interpolation method. At spaced time intervals, the volume of the anatomical structures was segmented to ascertain if any vital structure is in between PPT and might compromise the surgical success. To enhance the volume visualization of the reconstructed structures, different render transfer functions were used. Results: Real-time US volume reconstruction and rendering with more than 25 frames/s was only possible when rendering only three orthogonal slice views. When using the whole reconstructed volume one achieved 8-15 frames/s. 3 frames/s were reached when one introduce the segmentation and detection if some structure intersected the PPT. The proposed framework creates a virtual and intuitive platform that can be used to identify and validate a PPT to safely and accurately perform the puncture in percutaneous nephrolithotomy.
Fayad, Hadi; Odille, Freddy; Schmidt, Holger; Würslin, Christian; Küstner, Thomas; Feblinger, Jacques; Visvikis, Dimitris
2015-03-21
Respiratory motion is a source of artifacts in multimodality imaging such as PET/MR. Solutions include retrospective or prospective gating. They have however found limited use in clinical practice, since their increased overall acquisition duration to maintain overall image quality. More elaborate methods consist of using 4D MR datasets to extract spatial deformations in order to correct for the respiratory motion in PET. The main drawbacks of such approaches is the relatively long acquisition times associated with 4D MR imaging which is often incompatible with clinical PET/MR protocols. The objective of this work was to overcome these limitations by exploiting a generalized reconstruction by inversion of coupled systems (GRICS) approach. The methodology is based on a joint estimation of motion during the MR image reconstruction process, providing internal structure motion and associated deformation matrices for retrospective use in PET respiratory motion correction. This method was first validated on four MR volunteers and two PET/MR patient datasets by comparing GRICS generated MR images to 4D MR series obtained by retrospective gating. In a second step 4D PET datasets corresponding to acquired 4D MR images were simulated using the GATE Monte Carlo simulation platform. GRICS generated deformation matrices were subsequently used to correct respiratory motion in comparison to the 4D MR image based deformations both for the simulated and the two 4D PET/MR patient datasets. Results confirm that GRICS synchronized MR images correlate well with the acquired 4D MR series. Similarly, the use of GRICS for respiratory motion correction allows an equivalent percentage improvement on lesion contrast, position and size, considering the PET simulated tumors as well as PET real tumors. This work demonstrates the potential interest of using GRICS for PET respiratory motion correction in combined PET/MR using shorter duration acquisitions without the need for 4D MRI and
NASA Astrophysics Data System (ADS)
Fayad, Hadi; Odille, Freddy; Schmidt, Holger; Würslin, Christian; Küstner, Thomas; Feblinger, Jacques; Visvikis, Dimitris
2015-03-01
Respiratory motion is a source of artifacts in multimodality imaging such as PET/MR. Solutions include retrospective or prospective gating. They have however found limited use in clinical practice, since their increased overall acquisition duration to maintain overall image quality. More elaborate methods consist of using 4D MR datasets to extract spatial deformations in order to correct for the respiratory motion in PET. The main drawbacks of such approaches is the relatively long acquisition times associated with 4D MR imaging which is often incompatible with clinical PET/MR protocols. The objective of this work was to overcome these limitations by exploiting a generalized reconstruction by inversion of coupled systems (GRICS) approach. The methodology is based on a joint estimation of motion during the MR image reconstruction process, providing internal structure motion and associated deformation matrices for retrospective use in PET respiratory motion correction. This method was first validated on four MR volunteers and two PET/MR patient datasets by comparing GRICS generated MR images to 4D MR series obtained by retrospective gating. In a second step 4D PET datasets corresponding to acquired 4D MR images were simulated using the GATE Monte Carlo simulation platform. GRICS generated deformation matrices were subsequently used to correct respiratory motion in comparison to the 4D MR image based deformations both for the simulated and the two 4D PET/MR patient datasets. Results confirm that GRICS synchronized MR images correlate well with the acquired 4D MR series. Similarly, the use of GRICS for respiratory motion correction allows an equivalent percentage improvement on lesion contrast, position and size, considering the PET simulated tumors as well as PET real tumors. This work demonstrates the potential interest of using GRICS for PET respiratory motion correction in combined PET/MR using shorter duration acquisitions without the need for 4D MRI and
Kipritidis, John Keall, Paul J.; Siva, Shankar; Hofman, Michael S.; Callahan, Jason; Hicks, Rodney J.
2014-01-15
Purpose: CT ventilation imaging is a novel functional lung imaging modality based on deformable image registration. The authors present the first validation study of CT ventilation using positron emission tomography with{sup 68}Ga-labeled nanoparticles (PET-Galligas). The authors quantify this agreement for different CT ventilation metrics and PET reconstruction parameters. Methods: PET-Galligas ventilation scans were acquired for 12 lung cancer patients using a four-dimensional (4D) PET/CT scanner. CT ventilation images were then produced by applying B-spline deformable image registration between the respiratory correlated phases of the 4D-CT. The authors test four ventilation metrics, two existing and two modified. The two existing metrics model mechanical ventilation (alveolar air-flow) based on Hounsfield unit (HU) change (V{sub HU}) or Jacobian determinant of deformation (V{sub Jac}). The two modified metrics incorporate a voxel-wise tissue-density scaling (ρV{sub HU} and ρV{sub Jac}) and were hypothesized to better model the physiological ventilation. In order to assess the impact of PET image quality, comparisons were performed using both standard and respiratory-gated PET images with the former exhibiting better signal. Different median filtering kernels (σ{sub m} = 0 or 3 mm) were also applied to all images. As in previous studies, similarity metrics included the Spearman correlation coefficient r within the segmented lung volumes, and Dice coefficient d{sub 20} for the (0 − 20)th functional percentile volumes. Results: The best agreement between CT and PET ventilation was obtained comparing standard PET images to the density-scaled HU metric (ρV{sub HU}) with σ{sub m} = 3 mm. This leads to correlation values in the ranges 0.22 ⩽ r ⩽ 0.76 and 0.38 ⩽ d{sub 20} ⩽ 0.68, with r{sup ¯}=0.42±0.16 and d{sup ¯}{sub 20}=0.52±0.09 averaged over the 12 patients. Compared to Jacobian-based metrics, HU-based metrics lead to statistically significant
Ehrhardt, Jan; Werner, Rene; Saering, Dennis; Frenzel, Thorsten; Lu Wei; Low, Daniel; Handels, Heinz
2007-02-15
Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. 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. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patient's respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.
Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction.
Kazantsev, Daniil; Guo, Enyu; Kaestner, Anders; Lionheart, William R B; Bent, Julian; Withers, Philip J; Lee, Peter D
2016-01-01
X-ray imaging applications in medical and material sciences are frequently limited by the number of tomographic projections collected. The inversion of the limited projection data is an ill-posed problem and needs regularization. Traditional spatial regularization is not well adapted to the dynamic nature of time-lapse tomography since it discards the redundancy of the temporal information. In this paper, we propose a novel iterative reconstruction algorithm with a nonlocal regularization term to account for time-evolving datasets. The aim of the proposed nonlocal penalty is to collect the maximum relevant information in the spatial and temporal domains. With the proposed sparsity seeking approach in the temporal space, the computational complexity of the classical nonlocal regularizer is substantially reduced (at least by one order of magnitude). The presented reconstruction method can be directly applied to various big data 4D (x, y, z+time) tomographic experiments in many fields. We apply the proposed technique to modelled data and to real dynamic X-ray microtomography (XMT) data of high resolution. Compared to the classical spatio-temporal nonlocal regularization approach, the proposed method delivers reconstructed images of improved resolution and higher contrast while remaining significantly less computationally demanding. PMID:27002902
Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction
Kazantsev, Daniil; Guo, Enyu; Kaestner, Anders; Lionheart, William R. B.; Bent, Julian; Withers, Philip J.; Lee, Peter D.
2016-01-01
X-ray imaging applications in medical and material sciences are frequently limited by the number of tomographic projections collected. The inversion of the limited projection data is an ill-posed problem and needs regularization. Traditional spatial regularization is not well adapted to the dynamic nature of time-lapse tomography since it discards the redundancy of the temporal information. In this paper, we propose a novel iterative reconstruction algorithm with a nonlocal regularization term to account for time-evolving datasets. The aim of the proposed nonlocal penalty is to collect the maximum relevant information in the spatial and temporal domains. With the proposed sparsity seeking approach in the temporal space, the computational complexity of the classical nonlocal regularizer is substantially reduced (at least by one order of magnitude). The presented reconstruction method can be directly applied to various big data 4D (x, y, z+time) tomographic experiments in many fields. We apply the proposed technique to modelled data and to real dynamic X-ray microtomography (XMT) data of high resolution. Compared to the classical spatio-temporal nonlocal regularization approach, the proposed method delivers reconstructed images of improved resolution and higher contrast while remaining significantly less computationally demanding. PMID:27002902
Reconstruction of a 4D Particle Distribution Using UnderdeterminedPhase-Space Data
Rostamizadeh, Afshin
2005-08-10
A well defined 4D distribution that describes the transverse spatial coordinates (x,y) and momenta (x',y') of the particles that make up an intense ion beam is of great value to theorists in the field of particle beam physics. If such a distribution truthfully captures the characteristic of the actual beam, it can be used to initialize an extensive simulation, and can yield insight into the processes that affect beam quality. Creating a proper representative distribution of particles is a challenge because the problem is, in general, quite underdetermined. Data is collected through a pair of ''optical slit'' diagnostics which provide two 3D distributions, f(x,y,x') and f(x,y,y'); the challenge is to coalesce these into a full 4D distribution f(x,y,x',y'). Further difficulties are introduced because the data is collected at different longitudinal planes and must be ''remapped'' to a common plane, taking into account the convergence or divergence of the beam as well as any off-centering. This challenge was met by developing a suitable algorithm and implementing it as a ''plug-in'' for the popular scientific image analysis program ImageJ, written entirely in the Java programming language. The algorithm accomplishes the desired remapping and synthesizes a 4D particle distribution, using Monte-Carlo techniques. Preliminary results show that this reconstructed distribution is consistent with actual data that was gathered from the same experiment using a different diagnostic. Also, ''forward'' particle-in-cell (PIC) simulations, that use the reconstructed distribution, match actual data gathered downstream in the experiment. Both these results give us some indication that the reconstruction is being done correctly. In addition to the multi-particle synthesis, the plug-in allows for the easy loading of digital data and the output of various plots that are useful to both experimenters and theorists. It also provides a framework by which its applicability can be extended to
SU-D-207-04: GPU-Based 4D Cone-Beam CT Reconstruction Using Adaptive Meshing Method
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.
Fast analytic simulation toolkit for generation of 4D PET-MR data from real dynamic MR acquisitions
NASA Astrophysics Data System (ADS)
Tsoumpas, C.; Buerger, C.; Mollet, P.; Marsden, P. K.
2011-09-01
This work introduces and evaluates a fast analytic simulation toolkit (FAST) for simulating dynamic PET-MR data from real MR acquisitions. Realistic radiotracer values are assigned to segmented MR images. PET data are generated using analytic forward-projections (including attenuation and Poisson statistics) with the reconstruction software STIR, which is also used to produce the PET images that are spatially and temporally correlated with the real MR images. The simulation is compared with the GATE Monte Carlo package, which has more accurate physical modelling but it is 150 times slower compared to FAST for ten respiratory positions and 7000× slower, when repeating the simulation. The region of interest for mean values and coefficients of variation obtained with FAST and GATE, from 65 million and 104 million coincidences, respectively, were compared. Agreement between the two different simulation methods is good. In particular, the percentage differences of the mean values are: 10% for liver, and 19% for the myocardium and a warm lesion. The utility of FAST is demonstrated with the simulation of multiple volunteers with different breathing patterns. The package will be used for studying the performance of reconstruction, motion correction and attenuation correction algorithms for dynamic simultaneous PET-MR data.
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.
Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior
NASA Astrophysics Data System (ADS)
Ritschl, Ludwig; Sawall, Stefan; Knaup, Michael; Hess, Andreas; Kachelrieß, Marc
2012-03-01
Temporal-correlated image reconstruction, also known as 4D CT image reconstruction, is a big challenge in computed tomography. The reasons for incorporating the temporal domain into the reconstruction are motions of the scanned object, which would otherwise lead to motion artifacts. The standard method for 4D CT image reconstruction is extracting single motion phases and reconstructing them separately. These reconstructions can suffer from undersampling artifacts due to the low number of used projections in each phase. There are different iterative methods which try to incorporate some a priori knowledge to compensate for these artifacts. In this paper we want to follow this strategy. The cost function we use is a higher dimensional cost function which accounts for the sparseness of the measured signal in the spatial and temporal directions. This leads to the definition of a higher dimensional total variation. The method is validated using in vivo cardiac micro-CT mouse data. Additionally, we compare the results to phase-correlated reconstructions using the FDK algorithm and a total variation constrained reconstruction, where the total variation term is only defined in the spatial domain. The reconstructed datasets show strong improvements in terms of artifact reduction and low-contrast resolution compared to other methods. Thereby the temporal resolution of the reconstructed signal is not affected.
Sparsity-constrained PET image reconstruction with learned dictionaries.
Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie
2016-09-01
PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging. PMID:27494441
Sparsity-constrained PET image reconstruction with learned dictionaries
NASA Astrophysics Data System (ADS)
Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie
2016-09-01
PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.
NASA Astrophysics Data System (ADS)
El Naqa, Issam M.; Low, Daniel A.; Christensen, Gary E.; Parikh, Parag J.; Song, Joo Hyun; Nystrom, Michelle M.; Lu, Wei; Deasy, Joseph O.; Hubenschmidt, James P.; Wahab, Sasha H.; Mutic, Sasa; Singh, Anurag K.; Bradley, Jeffrey D.
2004-04-01
We are developing 4D-CT to provide breathing motion information (trajectories) for radiation therapy treatment planning of lung cancer. Potential applications include optimization of intensity-modulated beams in the presence of breathing motion and intra-fraction target volume margin determination for conformal therapy. The images are acquired using a multi-slice CT scanner while the patient undergoes simultaneous quantitative spirometry. At each couch position, the CT scanner is operated in ciné mode and acquires up to 15 scans of 12 slices each. Each CT scan is associated with the measured tidal volume for retrospective reconstruction of 3D CT scans at arbitrary tidal volumes. The specific tasks of this project involves the development of automated registration of internal organ motion (trajectories) during breathing. A modified least-squares based optical flow algorithm tracks specific features of interest by modifying the eigenvalues of gradient matrix (gradient structural tensor). Good correlations between the measured motion and spirometry-based tidal volume are observed and evidence of internal hysteresis is also detected.
Influence of Iterative Reconstruction Algorithms on PET Image Resolution
NASA Astrophysics Data System (ADS)
Karpetas, G. E.; Michail, C. M.; Fountos, G. P.; Valais, I. G.; Nikolopoulos, D.; Kandarakis, I. S.; Panayiotakis, G. S.
2015-09-01
The aim of the present study was to assess image quality of PET scanners through a thin layer chromatography (TLC) plane source. The source was simulated using a previously validated Monte Carlo model. The model was developed by using the GATE MC package and reconstructed images obtained with the STIR software for tomographic image reconstruction. The simulated PET scanner was the GE DiscoveryST. A plane source consisted of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrates, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the modulation transfer function (MTF). MTF curves were estimated from transverse reconstructed images of the plane source. Images were reconstructed by the maximum likelihood estimation (MLE)-OSMAPOSL, the ordered subsets separable paraboloidal surrogate (OSSPS), the median root prior (MRP) and OSMAPOSL with quadratic prior, algorithms. OSMAPOSL reconstruction was assessed by using fixed subsets and various iterations, as well as by using various beta (hyper) parameter values. MTF values were found to increase with increasing iterations. MTF also improves by using lower beta values. The simulated PET evaluation method, based on the TLC plane source, can be useful in the resolution assessment of PET scanners.
Lim, Chi Wan; Su, Yi; Yeo, Si Yong; Ng, Gillian Maria; Nguyen, Vinh Tan; Zhong, Liang; Tan, Ru San; Poh, Kian Keong; Chai, Ping
2014-01-01
We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial–temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities. PMID:24743555
NASA Astrophysics Data System (ADS)
Liu, Jiulong; Zhang, Xue; Zhang, Xiaoqun; Zhao, Hongkai; Gao, Yu; Thomas, David; Low, Daniel A.; Gao, Hao
2015-11-01
4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.
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.
Incorporating anatomical side information into PET reconstruction using nonlocal regularization.
Nguyen, Van-Giang; Lee, Soo-Jin
2013-10-01
With the introduction of combined positron emission tomography (PET)/computed tomography (CT) or PET/magnetic resonance imaging (MRI) scanners, there is an increasing emphasis on reconstructing PET images with the aid of the anatomical side information obtained from X-ray CT or MRI scanners. In this paper, we propose a new approach to incorporating prior anatomical information into PET reconstruction using the nonlocal regularization method. The nonlocal regularizer developed for this application is designed to selectively consider the anatomical information only when it is reliable. As our proposed nonlocal regularization method does not directly use anatomical edges or boundaries which are often used in conventional methods, it is not only free from additional processes to extract anatomical boundaries or segmented regions, but also more robust to the signal mismatch problem that is caused by the indirect relationship between the PET image and the anatomical image. We perform simulations with digital phantoms. According to our experimental results, compared to the conventional method based on the traditional local regularization method, our nonlocal regularization method performs well even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image. PMID:23744678
Bayesian PET image reconstruction incorporating anato-functional joint entropy
NASA Astrophysics Data System (ADS)
Tang, Jing; Rahmim, Arman
2009-12-01
We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff. Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.
An analytic reconstruction method for PET based on cubic splines
NASA Astrophysics Data System (ADS)
Kastis, George A.; Kyriakopoulou, Dimitra; Fokas, Athanasios S.
2014-03-01
PET imaging is an important nuclear medicine modality that measures in vivo distribution of imaging agents labeled with positron-emitting radionuclides. Image reconstruction is an essential component in tomographic medical imaging. In this study, we present the mathematical formulation and an improved numerical implementation of an analytic, 2D, reconstruction method called SRT, Spline Reconstruction Technique. This technique is based on the numerical evaluation of the Hilbert transform of the sinogram via an approximation in terms of 'custom made' cubic splines. It also imposes sinogram thresholding which restricts reconstruction only within object pixels. Furthermore, by utilizing certain symmetries it achieves a reconstruction time similar to that of FBP. We have implemented SRT in the software library called STIR and have evaluated this method using simulated PET data. We present reconstructed images from several phantoms. Sinograms have been generated at various Poison noise levels and 20 realizations of noise have been created at each level. In addition to visual comparisons of the reconstructed images, the contrast has been determined as a function of noise level. Further analysis includes the creation of line profiles when necessary, to determine resolution. Numerical simulations suggest that the SRT algorithm produces fast and accurate reconstructions at realistic noise levels. The contrast is over 95% in all phantoms examined and is independent of noise level.
FIRST: Fast Iterative Reconstruction Software for (PET) tomography
NASA Astrophysics Data System (ADS)
Herraiz, J. L.; España, S.; Vaquero, J. J.; Desco, M.; Udías, J. M.
2006-09-01
Small animal PET scanners require high spatial resolution and good sensitivity. To reconstruct high-resolution images in 3D-PET, iterative methods, such as OSEM, are superior to analytical reconstruction algorithms, although their high computational cost is still a serious drawback. The higher performance of modern computers could make iterative image reconstruction fast enough to be viable, provided we are able to deal with the large number of probability coefficients for the system response matrix in high-resolution PET scanners, which is a difficult task that prevents the algorithms from reaching peak computing performance. Considering all possible axial and in-plane symmetries, as well as certain quasi-symmetries, we have been able to reduce the memory requirements to store the system response matrix (SRM) well below 1 GB, which allows us to keep the whole response matrix of the system inside RAM of ordinary industry-standard computers, so that the reconstruction algorithm can achieve near peak performance. The elements of the SRM are stored as cubic spline profiles and matched to voxel size during reconstruction. In this way, the advantages of 'on-the-fly' calculation and of fully stored SRM are combined. The on-the-fly part of the calculation (matching the profile functions to voxel size) of the SRM accounts for 10-30% of the reconstruction time, depending on the number of voxels chosen. We tested our approach with real data from a commercial small animal PET scanner. The results (image quality and reconstruction time) show that the proposed technique is a feasible solution.
Iterative reconstruction for pet scanners with continuous scintillators.
Iriarte, Ana; Caffarena, Gabriel; Lopez-Fernandez, Mariano; Garcia-Carmona, Rodrigo; Otero, Abraham; Sorzano, Carlos O S; Marabini, Roberto
2015-08-01
Several technical developments have led to a comeback of the continuous scintillators in positron emission tomography (PET). Important differences exist between the resurgent continuous scintillators and the prevailing pixelated devices, which can translate into certain advantages of the former over the latter. However, if the peculiarities of the continuous scintillators are not considered in the iterative reconstruction in which the measured data is converted to images, these advantages will not be fully exploited. In this paper, we review which those peculiarities are and how they have been considered in the literature of PET reconstruction. In light of this review, we propose a new method to compute one of the key elements of the iterative schemes, the system matrix. Specifically, we substitute the traditional Gaussian approach to the so-called uncertainty term by a more general Monte Carlo estimation, and account for the effect of the optical photons, which cannot be neglected in continuous-scintillators devices. Finally, we gather in a single scheme all the elements of the iterative reconstruction that have been individually reformulated, in this or previous works, for continuous scintillators, providing the first reconstruction framework fully adapted to this type of detectors. The preliminary images obtained for a commercially available PET scanner show the benefits of adjusting the reconstruction to the nature of the scintillators. PMID:26736742
Zhang, Yu E-mail: qianjinfeng08@gmail.com; Wu, Xiuxiu; Yang, Wei; Feng, Qianjin E-mail: qianjinfeng08@gmail.com; Chen, Wufan
2014-11-01
Purpose: The use of 4D computed tomography (4D-CT) of the lung is important in lung cancer radiotherapy for tumor localization and treatment planning. Sometimes, dense sampling is not acquired along the superior–inferior direction. This disadvantage results in an interslice thickness that is much greater than in-plane voxel resolutions. Isotropic resolution is necessary for multiplanar display, but the commonly used interpolation operation blurs images. This paper presents a super-resolution (SR) reconstruction method to enhance 4D-CT resolution. Methods: The authors assume that the low-resolution images of different phases at the same position can be regarded as input “frames” to reconstruct high-resolution images. The SR technique is used to recover high-resolution images. Specifically, the Demons deformable registration algorithm is used to estimate the motion field between different “frames.” Then, the projection onto convex sets approach is implemented to reconstruct high-resolution lung images. Results: The performance of the SR algorithm is evaluated using both simulated and real datasets. Their method can generate clearer lung images and enhance image structure compared with cubic spline interpolation and back projection (BP) method. Quantitative analysis shows that the proposed algorithm decreases the root mean square error by 40.8% relative to cubic spline interpolation and 10.2% versus BP. Conclusions: A new algorithm has been developed to improve the resolution of 4D-CT. The algorithm outperforms the cubic spline interpolation and BP approaches by producing images with markedly improved structural clarity and greatly reduced artifacts.
PET image reconstruction: mean, variance, and optimal minimax criterion
NASA Astrophysics Data System (ADS)
Liu, Huafeng; Gao, Fei; Guo, Min; Xue, Liying; Nie, Jing; Shi, Pengcheng
2015-04-01
Given the noise nature of positron emission tomography (PET) measurements, it is critical to know the image quality and reliability as well as expected radioactivity map (mean image) for both qualitative interpretation and quantitative analysis. While existing efforts have often been devoted to providing only the reconstructed mean image, we present a unified framework for joint estimation of the mean and corresponding variance of the radioactivity map based on an efficient optimal min-max criterion. The proposed framework formulates the PET image reconstruction problem to be a transformation from system uncertainties to estimation errors, where the minimax criterion is adopted to minimize the estimation errors with possibly maximized system uncertainties. The estimation errors, in the form of a covariance matrix, express the measurement uncertainties in a complete way. The framework is then optimized by ∞-norm optimization and solved with the corresponding H∞ filter. Unlike conventional statistical reconstruction algorithms, that rely on the statistical modeling methods of the measurement data or noise, the proposed joint estimation stands from the point of view of signal energies and can handle from imperfect statistical assumptions to even no a priori statistical assumptions. The performance and accuracy of reconstructed mean and variance images are validated using Monte Carlo simulations. Experiments on phantom scans with a small animal PET scanner and real patient scans are also conducted for assessment of clinical potential.
Joint model of motion and anatomy for PET image reconstruction
Qiao Feng; Pan Tinsu; Clark, John W. Jr.; Mawlawi, Osama
2007-12-15
Anatomy-based positron emission tomography (PET) image enhancement techniques have been shown to have the potential for improving PET image quality. However, these techniques assume an accurate alignment between the anatomical and the functional images, which is not always valid when imaging the chest due to respiratory motion. In this article, we present a joint model of both motion and anatomical information by integrating a motion-incorporated PET imaging system model with an anatomy-based maximum a posteriori image reconstruction algorithm. The mismatched anatomical information due to motion can thus be effectively utilized through this joint model. A computer simulation and a phantom study were conducted to assess the efficacy of the joint model, whereby motion and anatomical information were either modeled separately or combined. The reconstructed images in each case were compared to corresponding reference images obtained using a quadratic image prior based maximum a posteriori reconstruction algorithm for quantitative accuracy. Results of these studies indicated that while modeling anatomical information or motion alone improved the PET image quantitation accuracy, a larger improvement in accuracy was achieved when using the joint model. In the computer simulation study and using similar image noise levels, the improvement in quantitation accuracy compared to the reference images was 5.3% and 19.8% when using anatomical or motion information alone, respectively, and 35.5% when using the joint model. In the phantom study, these results were 5.6%, 5.8%, and 19.8%, respectively. These results suggest that motion compensation is important in order to effectively utilize anatomical information in chest imaging using PET. The joint motion-anatomy model presented in this paper provides a promising solution to this problem.
Motion compensation for PET image reconstruction using deformable tetrahedral meshes
NASA Astrophysics Data System (ADS)
Manescu, P.; Ladjal, H.; Azencot, J.; Beuve, M.; Shariat, B.
2015-12-01
Respiratory-induced organ motion is a technical challenge to PET imaging. This motion induces displacements and deformation of the organs tissues, which need to be taken into account when reconstructing the spatial radiation activity. Classical image-based methods that describe motion using deformable image registration (DIR) algorithms cannot fully take into account the non-reproducibility of the respiratory internal organ motion nor the tissue volume variations that occur during breathing. In order to overcome these limitations, various biomechanical models of the respiratory system have been developed in the past decade as an alternative to DIR approaches. In this paper, we describe a new method of correcting motion artefacts in PET image reconstruction adapted to motion estimation models such as those based on the finite element method. In contrast with the DIR-based approaches, the radiation activity was reconstructed on deforming tetrahedral meshes. For this, we have re-formulated the tomographic reconstruction problem by introducing a time-dependent system matrix based calculated using tetrahedral meshes instead of voxelized images. The MLEM algorithm was chosen as the reconstruction method. The simulations performed in this study show that the motion compensated reconstruction based on tetrahedral deformable meshes has the capability to correct motion artefacts. Results demonstrate that, in the case of complex deformations, when large volume variations occur, the developed tetrahedral based method is more appropriate than the classical DIR-based one. This method can be used, together with biomechanical models controlled by external surrogates, to correct motion artefacts in PET images and thus reducing the need for additional internal imaging during the acquisition.
Sindoni, Alessandro; Minutoli, Fabio; Pontoriero, Antonio; Iatì, Giuseppe; Baldari, Sergio; Pergolizzi, Stefano
2016-06-01
In the past decade, Positron Emission Tomography (PET) has become a routinely used methodology for the assessment of solid tumors, which can detect functional abnormalities even before they become morphologically evident on conventional imaging. PET imaging has been reported to be useful in characterizing solitary pulmonary nodules, guiding biopsy, improving lung cancer staging, guiding therapy, monitoring treatment response and predicting outcome. This review focuses on the most relevant and recent literature findings, highlighting the current role of PET/CT and the evaluation of 4D-PET/CT modality for radiation therapy planning applications. Current evidence suggests that gross tumor volume delineation based on 4D-PET/CT information may be the best approach currently available for its delineation in thoracic cancers (lung and non-lung lesions). In our opinion, its use in this clinical setting is strongly encouraged, as it may improve patient treatment outcome in the setting of radiation therapy for cancers of the thoracic region, not only involving lung, but also lymph nodes and esophageal tissue. Literature results warrants further investigation in future prospective studies, especially in the setting of dose escalation. PMID:27133755
Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner
NASA Astrophysics Data System (ADS)
Ram Yu, A.; Kim, Jin Su
2015-10-01
Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data. Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.
Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging
Yu, Xingjian; Chen, Shuhang; Hu, Zhenghui; Liu, Meng; Chen, Yunmei; Shi, Pengcheng; Liu, Huafeng
2015-01-01
In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets. PMID:26540274
NASA Astrophysics Data System (ADS)
Bowen, S. R.; Nyflot, M. J.; Herrmann, C.; Groh, C. M.; Meyer, J.; Wollenweber, S. D.; Stearns, C. W.; Kinahan, P. E.; Sandison, G. A.
2015-05-01
Effective positron emission tomography / computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [18F]FDG. The lung lesion insert was driven by six different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses, and 2%-2 mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10-20%, treatment planning errors were 5-10%, and treatment delivery errors were 5-30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5-10% in PET/CT imaging, <5% in treatment planning, and <2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT
Bowen, S R; Nyflot, M J; Hermann, C; Groh, C; Meyer, J; Wollenweber, S D; Stearns, C W; Kinahan, P E; Sandison, G A
2015-01-01
Effective positron emission tomography/computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [18F]FDG. The lung lesion insert was driven by 6 different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy (VMAT) were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses (EUD), and 2%-2mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10–20%, treatment planning errors were 5–10%, and treatment delivery errors were 5–30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5–10% in PET/CT imaging, < 5% in treatment planning, and < 2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT
MAP reconstruction for Fourier rebinned TOF-PET data
NASA Astrophysics Data System (ADS)
Bai, Bing; Lin, Yanguang; Zhu, Wentao; Ren, Ran; Li, Quanzheng; Dahlbom, Magnus; DiFilippo, Frank; Leahy, Richard M.
2014-02-01
Time-of-flight (TOF) information improves the signal-to-noise ratio in positron emission tomography (PET). The computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple TOF bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either three-dimensional (3D) nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare the performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but with a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.
Evaluation of the spline reconstruction technique for PET
Kastis, George A. Kyriakopoulou, Dimitra; Gaitanis, Anastasios; Fernández, Yolanda; Hutton, Brian F.; Fokas, Athanasios S.
2014-04-15
Purpose: The spline reconstruction technique (SRT), based on the analytic formula for the inverse Radon transform, has been presented earlier in the literature. In this study, the authors present an improved formulation and numerical implementation of this algorithm and evaluate it in comparison to filtered backprojection (FBP). Methods: The SRT is based on the numerical evaluation of the Hilbert transform of the sinogram via an approximation in terms of “custom made” cubic splines. By restricting reconstruction only within object pixels and by utilizing certain mathematical symmetries, the authors achieve a reconstruction time comparable to that of FBP. The authors have implemented SRT in STIR and have evaluated this technique using simulated data from a clinical positron emission tomography (PET) system, as well as real data obtained from clinical and preclinical PET scanners. For the simulation studies, the authors have simulated sinograms of a point-source and three digital phantoms. Using these sinograms, the authors have created realizations of Poisson noise at five noise levels. In addition to visual comparisons of the reconstructed images, the authors have determined contrast and bias for different regions of the phantoms as a function of noise level. For the real-data studies, sinograms of an{sup 18}F-FDG injected mouse, a NEMA NU 4-2008 image quality phantom, and a Derenzo phantom have been acquired from a commercial PET system. The authors have determined: (a) coefficient of variations (COV) and contrast from the NEMA phantom, (b) contrast for the various sections of the Derenzo phantom, and (c) line profiles for the Derenzo phantom. Furthermore, the authors have acquired sinograms from a whole-body PET scan of an {sup 18}F-FDG injected cancer patient, using the GE Discovery ST PET/CT system. SRT and FBP reconstructions of the thorax have been visually evaluated. Results: The results indicate an improvement in FWHM and FWTM in both simulated and real
NASA Astrophysics Data System (ADS)
Vitanovski, Dime; Tsymbal, Alexey; Ionasec, Razvan; Georgescu, Bogdan; Zhou, Shaohua K.; Hornegger, Joachim; Comaniciu, Dorin
2011-03-01
Congenital heart defect (CHD) is the most common birth defect and a frequent cause of death for children. Tetralogy of Fallot (ToF) is the most often occurring CHD which affects in particular the pulmonary valve and trunk. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. While minimal invasive methods become common practice, imaging and non-invasive assessment tools become crucial components in the clinical setting. Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (cMRI) are techniques with complementary properties and ability to acquire multiple non-invasive and accurate scans required for advance evaluation and therapy planning. In contrary to CT which covers the full 4D information over the cardiac cycle, cMRI often acquires partial information, for example only one 3D scan of the whole heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. The data acquired in this way is called sparse cMRI. In this paper, we propose a regression-based approach for the reconstruction of the full 4D pulmonary trunk model from sparse MRI. The reconstruction approach is based on learning a distance function between the sparse MRI which needs to be completed and the 4D CT data with the full information used as the training set. The distance is based on the intrinsic Random Forest similarity which is learnt for the corresponding regression problem of predicting coordinates of unseen mesh points. Extensive experiments performed on 80 cardiac CT and MR sequences demonstrated the average speed of 10 seconds and accuracy of 0.1053mm mean absolute error for the proposed approach. Using the case retrieval workflow and local nearest neighbour regression with the learnt distance function appears to be competitive with respect to "black box" regression with immediate prediction of coordinates, while providing transparency to the
Online 4d Reconstruction Using Multi-Images Available Under Open Access
NASA Astrophysics Data System (ADS)
Ioannides, M.; Hadjiprocopi, A.; Doulamis, N.; Doulamis, A.; Protopapadakis, E.; Makantasis, K.; Santos, P.; Fellner, D.; Stork, A.; Balet, O.; Julien, M.; Weinlinger, G.; Johnson, P. S.; Klein, M.; Fritsch, D.
2013-07-01
The advent of technology in digital cameras and their incorporation into virtually any smart mobile device has led to an explosion of the number of photographs taken every day. Today, the number of images stored online and available freely has reached unprecedented levels. It is estimated that in 2011, there were over 100 billion photographs stored in just one of the major social media sites. This number is growing exponentially. Moreover, advances in the fields of Photogrammetry and Computer Vision have led to significant breakthroughs such as the Structure from Motion algorithm which creates 3D models of objects using their twodimensional photographs. The existence of powerful and affordable computational machinery not only the reconstruction of complex structures but also entire cities. This paper illustrates an overview of our methodology for producing 3D models of Cultural Heritage structures such as monuments and artefacts from 2D data (pictures, video), available on Internet repositories, social media, Google Maps, Bing, etc. We also present new approaches to semantic enrichment of the end results and their subsequent export to Europeana, the European digital library, for integrated, interactive 3D visualisation within regular web browsers using WebGl and X3D. Our main goal is to enable historians, architects, archaeologists, urban planners and affiliated professionals to reconstruct views of historical structures from millions of images floating around the web and interact with them.
Accelerated 4D Quantitative Single Point EPR Imaging Using Model-based Reconstruction
Jang, Hyungseok; Matsumoto, Shingo; Devasahayam, Nallathamby; Subramanian, Sankaran; Zhuo, Jiachen; Krishna, Murali C.; McMillan, Alan B
2014-01-01
Purpose EPRI has surfaced as a promising non-invasive imaging modality that is capable of imaging tissue oxygenation. Due to extremely short spin-spin relaxation time, EPRI benefits from single point imaging and inherently suffers from limited spatial and temporal resolution, preventing localization of small hypoxic tissues and differentiation of hypoxia dynamics, making accelerated imaging a crucial issue. Method In this study, methods for accelerated single point imaging were developed by combining a bilateral k-space extrapolation technique with model-based reconstruction that benefits from dense sampling in the parameter domain (measurement of the T2* decay of an FID). In bilateral k-space extrapolation, more k-space samples are obtained in a sparsely sampled region by bilaterally extrapolating data from temporally neighboring k-spaces. To improve the accuracy of T2* estimation, a principal component analysis (PCA)-based method was implemented. Result In a computer simulation and a phantom experiment, the proposed methods showed its capability for reliable T2* estimation with high acceleration (8-fold, 15-fold, and 30-fold accelerations for 61×61×61, 95×95×95, and 127×127×127 matrix, respectively). Conclusion By applying bilateral k-space extrapolation and model-based reconstruction, improved scan times with higher spatial resolution can be achieved in the current SP-EPRI modality. PMID:24803382
PET Image Reconstruction Using Information Theoretic Anatomical Priors
Somayajula, Sangeetha; Panagiotou, Christos; Rangarajan, Anand; Li, Quanzheng; Arridge, Simon R.
2011-01-01
We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using F18 Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors. PMID:20851790
Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography
NASA Astrophysics Data System (ADS)
Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G.; Liu, Chihray; Lu, Bo
2015-12-01
Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm ‘the common mask guided image reconstruction’ (c-MGIR). In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and ‘well’ solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the
NASA Astrophysics Data System (ADS)
Wilkinson, Paul; Chambers, Jonathan; Uhlemann, Sebastian; Meldrum, Philip; Smith, Alister; Dixon, Neil; Loke, Meng Heng
2016-02-01
Reliable tomographic inversion of geoelectrical monitoring data from unstable slopes relies critically on knowing the electrode positions, which may move over time. We develop and present an innovative inverse method to recover movements in both surface directions from geoelectrical measurements made on a grid of monitoring electrodes. For the first time, we demonstrate this method using field data from an active landslide to recover sequences of movement over timescales of days to years. Comparison with GPS measurements demonstrated an accuracy of within 10% of the electrode spacing, sufficient to correct the majority of artifacts that would occur in subsequent image reconstructions if incorrect positions are used. Over short timescales where the corresponding subsurface resistivity changes were smaller, the constraints could be relaxed and an order-of-magnitude better accuracy was achievable. This enabled the onset and acceleration of landslide activity to be detected with a temporal resolution of a few days.
4D reconstruction of the past: the image retrieval and 3D model construction pipeline
NASA Astrophysics Data System (ADS)
Hadjiprocopis, Andreas; Ioannides, Marinos; Wenzel, Konrad; Rothermel, Mathias; Johnsons, Paul S.; Fritsch, Dieter; Doulamis, Anastasios; Protopapadakis, Eftychios; Kyriakaki, Georgia; Makantasis, Kostas; Weinlinger, Guenther; Klein, Michael; Fellner, Dieter; Stork, Andre; Santos, Pedro
2014-08-01
One of the main characteristics of the Internet era we are living in, is the free and online availability of a huge amount of data. This data is of varied reliability and accuracy and exists in various forms and formats. Often, it is cross-referenced and linked to other data, forming a nexus of text, images, animation and audio enabled by hypertext and, recently, by the Web3.0 standard. Our main goal is to enable historians, architects, archaeolo- gists, urban planners and affiliated professionals to reconstruct views of historical monuments from thousands of images floating around the web. This paper aims to provide an update of our progress in designing and imple- menting a pipeline for searching, filtering and retrieving photographs from Open Access Image Repositories and social media sites and using these images to build accurate 3D models of archaeological monuments as well as enriching multimedia of cultural / archaeological interest with metadata and harvesting the end products to EU- ROPEANA. We provide details of how our implemented software searches and retrieves images of archaeological sites from Flickr and Picasa repositories as well as strategies on how to filter the results, on two levels; a) based on their built-in metadata including geo-location information and b) based on image processing and clustering techniques. We also describe our implementation of a Structure from Motion pipeline designed for producing 3D models using the large collection of 2D input images (>1000) retrieved from Internet Repositories.
NASA Astrophysics Data System (ADS)
Schroeder, Walter; Schulze, Wolfram; Wetter, Thomas; Chen, Chi-Hsien
2008-08-01
Three-dimensional (3D) body surface reconstruction is an important field in health care. A popular method for this purpose is laser scanning. However, using Photometric Stereo (PS) to record lumbar lordosis and the surface contour of the back poses a viable alternative due to its lower costs and higher flexibility compared to laser techniques and other methods of three-dimensional body surface reconstruction. In this work, we extended the traditional PS method and proposed a new method for obtaining surface and volume data of a moving object. The principle of traditional Photometric Stereo uses at least three images of a static object taken under different light sources to obtain 3D information of the object. Instead of using normal light, the light sources in the proposed method consist of the RGB-Color-Model's three colors: red, green and blue. A series of pictures taken with a video camera can now be separated into the different color channels. Each set of the three images can then be used to calculate the surface normals as a traditional PS. This method waives the requirement that the object imaged must be kept still as in almost all the other body surface reconstruction methods. By putting two cameras opposite to a moving object and lighting the object with the colored light, the time-varying surface (4D) data can easily be calculated. The obtained information can be used in many medical fields such as rehabilitation, diabetes screening or orthopedics.
G. S. CUNNINGHAM; A. LEHOVICH
2000-01-01
The Bayes Inference Engine (BIE) has been used to perform a 4D reconstruction of a first-pass radiotracer bolus distribution inside a CardioWest Total Artificial Heart, imaged with the University of Arizona's FastSPECT system. The BIE estimates parameter values that define the 3D model of the radiotracer distribution at each of 41 times spanning about two seconds. The 3D models have two components: a closed surface, composed of hi-quadratic Bezier triangular surface patches, that defines the interface between the part of the blood pool that contains radiotracer and the part that contains no radiotracer, and smooth voxel-to-voxel variations in intensity within the closed surface. Ideally, the surface estimates the ventricular wall location where the bolus is infused throughout the part of the blood pool contained by the right ventricle. The voxel-to-voxel variations are needed to model an inhomogeneously-mixed bolus. Maximum a posterior (MAP) estimates of the Bezier control points and voxel values are obtained for each time frame. We show new reconstructions using the Bezier surface models, and discuss estimates of ventricular volume as a function of time, ejection fraction, and wall motion. The computation time for our reconstruction process, which directly estimates complex 3D model parameters from the raw data, is performed in a time that is competitive with more traditional voxel-based methods (ML-EM, e.g.).
Image reconstruction for PET/CT scanners: past achievements and future challenges
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
NASA Astrophysics Data System (ADS)
Feygelman, V.; Nelms, B.
2013-06-01
As IMRT technology continues to evolve, so do the dosimetric QA methods. A historical review of those is presented, starting with longstanding techniques such as film and ion chamber in a phantom and progressing towards 3D and 4D dose reconstruction in the patient. Regarding patient-specific QA, we envision that the currently prevalent limited comparison of dose distributions in the phantom by γ-analysis will be eventually replaced by clinically meaningful patient dose analyses with improved sensitivity and specificity. In a larger sense, we envision a future of QA built upon lessons from the rich history of "quality" as a science and philosophy. This future will aim to improve quality (and ultimately reduce cost) via advanced commissioning processes that succeed in detecting and rooting out systematic errors upstream of patient treatment, thus reducing our reliance on, and the resource burden associated with, per-beam/per-plan inspection.
Investigation of optimization-based reconstruction with an image-total-variation constraint in PET
NASA Astrophysics Data System (ADS)
Zhang, Zheng; Ye, Jinghan; Chen, Buxin; Perkins, Amy E.; Rose, Sean; Sidky, Emil Y.; Kao, Chien-Min; Xia, Dan; Tung, Chi-Hua; Pan, Xiaochuan
2016-08-01
Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications.
Investigation of optimization-based reconstruction with an image-total-variation constraint in PET.
Zhang, Zheng; Ye, Jinghan; Chen, Buxin; Perkins, Amy E; Rose, Sean; Sidky, Emil Y; Kao, Chien-Min; Xia, Dan; Tung, Chi-Hua; Pan, Xiaochuan
2016-08-21
Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications. PMID:27452653
Iterative reconstruction methods for high-throughput PET tomographs.
Hamill, James; Bruckbauer, Thomas
2002-08-01
A fast iterative method is described for processing clinical PET scans acquired in three dimensions, that is, with no inter-plane septa, using standard computers to replace dedicated processors used until the late 1990s. The method is based on sinogram resampling, Fourier rebinning, Monte Carlo scatter simulation and iterative reconstruction using the attenuation-weighted OSEM method and a projector based on a Gaussian pixel model. Resampling of measured sinogram values occurs before Fourier rebinning, to minimize parallax and geometric distortions due to the circular geometry, and also to reduce the size of the sinogram. We analyse the geometrical and statistical effects of resampling, showing that the lines of response are positioned correctly and that resampling is equivalent to about 4 mm of post-reconstruction filtering. We also present phantom and patient results. In this approach, multi-bed clinical oncology scans can be ready for diagnosis within minutes. PMID:12200928
PET iterative reconstruction incorporating an efficient positron range correction method.
Bertolli, Ottavia; Eleftheriou, Afroditi; Cecchetti, Matteo; Camarlinghi, Niccolò; Belcari, Nicola; Tsoumpas, Charalampos
2016-02-01
Positron range is one of the main physical effects limiting the spatial resolution of positron emission tomography (PET) images. If positrons travel inside a magnetic field, for instance inside a nuclear magnetic resonance (MR) tomograph, the mean range will be smaller but still significant. In this investigation we examined a method to correct for the positron range effect in iterative image reconstruction by including tissue-specific kernels in the forward projection operation. The correction method was implemented within STIR library (Software for Tomographic Image Reconstruction). In order to obtain the positron annihilation distribution of various radioactive isotopes in water and lung tissue, simulations were performed with the Monte Carlo package GATE [Jan et al. 2004 [1
Data Acquisition and Image Reconstruction Systems from the miniPET Scanners to the CARDIOTOM Camera
Valastvan, I.; Imrek, J.; Hegyesi, G.; Molnar, J.; Novak, D.; Bone, D.; Kerek, A.
2007-11-26
Nuclear imaging devices play an important role in medical diagnosis as well as drug research. The first and second generation data acquisition systems and the image reconstruction library developed provide a unified hardware and software platform for the miniPET-I, miniPET-II small animal PET scanners and for the CARDIOTOM{sup TM}.
Guo, M; Nam, H; Li, R; Xing, L; Gao, H
2014-06-15
Purpose: 4D CT is routinely performed during radiation therapy treatment planning of thoracic and abdominal cancers. Compared with the cine mode, the helical mode is advantageous in temporal resolution. However, a low pitch (∼0.1) for 4D CT imaging is often required instead of the standard pitch (∼1) for static imaging, since standard image reconstruction based on analytic method requires the low-pitch scanning in order to satisfy the data sufficient condition when reconstructing each temporal frame individually. In comparison, the flexible iterative method enables the reconstruction of all temporal frames simultaneously, so that the image similarity among frames can be utilized to possibly perform high-pitch and sparse-view helical 4D CT imaging. The purpose of this work is to investigate such an exciting possibility for faster imaging with lower dose. Methods: A key for highpitch and sparse-view helical 4D CT imaging is the simultaneous reconstruction of all temporal frames using the prior that temporal frames are continuous along the temporal direction. In this work, such a prior is regularized through the sparsity transform based on spatiotemporal tensor framelet (TF) as a multilevel and high-order extension of total variation transform. Moreover, GPU-based fast parallel computing of X-ray transform and its adjoint together with split Bregman method is utilized for solving the 4D image reconstruction problem efficiently and accurately. Results: The simulation studies based on 4D NCAT phantoms were performed with various pitches (i.e., 0.1, 0.2, 0.5, and 1) and sparse views (i.e., 400 views per rotation instead of standard >2000 views per rotation), using 3D iterative individual reconstruction method based on 3D TF and 4D iterative simultaneous reconstruction method based on 4D TF respectively. Conclusion: The proposed TF-based simultaneous 4D image reconstruction method enables high-pitch and sparse-view helical 4D CT with lower dose and faster speed.
SU-E-J-153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments
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.
Optimizing modelling in iterative image reconstruction for preclinical pinhole PET
NASA Astrophysics Data System (ADS)
Goorden, Marlies C.; van Roosmalen, Jarno; van der Have, Frans; Beekman, Freek J.
2016-05-01
The recently developed versatile emission computed tomography (VECTor) technology enables high-energy SPECT and simultaneous SPECT and PET of small animals at sub-mm resolutions. VECTor uses dedicated clustered pinhole collimators mounted in a scanner with three stationary large-area NaI(Tl) gamma detectors. Here, we develop and validate dedicated image reconstruction methods that compensate for image degradation by incorporating accurate models for the transport of high-energy annihilation gamma photons. Ray tracing software was used to calculate photon transport through the collimator structures and into the gamma detector. Input to this code are several geometric parameters estimated from system calibration with a scanning 99mTc point source. Effects on reconstructed images of (i) modelling variable depth-of-interaction (DOI) in the detector, (ii) incorporating photon paths that go through multiple pinholes (‘multiple-pinhole paths’ (MPP)), and (iii) including various amounts of point spread function (PSF) tail were evaluated. Imaging 18F in resolution and uniformity phantoms showed that including large parts of PSFs is essential to obtain good contrast-noise characteristics and that DOI modelling is highly effective in removing deformations of small structures, together leading to 0.75 mm resolution PET images of a hot-rod Derenzo phantom. Moreover, MPP modelling reduced the level of background noise. These improvements were also clearly visible in mouse images. Performance of VECTor can thus be significantly improved by accurately modelling annihilation gamma photon transport.
Optimizing modelling in iterative image reconstruction for preclinical pinhole PET.
Goorden, Marlies C; van Roosmalen, Jarno; van der Have, Frans; Beekman, Freek J
2016-05-21
The recently developed versatile emission computed tomography (VECTor) technology enables high-energy SPECT and simultaneous SPECT and PET of small animals at sub-mm resolutions. VECTor uses dedicated clustered pinhole collimators mounted in a scanner with three stationary large-area NaI(Tl) gamma detectors. Here, we develop and validate dedicated image reconstruction methods that compensate for image degradation by incorporating accurate models for the transport of high-energy annihilation gamma photons. Ray tracing software was used to calculate photon transport through the collimator structures and into the gamma detector. Input to this code are several geometric parameters estimated from system calibration with a scanning (99m)Tc point source. Effects on reconstructed images of (i) modelling variable depth-of-interaction (DOI) in the detector, (ii) incorporating photon paths that go through multiple pinholes ('multiple-pinhole paths' (MPP)), and (iii) including various amounts of point spread function (PSF) tail were evaluated. Imaging (18)F in resolution and uniformity phantoms showed that including large parts of PSFs is essential to obtain good contrast-noise characteristics and that DOI modelling is highly effective in removing deformations of small structures, together leading to 0.75 mm resolution PET images of a hot-rod Derenzo phantom. Moreover, MPP modelling reduced the level of background noise. These improvements were also clearly visible in mouse images. Performance of VECTor can thus be significantly improved by accurately modelling annihilation gamma photon transport. PMID:27082049
Kim, D; Kang, S; Kim, T; Suh, T; Kim, S
2014-06-01
Purpose: In this paper, we implemented the four-dimensional (4D) digital tomosynthesis (DTS) imaging based on algebraic image reconstruction technique and total-variation minimization method in order to compensate the undersampled projection data and improve the image quality. Methods: The projection data were acquired as supposed the cone-beam computed tomography system in linear accelerator by the Monte Carlo simulation and the in-house 4D digital phantom generation program. We performed 4D DTS based upon simultaneous algebraic reconstruction technique (SART) among the iterative image reconstruction technique and total-variation minimization method (TVMM). To verify the effectiveness of this reconstruction algorithm, we performed systematic simulation studies to investigate the imaging performance. Results: The 4D DTS algorithm based upon the SART and TVMM seems to give better results than that based upon the existing method, or filtered-backprojection. Conclusion: The advanced image reconstruction algorithm for the 4D DTS would be useful to validate each intra-fraction motion during radiation therapy. In addition, it will be possible to give advantage to real-time imaging for the adaptive radiation therapy. This research was supported by Leading Foreign Research Institute Recruitment Program (Grant No.2009-00420) and Basic Atomic Energy Research Institute (BAERI); (Grant No. 2009-0078390) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP)
Harris, W; Zhang, Y; Ren, L; Yin, F
2014-06-01
Purpose: To investigate the feasibility of using nanoparticle markers to validate liver tumor motion together with a deformation field map-based four dimensional (4D) cone-beam computed tomography (CBCT) reconstruction method. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In this method, each phase of the 4D-CBCT is considered as a deformation of a prior CT volume. The DFM is solved by a motion modeling and free-form deformation (MM-FD) technique, using a data fidelity constraint and the deformation energy minimization. For liver imaging, there is low contrast of a liver tumor in on-board projections. A validation of liver tumor motion using implanted gold nanoparticles, along with the MM-FD deformation technique is implemented to reconstruct onboard 4D CBCT liver radiotherapy images. These nanoparticles were placed around the liver tumor to reflect the tumor positions in both CT simulation and on-board image acquisition. When reconstructing each phase of the 4D-CBCT, the migrations of the gold nanoparticles act as a constraint to regularize the deformation field, along with the data fidelity and the energy minimization constraints. In this study, multiple tumor diameters and positions were simulated within the liver for on-board 4D-CBCT imaging. The on-board 4D-CBCT reconstructed by the proposed method was compared with the “ground truth” image. Results: The preliminary data, which uses reconstruction for lung radiotherapy suggests that the advanced reconstruction algorithm including the gold nanoparticle constraint will Resultin volume percentage differences (VPD) between lesions in reconstructed images by MM-FD and “ground truth” on-board images of 11.5% (± 9.4%) and a center of mass shift of 1.3 mm (± 1.3 mm) for liver radiotherapy. Conclusion: The advanced MM-FD technique enforcing the additional constraints from gold nanoparticles, results in improved accuracy
Fast fully 3-D image reconstruction in PET using planograms.
Brasse, D; Kinahan, P E; Clackdoyle, R; Defrise, M; Comtat, C; Townsend, D W
2004-04-01
We present a method of performing fast and accurate three-dimensional (3-D) backprojection using only Fourier transform operations for line-integral data acquired by planar detector arrays in positron emission tomography. This approach is a 3-D extension of the two-dimensional (2-D) linogram technique of Edholm. By using a special choice of parameters to index a line of response (LOR) for a pair of planar detectors, rather than the conventional parameters used to index a LOR for a circular tomograph, all the LORs passing through a point in the field of view (FOV) lie on a 2-D plane in the four-dimensional (4-D) data space. Thus, backprojection of all the LORs passing through a point in the FOV corresponds to integration of a 2-D plane through the 4-D "planogram." The key step is that the integration along a set of parallel 2-D planes through the planogram, that is, backprojection of a plane of points, can be replaced by a 2-D section through the origin of the 4-D Fourier transform of the data. Backprojection can be performed as a sequence of Fourier transform operations, for faster implementation. In addition, we derive the central-section theorem for planogram format data, and also derive a reconstruction filter for both backprojection-filtering and filtered-backprojection reconstruction algorithms. With software-based Fourier transform calculations we provide preliminary comparisons of planogram backprojection to standard 3-D backprojection and demonstrate a reduction in computation time by a factor of approximately 15. PMID:15084067
Defrise, Michel; Gullberg, Grant T.
2006-04-05
We give an overview of the role of Physics in Medicine andBiology in development of tomographic reconstruction algorithms. We focuson imaging modalities involving ionizing radiation, CT, PET and SPECT,and cover a wide spectrum of reconstruction problems, starting withclassical 2D tomogra tomography in the 1970s up to 4D and 5D problemsinvolving dynamic imaging of moving organs.
Ma, C; Yin, Y
2015-06-15
Purpose: A method using four-dimensional(4D) PET/CT in design of radiation treatment planning was proposed and the target volume and radiation dose distribution changes relative to standard three-dimensional (3D) PET/CT were examined. Methods: A target deformable registration method was used by which the whole patient’s respiration process was considered and the effect of respiration motion was minimized when designing radiotherapy planning. The gross tumor volume of a non-small-cell lung cancer was contoured on the 4D FDG-PET/CT and 3D PET/CT scans by use of two different techniques: manual contouring by an experienced radiation oncologist using a predetermined protocol; another technique using a constant threshold of standardized uptake value (SUV) greater than 2.5. The target volume and radiotherapy dose distribution between VOL3D and VOL4D were analyzed. Results: For all phases, the average automatic and manually GTV volume was 18.61 cm3 (range, 16.39–22.03 cm3) and 31.29 cm3 (range, 30.11–35.55 cm3), respectively. The automatic and manually volume of merged IGTV were 27.82 cm3 and 49.37 cm3, respectively. For the manual contour, compared to 3D plan the mean dose for the left, right, and total lung of 4D plan have an average decrease 21.55%, 15.17% and 15.86%, respectively. The maximum dose of spinal cord has an average decrease 2.35%. For the automatic contour, the mean dose for the left, right, and total lung have an average decrease 23.48%, 16.84% and 17.44%, respectively. The maximum dose of spinal cord has an average decrease 1.68%. Conclusion: In comparison to 3D PET/CT, 4D PET/CT may better define the extent of moving tumors and reduce the contouring tumor volume thereby optimize radiation treatment planning for lung tumors.
Zhang, Y; Yin, F; Ren, L
2014-06-01
Purpose: To evaluate a 4D-CBCT reconstruction technique both geometrically and dosimetrically Methods: A prior-knowledge guided 4DC-BCT reconstruction method named the motion-modeling and free-form deformation (MM-FD) has been developed. MM-FD views each phase of the 4D-CBCT as a deformation of a prior CT volume. The deformation field is first solved by principal component analysis based motion modeling, followed by constrained free-form deformation.The 4D digital extended-cardiac- torso (XCAT) phantom was used for comprehensive evaluation. Based on a simulated 4D planning CT of a lung patient, 8 different scenarios were simulated to cover the typical on-board anatomical and respiratory variations: (1) synchronized and (2) unsynchronized motion amplitude change for body and tumor; tumor (3) shrinkage and (4) expansion; tumor average position shift in (5) superior-inferior (SI) direction, (6) anterior-posterior (AP) direction and (7) SI, AP and lateral directions altogether; and (8) tumor phase shift relative to the respiratory cycle of the body. Orthogonal-view 30° projections were simulated based on the eight patient scenarios to reconstruct on-board 4D-CBCTs. For geometric evaluation, the volume-percentage-difference (VPD) was calculated to assess the volumetric differences between the reconstructed and the ground-truth tumor.For dosimetric evaluation, a gated treatment plan was designed for the prior 4D-CT. The dose distributions were calculated on the reconstructed 4D-CBCTs and the ground-truth images for comparison. The MM-FD technique was compared with MM-only and FD-only techniques. Results: The average (±s.d.) VPD values of reconstructed tumors for MM-only, FDonly and MM-FD methods were 59.16%(± 26.66%), 75.98%(± 27.21%) and 5.22%(± 2.12%), respectively. The average min/max/mean dose (normalized to prescription) of the reconstructed tumors by MM-only, FD-only, MM-FD methods and ground-truth tumors were 78.0%/122.2%/108.2%, 13%/117.7%/86%, 58
Edge-Preserving PET Image Reconstruction Using Trust Optimization Transfer
Wang, Guobao; Qi, Jinyi
2014-01-01
Iterative image reconstruction for positron emission tomography (PET) can improve image quality by using spatial regularization. The most commonly used quadratic penalty often over-smoothes sharp edges and fine features in reconstructed images, while non-quadratic penalties can preserve edges and achieve higher contrast recovery. Existing optimization algorithms such as the expectation maximization (EM) and preconditioned conjugate gradient (PCG) algorithms work well for the quadratic penalty, but are less efficient for high-curvature or non-smooth edge-preserving regularizations. This paper proposes a new algorithm to accelerate edge-preserving image reconstruction by using two strategies: trust surrogate and optimization transfer descent. Trust surrogate approximates the original penalty by a smoother function at each iteration, but guarantees the algorithm to descend monotonically; Optimization transfer descent accelerates a conventional optimization transfer algorithm by using conjugate gradient and line search. Results of computer simulations and real 3D data show that the proposed algorithm converges much faster than the conventional EM and PCG for smooth edge-preserving regularization and can also be more efficient than the current state-of-art algorithms for the non-smooth ℓ1 regularization. PMID:25438302
NASA Astrophysics Data System (ADS)
Lee, Taek-Soo; Higuchi, Takahiro; Lautamäki, Riikka; Bengel, Frank M.; Tsui, Benjamin M. W.
2015-09-01
We evaluated the performance of a new 4D image reconstruction method for improved 4D gated myocardial perfusion (MP) SPECT using a task-based human observer study. We used a realistic 4D NURBS-based Cardiac-Torso (NCAT) phantom that models cardiac beating motion. Half of the population was normal; the other half had a regional hypokinetic wall motion abnormality. Noise-free and noisy projection data with 16 gates/cardiac cycle were generated using an analytical projector that included the effects of attenuation, collimator-detector response, and scatter (ADS), and were reconstructed using the 3D FBP without and 3D OS-EM with ADS corrections followed by different cut-off frequencies of a 4D linear post-filter. A 4D iterative maximum a posteriori rescaled-block (MAP-RBI)-EM image reconstruction method with ADS corrections was also used to reconstruct the projection data using various values of the weighting factor for its prior. The trade-offs between bias and noise were represented by the normalized mean squared error (NMSE) and averaged normalized standard deviation (NSDav), respectively. They were used to select reasonable ranges of the reconstructed images for use in a human observer study. The observers were trained with the simulated cine images and were instructed to rate their confidence on the absence or presence of a motion defect on a continuous scale. We then applied receiver operating characteristic (ROC) analysis and used the area under the ROC curve (AUC) index. The results showed that significant differences in detection performance among the different NMSE-NSDav combinations were found and the optimal trade-off from optimized reconstruction parameters corresponded to a maximum AUC value. The 4D MAP-RBI-EM with ADS correction, which had the best trade-off among the tested reconstruction methods, also had the highest AUC value, resulting in significantly better human observer detection performance when detecting regional myocardial wall motion
Lee, Taek-Soo; Higuchi, Takahiro; Lautamäki, Riikka; Bengel, Frank M.; Tsui, Benjamin M. W.
2015-01-01
We evaluated the performance of a new 4D image reconstruction method for improved 4D gated myocardial perfusion (MP) SPECT using a task-based human observer study. We used a realistic 4D NURBS-based Cardiac-Torso (NCAT) phantom that models cardiac beating motion. Half of the population was normal; the other half had a regional hypokinetic wall motion abnormality. Noise-free and noisy projection data with 16 gates/cardiac cycle were generated using an analytical projector that included the effects of attenuation, collimator-detector response, and scatter (ADS), and were reconstructed using the 3D FBP without and 3D OS-EM with ADS corrections followed by different cut-off frequencies of a 4D linear post-filter. A 4D iterative maximum a posteriori rescaled-block (MAP-RBI)-EM image reconstruction method with ADS corrections was also used to reconstruct the projection data using various values of the weighting factor for its prior. The trade-offs between bias and noise were represented by the normalized mean squared error (NMSE) and averaged normalized standard deviation (NSDav), respectively. They were used to select reasonable ranges of the reconstructed images for use in a human observer study. The observers were trained with the simulated cine images and were instructed to rate their confidence on the absence or presence of a motion defect on a continuous scale. We then applied receiver operating characteristic (ROC) analysis and used the area under the ROC curve (AUC) index. The results showed that significant differences in detection performance among the different NMSE-NSDav combinations were found and the optimal trade-off from optimized reconstruction parameters corresponded to a maximum AUC value. The 4D MAP-RBI-EM with ADS correction, which had the best trade-off among the tested reconstruction methods, also had the highest AUC value, resulting in significantly better human observer detection performance when detecting regional myocardial wall motion
Lee, Taek-Soo; Higuchi, Takahiro; Lautamäki, Riikka; Bengel, Frank M; Tsui, Benjamin M W
2015-09-01
We evaluated the performance of a new 4D image reconstruction method for improved 4D gated myocardial perfusion (MP) SPECT using a task-based human observer study. We used a realistic 4D NURBS-based Cardiac-Torso (NCAT) phantom that models cardiac beating motion. Half of the population was normal; the other half had a regional hypokinetic wall motion abnormality. Noise-free and noisy projection data with 16 gates/cardiac cycle were generated using an analytical projector that included the effects of attenuation, collimator-detector response, and scatter (ADS), and were reconstructed using the 3D FBP without and 3D OS-EM with ADS corrections followed by different cut-off frequencies of a 4D linear post-filter. A 4D iterative maximum a posteriori rescaled-block (MAP-RBI)-EM image reconstruction method with ADS corrections was also used to reconstruct the projection data using various values of the weighting factor for its prior. The trade-offs between bias and noise were represented by the normalized mean squared error (NMSE) and averaged normalized standard deviation (NSDav), respectively. They were used to select reasonable ranges of the reconstructed images for use in a human observer study. The observers were trained with the simulated cine images and were instructed to rate their confidence on the absence or presence of a motion defect on a continuous scale. We then applied receiver operating characteristic (ROC) analysis and used the area under the ROC curve (AUC) index. The results showed that significant differences in detection performance among the different NMSE-NSDav combinations were found and the optimal trade-off from optimized reconstruction parameters corresponded to a maximum AUC value. The 4D MAP-RBI-EM with ADS correction, which had the best trade-off among the tested reconstruction methods, also had the highest AUC value, resulting in significantly better human observer detection performance when detecting regional myocardial wall motion
Lee, Soyoung; Yan, Guanghua; Lu, Bo; Kahler, Darren; Li, Jonathan G; Sanjiv, Samat S
2015-01-01
Four-dimensional, cone-beam CT (4D CBCT) substantially reduces respiration-induced motion blurring artifacts in three-dimension (3D) CBCT. However, the image quality of 4D CBCT is significantly degraded which may affect its accuracy in localizing a mobile tumor for high-precision, image-guided radiation therapy (IGRT). The purpose of this study was to investigate the impact of scanning parameters hereinafter collectively referred to as scanning sequence) and breathing patterns on the image quality and the accuracy of computed tumor trajectory for a commercial 4D CBCT system, in preparation for its clinical implementation. We simulated a series of periodic and aperiodic sinusoidal breathing patterns with a respiratory motion phantom. The aperiodic pattern was created by varying the period or amplitude of individual sinusoidal breathing cycles. 4D CBCT scans of the phantom were acquired with a manufacturer-supplied scanning sequence (4D-S-slow) and two in-house modified scanning sequences (4D-M-slow and 4D-M-fast). While 4D-S-slow used small field of view (FOV), partial rotation (200°), and no imaging filter, 4D-M-slow and 4D-M-fast used medium FOV, full rotation, and the F1 filter. The scanning speed was doubled in 4D-M-fast (100°/min gantry rotation). The image quality of the 4D CBCT scans was evaluated using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and motion blurring ratio (MBR). The trajectory of the moving target was reconstructed by registering each phase of the 4D CBCT with a reference CT. The root-mean-squared-error (RMSE) analysis was used to quantify its accuracy. Significant decrease in CNR and SNR from 3D CBCT to 4D CBCT was observed. The 4D-S-slow and 4D-M-fast scans had comparable image quality, while the 4D-M-slow scans had better performance due to doubled projections. Both CNR and SNR decreased slightly as the breathing period increased, while no dependence on the amplitude was observed. The difference of both CNR and SNR
LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation
Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.
2015-01-01
Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanner. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present an LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3-D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the nonnegative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which
LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation
NASA Astrophysics Data System (ADS)
Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.
2015-01-01
Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanners. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present a LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the non-negative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which
Shieh, Chun-Chien; Kipritidis, John; O’Brien, Ricky T.; Kuncic, Zdenka; Keall, Paul J.
2014-01-01
Purpose: Respiratory signal, binning method, and reconstruction algorithm are three major controllable factors affecting image quality in thoracic 4D cone-beam CT (4D-CBCT), which is widely used in image guided radiotherapy (IGRT). Previous studies have investigated each of these factors individually, but no integrated sensitivity analysis has been performed. In addition, projection angular spacing is also a key factor in reconstruction, but how it affects image quality is not obvious. An investigation of the impacts of these four factors on image quality can help determine the most effective strategy in improving 4D-CBCT for IGRT. Methods: Fourteen 4D-CBCT patient projection datasets with various respiratory motion features were reconstructed with the following controllable factors: (i) respiratory signal (real-time position management, projection image intensity analysis, or fiducial marker tracking), (ii) binning method (phase, displacement, or equal-projection-density displacement binning), and (iii) reconstruction algorithm [Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), or adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS)]. The image quality was quantified using signal-to-noise ratio (SNR), contrast-to-noise ratio, and edge-response width in order to assess noise/streaking and blur. The SNR values were also analyzed with respect to the maximum, mean, and root-mean-squared-error (RMSE) projection angular spacing to investigate how projection angular spacing affects image quality. Results: The choice of respiratory signals was found to have no significant impact on image quality. Displacement-based binning was found to be less prone to motion artifacts compared to phase binning in more than half of the cases, but was shown to suffer from large interbin image quality variation and large projection angular gaps. Both MKB and ASD-POCS resulted in noticeably improved image quality almost 100% of the time relative to FDK. In addition, SNR
Shieh, Chun-Chien; Kipritidis, John; O’Brien, Ricky T.; Keall, Paul J.; Kuncic, Zdenka
2014-04-15
Purpose: Respiratory signal, binning method, and reconstruction algorithm are three major controllable factors affecting image quality in thoracic 4D cone-beam CT (4D-CBCT), which is widely used in image guided radiotherapy (IGRT). Previous studies have investigated each of these factors individually, but no integrated sensitivity analysis has been performed. In addition, projection angular spacing is also a key factor in reconstruction, but how it affects image quality is not obvious. An investigation of the impacts of these four factors on image quality can help determine the most effective strategy in improving 4D-CBCT for IGRT. Methods: Fourteen 4D-CBCT patient projection datasets with various respiratory motion features were reconstructed with the following controllable factors: (i) respiratory signal (real-time position management, projection image intensity analysis, or fiducial marker tracking), (ii) binning method (phase, displacement, or equal-projection-density displacement binning), and (iii) reconstruction algorithm [Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), or adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS)]. The image quality was quantified using signal-to-noise ratio (SNR), contrast-to-noise ratio, and edge-response width in order to assess noise/streaking and blur. The SNR values were also analyzed with respect to the maximum, mean, and root-mean-squared-error (RMSE) projection angular spacing to investigate how projection angular spacing affects image quality. Results: The choice of respiratory signals was found to have no significant impact on image quality. Displacement-based binning was found to be less prone to motion artifacts compared to phase binning in more than half of the cases, but was shown to suffer from large interbin image quality variation and large projection angular gaps. Both MKB and ASD-POCS resulted in noticeably improved image quality almost 100% of the time relative to FDK. In addition, SNR
Initial experience in primal-dual optimization reconstruction from sparse-PET patient data
NASA Astrophysics Data System (ADS)
Zhang, Zheng; Ye, Jinghan; Chen, Buxin; Perkins, Amy E.; Rose, Sean; Sidky, Emil Y.; Kao, Chien-Min; Xia, Dan; Tung, Chi-Hua; Pan, Xiaochuan
2016-03-01
There exists interest in designing a PET system with reduced detectors due to cost concerns, while not significantly compromising the PET utility. Recently developed optimization-based algorithms, which have demonstrated the potential clinical utility in image reconstruction from sparse CT data, may be used for enabling such design of innovative PET systems. In this work, we investigate a PET configuration with reduced number of detectors, and carry out preliminary studies from patient data collected by use of such sparse-PET configuration. We consider an optimization problem combining Kullback-Leibler (KL) data fidelity with an image TV constraint, and solve it by using a primal-dual optimization algorithm developed by Chambolle and Pock. Results show that advanced algorithms may enable the design of innovative PET configurations with reduced number of detectors, while yielding potential practical PET utilities.
SU-D-17A-03: 5D Respiratory Motion Model Based Iterative Reconstruction Method for 4D Cone-Beam CT
Gao, Y; Thomas, D; Low, D; Gao, H
2014-06-01
Purpose: The purpose of this work is to develop a new iterative reconstruction method for 4D cone-beam CT (CBCT) based on a published time-independent 5D respiratory motion model. The proposed method will offer a single high-resolution image at a user-selected breathing phase and the 5D motion model parameters, which could be used to generate the breathing pattern during the CT acquisition. Methods: 5D respiratory motion model was proposed for accurately modeling the motion of lung and lung tumor tissues. 4D images are then parameterized by a reference image, measured breathing amplitude, breathing rate, two time-independent vector fields that describe the 5D model parameters, and a scalar field that describes the change in HU as a function of breathing amplitude. In contrast with the traditional method of reconstructing multiple temporal image phases to reduce respiratory artifact, 5D model based method simplify the problem into the reconstruction of a single reference image and the 5D motion model parameters. The reconstruction formulation of the reference image and scalar and vector fields is a nonlinear least-square optimization problem that consists of solving the reference image and fields alternately, in which the reference image is regularized with the total variation sparsity transform and the vector fields are solved through linearizations regularized by the H1 norm. 2D lung simulations were performed in this proof-of-concept study. Results: The breathing amplitude, its rate, and the corresponding scalar and vector fields were generated from a patient case. Compared with filtered backprojection method and sparsity regularized iterative method for the phase-by-phase reconstruction, the proposed 5D motion model based method yielded improved image quality. Conclusion: Based on 5D respiratory motion model, we have developed a new iterative reconstruction method for 4D CBCT that has the potential for improving image quality while providing needed on
NASA Astrophysics Data System (ADS)
Nam, Woo Hyun; Ahn, Il Jun; Kim, Kyeong Min; Kim, Byung Il; Ra, Jong Beom
2013-10-01
Positron emission tomography (PET) is widely used for diagnosis and follow up assessment of radiotherapy. However, thoracic and abdominal PET suffers from false staging and incorrect quantification of the radioactive uptake of lesion(s) due to respiratory motion. Furthermore, respiratory motion-induced mismatch between a computed tomography (CT) attenuation map and PET data often leads to significant artifacts in the reconstructed PET image. To solve these problems, we propose a unified framework for respiratory-matched attenuation correction and motion compensation of respiratory-gated PET. For the attenuation correction, the proposed algorithm manipulates a 4D CT image virtually generated from two low-dose inhale and exhale CT images, rather than a real 4D CT image which significantly increases the radiation burden on a patient. It also utilizes CT-driven motion fields for motion compensation. To realize the proposed algorithm, we propose an improved region-based approach for non-rigid registration between body CT images, and we suggest a selection scheme of 3D CT images that are respiratory-matched to each respiratory-gated sinogram. In this work, the proposed algorithm was evaluated qualitatively and quantitatively by using patient datasets including lung and/or liver lesion(s). Experimental results show that the method can provide much clearer organ boundaries and more accurate lesion information than existing algorithms by utilizing two low-dose CT images.
Lee, S; Lu, B; Samant, S
2014-06-01
Purpose: To investigate the effects of scanning parameters and respiratory patterns on the image quality for 4-dimensional cone-beam computed tomography(4D-CBCT) imaging, and assess the accuracy of computed tumor trajectory for lung imaging using registration of phased 4D-CBCT imaging with treatment planning-CT. Methods: We simulated a periodic and non-sinusoidal respirations with various breathing periods and amplitudes using a respiratory phantom(Quasar, Modus Medical Devices Inc) to acquire respiration-correlated 4D-CBCT images. 4D-CBCT scans(Elekta Oncology Systems Ltd) were performed with different scanning parameters for collimation size(e.g., small and medium field-of-views) and scanning speed(e.g., slow 50°·min{sup −1}, fast 100°·min{sup −1}). Using a standard CBCT-QA phantom(Catphan500, The Phantom Laboratory), the image qualities of all phases in 4D-CBCT were evaluated with contrast-to-noise ratio(CNR) for lung tissue and uniformity in each module. Using a respiratory phantom, the target imaging in 4D-CBCT was compared to 3D-CBCT target image. The target trajectory from 10-respiratory phases in 4D-CBCT was extracted using an automatic image registration and subsequently assessed the accuracy by comparing with actual motion of the target. Results: Image analysis indicated that a short respiration with a small amplitude resulted in superior CNR and uniformity. Smaller variation of CNR and uniformity was present amongst different respiratory phases. The small field-of-view with a partial scan using slow scan can improve CNR, but degraded uniformity. Large amplitude of respiration can degrade image quality. RMS of voxel densities in tumor area of 4D-CBCT images between sinusoidal and non-sinusoidal motion exhibited no significant difference. The maximum displacement errors of motion trajectories were less than 1.0 mm and 13.5 mm, for sinusoidal and non-sinusoidal breathings, respectively. The accuracy of motion reconstruction showed good overall
Shigemitsu, Yoshiki; Ikeya, Teppei; Yamamoto, Akihiro; Tsuchie, Yuusuke; Mishima, Masaki; Smith, Brian O; Güntert, Peter; Ito, Yutaka
2015-02-01
Despite their advantages in analysis, 4D NMR experiments are still infrequently used as a routine tool in protein NMR projects due to the long duration of the measurement and limited digital resolution. Recently, new acquisition techniques for speeding up multidimensional NMR experiments, such as nonlinear sampling, in combination with non-Fourier transform data processing methods have been proposed to be beneficial for 4D NMR experiments. Maximum entropy (MaxEnt) methods have been utilised for reconstructing nonlinearly sampled multi-dimensional NMR data. However, the artefacts arising from MaxEnt processing, particularly, in NOESY spectra have not yet been clearly assessed in comparison with other methods, such as quantitative maximum entropy, multidimensional decomposition, and compressed sensing. We compared MaxEnt with other methods in reconstructing 3D NOESY data acquired with variously reduced sparse sampling schedules and found that MaxEnt is robust, quick and competitive with other methods. Next, nonlinear sampling and MaxEnt processing were applied to 4D NOESY experiments, and the effect of the artefacts of MaxEnt was evaluated by calculating 3D structures from the NOE-derived distance restraints. Our results demonstrated that sufficiently converged and accurate structures (RMSD of 0.91Å to the mean and 1.36Å to the reference structures) were obtained even with NOESY spectra reconstructed from 1.6% randomly selected sampling points for indirect dimensions. This suggests that 3D MaxEnt processing in combination with nonlinear sampling schedules is still a useful and advantageous option for rapid acquisition of high-resolution 4D NOESY spectra of proteins. PMID:25545060
NASA Astrophysics Data System (ADS)
Hansis, Eberhard; Schomberg, Hermann; Erhard, Klaus; Dössel, Olaf; Grass, Michael
2009-02-01
The tomographic reconstruction of the beating heart requires dedicated methods. One possibility is gated reconstruction, where only data corresponding to a certain motion state are incorporated. Another one is motioncompensated reconstruction with a pre-computed motion vector field, which requires a preceding estimation of the motion. Here, results of a new approach are presented: simultaneous reconstruction of a three-dimensional object and its motion over time, yielding a fully four-dimensional representation. The object motion is modeled by a time-dependent elastic transformation. The reconstruction is carried out with an iterative gradient-descent algorithm which simultaneously optimizes the three-dimensional image and the motion parameters. The method was tested on a simulated rotational X-ray acquisition of a dynamic coronary artery phantom, acquired on a C-arm system with a slowly rotating C-arm. Accurate reconstruction of both absorption coefficient and motion could be achieved. First results from experiments on clinical rotational X-ray coronary angiography data are shown. The resulting reconstructions enable the analysis of both static properties, such as vessel geometry and cross-sectional areas, and dynamic properties, like magnitude, speed, and synchrony of motion during the cardiac cycle.
Wobbling and LSF-based maximum likelihood expectation maximization reconstruction for wobbling PET
NASA Astrophysics Data System (ADS)
Kim, Hang-Keun; Son, Young-Don; Kwon, Dae-Hyuk; Joo, Yohan; Cho, Zang-Hee
2016-04-01
Positron emission tomography (PET) is a widely used imaging modality; however, the PET spatial resolution is not yet satisfactory for precise anatomical localization of molecular activities. Detector size is the most important factor because it determines the intrinsic resolution, which is approximately half of the detector size and determines the ultimate PET resolution. Detector size, however, cannot be made too small because both the decreased detection efficiency and the increased septal penetration effect degrade the image quality. A wobbling and line spread function (LSF)-based maximum likelihood expectation maximization (WL-MLEM) algorithm, which combined the MLEM iterative reconstruction algorithm with wobbled sampling and LSF-based deconvolution using the system matrix, was proposed for improving the spatial resolution of PET without reducing the scintillator or detector size. The new algorithm was evaluated using a simulation, and its performance was compared with that of the existing algorithms, such as conventional MLEM and LSF-based MLEM. Simulations demonstrated that the WL-MLEM algorithm yielded higher spatial resolution and image quality than the existing algorithms. The WL-MLEM algorithm with wobbling PET yielded substantially improved resolution compared with conventional algorithms with stationary PET. The algorithm can be easily extended to other iterative reconstruction algorithms, such as maximum a priori (MAP) and ordered subset expectation maximization (OSEM). The WL-MLEM algorithm with wobbling PET may offer improvements in both sensitivity and resolution, the two most sought-after features in PET design.
Full 3-D cluster-based iterative image reconstruction tool for a small animal PET camera
NASA Astrophysics Data System (ADS)
Valastyán, I.; Imrek, J.; Molnár, J.; Novák, D.; Balkay, L.; Emri, M.; Trón, L.; Bükki, T.; Kerek, A.
2007-02-01
Iterative reconstruction methods are commonly used to obtain images with high resolution and good signal-to-noise ratio in nuclear imaging. The aim of this work was to develop a scalable, fast, cluster based, fully 3-D iterative image reconstruction package for our small animal PET camera, the miniPET. The reconstruction package is developed to determine the 3-D radioactivity distribution from list mode type of data sets and it can also simulate noise-free projections of digital phantoms. We separated the system matrix generation and the fully 3-D iterative reconstruction process. As the detector geometry is fixed for a given camera, the system matrix describing this geometry is calculated only once and used for every image reconstruction, making the process much faster. The Poisson and the random noise sensitivity of the ML-EM iterative algorithm were studied for our small animal PET system with the help of the simulation and reconstruction tool. The reconstruction tool has also been tested with data collected by the miniPET from a line and a cylinder shaped phantom and also a rat.
Inter-update Metz filtering as regularization for variable block-ART in PET reconstruction
NASA Astrophysics Data System (ADS)
Sadki, Mustapha; San-Martin, Maite T.
2005-03-01
Positron Emission Tomography (PET) is a technology that uses short-lived radio nuclides altered by disease and precede changes that can be visualized by cross-sectional imaging. Over the last decade, this technique has become an important clinical tool for detection of tumors, follow-up treatment and drug research, providing an understanding of dynamic physiological processes. Since PET needs improved reconstruction algorithms to facilitate clinical diagnosis, we will investigate an improved iterative algorithm. Amongst current algorithms applied for PET reconstruction, ART was first proposed as a method of reconstruction from CT projections. With appropriate tuning, the convergence of these algorithms could be very fast indeed. However, the quality of reconstruction using these methods has not been thoroughly investigated. We study a variant of these algorithms. We present the state of the art, review well-known ART and investigate an optimum dynamically-changing block structure for the not yet fully explored variable-Block ART, which uses jointly the Inter-Update Metz filter for regularization and exploits the full symmetries in PET scanners. This reveals significant acceleration of initial convergence to an acceptable reconstruction of inconsistent cases. To assess the quality and analyze any discrepancy of the reconstructed images, two figures of merit (FOMs) are used to evaluate two 3D Data phantoms acquired on a GE-Advance scanner for high statistics.
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.
Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model
NASA Astrophysics Data System (ADS)
Walker, M. D.; Asselin, M.-C.; Julyan, P. J.; Feldmann, M.; Talbot, P. S.; Jones, T.; Matthews, J. C.
2011-02-01
Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [11C]DASB and [15O]H2O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [11C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [15O]H2O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.
Single-Cell Tracking with PET using a Novel Trajectory Reconstruction Algorithm
Lee, Keum Sil; Kim, Tae Jin
2015-01-01
Virtually all biomedical applications of positron emission tomography (PET) use images to represent the distribution of a radiotracer. However, PET is increasingly used in cell tracking applications, for which the “imaging” paradigm may not be optimal. Here we investigate an alternative approach, which consists in reconstructing the time-varying position of individual radiolabeled cells directly from PET measurements. As a proof of concept, we formulate a new algorithm for reconstructing the trajectory of one single moving cell directly from list-mode PET data. We model the trajectory as a 3D B-spline function of the temporal variable and use non-linear optimization to minimize the mean-square distance between the trajectory and the recorded list-mode coincidence events. Using Monte Carlo simulations (GATE), we show that this new algorithm can track a single source moving within a small-animal PET system with <3 mm accuracy provided that the activity of the cell [Bq] is greater than four times its velocity [mm/s]. The algorithm outperforms conventional ML-EM as well as the “minimum distance” method used for positron emission particle tracking (PEPT). The new method was also successfully validated using experimentally acquired PET data. In conclusion, we demonstrated the feasibility of a new method for tracking a single moving cell directly from PET list-mode data, at the whole-body level, for physiologically relevant activities and velocities. PMID:25423651
Image reconstructions from super-sampled data sets with resolution modeling in PET imaging
Li, Yusheng; Matej, Samuel; Metzler, Scott D.
2014-01-01
Purpose: Spatial resolution in positron emission tomography (PET) is still a limiting factor in many imaging applications. To improve the spatial resolution for an existing scanner with fixed crystal sizes, mechanical movements such as scanner wobbling and object shifting have been considered for PET systems. Multiple acquisitions from different positions can provide complementary information and increased spatial sampling. The objective of this paper is to explore an efficient and useful reconstruction framework to reconstruct super-resolution images from super-sampled low-resolution data sets. Methods: The authors introduce a super-sampling data acquisition model based on the physical processes with tomographic, downsampling, and shifting matrices as its building blocks. Based on the model, we extend the MLEM and Landweber algorithms to reconstruct images from super-sampled data sets. The authors also derive a backprojection-filtration-like (BPF-like) method for the super-sampling reconstruction. Furthermore, they explore variant methods for super-sampling reconstructions: the separate super-sampling resolution-modeling reconstruction and the reconstruction without downsampling to further improve image quality at the cost of more computation. The authors use simulated reconstruction of a resolution phantom to evaluate the three types of algorithms with different super-samplings at different count levels. Results: Contrast recovery coefficient (CRC) versus background variability, as an image-quality metric, is calculated at each iteration for all reconstructions. The authors observe that all three algorithms can significantly and consistently achieve increased CRCs at fixed background variability and reduce background artifacts with super-sampled data sets at the same count levels. For the same super-sampled data sets, the MLEM method achieves better image quality than the Landweber method, which in turn achieves better image quality than the BPF-like method. The
A Robust State-Space Kinetics-Guided Framework for Dynamic PET Image Reconstruction
Tong, S; Alessio, A M; Kinahan, P E; Liu, H; Shi, P
2011-01-01
Dynamic PET image reconstruction is a challenging issue due to the low SNR and the large quantity of spatio-temporal data. We propose a robust state-space image reconstruction (SSIR) framework for activity reconstruction in dynamic PET. Unlike statistically-based frame-by-frame methods, tracer kinetic modeling is incorporated to provide physiological guidance for the reconstruction, harnessing the temporal information of the dynamic data. Dynamic reconstruction is formulated in a state-space representation, where a compartmental model describes the kinetic processes in a continuous-time system equation, and the imaging data is expressed in a discrete measurement equation. Tracer activity concentrations are treated as the state variables, and are estimated from the dynamic data. Sampled-data H∞ filtering is adopted for robust estimation. H∞ filtering makes no assumptions on the system and measurement statistics, and guarantees bounded estimation error for finite-energy disturbances, leading to robust performance for dynamic data with low SNR and/or errors. This alternative reconstruction approach could help to deal with unpredictable situations in imaging (e.g. data corruption from failed detector blocks) or inaccurate noise models. Experiments on synthetic phantom and patient PET data are performed to demonstrate feasibility of the SSIR framework, and to explore its potential advantages over frame-by-frame statistical reconstruction approaches. PMID:21441650
Reconstruction for 3D PET Based on Total Variation Constrained Direct Fourier Method
Yu, Haiqing; Chen, Zhi; Zhang, Heye; Loong Wong, Kelvin Kian; Chen, Yunmei; Liu, Huafeng
2015-01-01
This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventional 2D reconstruction algorithms. Given the locally piece-wise constant nature of PET images, we introduce the total variation (TV) based reconstruction schemes. More specifically, we formulate the 2D PET reconstruction problem as an optimization problem, whose objective function consists of TV norm of the reconstructed image and the data fidelity term measuring the consistency between the reconstructed image and sinogram. To solve the resulting minimization problem, we apply an efficient methods called the Bregman operator splitting algorithm with variable step size (BOSVS). Experiments based on Monte Carlo simulated data and real data are conducted as validations. The experiment results show that the proposed method produces higher accuracy than conventional direct Fourier (DF) (bias in BOSVS is 70% of ones in DF, variance of BOSVS is 80% of ones in DF). PMID:26398232
Comparison of reconstruction methods and quantitative accuracy in Siemens Inveon PET scanner
NASA Astrophysics Data System (ADS)
Ram Yu, A.; Kim, Jin Su; Kang, Joo Hyun; Moo Lim, Sang
2015-04-01
PET reconstruction is key to the quantification of PET data. To our knowledge, no comparative study of reconstruction methods has been performed to date. In this study, we compared reconstruction methods with various filters in terms of their spatial resolution, non-uniformities (NU), recovery coefficients (RCs), and spillover ratios (SORs). In addition, the linearity of reconstructed radioactivity between linearity of measured and true concentrations were also assessed. A Siemens Inveon PET scanner was used in this study. Spatial resolution was measured with NEMA standard by using a 1 mm3 sized 18F point source. Image quality was assessed in terms of NU, RC and SOR. To measure the effect of reconstruction algorithms and filters, data was reconstructed using FBP, 3D reprojection algorithm (3DRP), ordered subset expectation maximization 2D (OSEM 2D), and maximum a posteriori (MAP) with various filters or smoothing factors (β). To assess the linearity of reconstructed radioactivity, image quality phantom filled with 18F was used using FBP, OSEM and MAP (β =1.5 & 5 × 10-5). The highest achievable volumetric resolution was 2.31 mm3 and the highest RCs were obtained when OSEM 2D was used. SOR was 4.87% for air and 3.97% for water, obtained OSEM 2D reconstruction was used. The measured radioactivity of reconstruction image was proportional to the injected one for radioactivity below 16 MBq/ml when FBP or OSEM 2D reconstruction methods were used. By contrast, when the MAP reconstruction method was used, activity of reconstruction image increased proportionally, regardless of the amount of injected radioactivity. When OSEM 2D or FBP were used, the measured radioactivity concentration was reduced by 53% compared with true injected radioactivity for radioactivity <16 MBq/ml. The OSEM 2D reconstruction method provides the highest achievable volumetric resolution and highest RC among all the tested methods and yields a linear relation between the measured and true
Berkels, Benjamin; Rumpf, Martin; Bauer, Sebastian; Ettl, Svenja; Arold, Oliver; Hornegger, Joachim
2013-09-15
Purpose: The intraprocedural tracking of respiratory motion has the potential to substantially improve image-guided diagnosis and interventions. The authors have developed a sparse-to-dense registration approach that is capable of recovering the patient's external 3D body surface and estimating a 4D (3D + time) surface motion field from sparse sampling data and patient-specific prior shape knowledge.Methods: The system utilizes an emerging marker-less and laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is recovered, which describes the spatio-temporal 4D deformation of the complete patient body surface, depending on the type and state of respiration. It yields both a reconstruction of the instantaneous patient shape and a high-dimensional respiratory surrogate for respiratory motion tracking. The method is validated on a 4D CT respiration phantom and evaluated on both real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured-light scanner.Results: In the experiments, the authors estimated surface motion fields with the proposed algorithm on 256 datasets from 16 subjects and in different respiration states, achieving a mean surface reconstruction accuracy of ±0.23 mm with respect to ground truth data—down from a mean initial surface mismatch of 5.66 mm. The 95th percentile of the local residual mesh-to-mesh distance after registration did not exceed 1.17 mm for any subject. On average, the total runtime of our proof of concept CPU implementation is 2.3 s per frame, outperforming related work substantially.Conclusions: In external beam radiation therapy, the approach holds potential for patient monitoring during treatment using the reconstructed surface, and for motion-compensated dose delivery using
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.
Soultan, D; Murphy, J; James, C; Hoh, C; Moiseenko, V; Cervino, L; Gill, B
2015-06-15
Purpose: To assess the accuracy of internal target volume (ITV) segmentation of lung tumors for treatment planning of simultaneous integrated boost (SIB) radiotherapy as seen in 4D PET/CT images, using a novel 3D-printed phantom. Methods: The insert mimics high PET tracer uptake in the core and 50% uptake in the periphery, by using a porous design at the periphery. A lung phantom with the insert was placed on a programmable moving platform. Seven breathing waveforms of ideal and patient-specific respiratory motion patterns were fed to the platform, and 4D PET/CT scans were acquired of each of them. CT images were binned into 10 phases, and PET images were binned into 5 phases following the clinical protocol. Two scenarios were investigated for segmentation: a gate 30–70 window, and no gating. The radiation oncologist contoured the outer ITV of the porous insert with on CT images, while the internal void volume with 100% uptake was contoured on PET images for being indistinguishable from the outer volume in CT images. Segmented ITVs were compared to the expected volumes based on known target size and motion. Results: 3 ideal breathing patterns, 2 regular-breathing patient waveforms, and 2 irregular-breathing patient waveforms were used for this study. 18F-FDG was used as the PET tracer. The segmented ITVs from CT closely matched the expected motion for both no gating and gate 30–70 window, with disagreement of contoured ITV with respect to the expected volume not exceeding 13%. PET contours were seen to overestimate volumes in all the cases, up to more than 40%. Conclusion: 4DPET images of a novel 3D printed phantom designed to mimic different uptake values were obtained. 4DPET contours overestimated ITV volumes in all cases, while 4DCT contours matched expected ITV volume values. Investigation of the cause and effects of the discrepancies is undergoing.
Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework
NASA Astrophysics Data System (ADS)
Matej, Samuel; Daube-Witherspoon, Margaret E.; Karp, Joel S.
2016-05-01
Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of time-of-flight (TOF) scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (DIRECT: direct image reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias versus variance performance to iterative TOF reconstruction with a matched resolution model.
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.
Novosad, Philip; Reader, Andrew J
2016-06-21
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions
NASA Astrophysics Data System (ADS)
Novosad, Philip; Reader, Andrew J.
2016-06-01
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral
List mode reconstruction for PET with motion compensation: A simulation study
Qi, Jinyi; Huesman, Ronald H.
2002-07-03
Motion artifacts can be a significant factor that limits the image quality in high-resolution PET. Surveillance systems have been developed to track the movements of the subject during a scan. Development of reconstruction algorithms that are able to compensate for the subject motion will increase the potential of PET. In this paper we present a list mode likelihood reconstruction algorithm with the ability of motion compensation. The subject moti is explicitly modeled in the likelihood function. The detections of each detector pair are modeled as a Poisson process with time vary ingrate function. The proposed method has several advantages over the existing methods. It uses all detected events and does not introduce any interpolation error. Computer simulations show that the proposed method can compensate simulated subject movements and that the reconstructed images have no visible motion artifacts.
List mode reconstruction for PET with motion compensation: A simulation study
Qi, Jinyi; Huesman, Ronald H.
2002-07-01
Motion artifacts can be a significant factor that limits the image quality in high-resolution PET. Surveillance systems have been developed to track the movements of the subject during a scan. Development of reconstruction algorithms that are able to compensate for the subject motion will increase the potential of PET. In this paper we present a list mode likelihood reconstruction algorithm with the ability of motion compensation. The subject motion is explicitly modeled in the likelihood function. The detections of each detector pair are modeled as a Poisson process with time-varying rate function. The proposed method has several advantages over the existing methods. It uses all detected events and does not introduce any interpolation error. Computer simulations show that the proposed method can compensate simulated subject movements and that the reconstructed images have no visible motion artifacts.
Regional MLEM reconstruction strategy for PET-based treatment verification in ion beam radiotherapy
NASA Astrophysics Data System (ADS)
Gianoli, Chiara; Bauer, Julia; Riboldi, Marco; De Bernardi, Elisabetta; Fattori, Giovanni; Baselli, Giuseppe; Debus, Jürgen; Parodi, Katia; Baroni, Guido
2014-11-01
In ion beam radiotherapy, PET-based treatment verification provides a consistency check of the delivered treatment with respect to a simulation based on the treatment planning. In this work the region-based MLEM reconstruction algorithm is proposed as a new evaluation strategy in PET-based treatment verification. The comparative evaluation is based on reconstructed PET images in selected regions, which are automatically identified on the expected PET images according to homogeneity in activity values. The strategy was tested on numerical and physical phantoms, simulating mismatches between the planned and measured β+ activity distributions. The region-based MLEM reconstruction was demonstrated to be robust against noise and the sensitivity of the strategy results were comparable to three voxel units, corresponding to 6 mm in numerical phantoms. The robustness of the region-based MLEM evaluation outperformed the voxel-based strategies. The potential of the proposed strategy was also retrospectively assessed on patient data and further clinical validation is envisioned.
Conditional entropy maximization for PET image reconstruction using adaptive mesh model.
Zhu, Hongqing; Shu, Huazhong; Zhou, Jian; Dai, Xiubin; Luo, Limin
2007-04-01
Iterative image reconstruction algorithms have been widely used in the field of positron emission tomography (PET). However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations is high. In this paper, we propose a new algorithm to reconstruct an image from the PET emission projection data by using the conditional entropy maximization and the adaptive mesh model. In a traditional tomography reconstruction method, the reconstructed image is directly computed in the pixel domain. Unlike this kind of methods, the proposed approach is performed by estimating the nodal values from the observed projection data in a mesh domain. In our method, the initial Delaunay triangulation mesh is generated from a set of randomly selected pixel points, and it is then modified according to the pixel intensity value of the estimated image at each iteration step in which the conditional entropy maximization is used. The advantage of using the adaptive mesh model for image reconstruction is that it provides a natural spatially adaptive smoothness mechanism. In experiments using the synthetic and clinical data, it is found that the proposed algorithm is more robust to noise compared to the common pixel-based MLEM algorithm and mesh-based MLEM with a fixed mesh structure. PMID:17368841
Event-by-event PET image reconstruction using list-mode origin ensembles algorithm
NASA Astrophysics Data System (ADS)
Andreyev, Andriy
2016-03-01
There is a great demand for real time or event-by-event (EBE) image reconstruction in emission tomography. Ideally, as soon as event has been detected by the acquisition electronics, it needs to be used in the image reconstruction software. This would greatly speed up the image reconstruction since most of the data will be processed and reconstructed while the patient is still undergoing the scan. Unfortunately, the current industry standard is that the reconstruction of the image would not start until all the data for the current image frame would be acquired. Implementing an EBE reconstruction for MLEM family of algorithms is possible, but not straightforward as multiple (computationally expensive) updates to the image estimate are required. In this work an alternative Origin Ensembles (OE) image reconstruction algorithm for PET imaging is converted to EBE mode and is investigated whether it is viable alternative for real-time image reconstruction. In OE algorithm all acquired events are seen as points that are located somewhere along the corresponding line-of-responses (LORs), together forming a point cloud. Iteratively, with a multitude of quasi-random shifts following the likelihood function the point cloud converges to a reflection of an actual radiotracer distribution with the degree of accuracy that is similar to MLEM. New data can be naturally added into the point cloud. Preliminary results with simulated data show little difference between regular reconstruction and EBE mode, proving the feasibility of the proposed approach.
On the assessment of spatial resolution of PET systems with iterative image reconstruction
NASA Astrophysics Data System (ADS)
Gong, Kuang; Cherry, Simon R.; Qi, Jinyi
2016-03-01
Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.
High resolution image reconstruction method for a double-plane PET system with changeable spacing
NASA Astrophysics Data System (ADS)
Gu, Xiao-Yue; Zhou, Wei; Li, Lin; Wei, Long; Yin, Peng-Fei; Shang, Lei-Min; Yun, Ming-Kai; Lu, Zhen-Rui; Huang, Xian-Chao
2016-05-01
Breast-dedicated positron emission tomography (PET) imaging techniques have been developed in recent years. Their capacities to detect millimeter-sized breast tumors have been the subject of many studies. Some of them have been confirmed with good results in clinical applications. With regard to biopsy application, a double-plane detector arrangement is practicable, as it offers the convenience of breast immobilization. However, the serious blurring effect of the double-plane PET, with changeable spacing for different breast sizes, should be studied. We investigated a high resolution reconstruction method applicable for a double-plane PET. The distance between the detector planes is changeable. Geometric and blurring components were calculated in real-time for different detector distances, and accurate geometric sensitivity was obtained with a new tube area model. Resolution recovery was achieved by estimating blurring effects derived from simulated single gamma response information. The results showed that the new geometric modeling gave a more finite and smooth sensitivity weight in the double-plane PET. The blurring component yielded contrast recovery levels that could not be reached without blurring modeling, and improved visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images were achieved with the blurring component. Statistical noise had lower variance at the voxel level with blurring modeling at matched resolution, compared to without blurring modeling. In distance-changeable double-plane PET, finite resolution modeling during reconstruction achieved resolution recovery, without noise amplification. Supported by Knowledge Innovation Project of The Chinese Academy of Sciences (KJCX2-EW-N06)
The SRT reconstruction algorithm for semiquantification in PET imaging
Kastis, George A.; Gaitanis, Anastasios; Samartzis, Alexandros P.; Fokas, Athanasios S.
2015-10-15
Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of {sup 18}F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT
EM reconstruction of dual isotope PET using staggered injections and prompt gamma positron emitters
Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna
2014-01-01
Purpose: The aim of dual isotope positron emission tomography (DIPET) is to create two separate images of two coinjected PET radiotracers. DIPET shortens the duration of the study, reduces patient discomfort, and produces perfectly coregistered images compared to the case when two radiotracers would be imaged independently (sequential PET studies). Reconstruction of data from such simultaneous acquisition of two PET radiotracers is difficult because positron decay of any isotope creates only 511 keV photons; therefore, the isotopes cannot be differentiated based on the detected energy. Methods: Recently, the authors have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as 18F or 11C) and radiotracer B in which positron decay is accompanied by the emission of a high-energy (HE) prompt gamma (such as 38K or 60Cu). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons allow the authors to identify the lines-of-response (LORs) of isotope B. These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm. In this work the authors propose a new version of the DIPET EM-based reconstruction algorithm that allows the authors to include an additional, independent estimate of radiotracer A distribution which may be obtained if radioisotopes are administered using a staggered injections method. In this work the method is tested on simple simulations of static PET acquisitions. Results: The authors’ experiments performed using Monte-Carlo simulations with static acquisitions demonstrate that the combined method provides better results (crosstalk errors decrease by up to 50%) than the positron-gamma DIPET method or staggered injections alone. Conclusions: The authors demonstrate that the authors’ new EM algorithm which combines information from triple coincidences with prompt gammas and staggered injections improves
Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms for the IndyPET scanner.
Frese, Thomas; Rouze, Ned C; Bouman, Charles A; Sauer, Ken; Hutchins, Gary D
2003-02-01
We quantitatively compare filtered backprojection (FBP), expectation-maximization (EM), and Bayesian reconstruction algorithms as applied to the IndyPET scanner--a dedicated research scanner which has been developed for small and intermediate field of view imaging applications. In contrast to previous approaches that rely on Monte Carlo simulations, a key feature of our investigation is the use of an empirical system kernel determined from scans of line source phantoms. This kernel is incorporated into the forward model of the EM and Bayesian algorithms to achieve resolution recovery. Three data sets are used, data collected on the IndyPET scanner using a bar phantom and a Hoffman three-dimensional brain phantom, and simulated data containing a hot lesion added to a uniform background. Reconstruction quality is analyzed quantitatively in terms of bias-variance measures (bar phantom) and mean square error (lesion phantom). We observe that without use of the empirical system kernel, the FBP, EM, and Bayesian algorithms give similar performance. However, with the inclusion of the empirical kernel, the iterative algorithms provide superior reconstructions compared with FBP, both in terms of visual quality and quantitative measures. Furthermore, Bayesian methods outperform EM. We conclude that significant improvements in reconstruction quality can be realized by combining accurate models of the system response with Bayesian reconstruction algorithms. PMID:12716002
Pragmatic fully 3D image reconstruction for the MiCES mouse imaging PET scanner
NASA Astrophysics Data System (ADS)
Lee, Kisung; Kinahan, Paul E.; Fessler, Jeffrey A.; Miyaoka, Robert S.; Janes, Marie; Lewellen, Tom K.
2004-10-01
We present a pragmatic approach to image reconstruction for data from the micro crystal elements system (MiCES) fully 3D mouse imaging positron emission tomography (PET) scanner under construction at the University of Washington. Our approach is modelled on fully 3D image reconstruction used in clinical PET scanners, which is based on Fourier rebinning (FORE) followed by 2D iterative image reconstruction using ordered-subsets expectation-maximization (OSEM). The use of iterative methods allows modelling of physical effects (e.g., statistical noise, detector blurring, attenuation, etc), while FORE accelerates the reconstruction process by reducing the fully 3D data to a stacked set of independent 2D sinograms. Previous investigations have indicated that non-stationary detector point-spread response effects, which are typically ignored for clinical imaging, significantly impact image quality for the MiCES scanner geometry. To model the effect of non-stationary detector blurring (DB) in the FORE+OSEM(DB) algorithm, we have added a factorized system matrix to the ASPIRE reconstruction library. Initial results indicate that the proposed approach produces an improvement in resolution without an undue increase in noise and without a significant increase in the computational burden. The impact on task performance, however, remains to be evaluated.
NASA Astrophysics Data System (ADS)
Liang, Yicheng; Peng, Hao
2015-02-01
Depth-of-interaction (DOI) poses a major challenge for a PET system to achieve uniform spatial resolution across the field-of-view, particularly for small animal and organ-dedicated PET systems. In this work, we implemented an analytical method to model system matrix for resolution recovery, which was then incorporated in PET image reconstruction on a graphical processing unit platform, due to its parallel processing capacity. The method utilizes the concepts of virtual DOI layers and multi-ray tracing to calculate the coincidence detection response function for a given line-of-response. The accuracy of the proposed method was validated for a small-bore PET insert to be used for simultaneous PET/MR breast imaging. In addition, the performance comparisons were studied among the following three cases: 1) no physical DOI and no resolution modeling; 2) two physical DOI layers and no resolution modeling; and 3) no physical DOI design but with a different number of virtual DOI layers. The image quality was quantitatively evaluated in terms of spatial resolution (full-width-half-maximum and position offset), contrast recovery coefficient and noise. The results indicate that the proposed method has the potential to be used as an alternative to other physical DOI designs and achieve comparable imaging performances, while reducing detector/system design cost and complexity.
Liang, Yicheng; Peng, Hao
2015-02-01
Depth-of-interaction (DOI) poses a major challenge for a PET system to achieve uniform spatial resolution across the field-of-view, particularly for small animal and organ-dedicated PET systems. In this work, we implemented an analytical method to model system matrix for resolution recovery, which was then incorporated in PET image reconstruction on a graphical processing unit platform, due to its parallel processing capacity. The method utilizes the concepts of virtual DOI layers and multi-ray tracing to calculate the coincidence detection response function for a given line-of-response. The accuracy of the proposed method was validated for a small-bore PET insert to be used for simultaneous PET/MR breast imaging. In addition, the performance comparisons were studied among the following three cases: 1) no physical DOI and no resolution modeling; 2) two physical DOI layers and no resolution modeling; and 3) no physical DOI design but with a different number of virtual DOI layers. The image quality was quantitatively evaluated in terms of spatial resolution (full-width-half-maximum and position offset), contrast recovery coefficient and noise. The results indicate that the proposed method has the potential to be used as an alternative to other physical DOI designs and achieve comparable imaging performances, while reducing detector/system design cost and complexity. PMID:25591118
NASA Astrophysics Data System (ADS)
Lartizien, Carole; Kinahan, Paul E.; Comtat, Claude; Lin, Michael; Swensson, Richard G.; Trebossen, Regine; Bendriem, Bernard
2000-04-01
This work presents initial results from observer detection performance studies using the same volume visualization software tools that are used in clinical PET oncology imaging. Research into the FORE+OSEM and FORE+AWOSEM statistical image reconstruction methods tailored to whole- body 3D PET oncology imaging have indicated potential improvements in image SNR compared to currently used analytic reconstruction methods (FBP). To assess the resulting impact of these reconstruction methods on the performance of human observers in detecting and localizing tumors, we use a non- Monte Carlo technique to generate multiple statistically accurate realizations of 3D whole-body PET data, based on an extended MCAT phantom and with clinically realistic levels of statistical noise. For each realization, we add a fixed number of randomly located 1 cm diam. lesions whose contrast is varied among pre-calibrated values so that the range of true positive fractions is well sampled. The observer is told the number of tumors and, similar to the AFROC method, asked to localize all of them. The true positive fraction for the three algorithms (FBP, FORE+OSEM, FORE+AWOSEM) as a function of lesion contrast is calculated, although other protocols could be compared. A confidence level for each tumor is also recorded for incorporation into later AFROC analysis.
Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy
Tang, Jing; Rahmim, Arman
2015-01-01
A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE
Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy
NASA Astrophysics Data System (ADS)
Tang, Jing; Rahmim, Arman
2015-01-01
A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE
Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M.; El Fakhri, Georges
2013-01-01
Purpose: Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Methods: Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. Results: At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%–29% and 32%–70% for 50 × 106 and 10 × 106 detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40–50 iterations), while more than 500 iterations were needed for CG. Conclusions: The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method. PMID:24089922
Mikhaylova, E.; Kolstein, M.; De Lorenzo, G.; Chmeissani, M.
2014-01-01
A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm3) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics. PMID:25018777
Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M
2014-07-01
A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm(3)) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics. PMID:25018777
Reconstruction of an input function from a dynamic PET water image using multiple tissue curves.
Kudomi, Nobuyuki; Maeda, Yukito; Yamamoto, Yuka; Nishiyama, Yoshihiro
2016-08-01
Quantification of cerebral blood flow (CBF) is important for the understanding of normal and pathologic brain physiology. When CBF is assessed using PET with [Formula: see text] (15)O or C(15)O2, its calculation requires an arterial input function, which generally requires invasive arterial blood sampling. The aim of the present study was to develop a new technique to reconstruct an image derived input function (IDIF) from a dynamic [Formula: see text] (15)O PET image as a completely non-invasive approach. Our technique consisted of using a formula to express the input using tissue curve with rate constant parameter. For multiple tissue curves extracted from the dynamic image, the rate constants were estimated so as to minimize the sum of the differences of the reproduced inputs expressed by the extracted tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects (n = 29) and was compared to the blood sampling method. Simulation studies were performed to examine the magnitude of potential biases in CBF and to optimize the number of multiple tissue curves used for the input reconstruction. In the PET study, the estimated IDIFs were well reproduced against the measured ones. The difference between the calculated CBF values obtained using the two methods was small as around <8% and the calculated CBF values showed a tight correlation (r = 0.97). The simulation showed that errors associated with the assumed parameters were <10%, and that the optimal number of tissue curves to be used was around 500. Our results demonstrate that IDIF can be reconstructed directly from tissue curves obtained through [Formula: see text] (15)O PET imaging. This suggests the possibility of using a completely non-invasive technique to assess CBF in patho-physiological studies. PMID:27401833
Reconstruction of an input function from a dynamic PET water image using multiple tissue curves
NASA Astrophysics Data System (ADS)
Kudomi, Nobuyuki; Maeda, Yukito; Yamamoto, Yuka; Nishiyama, Yoshihiro
2016-08-01
Quantification of cerebral blood flow (CBF) is important for the understanding of normal and pathologic brain physiology. When CBF is assessed using PET with {{\\text{H}}2} 15O or C15O2, its calculation requires an arterial input function, which generally requires invasive arterial blood sampling. The aim of the present study was to develop a new technique to reconstruct an image derived input function (IDIF) from a dynamic {{\\text{H}}2} 15O PET image as a completely non-invasive approach. Our technique consisted of using a formula to express the input using tissue curve with rate constant parameter. For multiple tissue curves extracted from the dynamic image, the rate constants were estimated so as to minimize the sum of the differences of the reproduced inputs expressed by the extracted tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects (n = 29) and was compared to the blood sampling method. Simulation studies were performed to examine the magnitude of potential biases in CBF and to optimize the number of multiple tissue curves used for the input reconstruction. In the PET study, the estimated IDIFs were well reproduced against the measured ones. The difference between the calculated CBF values obtained using the two methods was small as around <8% and the calculated CBF values showed a tight correlation (r = 0.97). The simulation showed that errors associated with the assumed parameters were <10%, and that the optimal number of tissue curves to be used was around 500. Our results demonstrate that IDIF can be reconstructed directly from tissue curves obtained through {{\\text{H}}2} 15O PET imaging. This suggests the possibility of using a completely non-invasive technique to assess CBF in patho-physiological studies.
Three-dimensional image reconstruction for PET by multi-slice rebinning and axial image filtering.
Lewittt, R M; Muehllehner, G; Karpt, J S
1994-03-01
A fast method is described for reconstructing volume images from three-dimensional (3D) coincidence data in positron emission tomography (PET). The reconstruction method makes use of all coincidence data acquired by high-sensitivity PET systems that do not have inter-slice absorbers (septa) to restrict the axial acceptance angle. The reconstruction method requires only a small amount of storage and computation, making it well suited for dynamic and whole-body studies. The method consists of three steps: (i) rebinning of coincidence data into a stack of 2D sinograms; (ii) slice-by-slice reconstruction of the sinogram associated with each slice to produce a preliminary 3D image having strong blurring in the axial (z) direction, but with different blurring at different z positions; and (iii) spatially variant filtering of the 3D image in the axial direction (i.e. 1D filtering in z for each x-y column) to produce the final image. The first step involves a new form of the rebinning operation in which multiple sinograms are incremented for each oblique coincidence line (multi-slice rebinning). The axial filtering step is formulated and implemented using the singular value decomposition (SVD). The method has been applied successfully to simulated data and to measured data for different kinds of phantom (multiple point sources, multiple discs, a cylinder with cold spheres, and a 3D brain phantom). PMID:15551583
Improving lesion detectability in PET imaging with a penalized likelihood reconstruction algorithm
NASA Astrophysics Data System (ADS)
Wangerin, Kristen A.; Ahn, Sangtae; Ross, Steven G.; Kinahan, Paul E.; Manjeshwar, Ravindra M.
2015-03-01
Ordered Subset Expectation Maximization (OSEM) is currently the most widely used image reconstruction algorithm for clinical PET. However, OSEM does not necessarily provide optimal image quality, and a number of alternative algorithms have been explored. We have recently shown that a penalized likelihood image reconstruction algorithm using the relative difference penalty, block sequential regularized expectation maximization (BSREM), achieves more accurate lesion quantitation than OSEM, and importantly, maintains acceptable visual image quality in clinical wholebody PET. The goal of this work was to evaluate lesion detectability with BSREM versus OSEM. We performed a twoalternative forced choice study using 81 patient datasets with lesions of varying contrast inserted into the liver and lung. At matched imaging noise, BSREM and OSEM showed equivalent detectability in the lungs, and BSREM outperformed OSEM in the liver. These results suggest that BSREM provides not only improved quantitation and clinically acceptable visual image quality as previously shown but also improved lesion detectability compared to OSEM. We then modeled this detectability study, applying both nonprewhitening (NPW) and channelized Hotelling (CHO) model observers to the reconstructed images. The CHO model observer showed good agreement with the human observers, suggesting that we can apply this model to future studies with varying simulation and reconstruction parameters.
LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms
Kadrmas, Dan J
2010-01-01
Iterative statistical reconstruction methods are becoming the standard in positron emission tomography (PET). Conventional maximum-likelihood expectation-maximization (MLEM) and ordered-subsets (OSEM) algorithms act on data which has been pre-processed into corrected, evenly-spaced histograms; however, such pre-processing corrupts the Poisson statistics. Recent advances have incorporated attenuation, scatter, and randoms compensation into the iterative reconstruction. The objective of this work was to incorporate the remaining preprocessing steps, including arc correction, to reconstruct directly from raw unevenly-spaced line-of-response (LOR) histograms. This exactly preserves Poisson statistics and full spatial information in a manner closely related to listmode ML, making full use of the ML statistical model. The LOR-OSEM algorithm was implemented using a rotation-based projector which maps directly to the unevenly-spaced LOR grid. Simulation and phantom experiments were performed to characterize resolution, contrast, and noise properties for 2D PET. LOR-OSEM provided a beneficial noise-resolution tradeoff, outperforming AW-OSEM by about the same margin that AW-OSEM outperformed pre-corrected OSEM. The relationship between LOR-ML and listmode ML algorithms was explored, and implementation differences are discussed. LOR-OSEM is a viable alternative to AW-OSEM for histogram-based reconstruction with improved spatial resolution and noise properties. PMID:15566171
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
Sinogram bow-tie filtering in FBP PET reconstruction.
Abella, M; Vaquero, J J; Soto-Montenegro, M L; Lage, E; Desco, M
2009-05-01
Low-pass filtering of sinograms in the radial direction is the most common practice to limit noise amplification in filtered back projection (FBP) reconstruction of positron emission tomography studies. Other filtering strategies have been proposed to prevent the loss in resolution due to low-pass radial filters, although results have been diverse. Using the well-known properties of the Fourier transform of a sinogram, the authors defined a binary mask that matches the expected shape of the support region in the Fourier domain of the sinogram ("bow tie"). This mask was smoothed by a convolution with a ten-point Gaussian kernel which not only avoids ringing but also introduces a pre-emphasis at low frequencies. A new filtering scheme for FBP is proposed, comprising this smoothed bow-tie filter combined with a standard radial filter and an axial filter. The authors compared the performance of the bow-tie filtering scheme with that of other previously reported methods: Standard radial filtering, angular filtering, and stackgram-domain filtering. All the quantitative data in the comparisons refer to a baseline reconstruction using a ramp filter only. When using the smallest size of the Gaussian kernel in the stackgram domain, the authors achieved a noise reduction of 33% at the cost of degrading radial and tangential resolutions (14.5% and 16%, respectively, for cubic interpolation). To reduce the noise by 30%, the angular filter produced a larger degradation of contrast (3%) and tangential resolution (46% at 10 mm from the center of the field of view) and showed noticeable artifacts in the form of circular blurring dependent on the distance to the center of the field of view. For a similar noise reduction (33%), the proposed bow-tie filtering scheme yielded optimum results in resolution (gain in radial resolution of 10%) and contrast (1% increase) when compared with any of the other filters alone. Experiments with rodent images showed noticeable image quality
NASA Astrophysics Data System (ADS)
Tang, Jing; Rahmim, Arman; Lautamäki, Riikka; Lodge, Martin A.; Bengel, Frank M.; Tsui, Benjamin M. W.
2009-05-01
The purpose of this study is to optimize the dynamic Rb-82 cardiac PET acquisition and reconstruction protocols for maximum myocardial perfusion defect detection using realistic simulation data and task-based evaluation. Time activity curves (TACs) of different organs under both rest and stress conditions were extracted from dynamic Rb-82 PET images of five normal patients. Combined SimSET-GATE Monte Carlo simulation was used to generate nearly noise-free cardiac PET data from a time series of 3D NCAT phantoms with organ activities modeling different pre-scan delay times (PDTs) and total acquisition times (TATs). Poisson noise was added to the nearly noise-free projections and the OS-EM algorithm was applied to generate noisy reconstructed images. The channelized Hotelling observer (CHO) with 32× 32 spatial templates corresponding to four octave-wide frequency channels was used to evaluate the images. The area under the ROC curve (AUC) was calculated from the CHO rating data as an index for image quality in terms of myocardial perfusion defect detection. The 0.5 cycle cm-1 Butterworth post-filtering on OS-EM (with 21 subsets) reconstructed images generates the highest AUC values while those from iteration numbers 1 to 4 do not show different AUC values. The optimized PDTs for both rest and stress conditions are found to be close to the cross points of the left ventricular chamber and myocardium TACs, which may promote an individualized PDT for patient data processing and image reconstruction. Shortening the TATs for <~3 min from the clinically employed acquisition time does not affect the myocardial perfusion defect detection significantly for both rest and stress studies.
Tohme, Michel S.; Qi, Jinyi
2010-01-01
Purpose: The accuracy of the system model that governs the transformation from the image space to the projection space in positron emission tomography (PET) greatly affects the quality of reconstructed images. For efficient computation in iterative reconstructions, the system model in PET can be factored into a product of geometric projection and sinogram blurring function. To further speed up reconstruction, fully 3D PET data can be rebinned into a stack of 2D sinograms and then be reconstructed using 2D iterative algorithms. The purpose of this work is to develop a method to estimate the sinogram blurring function to be used in reconstruction of Fourier-rebinned data. Methods: In a previous work, the authors developed an approach to estimating the sinogram blurring function of nonrebinned PET data from experimental scans of point sources. In this study, the authors extend this method to the estimation of sinogram blurring function for Fourier-rebinned PET data. A point source was scanned at a set of sampled positions in the microPET II scanner. The sinogram blurring function is considered to be separable between the transaxial and axial directions. A radially and angularly variant 2D blurring function is estimated from Fourier-rebinned point source scans to model the transaxial blurring with consideration of the detector block structure of the scanner; a space-variant 1D blurring kernel along the axial direction is estimated separately to model the correlation between neighboring planes due to detector intrinsic blurring and Fourier rebinning. The estimated sinogram blurring function is incorporated in a 2D maximum a posteriori (MAP) reconstruction algorithm for image reconstruction. Results: Physical phantom experiments were performed on the microPET II scanner to validate the proposed method. The authors compared the proposed method to 2D MAP reconstruction without sinogram blurring model and 2D MAP reconstruction with a Monte Carlo based blurring model. The
Reconstruction of signal in plastic scintillator of PET using Tikhonov regularization.
Raczynski, Lech
2015-08-01
The new concept of Time of Flight Positron Emission Tomography (TOF-PET) detection system, which allows for single bed imaging of the whole human body, is currently under development at the Jagiellonian University. The Jagiellonian-PET (J-PET) detector improves the TOF resolution due to the use of fast plastic scintillators. Since registration of the waveform of signals with duration times of few nanoseconds is not feasible, a novel front-end electronics allowing for sampling in a voltage domain at four thresholds was developed. To take fully advantage of these fast signals a novel scheme of recovery of the waveform of the signal, based on idea from the Tikhonov regularization method, is presented. From the Bayes theory the properties of regularized solution, especially its covariance matrix, may be easily derived. This step is crucial to introduce and prove the formula for calculations of the signal recovery error. The method is tested using signals registered by means of the single detection module of the J-PET detector built out from the 30 cm long plastic scintillator strip. It is shown that using the recovered waveform of the signals, instead of samples at four voltage levels alone, improves the spatial resolution of the hit position reconstruction from 1.05 cm to 0.94 cm. Moreover, the obtained result is only slightly worse than the one evaluated using the original raw-signal. The spatial resolution calculated under these conditions is equal to 0.93 cm. PMID:26736869
Statistical image reconstruction methods for simultaneous emission/transmission PET scans
Erdogan, H.; Fessler, J.A.
1996-12-31
Transmission scans are necessary for estimating the attenuation correction factors (ACFs) to yield quantitatively accurate PET emission images. To reduce the total scan time, post-injection transmission scans have been proposed in which one can simultaneously acquire emission and transmission data using rod sources and sinogram windowing. However, since the post-injection transmission scans are corrupted by emission coincidences, accurate correction for attenuation becomes more challenging. Conventional methods (emission subtraction) for ACF computation from post-injection scans are suboptimal and require relatively long scan times. We introduce statistical methods based on penalized-likelihood objectives to compute ACFs and then use them to reconstruct lower noise PET emission images from simultaneous transmission/emission scans. Simulations show the efficacy of the proposed methods. These methods improve image quality and SNR of the estimates as compared to conventional methods.
Assessment of a fully 3D Monte Carlo reconstruction method for preclinical PET with iodine-124
NASA Astrophysics Data System (ADS)
Moreau, M.; Buvat, I.; Ammour, L.; Chouin, N.; Kraeber-Bodéré, F.; Chérel, M.; Carlier, T.
2015-03-01
Iodine-124 is a radionuclide well suited to the labeling of intact monoclonal antibodies. Yet, accurate quantification in preclinical imaging with I-124 is challenging due to the large positron range and a complex decay scheme including high-energy gammas. The aim of this work was to assess the quantitative performance of a fully 3D Monte Carlo (MC) reconstruction for preclinical I-124 PET. The high-resolution small animal PET Inveon (Siemens) was simulated using GATE 6.1. Three system matrices (SM) of different complexity were calculated in addition to a Siddon-based ray tracing approach for comparison purpose. Each system matrix accounted for a more or less complete description of the physics processes both in the scanned object and in the PET scanner. One homogeneous water phantom and three heterogeneous phantoms including water, lungs and bones were simulated, where hot and cold regions were used to assess activity recovery as well as the trade-off between contrast recovery and noise in different regions. The benefit of accounting for scatter, attenuation, positron range and spurious coincidences occurring in the object when calculating the system matrix used to reconstruct I-124 PET images was highlighted. We found that the use of an MC SM including a thorough modelling of the detector response and physical effects in a uniform water-equivalent phantom was efficient to get reasonable quantitative accuracy in homogeneous and heterogeneous phantoms. Modelling the phantom heterogeneities in the SM did not necessarily yield the most accurate estimate of the activity distribution, due to the high variance affecting many SM elements in the most sophisticated SM.
NASA Astrophysics Data System (ADS)
Ahn, Sangtae; Ross, Steven G.; Asma, Evren; Miao, Jun; Jin, Xiao; Cheng, Lishui; Wollenweber, Scott D.; Manjeshwar, Ravindra M.
2015-08-01
Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.
Linear array implementation of the EM algorithm for PET image reconstruction
Rajan, K.; Patnaik, L.M.; Ramakrishna, J.
1995-08-01
The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution back projection algorithms. However, the PET image reconstruction based on the EM algorithm is computationally burdensome for today`s single processor systems. In addition, a large memory is required for the storage of the image, projection data, and the probability matrix. Since the computations are easily divided into tasks executable in parallel, multiprocessor configurations are the ideal choice for fast execution of the EM algorithms. In tis study, the authors attempt to overcome these two problems by parallelizing the EM algorithm on a multiprocessor systems. The parallel EM algorithm on a linear array topology using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PE`s) has been implemented. The performance of the EM algorithm on a 386/387 machine, IBM 6000 RISC workstation, and on the linear array system is discussed and compared. The results show that the computational speed performance of a linear array using 8 DSP chips as PE`s executing the EM image reconstruction algorithm is about 15.5 times better than that of the IBM 6000 RISC workstation. The novelty of the scheme is its simplicity. The linear array topology is expandable with a larger number of PE`s. The architecture is not dependant on the DSP chip chosen, and the substitution of the latest DSP chip is straightforward and could yield better speed performance.
Ahn, Sangtae; Ross, Steven G; Asma, Evren; Miao, Jun; Jin, Xiao; Cheng, Lishui; Wollenweber, Scott D; Manjeshwar, Ravindra M
2015-08-01
Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs. PMID:26158503
Maughan, N; Conti, M; Parikh, P; Faul, D; Laforest, R
2015-06-15
Purpose: Imaging Y-90 microspheres with PET/MRI following hepatic radioembolization has the potential for predicting treatment outcome and, in turn, improving patient care. The positron decay branching ratio, however, is very small (32 ppm), yielding images with poor statistics even when therapy doses are used. Our purpose is to find PET reconstruction parameters that maximize the PET recovery coefficients and minimize noise. Methods: An initial 7.5 GBq of Y-90 chloride solution was used to fill an ACR phantom for measurements with a PET/MRI scanner (Siemens Biograph mMR). Four hot cylinders and a warm background activity volume of the phantom were filled with a 10:1 ratio. Phantom attenuation maps were derived from scaled CT images of the phantom and included the MR phased array coil. The phantom was imaged at six time points between 7.5–1.0 GBq total activity over a period of eight days. PET images were reconstructed via OP-OSEM with 21 subsets and varying iteration number (1–5), post-reconstruction filter size (5–10 mm), and either absolute or relative scatter correction. Recovery coefficients, SNR, and noise were measured as well as total activity in the phantom. Results: For the 120 different reconstructions, recovery coefficients ranged from 0.1–0.6 and improved with increasing iteration number and reduced post-reconstruction filter size. SNR, however, improved substantially with lower iteration numbers and larger post-reconstruction filters. From the phantom data, we found that performing 2 iterations, 21 subsets, and applying a 5 mm Gaussian post-reconstruction filter provided optimal recovery coefficients at a moderate noise level for a wide range of activity levels. Conclusion: The choice of reconstruction parameters for Y-90 PET images greatly influences both the accuracy of measurements and image quality. We have found reconstruction parameters that provide optimal recovery coefficients with minimized noise. Future work will include the effects
Multi-ray-based system matrix generation for 3D PET reconstruction.
Moehrs, Sascha; Defrise, Michel; Belcari, Nicola; Guerra, Alberto Del; Bartoli, Antonietta; Fabbri, Serena; Zanetti, Gianluigi
2008-12-01
Iterative image reconstruction algorithms for positron emission tomography (PET) require a sophisticated system matrix (model) of the scanner. Our aim is to set up such a model offline for the YAP-(S)PET II small animal imaging tomograph in order to use it subsequently with standard ML-EM (maximum-likelihood expectation maximization) and OSEM (ordered subset expectation maximization) for fully three-dimensional image reconstruction. In general, the system model can be obtained analytically, via measurements or via Monte Carlo simulations. In this paper, we present the multi-ray method, which can be considered as a hybrid method to set up the system model offline. It incorporates accurate analytical (geometric) considerations as well as crystal depth and crystal scatter effects. At the same time, it has the potential to model seamlessly other physical aspects such as the positron range. The proposed method is based on multiple rays which are traced from/to the detector crystals through the image volume. Such a ray-tracing approach itself is not new; however, we derive a novel mathematical formulation of the approach and investigate the positioning of the integration (ray-end) points. First, we study single system matrix entries and show that the positioning and weighting of the ray-end points according to Gaussian integration give better results compared to equally spaced integration points (trapezoidal integration), especially if only a small number of integration points (rays) are used. Additionally, we show that, for a given variance of the single matrix entries, the number of rays (events) required to calculate the whole matrix is a factor of 20 larger when using a pure Monte-Carlo-based method. Finally, we analyse the quality of the model by reconstructing phantom data from the YAP-(S)PET II scanner. PMID:19001696
Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET
NASA Astrophysics Data System (ADS)
Rezaei, Ahmadreza; Michel, Christian; Casey, Michael E.; Nuyts, Johan
2016-02-01
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.
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.
Fat-constrained 18F-FDG PET reconstruction using Dixon MR imaging and the origin ensemble algorithm
NASA Astrophysics Data System (ADS)
Wülker, Christian; Heinzer, Susanne; Börnert, Peter; Renisch, Steffen; Prevrhal, Sven
2015-03-01
Combined PET/MR imaging allows to incorporate the high-resolution anatomical information delivered by MRI into the PET reconstruction algorithm for improvement of PET accuracy beyond standard corrections. We used the working hypothesis that glucose uptake in adipose tissue is low. Thus, our aim was to shift 18F-FDG PET signal into image regions with a low fat content. Dixon MR imaging can be used to generate fat-only images via the water/fat chemical shift difference. On the other hand, the Origin Ensemble (OE) algorithm, a novel Markov chain Monte Carlo method, allows to reconstruct PET data without the use of forward- and back projection operations. By adequate modifications to the Markov chain transition kernel, it is possible to include anatomical a priori knowledge into the OE algorithm. In this work, we used the OE algorithm to reconstruct PET data of a modified IEC/NEMA Body Phantom simulating body water/fat composition. Reconstruction was performed 1) natively, 2) informed with the Dixon MR fat image to down-weight 18F-FDG signal in fatty tissue compartments in favor of adjacent regions, and 3) informed with the fat image to up-weight 18F-FDG signal in fatty tissue compartments, for control purposes. Image intensity profiles confirmed the visibly improved contrast and reduced partial volume effect at water/fat interfaces. We observed a 17+/-2% increased SNR of hot lesions surrounded by fat, while image quality was almost completely retained in fat-free image regions. An additional in vivo experiment proved the applicability of the presented technique in practice, and again verified the beneficial impact of fat-constrained OE reconstruction on PET image quality.
Isotope specific resolution recovery image reconstruction in high resolution PET imaging
Kotasidis, Fotis A.; Angelis, Georgios I.; Anton-Rodriguez, Jose; Matthews, Julian C.; Reader, Andrew J.; Zaidi, Habib
2014-05-15
Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. Methods: In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. Results: The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Conclusions: Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution
Hardcastle, Nicholas; Hofman, Michael S.; Hicks, Rodney J.; Callahan, Jason; Kron, Tomas; MacManus, Michael P.; Ball, David L.; Jackson, Price; Siva, Shankar
2015-09-01
Purpose: Measuring changes in lung perfusion resulting from radiation therapy dose requires registration of the functional imaging to the radiation therapy treatment planning scan. This study investigates registration accuracy and utility for positron emission tomography (PET)/computed tomography (CT) perfusion imaging in radiation therapy for non–small cell lung cancer. Methods: {sup 68}Ga 4-dimensional PET/CT ventilation-perfusion imaging was performed before, during, and after radiation therapy for 5 patients. Rigid registration and deformable image registration (DIR) using B-splines and Demons algorithms was performed with the CT data to obtain a deformation map between the functional images and planning CT. Contour propagation accuracy and correspondence of anatomic features were used to assess registration accuracy. Wilcoxon signed-rank test was used to determine statistical significance. Changes in lung perfusion resulting from radiation therapy dose were calculated for each registration method for each patient and averaged over all patients. Results: With B-splines/Demons DIR, median distance to agreement between lung contours reduced modestly by 0.9/1.1 mm, 1.3/1.6 mm, and 1.3/1.6 mm for pretreatment, midtreatment, and posttreatment (P<.01 for all), and median Dice score between lung contours improved by 0.04/0.04, 0.05/0.05, and 0.05/0.05 for pretreatment, midtreatment, and posttreatment (P<.001 for all). Distance between anatomic features reduced with DIR by median 2.5 mm and 2.8 for pretreatment and midtreatment time points, respectively (P=.001) and 1.4 mm for posttreatment (P>.2). Poorer posttreatment results were likely caused by posttreatment pneumonitis and tumor regression. Up to 80% standardized uptake value loss in perfusion scans was observed. There was limited change in the loss in lung perfusion between registration methods; however, Demons resulted in larger interpatient variation compared with rigid and B-splines registration
The impact of reconstruction algorithms and time of flight information on PET/CT image quality
Suljic, Alen; Tomse, Petra; Jensterle, Luka; Skrk, Damijan
2015-01-01
Background The aim of the study was to explore the influence of various time-of-flight (TOF) and non-TOF reconstruction algorithms on positron emission tomography/computer tomography (PET/CT) image quality. Materials and methods. Measurements were performed with a triple line source phantom, consisting of capillaries with internal diameter of ∼ 1 mm and standard Jaszczak phantom. Each of the data sets was reconstructed using analytical filtered back projection (FBP) algorithm, iterative ordered subsets expectation maximization (OSEM) algorithm (4 iterations, 24 subsets) and iterative True-X algorithm incorporating a specific point spread function (PSF) correction (4 iterations, 21 subsets). Baseline OSEM (2 iterations, 8 subsets) was included for comparison. Procedures were undertaken following the National Electrical Manufacturers Association (NEMA) NU-2-2001 protocol. Results Measurement of spatial resolution in full width at half maximum (FWHM) was 5.2 mm, 4.5 mm and 2.9 mm for FBP, OSEM and True-X; and 5.1 mm, 4.5 mm and 2.9 mm for FBP+TOF, OSEM+TOF and True-X+TOF respectively. Assessment of reconstructed Jaszczak images at different concentration ratios showed that incorporation of TOF information improves cold contrast, while hot contrast only slightly, however the most prominent improvement could be seen in background variability - noise reduction. Conclusions On the basis of the results of investigation we concluded, that incorporation of TOF information in reconstruction algorithm mostly affects reduction of the background variability (levels of noise in the image), while the improvement of spatial resolution due to incorporation of TOF information is negligible. Comparison of traditional and modern reconstruction algorithms showed that analytical FBP yields comparable results in some parameter measurements, such as cold contrast and relative count error. Iterative methods show highest levels of hot contrast, when TOF and PSF corrections were applied
NASA Astrophysics Data System (ADS)
Cao, Xiaoqing; Xie, Qingguo; Xiao, Peng
2015-01-01
List mode format is commonly used in modern positron emission tomography (PET) for image reconstruction due to certain special advantages. In this work, we proposed a list mode based regularized relaxed ordered subset (LMROS) algorithm for static PET imaging. LMROS is able to work with regularization terms which can be formulated as twice differentiable convex functions. Such a versatility would make LMROS a convenient and general framework for fulfilling different regularized list mode reconstruction methods. LMROS was applied to two simulated undersampling PET imaging scenarios to verify its effectiveness. Convex quadratic function, total variation constraint, non-local means and dictionary learning based regularization methods were successfully realized for different cases. The results showed that the LMROS algorithm was effective and some regularization methods greatly reduced the distortions and artifacts caused by undersampling.
NASA Astrophysics Data System (ADS)
Lougovski, A.; Hofheinz, F.; Maus, J.; Schramm, G.; Will, E.; van den Hoff, J.
2014-02-01
The aim of this study is the evaluation of on-the-fly volume of intersection computation for system’s geometry modelling in 3D PET image reconstruction. For this purpose we propose a simple geometrical model in which the cubic image voxels on the given Cartesian grid are approximated with spheres and the rectangular tubes of response (ToRs) are approximated with cylinders. The model was integrated into a fully 3D list-mode PET reconstruction for performance evaluation. In our model the volume of intersection between a voxel and the ToR is only a function of the impact parameter (the distance between voxel centre to ToR axis) but is independent of the relative orientation of voxel and ToR. This substantially reduces the computational complexity of the system matrix calculation. Based on phantom measurements it was determined that adjusting the diameters of the spherical voxel size and the ToR in such a way that the actual voxel and ToR volumes are conserved leads to the best compromise between high spatial resolution, low noise, and suppression of Gibbs artefacts in the reconstructed images. Phantom as well as clinical datasets from two different PET systems (Siemens ECAT HR+ and Philips Ingenuity-TF PET/MR) were processed using the developed and the respective vendor-provided (line of intersection related) reconstruction algorithms. A comparison of the reconstructed images demonstrated very good performance of the new approach. The evaluation showed the respective vendor-provided reconstruction algorithms to possess 34-41% lower resolution compared to the developed one while exhibiting comparable noise levels. Contrary to explicit point spread function modelling our model has a simple straight-forward implementation and it should be easy to integrate into existing reconstruction software, making it competitive to other existing resolution recovery techniques.
Quantitative accuracy of MAP reconstruction for dynamic PET imaging in small animals
Cheng, Ju-Chieh (Kevin); Shoghi, Kooresh; Laforest, Richard
2012-01-01
Purpose: Iterative reconstruction algorithms are becoming more commonly employed in positron emission tomography (PET) imaging; however, the quantitative accuracy of the reconstructed images still requires validation for various levels of contrast and counting statistics. Methods: The authors present an evaluation of the quantitative accuracy of the 3D maximum a posteriori (3D-MAP) image reconstruction algorithm for dynamic PET imaging with comparisons to two of the most widely used reconstruction algorithms: the 2D filtered-backprojection (2D-FBP) and 2D-ordered subsets expectation maximization (2D-OSEM) on the Siemens microPET scanners. The study was performed for various levels of count density encountered in typical dynamic scanning as well as the imaging of cardiac activity concentration in small animal studies on the Focus 120. Specially designed phantoms were used for evaluation of the spatial resolution, image quality, and quantitative accuracy. A normal mouse was employed to evaluate the accuracy of the blood time activity concentration extracted from left ventricle regions of interest (ROIs) within the images as compared to the actual blood activity concentration measured from arterial blood sampling. Results: For MAP reconstructions, the spatial resolution and contrast have been found to reach a stable value after 20 iterations independent of the β values (i.e., hyper parameter which controls the weight of the penalty term) and count density within the frame. The spatial resolution obtained with 3D-MAP reaches values of ∼1.0 mm with a β of 0.01 while the 2D-FBP has value of 1.8 mm and 2D-OSEM has a value of 1.6 mm. It has been observed that the lower the hyper parameter β used in MAP, more iterations are needed to reach the stable noise level (i.e., image roughness). The spatial resolution is improved by using a lower β value at the expense of higher image noise. However, with similar noise level the spatial resolution achieved by 3D-MAP was
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
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
Bayesian reconstruction of photon interaction sequences for high-resolution PET detectors
Pratx, Guillem
2013-01-01
Realizing the full potential of high-resolution positron emission tomography (PET) systems involves accurately positioning events in which the annihilation photon deposits all its energy across multiple detector elements. Reconstructing the complete sequence of interactions of each photon provides a reliable way to select the earliest interaction because it ensures that all the interactions are consistent with one another. Bayesian estimation forms a natural framework to maximize the consistency of the sequence with the measurements while taking into account the physics of γ-ray transport. An inherently statistical method, it accounts for the uncertainty in the measured energy and position of each interaction. An algorithm based on maximum a posteriori (MAP) was evaluated for computer simulations. For a high-resolution PET system based on cadmium zinc telluride detectors, 93.8% of the recorded coincidences involved at least one photon multiple-interactions event (PMIE). The MAP estimate of the first interaction was accurate for 85.2% of the single photons. This represents a two-fold reduction in the number of mispositioned events compared to minimum pair distance, a simpler yet efficient positioning method. The point-spread function of the system presented lower tails and higher peak value when MAP was used. This translated into improved image quality, which we quantified by studying contrast and spatial resolution gains. PMID:19652293
NASA Astrophysics Data System (ADS)
Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib
2016-02-01
Time-of-flight (TOF) positron emission tomography (PET) technology has recently regained popularity in clinical PET studies for improving image quality and lesion detectability. Using TOF information, the spatial location of annihilation events is confined to a number of image voxels along each line of response, thereby the cross-dependencies of image voxels are reduced, which in turns results in improved signal-to-noise ratio and convergence rate. In this work, we propose a novel approach to further improve the convergence of the expectation maximization (EM)-based TOF PET image reconstruction algorithm through subsetization of emission data over TOF bins as well as azimuthal bins. Given the prevalence of TOF PET, we elaborated the practical and efficient implementation of TOF PET image reconstruction through the pre-computation of TOF weighting coefficients while exploiting the same in-plane and axial symmetries used in pre-computation of geometric system matrix. In the proposed subsetization approach, TOF PET data were partitioned into a number of interleaved TOF subsets, with the aim of reducing the spatial coupling of TOF bins and therefore to improve the convergence of the standard maximum likelihood expectation maximization (MLEM) and ordered subsets EM (OSEM) algorithms. The comparison of on-the-fly and pre-computed TOF projections showed that the pre-computation of the TOF weighting coefficients can considerably reduce the computation time of TOF PET image reconstruction. The convergence rate and bias-variance performance of the proposed TOF subsetization scheme were evaluated using simulated, experimental phantom and clinical studies. Simulations demonstrated that as the number of TOF subsets is increased, the convergence rate of MLEM and OSEM algorithms is improved. It was also found that for the same computation time, the proposed subsetization gives rise to further convergence. The bias-variance analysis of the experimental NEMA phantom and a clinical
Sun, H; Pistorious, S
2014-08-15
Introduction: Scattered coincidences in PET are generally taken as noise, which reduces image contrast and compromises quantification. We have developed a method, with promising results, to reconstruct activity distribution from scattered PET events instead of simply correcting for them. The implementation of this method on clinical PET scanners is however limited by the currently available detector energy resolution. With low energy resolution we lose the ability to distinguish scattered coincidences from true events based on the measured photon energy. In addition the two circular arcs used to confine the source position for a scattered event cannot be accurately defined. Method: This paper presents a modification to this approach which accounts for limited energy resolution. A measured event is split into a true and a scattered component each with different probabilities based on the position of the pair of photon energies in the energy spectrum. For the scattered component, we model the photon energy with a Gaussian distribution and the upper and lower energy limits can be estimated and used to define inner and outer circular arcs to confine the source position. The true and scattered components for each measured event were reconstructed using our Generalized Scatter reconstruction algorithm. Results and Conclusion: The results show that the contrast and noise properties were improved by 6–9% and 2–4% respectively. This demonstrates that the performance of the algorithm is less sensitive to the energy resolution and that incorporating scattered photons into reconstruction brings more benefits than simply rejecting them.
Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frederic
2015-02-15
Purpose: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy–Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. Methods: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy–Richardson deconvolution algorithm to the current estimation of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Results: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. Conclusions: This study demonstrates the feasibility of incorporating the Lucy–Richardson deconvolution associated with a
Huang, Jianming; Chen, Fengrong; Jian, Guojian; Ye, Zhiyang; Wang, Zimin; Liu, Haoyuan; Kang, Yifan
2015-01-01
Ligament reconstruction is an effective therapy for anterior cruciate ligament (ACL) rupture. Polyethylene terephthalate (PET) artificial ligaments have recently gained popularity in clinical ACL reconstruction for its advantage in the improvement of keen function. However, the application of PET in clinical treatment is limited by its poor bioactivity and biocompatibility. Recently, bone marrow-derived mesenchymal stem cells (BMSCs) have been widely studied in regenerative medical therapy due to their multi-lineage differentiation. Previous study also indicated that BMSCs may promote the healing of tendon-bone interface of injured ligament. We speculate that BMSCs may enhance the curative effect of PET artificial ligament on the tendon-bone-healing in ligament reconstruction. In this study, the PET materials were first modified with sodium hydroxide hydrolysis and GRGDSPC peptide which was able to improve its bioactivity and biocompatibility. Then, the effects of modified PET materials on the adhesion, proliferation and differentiation of BMSCs were examined. The in vitro co-culture of BMSCs and modified PET showed the modified PET promoted the adhesion, proliferation and differentiation of BMSCs. Further, the effect of culture complex of BMSCs and modified PET artificial ligament co-culture system on the injured ligament reconstruction was investigated in vivo. Results showed not only better growth and differentiation of BMSCs but also satisfactory healing of the injured ligament was observed after implantation of this culture complex into the injured ligament of rabbits. Our study provides a brand-new solution for ACL reconstruction. PMID:26221227
High-speed computation of the EM algorithm for PET image reconstruction
Rajan, K.; Patnaik, L.M.; Ramakrishna, J. )
1994-10-01
The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution backprojection algorithms. However, two major drawbacks have impeded the routine use of the EM algorithm, namely, the long computational time due to slow convergence and the large memory required for the storage of the image, projection data and the probability matrix. In this study, the authors attempts to solve these two problems by parallelizing the EM algorithm on a multiprocessor system. The authors have implemented an extended hypercube (EH) architecture for the high-speed computation of the EM algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs). The authors discuss and compare the performance of the EM algorithm on a 386/387 machine, CD 4360 mainframe, and on the EH system. The results show that the computational speed performance of an EH using DSP chips as PEs executing the EM image reconstruction algorithm is about 130 times better than that of the CD 4360 mainframe. The EH topology is expandable with more number of PEs.
Read, Charlotte; Branford, Olivier A; Verjee, Liaquat S; Wood, Simon H
2015-08-01
Late presenting and recurrent sternal wound infections post-sternotomy are difficult to treat, with the clinical picture not necessarily reflecting the underlying problem. As a result of our experience, we suggest that these chronic cases should be managed using a different algorithm to acute sternal wound infection. Positron emission tomography combined with computerized tomography (PET-CT) imaging may be potentially useful in enabling accurate localization of disease sites, which guides adequate debridement prior to definitive reconstruction. It may also allow for disease surveillance and monitoring of the response to antimicrobial treatment. We present three cases which support the need for pre-operative imaging using PET-CT. PMID:25986418
Kim, Hyeon Sik; Cho, Sang-Geon; Kim, Ju Han; Kwon, Seong Young; Lee, Byeong-il; Bom, Hee-Seung
2014-01-01
Objective(s): In order to evaluate the effect of post-reconstruction Gaussian filtering on image quality and myocardial blood flow (MBF) measurement by dynamic N-13 ammonia positron emission tomography (PET), we compared various reconstruction and filtering methods with image characteristics. Methods: Dynamic PET images of three patients with coronary artery disease (male-female ratio of 2:1; age: 57, 53, and 76 years) were reconstructed, using filtered back projection (FBP) and ordered subset expectation maximization (OSEM) methods. OSEM reconstruction consisted of OSEM_2I, OSEM_4I, and OSEM_6I with 2, 4, and 6 iterations, respectively. The images, reconstructed and filtered by Gaussian filters of 5, 10, and 15 mm, were obtained, as well as non-filtered images. Visual analysis of image quality (IQ) was performed using a 3-grade scoring system by 2 independent readers, blinded to the reconstruction and filtering methods of stress images. Then, signal-to-noise ratio (SNR) was calculated by noise and contrast recovery (CR). Stress and rest MBF and coronary flow reserve (CFR) were obtained for each method. IQ scores, stress and rest MBF, and CFR were compared between the methods, using Chi-square and Kruskal-Wallis tests. Results: In the visual analysis, IQ was significantly higher by 10 mm Gaussian filtering, compared to other sizes of filter (P<0.001 for both readers). However, no significant difference of IQ was found between FBP and various numbers of iteration in OSEM (P=0.923 and 0.855 for readers 1 and 2, respectively). SNR was significantly higher in 10 mm Gaussian filter. There was a significant difference in stress and rest MBF between several vascular territories. However CFR was not significantly different according to various filtering methods. Conclusion: Post-reconstruction Gaussian filtering with a filter size of 10 mm significantly enhances the IQ of N-13 ammonia PET-CT, without changing the results of CFR calculation. PMID:27408866
NASA Astrophysics Data System (ADS)
Gajos, A.; Kamińska, D.; Czerwiński, E.; Alfs, D.; Bednarski, T.; Białas, P.; Głowacz, B.; Gorgol, M.; Jasińska, B.; Kapłon, Ł.; Korcyl, G.; Kowalski, P.; Kozik, T.; Krzemień, W.; Kubicz, E.; Mohammed, M.; Niedźwiecki, Sz.; Pałka, M.; Pawlik-Niedźwiecka, M.; Raczyński, L.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Słomski, A.; Strzelecki, A.; Wieczorek, A.; Wiślicki, W.; Zgardzińska, B.; Zieliński, M.; Moskal, P.
2016-05-01
This work reports on a new reconstruction algorithm allowing us to reconstruct the decays of ortho-positronium atoms into three photons using the places and times of photons recorded in the detector. The method is based on trilateration and allows for a simultaneous reconstruction of both location and time of the decay. Results of resolution tests of the new reconstruction in the J-PET detector based on Monte Carlo simulations are presented, which yield a spatial resolution at the level of 2 cm (FWHM) for X and Y and at the level of 1 cm (FWHM) for Z available with the present resolution of J-PET after application of a kinematic fit. Prospects of employment of this method for studying angular correlations of photons in decays of polarized ortho-positronia for the needs of tests of CP and CPT discrete symmetries are also discussed. The new reconstruction method allows for discrimination of background from random three-photon coincidences as well as for application of a novel method for determination of the linear polarization of ortho-positronium atoms, which is also introduced in this work.
NASA Astrophysics Data System (ADS)
Wilhelm, Bruno; Jenny, Jean-Philippe; Arnaud, Fabien; Sabatier, Pierre; Giguet-Covex, Charline; Mélo, Alain; Fanget, Bernard; Malet, Emmanuel; Perga, Marie-Elodie
2014-05-01
A high-resolution sedimentological study of the large Lake Bourget (French Alps, 231m a.s.l., 45°45'55N, 5°51'45E) was conducted to reconstruct the flood frequency and intensity (or magnitude) in the area over the last 350 years. Particular emphasis was placed on investigating the spatio-temporal distribution of flood deposits in this large lake basin. The thicknesses of deposits resulting from 30 flood events of the Rhône River were collected over a set of 24 short sediment cores. Deposit thicknesses were compared with instrumental data for the Rhône River discharge for the period from 1853 to 2010. The results show that flood frequency and intensity cannot be reliably reconstructed from a single core because of the inhomogeneous flood-deposit geometry in such a large lake. From all documented flood-deposit thicknesses, volumes of sediment brought into the lake during each flood event were computed through a kriging procedure and compared with the historical instrumental data. The results show that reconstructed sediment volumes are well correlated to maximal flood discharges. This significant correlation suggests that the increase of embankment and dam settlements on the Rhône River during the last 150 years has not significantly affected the transport of the smallest sediment fraction during major flood events. Hence, assessment of the flood-sediment volumes deposited in the large Lake Bourget allowed to reliably reconstruct the flood frequency and intensity of the past Rhône River floods.
Schaefferkoetter, Joshua; Casey, Michael; Townsend, David; Fakhri, Georges El
2013-01-01
Time-of-flight (TOF) and point spread function (PSF) modeling have been shown to improve PET reconstructions, but the impact on physicians in the clinical setting has not been thoroughly investigated. A lesion detection and localization study was performed using simulated lesions in real patient images. Four reconstruction schemes were considered: ordinary Poisson OSEM (OP) alone and combined with TOF, PSF, and TOF+PSF. The images were presented to physicians experienced in reading PET images, and the performance of each was quantified using localization receiver operating characteristic (LROC). Numerical observers (non-prewhitening and Hotelling) were used to identify optimal reconstruction parameters, and observer SNR was compared to the performance of the physicians. The numerical models showed good agreement with human performance, and best performance was achieved by both when using TOF+PSF. These findings suggest a large potential benefit of TOF+PSF for oncology PET studies, especially in the detection of small, low-intensity, focal disease in larger patients. PMID:23403399
Mustafovic, Sanida; Thielemans, Kris
2004-04-01
In this paper, we study the resolution properties of those algorithms where a filtering step is applied after every iteration. As concrete examples we take filtered preconditioned gradient descent algorithms for the Poisson log likelihood for PET emission data. For nonlinear estimators, resolution can be characterized in terms of the linearized local impulse response (LLIR). We provide analytic approximations for the LLIR for the class of algorithms mentioned above. Our expressions clearly show that when interiteration filtering (with linear filters) is used, the resolution properties are, in most cases, spatially varying, object dependent and asymmetric. These nonuniformities are solely due to the interaction between the filtering step and the Poisson noise model. This situation is similar to penalized likelihood reconstructions as studied previously in the literature. In contrast, nonregularized and postfiltered maximum-likelihood expectation maximization (MLEM) produce images with nearly "perfect" uniform resolution when convergence is reached. We use the analytic expressions for the LLIR to propose three different approaches to obtain nearly object independent and uniform resolution. Two of them are based on calculating filter coefficients on a pixel basis, whereas the third one chooses an appropriate preconditioner. These three approaches are tested on simulated data for the filtered MLEM algorithm or the filtered separable paraboloidal surrogates algorithm. The evaluation confirms that images obtained using our proposed regularization methods have nearly object independent and uniform resolution. PMID:15084069
NASA Astrophysics Data System (ADS)
Jian, Y.; Yao, R.; Mulnix, T.; Jin, X.; Carson, R. E.
2015-01-01
Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners—the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [11C]AFM rats imaged on the HRRT and [11C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods.
NASA Astrophysics Data System (ADS)
Gravel, Paul; Verhaeghe, Jeroen; Reader, Andrew J.
2013-01-01
This work explores the feasibility and impact of including both the motion correction and the image registration transformation parameters from positron emission tomography (PET) image space to magnetic resonance (MR), or stereotaxic, image space within the system matrix of PET image reconstruction. This approach is motivated by the fields of neuroscience and psychiatry, where PET is used to investigate differences in activation patterns between different groups of participants, requiring all images to be registered to a common spatial atlas. Currently, image registration is performed after image reconstruction which introduces interpolation effects into the final image. Furthermore, motion correction (also requiring registration) introduces a further level of interpolation, and the overall result of these operations can lead to resolution degradation and possibly artifacts. It is important to note that performing such operations on a post-reconstruction basis means, strictly speaking, that the final images are not ones which maximize the desired objective function (e.g. maximum likelihood (ML), or maximum a posteriori reconstruction (MAP)). To correctly seek parameter estimates in the desired spatial atlas which are in accordance with the chosen reconstruction objective function, it is necessary to include the transformation parameters for both motion correction and registration within the system modeling stage of image reconstruction. Such an approach not only respects the statistically chosen objective function (e.g. ML or MAP), but furthermore should serve to reduce the interpolation effects. To evaluate the proposed method, this work investigates registration (including motion correction) using 2D and 3D simulations based on the high resolution research tomograph (HRRT) PET scanner geometry, with and without resolution modeling, using the ML expectation maximization (MLEM) reconstruction algorithm. The quality of reconstruction was assessed using bias
NASA Astrophysics Data System (ADS)
Rank, Christopher M.; Heußer, Thorsten; Flach, Barbara; Brehm, Marcus; Kachelrieß, Marc
2015-03-01
We propose a new method for PET/MR respiratory motion compensation, which is based on a 3D-2D registration of strongly undersampled MR data and a) runs in parallel with the PET acquisition, b) can be interlaced with clinical MR sequences, and c) requires less than one minute of the total MR acquisition time per bed position. In our simulation study, we applied a 3D encoded radial stack-of-stars sampling scheme with 160 radial spokes per slice and an acquisition time of 38 s. Gated 4D MR images were reconstructed using a 4D iterative reconstruction algorithm. Based on these images, motion vector fields were estimated using our newly-developed 3D-2D registration framework. A 4D PET volume of a patient with eight hot lesions in the lungs and upper abdomen was simulated and MoCo 4D PET images were reconstructed based on the motion vector fields derived from MR. For evaluation, average SUVmean values of the artificial lesions were determined for a 3D, a gated 4D, a MoCo 4D and a reference (with ten-fold measurement time) gated 4D reconstruction. Compared to the reference, 3D reconstructions yielded an underestimation of SUVmean values due to motion blurring. In contrast, gated 4D reconstructions showed the highest variation of SUVmean due to low statistics. MoCo 4D reconstructions were only slightly affected by these two sources of uncertainty resulting in a significant visual and quantitative improvement in terms of SUVmean values. Whereas temporal resolution was comparable to the gated 4D images, signal-to-noise ratio and contrast-to-noise ratio were close to the 3D reconstructions.
NASA Astrophysics Data System (ADS)
Hong, Inki; Cho, Sanghee; Michel, Christian J.; Casey, Michael E.; Schaefferkoetter, Joshua D.
2014-09-01
A new data handling method is presented for improving the image noise distribution and reducing bias when reconstructing very short frames from low count dynamic PET acquisition. The new method termed ‘Complementary Frame Reconstruction’ (CFR) involves the indirect formation of a count-limited emission image in a short frame through subtraction of two frames with longer acquisition time, where the short time frame data is excluded from the second long frame data before the reconstruction. This approach can be regarded as an alternative to the AML algorithm recently proposed by Nuyts et al, as a method to reduce the bias for the maximum likelihood expectation maximization (MLEM) reconstruction of count limited data. CFR uses long scan emission data to stabilize the reconstruction and avoids modification of algorithms such as MLEM. The subtraction between two long frame images, naturally allows negative voxel values and significantly reduces bias introduced in the final image. Simulations based on phantom and clinical data were used to evaluate the accuracy of the reconstructed images to represent the true activity distribution. Applicability to determine the arterial input function in human and small animal studies is also explored. In situations with limited count rate, e.g. pediatric applications, gated abdominal, cardiac studies, etc., or when using limited doses of short-lived isotopes such as 15O-water, the proposed method will likely be preferred over independent frame reconstruction to address bias and noise issues.
Keall, P; Pollock, S; Yang, J; Diehn, M; Berger, J; Graves, E; Loo, B; Yamamoto, T
2014-06-01
comprehension and capability. Supported by NIH/NCI R01 CA 093626, Stanford BioX Interdisciplinary Initiatives Program, NHMRC Australia Fellowship, and Kwanjeong Educational Foundation. GE Healthcare provided the Respiratory Gating Toolbox for 4D-PET image reconstruction. Stanford University owns US patent #E7955270 which is unlicensed to any commercial entity.
Xiang, Liangzhong; Wang, Bo; Ji, Lijun; Jiang, Huabei
2013-01-01
Photoacoustic tomography (PAT) offers three-dimensional (3D) structural and functional imaging of living biological tissue with label-free, optical absorption contrast. These attributes lend PAT imaging to a wide variety of applications in clinical medicine and preclinical research. Despite advances in live animal imaging with PAT, there is still a need for 3D imaging at centimeter depths in real-time. We report the development of four dimensional (4D) PAT, which integrates time resolutions with 3D spatial resolution, obtained using spherical arrays of ultrasonic detectors. The 4D PAT technique generates motion pictures of imaged tissue, enabling real time tracking of dynamic physiological and pathological processes at hundred micrometer-millisecond resolutions. The 4D PAT technique is used here to image needle-based drug delivery and pharmacokinetics. We also use this technique to monitor 1) fast hemodynamic changes during inter-ictal epileptic seizures and 2) temperature variations during tumor thermal therapy. PMID:23346370
NASA Astrophysics Data System (ADS)
Xiang, Liangzhong; Wang, Bo; Ji, Lijun; Jiang, Huabei
2013-01-01
Photoacoustic tomography (PAT) offers three-dimensional (3D) structural and functional imaging of living biological tissue with label-free, optical absorption contrast. These attributes lend PAT imaging to a wide variety of applications in clinical medicine and preclinical research. Despite advances in live animal imaging with PAT, there is still a need for 3D imaging at centimeter depths in real-time. We report the development of four dimensional (4D) PAT, which integrates time resolutions with 3D spatial resolution, obtained using spherical arrays of ultrasonic detectors. The 4D PAT technique generates motion pictures of imaged tissue, enabling real time tracking of dynamic physiological and pathological processes at hundred micrometer-millisecond resolutions. The 4D PAT technique is used here to image needle-based drug delivery and pharmacokinetics. We also use this technique to monitor 1) fast hemodynamic changes during inter-ictal epileptic seizures and 2) temperature variations during tumor thermal therapy.
2014-01-01
Background To assess the feasibility and benefit of integrating four-dimensional (4D) Positron Emission Tomography (PET) – computed tomography (CT) for liver stereotactic body radiation therapy (SBRT) planning. Methods 8 patients with 14 metastases were accrued in the study. They all underwent a non-gated PET and a 4D PET centered on the liver. The same CT scan was used for attenuation correction, registration, and considered the planning CT for SBRT planning. Six PET phases were reconstructed for each 4D PET. By applying an individualized threshold to the 4D PET, a Biological Internal Target Volume (BITV) was generated for each lesion. A gated Planning Target Volume (PTVg) was created by adding 3 mm to account for set-up margins. This volume was compared to a manual Planning Target Volume (PTV) delineated with the help of a semi-automatic Biological Target Volume (BTV) obtained from the non-gated exam. A 5 mm radial and a 10 mm craniocaudal margins were applied to account for tumor motion and set-up margins to create the PTV. Results One undiagnosed liver metastasis was discovered thanks to the 4D PET. The semi-automatic BTV were significantly smaller than the BITV (p = 0.0031). However, after applying adapted margins, 4D PET allowed a statistically significant decrease in the PTVg as compared to the PTV (p = 0.0052). Conclusions In comparison to non-gated PET, 4D PET may better define the respiratory movements of liver targets and improve SBRT planning for liver metastases. Furthermore, non respiratory-gated PET exams can both misdiagnose liver metastases and underestimate the real internal target volumes. PMID:24885897
Maximum likelihood reconstruction in fully 3D PET via the SAGE algorithm
Ollinger, J.M.; Goggin, A.S.
1996-12-31
The SAGE and ordered subsets algorithms have been proposed as fast methods to compute penalized maximum likelihood estimates in PET. We have implemented both for use in fully 3D PET and completed a preliminary evaluation. The technique used to compute the transition matrix is fully described. The evaluation suggests that the ordered subsets algorithm converges much faster than SAGE, but that it stops short of the optimal solution.
NASA Astrophysics Data System (ADS)
Kim, Kyungsang; Son, Young Don; Bresler, Yoram; Cho, Zang Hee; Ra, Jong Beom; Ye, Jong Chul
2015-03-01
Dynamic positron emission tomography (PET) is widely used to measure changes in the bio-distribution of radiopharmaceuticals within particular organs of interest over time. However, to retain sufficient temporal resolution, the number of photon counts in each time frame must be limited. Therefore, conventional reconstruction algorithms such as the ordered subset expectation maximization (OSEM) produce noisy reconstruction images, thus degrading the quality of the extracted time activity curves (TACs). To address this issue, many advanced reconstruction algorithms have been developed using various spatio-temporal regularizations. In this paper, we extend earlier results and develop a novel temporal regularization, which exploits the self-similarity of patches that are collected in dynamic images. The main contribution of this paper is to demonstrate that the correlation of patches can be exploited using a low-rank constraint that is insensitive to global intensity variations. The resulting optimization framework is, however, non-Lipschitz and non-convex due to the Poisson log-likelihood and low-rank penalty terms. Direct application of the conventional Poisson image deconvolution by an augmented Lagrangian (PIDAL) algorithm is, however, problematic due to its large memory requirements, which prevents its parallelization. Thus, we propose a novel optimization framework using the concave-convex procedure (CCCP) by exploiting the Legendre-Fenchel transform, which is computationally efficient and parallelizable. In computer simulation and a real in vivo experiment using a high-resolution research tomograph (HRRT) scanner, we confirm that the proposed algorithm can improve image quality while also extracting more accurate region of interests (ROI) based kinetic parameters. Furthermore, we show that the total reconstruction time for HRRT PET is significantly accelerated using our GPU implementation, which makes the algorithm very practical in clinical environments.
Llacer, J.; Veklerov, E.; Nolan, D. ); Grafton, S.T.; Mazziotta, J.C.; Hawkins, R.A.; Hoh, C.K.; Hoffman, E.J. )
1990-10-01
This paper will report on the progress to date in carrying out Receiver Operating Characteristics (ROC) studies comparing Maximum Likelihood Estimator (MLE) and Filtered Backprojection (FBP) reconstructions of normal and abnormal human brain PET data in a clinical setting. A previous statistical study of reconstructions of the Hoffman brain phantom with real data indicated that the pixel-to-pixel standard deviation in feasible MLE images is approximately proportional to the square root of the number of counts in a region, as opposed to a standard deviation which is high and largely independent of the number of counts in FBP. A preliminary ROC study carried out with 10 non-medical observers performing a relatively simple detectability task indicates that, for the majority of observers, lower standard deviation translates itself into a statistically significant detectability advantage in MLE reconstructions. The initial results of ongoing tests with four experienced neurologists/nuclear medicine physicians are presented. Normal cases of {sup 18}F -- fluorodeoxyglucose (FDG) cerebral metabolism studies and abnormal cases in which a variety of lesions have been introduced into normal data sets have been evaluated. We report on the results of reading the reconstructions of 90 data sets, each corresponding to a single brain slice. It has become apparent that the design of the study based on reading single brain slices is too insensitive and we propose a variation based on reading three consecutive slices at a time, rating only the center slice. 9 refs., 2 figs., 1 tab.
List-mode PET image reconstruction for motion correction using the Intel XEON PHI co-processor
NASA Astrophysics Data System (ADS)
Ryder, W. J.; Angelis, G. I.; Bashar, R.; Gillam, J. E.; Fulton, R.; Meikle, S.
2014-03-01
List-mode image reconstruction with motion correction is computationally expensive, as it requires projection of hundreds of millions of rays through a 3D array. To decrease reconstruction time it is possible to use symmetric multiprocessing computers or graphics processing units. The former can have high financial costs, while the latter can require refactoring of algorithms. The Xeon Phi is a new co-processor card with a Many Integrated Core architecture that can run 4 multiple-instruction, multiple data threads per core with each thread having a 512-bit single instruction, multiple data vector register. Thus, it is possible to run in the region of 220 threads simultaneously. The aim of this study was to investigate whether the Xeon Phi co-processor card is a viable alternative to an x86 Linux server for accelerating List-mode PET image reconstruction for motion correction. An existing list-mode image reconstruction algorithm with motion correction was ported to run on the Xeon Phi coprocessor with the multi-threading implemented using pthreads. There were no differences between images reconstructed using the Phi co-processor card and images reconstructed using the same algorithm run on a Linux server. However, it was found that the reconstruction runtimes were 3 times greater for the Phi than the server. A new version of the image reconstruction algorithm was developed in C++ using OpenMP for mutli-threading and the Phi runtimes decreased to 1.67 times that of the host Linux server. Data transfer from the host to co-processor card was found to be a rate-limiting step; this needs to be carefully considered in order to maximize runtime speeds. When considering the purchase price of a Linux workstation with Xeon Phi co-processor card and top of the range Linux server, the former is a cost-effective computation resource for list-mode image reconstruction. A multi-Phi workstation could be a viable alternative to cluster computers at a lower cost for medical imaging
Li, K; Safavi-Naeini, M; Franklin, D R; Han, Z; Rosenfeld, A B; Hutton, B; Lerch, M L F
2015-09-01
A common approach to improving the spatial resolution of small animal PET scanners is to reduce the size of scintillation crystals and/or employ high resolution pixellated semiconductor detectors. The large number of detector elements results in the system matrix--an essential part of statistical iterative reconstruction algorithms--becoming impractically large. In this paper, we propose a methodology for system matrix modelling which utilises a virtual single-layer detector ring to greatly reduce the size of the system matrix without sacrificing precision. Two methods for populating the system matrix are compared; the first utilises a geometrically-derived system matrix based on Siddon's ray tracer method with the addition of an accurate detector response function, while the second uses Monte Carlo simulation to populate the system matrix. The effectiveness of both variations of the proposed technique is demonstrated via simulations of PETiPIX, an ultra high spatial resolution small animal PET scanner featuring high-resolution DoI capabilities, which has previously been simulated and characterised using classical image reconstruction methods. Compression factors of 5 x 10(7) and 2.5 x 10(7)are achieved using this methodology for the system matrices produced using the geometric and Monte Carlo-based approaches, respectively, requiring a total of 0.5-1.2 GB of memory-resident storage. Images reconstructed from Monte Carlo simulations of various point source and phantom models, produced using system matrices generated via both geometric and simulation methods, are used to evaluate the quality of the resulting system matrix in terms of achievable spatial resolution and the CRC, CoV and CW-SSIM index image quality metrics. The Monte Carlo-based system matrix is shown to provide the best image quality at the cost of substantial one-off computational effort and a lower (but still practical) compression factor. Finally, a straightforward extension of the virtual ring
NASA Astrophysics Data System (ADS)
Zhou, Jian; Qi, Jinyi
2011-10-01
Statistically based iterative image reconstruction has been widely used in positron emission tomography (PET) imaging. The quality of reconstructed images depends on the accuracy of the system matrix that defines the mapping from the image space to the data space. However, an accurate system matrix is often associated with high computation cost and huge storage requirement. In this paper, we present a method to address this problem using sparse matrix factorization and graphics processor unit (GPU) acceleration. We factor the accurate system matrix into three highly sparse matrices: a sinogram blurring matrix, a geometric projection matrix and an image blurring matrix. The geometrical projection matrix is precomputed based on a simple line integral model, while the sinogram and image blurring matrices are estimated from point-source measurements. The resulting factored system matrix has far less nonzero elements than the original system matrix, which substantially reduces the storage and computation cost. The smaller matrix size also allows an efficient implementation of the forward and backward projectors on a GPU, which often has a limited memory space. Our experimental studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction, while achieving better performance than existing factorization methods.
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
High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid
Cabello, J.; Gillam, J. E.; Rafecas, M.
2012-01-01
Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM) to store, which additionally leads to long reconstruction times. These two problems are partly overcome by exploiting spherical symmetries, although computation time is still slower compared to non-overlapping basis functions. In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU) technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. The reconstruction time achieved using spherical basis functions on a GPU was 4.3 times faster than the Central Processing Unit (CPU) and 2.5 times faster than a CPU-multi-core parallel implementation using eight cores. Overwriting hazards are minimized by combining a random line of response ordering and constrained atomic writing. Small differences in image quality were observed between implementations. PMID:22548047
Veltchev, I; Fourkal, E; Doss, M; Ma, C; Meyer, J; Yu, M; Horwitz, E
2014-06-01
Purpose: In the past few years there have been numerous proposals for 3D dose reconstruction from the PET-CT imaging of patients undergoing radioembolization treatment of the liver with yttrium-90 microspheres. One of the most promising techniques uses convolution of the measured PET activity distribution with a pre-calculated Monte Carlo dose deposition kernel. The goal of the present study is to experimentally verify the accuracy of this method and to analyze the significance of various error sources. Methods: Optically stimulated luminescence detectors (OSLD) were used (NanoDot, Landauer) in this experiment. Two detectors were mounted on the central axis of a cylinder filled with water solution of yttrium-90 chloride. The total initial activity was 90mCi. The cylinder was inserted in a larger water phantom and scanned on a Siemens Biograph 16 Truepoint PET-CT scanner. Scans were performed daily over a period of 20 days to build a calibration curve for the measured absolute activity spanning 7 yttrium-90 half-lives. The OSLDs were mounted in the phantom for a predetermined period of time in order to record 2Gy dose. The measured dose was then compared to the dose reconstructed from the activity density at the location of each dosimeter. Results: Thorough error analysis of the dose reconstruction algorithm takes into account the uncertainties in the absolute PET activity, branching ratios, and nonlinearity of the calibration curve. The measured dose for 105-minute exposure on day 10 of the experiment was 219(11)cGy, while the reconstructed dose at the location of the detector was 215(47)cGy. Conclusion: We present the first experimental verification of the accuracy of the convolution algorithm for absolute dose reconstruction of yttrium-90 microspheres. The excellent agreement between the measured and calculated point doses will encourage the broad clinical adoption of the convolution-based dose reconstruction algorithm, making future quantitative dose
Cheng, Xiaoyin; Bayer, Christine; Maftei, Constantin-Alin; Astner, Sabrina T; Vaupel, Peter; Ziegler, Sibylle I; Shi, Kuangyu
2014-01-20
Compared to indirect methods, direct parametric image reconstruction (PIR) has the advantage of high quality and low statistical errors. However, it is not yet clear if this improvement in quality is beneficial for physiological quantification. This study aimed to evaluate direct PIR for the quantification of tumor hypoxia using the hypoxic fraction (HF) assessed from immunohistological data as a physiological reference. Sixteen mice with xenografted human squamous cell carcinomas were scanned with dynamic [18F]FMISO PET. Afterward, tumors were sliced and stained with H&E and the hypoxia marker pimonidazole. The hypoxic signal was segmented using k-means clustering and HF was specified as the ratio of the hypoxic area over the viable tumor area. The parametric Patlak slope images were obtained by indirect voxel-wise modeling on reconstructed images using filtered back projection and ordered-subset expectation maximization (OSEM) and by direct PIR (e.g., parametric-OSEM, POSEM). The mean and maximum Patlak slopes of the tumor area were investigated and compared with HF. POSEM resulted in generally higher correlations between slope and HF among the investigated methods. A strategy for the delineation of the hypoxic tumor volume based on thresholding parametric images at half maximum of the slope is recommended based on the results of this study. PMID:24351879
NASA Astrophysics Data System (ADS)
Cheng, Xiaoyin; Bayer, Christine; Maftei, Constantin-Alin; Astner, Sabrina T.; Vaupel, Peter; Ziegler, Sibylle I.; Shi, Kuangyu
2014-01-01
Compared to indirect methods, direct parametric image reconstruction (PIR) has the advantage of high quality and low statistical errors. However, it is not yet clear if this improvement in quality is beneficial for physiological quantification. This study aimed to evaluate direct PIR for the quantification of tumor hypoxia using the hypoxic fraction (HF) assessed from immunohistological data as a physiological reference. Sixteen mice with xenografted human squamous cell carcinomas were scanned with dynamic [18F]FMISO PET. Afterward, tumors were sliced and stained with H&E and the hypoxia marker pimonidazole. The hypoxic signal was segmented using k-means clustering and HF was specified as the ratio of the hypoxic area over the viable tumor area. The parametric Patlak slope images were obtained by indirect voxel-wise modeling on reconstructed images using filtered back projection and ordered-subset expectation maximization (OSEM) and by direct PIR (e.g., parametric-OSEM, POSEM). The mean and maximum Patlak slopes of the tumor area were investigated and compared with HF. POSEM resulted in generally higher correlations between slope and HF among the investigated methods. A strategy for the delineation of the hypoxic tumor volume based on thresholding parametric images at half maximum of the slope is recommended based on the results of this study.
PET/CT (and CT) instrumentation, image reconstruction and data transfer for radiotherapy planning.
Sattler, Bernhard; Lee, John A; Lonsdale, Markus; Coche, Emmanuel
2010-09-01
The positron emission tomography in combination with CT in hybrid, cross-modality imaging systems (PET/CT) gains more and more importance as a part of the treatment-planning procedure in radiotherapy. Positron emission tomography (PET), as a integral part of nuclear medicine imaging and non-invasive imaging technique, offers the visualization and quantification of pre-selected tracer metabolism. In combination with the structural information from CT, this molecular imaging technique has great potential to support and improve the outcome of the treatment-planning procedure prior to radiotherapy. By the choice of the PET-Tracer, a variety of different metabolic processes can be visualized. First and foremost, this is the glucose metabolism of a tissue as well as for instance hypoxia or cell proliferation. This paper comprises the system characteristics of hybrid PET/CT systems. Acquisition and processing protocols are described in general and modifications to cope with the special needs in radiooncology. This starts with the different position of the patient on a special table top, continues with the use of the same fixation material as used for positioning of the patient in radiooncology while simulation and irradiation and leads to special processing protocols that include the delineation of the volumes that are subject to treatment planning and irradiation (PTV, GTV, CTV, etc.). General CT acquisition and processing parameters as well as the use of contrast enhancement of the CT are described. The possible risks and pitfalls the investigator could face during the hybrid-imaging procedure are explained and listed. The interdisciplinary use of different imaging modalities implies a increase of the volume of data created. These data need to be stored and communicated fast, safe and correct. Therefore, the DICOM-Standard provides objects and classes for this purpose (DICOM RT). Furthermore, the standard DICOM objects and classes for nuclear medicine (NM, PT) and
Ahmad, Munir; Shahzad, Tasawar; Masood, Khalid; Rashid, Khalid; Tanveer, Muhammad; Iqbal, Rabail; Hussain, Nasir; Shahid, Abubakar; Fazal-E-Aleem
2016-06-01
Emission tomographic image reconstruction is an ill-posed problem due to limited and noisy data and various image-degrading effects affecting the data and leads to noisy reconstructions. Explicit regularization, through iterative reconstruction methods, is considered better to compensate for reconstruction-based noise. Local smoothing and edge-preserving regularization methods can reduce reconstruction-based noise. However, these methods produce overly smoothed images or blocky artefacts in the final image because they can only exploit local image properties. Recently, non-local regularization techniques have been introduced, to overcome these problems, by incorporating geometrical global continuity and connectivity present in the objective image. These techniques can overcome drawbacks of local regularization methods; however, they also have certain limitations, such as choice of the regularization function, neighbourhood size or calibration of several empirical parameters involved. This work compares different local and non-local regularization techniques used in emission tomographic imaging in general and emission computed tomography in specific for improved quality of the resultant images. PMID:26714680
Van Slambrouck, Katrien; Stute, Simon; Comtat, Claude; Sibomana, Merence; van Velden, Floris H P; Boellaard, Ronald; Nuyts, Johan
2015-01-01
Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling. Two reconstruction methods with bias reduction properties that do not use strict Poisson optimization are presented and compared to each other, to filtered backprojection (FBP), and to MLEM. The first method is an extension of NEGML, where the Poisson distribution is replaced by a Gaussian distribution for low count data points. The transition point between the Gaussian and the Poisson regime is a parameter of the model. The second method is a simplification of ABML. ABML has a lower and upper bound for the reconstructed image whereas AML has the upper bound set to infinity. AML uses a negative lower bound to obtain bias reduction properties. Different choices of the lower bound are studied. The parameter of both algorithms determines the effectiveness of the bias reduction and should be chosen large enough to ensure bias-free images. This means that both algorithms become more similar to least squares algorithms, which turned out to be necessary to obtain bias-free reconstructions. This comes at the cost of increased variance. Nevertheless, NEGML and AML have lower variance than FBP. Furthermore, randoms handling has a large influence on the bias. Reconstruction with smoothed randoms results in lower bias compared to reconstruction with unsmoothed randoms or randoms precorrected data. However, NEGML and AML yield both bias-free images for large values of their parameter. PMID:25137726
Garcia, Marie-Paule; Charil, Arnaud; Callaghan, Paul; Wimberley, Catriona; Busso, Florian; Gregoire, Marie-Claude; Bardies, Manuel; Reilhac, Anthonin
2016-07-01
A wide range of medical imaging applications benefits from the availability of realistic ground truth data. In the case of positron emission tomography (PET), ground truth data is crucial to validate processing algorithms and assessing their performances. The design of such ground truth data often relies on Monte-Carlo simulation techniques. Since the creation of a large dataset is not trivial both in terms of computing time and realism, we propose the OSSI-PET database containing 350 simulated [(11)C]Raclopride dynamic scans for rats, created specifically for the Inveon pre-clinical PET scanner. The originality of this database lies on the availability of several groups of scans with controlled biological variations in the striata. Besides, each group consists of a large number of realizations (i.e., noise replicates). We present the construction methodology of this database using rat pharmacokinetic and anatomical models. A first application using the OSSI-PET database is presented. Several commonly used reconstruction techniques were compared in terms of image quality, accuracy and variability of the activity estimates and of the computed kinetic parameters. The results showed that OP-OSEM3D iterative reconstruction method outperformed the other tested methods. Analytical methods such as FBP2D and 3DRP also produced satisfactory results. However, FORE followed by OSEM2D reconstructions should be avoided. Beyond the illustration of the potential of the database, this application will help scientists to understand the different sources of noise and bias that can occur at the different steps in the processing and will be very useful for choosing appropriate reconstruction methods and parameters. PMID:26863655
Angelis, G I; Reader, A J; Kotasidis, F A; Lionheart, W R; Matthews, J C
2011-07-01
Iterative expectation maximization (EM) techniques have been extensively used to solve maximum likelihood (ML) problems in positron emission tomography (PET) image reconstruction. Although EM methods offer a robust approach to solving ML problems, they usually suffer from slow convergence rates. The ordered subsets EM (OSEM) algorithm provides significant improvements in the convergence rate, but it can cycle between estimates converging towards the ML solution of each subset. In contrast, gradient-based methods, such as the recently proposed non-monotonic maximum likelihood (NMML) and the more established preconditioned conjugate gradient (PCG), offer a globally convergent, yet equally fast, alternative to OSEM. Reported results showed that NMML provides faster convergence compared to OSEM; however, it has never been compared to other fast gradient-based methods, like PCG. Therefore, in this work we evaluate the performance of two gradient-based methods (NMML and PCG) and investigate their potential as an alternative to the fast and widely used OSEM. All algorithms were evaluated using 2D simulations, as well as a single [(11)C]DASB clinical brain dataset. Results on simulated 2D data show that both PCG and NMML achieve orders of magnitude faster convergence to the ML solution compared to MLEM and exhibit comparable performance to OSEM. Equally fast performance is observed between OSEM and PCG for clinical 3D data, but NMML seems to perform poorly. However, with the addition of a preconditioner term to the gradient direction, the convergence behaviour of NMML can be substantially improved. Although PCG is a fast convergent algorithm, the use of a (bent) line search increases the complexity of the implementation, as well as the computational time involved per iteration. Contrary to previous reports, NMML offers no clear advantage over OSEM or PCG, for noisy PET data. Therefore, we conclude that there is little evidence to replace OSEM as the algorithm of choice for
Comparison of basis functions for 3D PET reconstruction using a Monte Carlo system matrix
NASA Astrophysics Data System (ADS)
Cabello, Jorge; Rafecas, Magdalena
2012-04-01
In emission tomography, iterative statistical methods are accepted as the reconstruction algorithms that achieve the best image quality. The accuracy of these methods relies partly on the quality of the system response matrix (SRM) that characterizes the scanner. The more physical phenomena included in the SRM, the higher the SRM quality, and therefore higher image quality is obtained from the reconstruction process. High-resolution small animal scanners contain as many as 103-104 small crystal pairs, while the field of view (FOV) is divided into hundreds of thousands of small voxels. These two characteristics have a significant impact on the number of elements to be calculated in the SRM. Monte Carlo (MC) methods have gained popularity as a way of calculating the SRM, due to the increased accuracy achievable, at the cost of introducing some statistical noise and long simulation times. In the work presented here the SRM is calculated using MC methods exploiting the cylindrical symmetries of the scanner, significantly reducing the simulation time necessary to calculate a high statistical quality SRM and the storage space necessary. The use of cylindrical symmetries makes polar voxels a convenient basis function. Alternatively, spherically symmetric basis functions result in improved noise properties compared to cubic and polar basis functions. The quality of reconstructed images using polar voxels, spherically symmetric basis functions on a polar grid, cubic voxels and post-reconstruction filtered polar and cubic voxels is compared from a noise and spatial resolution perspective. This study demonstrates that polar voxels perform as well as cubic voxels, reducing the simulation time necessary to calculate the SRM and the disk space necessary to store it. Results showed that spherically symmetric functions outperform polar and cubic basis functions in terms of noise properties, at the cost of slightly degraded spatial resolution, larger SRM file size and longer
Spatially Variant Resolution Modelling for Iterative List-Mode PET Reconstruction.
Bickell, Matthew G; Zhou, Lin; Nuyts, Johan
2016-07-01
A spatially variant resolution modelling technique is presented which estimates the system matrix on-the-fly during iterative list-mode reconstruction. This is achieved by redistributing the endpoints of each list-mode event according to derived probability density functions describing the detector response function and photon acollinearity, at each iteration during the reconstruction. Positron range is modelled using an image-based convolution. When applying this technique it is shown that the maximum-likelihood expectation maximisation (MLEM) algorithm is not compatible with an obvious acceleration strategy. The image space reconstruction algorithm (ISRA), however, after being adapted to a list-mode based implementation, is well-suited to the implementation of the model. A comparison of ISRA and MLEM is made to confirm that ISRA is a suitable alternative to MLEM. We demonstrate that this model agrees with measured point spread functions and we present results showing an improvement in resolution recovery, particularly for off-centre objects, as compared to commercially available software, as well as the standard technique of using a stationary Gaussian convolution to model the resolution, for equal iterations and only slightly higher computation time. PMID:26886967
Interactive animation of 4D performance capture.
Casas, Dan; Tejera, Margara; Guillemaut, Jean-Yves; Hilton, Adrian
2013-05-01
A 4D parametric motion graph representation is presented for interactive animation from actor performance capture in a multiple camera studio. The representation is based on a 4D model database of temporally aligned mesh sequence reconstructions for multiple motions. High-level movement controls such as speed and direction are achieved by blending multiple mesh sequences of related motions. A real-time mesh sequence blending approach is introduced, which combines the realistic deformation of previous nonlinear solutions with efficient online computation. Transitions between different parametric motion spaces are evaluated in real time based on surface shape and motion similarity. Four-dimensional parametric motion graphs allow real-time interactive character animation while preserving the natural dynamics of the captured performance. PMID:23492379
Fourkal, E.; Veltchev, I.; Lin, M.; Meyer, J.; Koren, S.; Doss, M.; Yu, J. Q.
2013-08-15
Purpose: The introduction of radioembolization with microspheres represents a significant step forward in the treatment of patients with metastatic disease to the liver. This technique uses semiempirical formulae based on body surface area or liver and target volumes to calculate the required total activity for a given patient. However, this treatment modality lacks extremely important information, which is the three-dimensional (3D) dose delivered by microspheres to different organs after their administration. The absence of this information dramatically limits the clinical efficacy of this modality, specifically the predictive power of the treatment. Therefore, the aim of this study is to develop a 3D dose calculation technique that is based on the PET imaging of the infused microspheres.Methods: The Fluka Monte Carlo code was used to calculate the voxel dose kernel for {sup 90}Y source with voxel size equal to that of the PET scan. The measured PET activity distribution was converted to total activity distribution for the subsequent convolution with the voxel dose kernel to obtain the 3D dose distribution. In addition, dose-volume histograms were generated to analyze the dose to the tumor and critical structures.Results: The 3D inpatient dose distribution can be reconstructed from the PET data of a patient scanned after the infusion of microspheres. A total of seven patients have been analyzed so far using the proposed reconstruction method. Four patients underwent treatment with SIR-Spheres for liver metastases from colorectal cancer and three patients were treated with Therasphere for hepatocellular cancer. A total of 14 target tumors were contoured on post-treatment PET-CT scans for dosimetric evaluation. Mean prescription activity was 1.7 GBq (range: 0.58–3.8 GBq). The resulting mean maximum measured dose to targets was 167 Gy (range: 71–311 Gy). Mean minimum dose to 70% of target (D70) was 68 Gy (range: 25–155 Gy). Mean minimum dose to 90% of target
NASA Astrophysics Data System (ADS)
de Jesús Ochoa Domínguez, Humberto; Máynez, Leticia Ortega; Villegas, Osslan Osiris Vergara; Castillo, Nelly Gordillo; Sánchez, Vianey Guadalupe Cruz; Casas, Efrén David Gutiérrez
2011-10-01
The data obtained from a PET system tend to be noisy because of the limitations of the current instrumentation and the detector efficiency. This problem is particularly severe in images of small animals as the noise contaminates areas of interest within small organs. Therefore, denoising becomes a challenging task. In this paper, a novel wavelet-based regularization and edge preservation method is proposed to reduce such noise. To demonstrate this method, image reconstruction using a small mouse 18F NEMA phantom and a 18F mouse was performed. Investigation on the effects of the image quality was addressed for each reconstruction case. Results show that the proposed method drastically reduces the noise and preserves the image details.
NASA Technical Reports Server (NTRS)
1956-01-01
This Photograph taken in 1956 shows the first of three R4D Skytrain aircraft on the ramp behind the NACA High-Speed Flight Station. Note the designation 'United States NACA' on the side of the aircraft. NACA stood for the National Advisory Committee for Aeronautics, which evolved into the National Aeronautics and Space Administration (NASA) in 1958. The R4D Skytrain was one of the early workhorses for NACA and NASA at Edwards Air Force Base, California, from 1952 to 1984. Designated the R4D by the U.S. Navy, the aircraft was called the C-47 by the U.S. Army and U.S. Air Force and the DC-3 by its builder, Douglas Aircraft. Nearly everyone called it the 'Gooney Bird.' In 1962, Congress consolidated the military-service designations and called all of them the C-47. After that date, the R4D at NASA's Flight Research Center (itself redesignated the Dryden Flight Research Center in 1976) was properly called a C-47. Over the 32 years it was used at Edwards, three different R4D/C-47s were used to shuttle personnel and equipment between NACA/NASA Centers and test locations throughout the country and for other purposes. One purpose was landing on 'dry' lakebeds used as alternate landing sites for the X-15, to determine whether their surfaces were hard (dry) enough for the X-15 to land on in case an emergency occurred after its launch and before it could reach Rogers Dry Lake at Edwards Air Force Base. The R4D/C-47 served a variety of needs, including serving as the first air-tow vehicle for the M2-F1 lifting body (which was built of mahogany plywood). The C-47 (as it was then called) was used for 77 tows before the M2-F1 was retired for more advanced lifting bodies that were dropped from the NASA B-52 'Mothership.' The R4D also served as a research aircraft. It was used to conduct early research on wing-tip-vortex flow visualization as well as checking out the NASA Uplink Control System. The first Gooney Bird was at the NACA High-Speed Flight Research Station (now the Dryden
NASA Astrophysics Data System (ADS)
Zhang, Xuezhu; Stortz, Greg; Sossi, Vesna; Thompson, Christopher J.; Retière, Fabrice; Kozlowski, Piotr; Thiessen, Jonathan D.; Goertzen, Andrew L.
2013-12-01
In this study we present a method of 3D system response calculation for analytical computer simulation and statistical image reconstruction for a magnetic resonance imaging (MRI) compatible positron emission tomography (PET) insert system that uses a dual-layer offset (DLO) crystal design. The general analytical system response functions (SRFs) for detector geometric and inter-crystal penetration of coincident crystal pairs are derived first. We implemented a 3D ray-tracing algorithm with 4π sampling for calculating the SRFs of coincident pairs of individual DLO crystals. The determination of which detector blocks are intersected by a gamma ray is made by calculating the intersection of the ray with virtual cylinders with radii just inside the inner surface and just outside the outer-edge of each crystal layer of the detector ring. For efficient ray-tracing computation, the detector block and ray to be traced are then rotated so that the crystals are aligned along the X-axis, facilitating calculation of ray/crystal boundary intersection points. This algorithm can be applied to any system geometry using either single-layer (SL) or multi-layer array design with or without offset crystals. For effective data organization, a direct lines of response (LOR)-based indexed histogram-mode method is also presented in this work. SRF calculation is performed on-the-fly in both forward and back projection procedures during each iteration of image reconstruction, with acceleration through use of eight-fold geometric symmetry and multi-threaded parallel computation. To validate the proposed methods, we performed a series of analytical and Monte Carlo computer simulations for different system geometry and detector designs. The full-width-at-half-maximum of the numerical SRFs in both radial and tangential directions are calculated and compared for various system designs. By inspecting the sinograms obtained for different detector geometries, it can be seen that the DLO crystal
NASA Astrophysics Data System (ADS)
Taillandier-Thomas, Thibault; Roux, Stéphane; Hild, François
2016-07-01
Based on the assumption that the time evolution of a sample observed by computed tomography requires many less parameters than the definition of the microstructure itself, it is proposed to reconstruct these changes based on the initial state (using computed tomography) and very few radiographs acquired at fixed intervals of time. This Letter presents a proof of concept that for a fatigue cracked sample its kinematics can be tracked from no more than two radiographs in situations where a complete 3D view would require several hundreds of radiographs. This 2 order of magnitude gain opens the way to a "computed" 4D tomography, which complements the recent progress achieved in fast or ultrafast computed tomography, which is based on beam brightness, detector sensitivity, and signal acquisition technologies.
NASA Astrophysics Data System (ADS)
Bowen, Spencer L.; Byars, Larry G.; Michel, Christian J.; Chonde, Daniel B.; Catana, Ciprian
2013-10-01
Kinetic parameters estimated from dynamic 18F-fluorodeoxyglucose (18F-FDG) PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For ordered subsets expectation maximization (OSEM), image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting 18F-FDG dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation geometric transfer matrix PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in cerebral metabolic rate of glucose estimates, although by less than 5% in most
4D-DSA and 4D fluoroscopy: preliminary implementation
NASA Astrophysics Data System (ADS)
Mistretta, C. A.; Oberstar, E.; Davis, B.; Brodsky, E.; Strother, C. M.
2010-04-01
We have described methods that allow highly accelerated MRI using under-sampled acquisitions and constrained reconstruction. One is a hybrid acquisition involving the constrained reconstruction of time dependent information obtained from a separate scan of longer duration. We have developed reconstruction algorithms for DSA that allow use of a single injection to provide the temporal data required for flow visualization and the steady state data required for construction of a 3D-DSA vascular volume. The result is time resolved 3D volumes with typical resolution of 5123 at frame rates of 20-30 fps. Full manipulation of these images is possible during each stage of vascular filling thereby allowing for simplified interpretation of vascular dynamics. For intravenous angiography this time resolved 3D capability overcomes the vessel overlap problem that greatly limited the use of conventional intravenous 2D-DSA. Following further hardware development, it will be also be possible to rotate fluoroscopic volumes for use as roadmaps that can be viewed at arbitrary angles without a need for gantry rotation. The most precise implementation of this capability requires availability of biplane fluoroscopy data. Since the reconstruction of 3D volumes presently suppresses the contrast in the soft tissue, the possibility of using these techniques to derive complete indications of perfusion deficits based on cerebral blood volume (CBV), mean transit time (MTT) and time to peak (TTP) parameters requires further investigation. Using MATLAB post-processing, successful studies in animals and humans done in conjunction with both intravenous and intra-arterial injections have been completed. Real time implementation is in progress.
Pulmonary imaging using respiratory motion compensated simultaneous PET/MR
Dutta, Joyita; Huang, Chuan; Li, Quanzheng; El Fakhri, Georges
2015-01-01
Purpose: Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies. Methods: The authors have developed an MCIR framework based on maximum a posteriori or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing k-t FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner. Results: The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%–107% for 14 mm diameter lung lesions and 39%–120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors’ results show that the MC image yields 19%–190% increase in the CNR of high-intensity features of interest affected by
Attili, A; Vignati, A; Giordanengo, S; Kraan, A; Dalmasso, F; Battistoni, G
2015-06-15
Purpose: Ion beam therapy is sensitive to uncertainties from treatment planning and dose delivery. PET imaging of induced positron emitter distributions is a practical approach for in vivo, in situ verification of ion beam treatments. Treatment verification is usually done by comparing measured activity distributions with reference distributions, evaluated in nominal conditions. Although such comparisons give valuable information on treatment quality, a proper clinical evaluation of the treatment ultimately relies on the knowledge of the actual delivered dose. Analytical deconvolution methods relating activity and dose have been studied in this context, but were not clinically applied. In this work we present a feasibility study of an alternative approach for dose reconstruction from activity data, which is based on relating variations in accumulated activity to tissue density variations. Methods: First, reference distributions of dose and activity were calculated from the treatment plan and CT data. Then, the actual measured activity data were cumulatively matched with the reference activity distributions to obtain a set of activity-equivalent path lengths (AEPLs) along the rays of the pencil beams. Finally, these AEPLs were used to deform the original dose distribution, yielding the actual delivered dose. The method was tested by simulating a proton therapy treatment plan delivering 2 Gy on a homogeneous water phantom (the reference), which was compared with the same plan delivered on a phantom containing inhomogeneities. Activity and dose distributions were were calculated by means of the FLUKA Monte Carlo toolkit. Results: The main features of the observed dose distribution in the inhomogeneous situation were reproduced using the AEPL approach. Variations in particle range were reproduced and the positions, where these deviations originated, were properly identified. Conclusions: For a simple inhomogeneous phantom the 3D dose reconstruction from PET
NASA Astrophysics Data System (ADS)
Scheins, J. J.; Vahedipour, K.; Pietrzyk, U.; Shah, N. J.
2015-12-01
For high-resolution, iterative 3D PET image reconstruction the efficient implementation of forward-backward projectors is essential to minimise the calculation time. Mathematically, the projectors are summarised as a system response matrix (SRM) whose elements define the contribution of image voxels to lines-of-response (LORs). In fact, the SRM easily comprises billions of non-zero matrix elements to evaluate the tremendous number of LORs as provided by state-of-the-art PET scanners. Hence, the performance of iterative algorithms, e.g. maximum-likelihood-expectation-maximisation (MLEM), suffers from severe computational problems due to the intensive memory access and huge number of floating point operations. Here, symmetries occupy a key role in terms of efficient implementation. They reduce the amount of independent SRM elements, thus allowing for a significant matrix compression according to the number of exploitable symmetries. With our previous work, the PET REconstruction Software TOolkit (PRESTO), very high compression factors (>300) are demonstrated by using specific non-Cartesian voxel patterns involving discrete polar symmetries. In this way, a pre-calculated memory-resident SRM using complex volume-of-intersection calculations can be achieved. However, our original ray-driven implementation suffers from addressing voxels, projection data and SRM elements in disfavoured memory access patterns. As a consequence, a rather limited numerical throughput is observed due to the massive waste of memory bandwidth and inefficient usage of cache respectively. In this work, an advantageous symmetry-driven evaluation of the forward-backward projectors is proposed to overcome these inefficiencies. The polar symmetries applied in PRESTO suggest a novel organisation of image data and LOR projection data in memory to enable an efficient single instruction multiple data vectorisation, i.e. simultaneous use of any SRM element for symmetric LORs. In addition, the calculation
NASA Astrophysics Data System (ADS)
Moskal, P.; Zoń, N.; Bednarski, T.; Białas, P.; Czerwiński, E.; Gajos, A.; Kamińska, D.; Kapłon, Ł.; Kochanowski, A.; Korcyl, G.; Kowal, J.; Kowalski, P.; Kozik, T.; Krzemień, W.; Kubicz, E.; Niedźwiecki, Sz.; Pałka, M.; Raczyński, L.; Rudy, Z.; Rundel, O.; Salabura, P.; Sharma, N. G.; Silarski, M.; Słomski, A.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Wiślicki, W.; Zieliński, M.
2015-03-01
A novel method of hit time and hit position reconstruction in scintillator detectors is described. The method is based on comparison of detector signals with results stored in a library of synchronized model signals registered for a set of well-defined positions of scintillation points. The hit position is reconstructed as the one corresponding to the signal from the library which is most similar to the measurement signal. The time of the interaction is determined as a relative time between the measured signal and the most similar one in the library. A degree of similarity of measured and model signals is defined as the distance between points representing the measurement- and model-signal in the multi-dimensional measurement space. Novelty of the method lies also in the proposed way of synchronization of model signals enabling direct determination of the difference between time-of-flights (TOF) of annihilation quanta from the annihilation point to the detectors. The introduced method was validated using experimental data obtained by means of the double strip prototype of the J-PET detector and 22Na sodium isotope as a source of annihilation gamma quanta. The detector was built out from plastic scintillator strips with dimensions of 5 mm×19 mm×300 mm, optically connected at both sides to photomultipliers, from which signals were sampled by means of the Serial Data Analyzer. Using the introduced method, the spatial and TOF resolution of about 1.3 cm (σ) and 125 ps (σ) were established, respectively.
4-D OCT in Developmental Cardiology
NASA Astrophysics Data System (ADS)
Jenkins, Michael W.; Rollins, Andrew M.
Although strong evidence exists to suggest that altered cardiac function can lead to CHDs, few studies have investigated the influential role of cardiac function and biophysical forces on the development of the cardiovascular system due to a lack of proper in vivo imaging tools. 4-D imaging is needed to decipher the complex spatial and temporal patterns of biomechanical forces acting upon the heart. Numerous solutions over the past several years have demonstrated 4-D OCT imaging of the developing cardiovascular system. This chapter will focus on these solutions and explain their context in the evolution of 4-D OCT imaging. The first sections describe the relevant techniques (prospective gating, direct 4-D imaging, retrospective gating), while later sections focus on 4-D Doppler imaging and measurements of force implementing 4-D OCT Doppler. Finally, the techniques are summarized, and some possible future directions are discussed.
Ha, S.; Matej, S.; Ispiryan, M.; Mueller, K.
2013-01-01
We describe a GPU-accelerated framework that efficiently models spatially (shift) variant system response kernels and performs forward- and back-projection operations with these kernels for the DIRECT (Direct Image Reconstruction for TOF) iterative reconstruction approach. Inherent challenges arise from the poor memory cache performance at non-axis aligned TOF directions. Focusing on the GPU memory access patterns, we utilize different kinds of GPU memory according to these patterns in order to maximize the memory cache performance. We also exploit the GPU instruction-level parallelism to efficiently hide long latencies from the memory operations. Our experiments indicate that our GPU implementation of the projection operators has slightly faster or approximately comparable time performance than FFT-based approaches using state-of-the-art FFTW routines. However, most importantly, our GPU framework can also efficiently handle any generic system response kernels, such as spatially symmetric and shift-variant as well as spatially asymmetric and shift-variant, both of which an FFT-based approach cannot cope with. PMID:23531763
Vauclin, S; Michel, C; Buvat, I; Doyeux, K; Edet-Sanson, A; Vera, P; Gardin, I; Hapdey, S
2015-01-01
In PET/CT thoracic imaging, respiratory motion reduces image quality. A solution consists in performing respiratory gated PET acquisitions. The aim of this study was to generate clinically realistic Monte-Carlo respiratory PET data, obtained using the 4D-NCAT numerical phantom and the GATE simulation tool, to assess the impact of respiratory motion and respiratory-motion compensation in PET on lesion detection and volume measurement. To obtain reconstructed images as close as possible to those obtained in clinical conditions, a particular attention was paid to apply to the simulated data the same correction and reconstruction processes as those applied to real clinical data. The simulations required 140,000h (CPU) generating 1.5 To of data (98 respiratory gated and 49 ungated scans). Calibration phantom and patient reconstructed images from the simulated data were visually and quantitatively very similar to those obtained in clinical studies. The lesion detectability was higher when the better trade-off between lesion movement limitation (compared to ungated acquisitions) and image statistic preservation is considered (respiratory cycle sampling in 3 frames). We then compared the lesion volumes measured on conventional PET acquisitions versus respiratory gated acquisitions, using an automatic segmentation method and a 40%-threshold approach. A time consuming initial manual exclusion of noisy structures needed with the 40%-threshold was not necessary when the automatic method was used. The lesion detectability along with the accuracy of tumor volume estimates was largely improved with the gated compared to ungated PET images. PMID:25459525
TU-C-BRD-01: Image Guided SBRT I: Multi-Modality 4D Imaging
Cai, J; Mageras, G; Pan, T
2014-06-15
Motion management is one of the critical technical challenges for radiation therapy. 4D imaging has been rapidly adopted as essential tool to assess organ motion associated with respiratory breathing. A variety of 4D imaging techniques have been developed and are currently under development based on different imaging modalities such as CT, MRI, PET, and CBCT. Each modality provides specific and complementary information about organ and tumor respiratory motion. Effective use of each different technique or combined use of different techniques can introduce a comprehensive management of tumor motion. Specifically, these techniques have afforded tremendous opportunities to better define and delineate tumor volumes, more accurately perform patient positioning, and effectively apply highly conformal therapy techniques such as IMRT and SBRT. Successful implementation requires good understanding of not only each technique, including unique features, limitations, artifacts, imaging acquisition and process, but also how to systematically apply the information obtained from different imaging modalities using proper tools such as deformable image registration. Furthermore, it is important to understand the differences in the effects of breathing variation between different imaging modalities. A comprehensive motion management strategy using multi-modality 4D imaging has shown promise in improving patient care, but at the same time faces significant challenges. This session will focuses on the current status and advances in imaging respiration-induced organ motion with different imaging modalities: 4D-CT, 4D-MRI, 4D-PET, and 4D-CBCT/DTS. Learning Objectives: Understand the need and role of multimodality 4D imaging in radiation therapy. Understand the underlying physics behind each 4D imaging technique. Recognize the advantages and limitations of each 4D imaging technique.
NASA Astrophysics Data System (ADS)
Cheng, Ju-Chieh Kevin; Rahmim, Arman; Blinder, Stephan; Camborde, Marie-Laure; Raywood, Kelvin; Sossi, Vesna
2007-04-01
We describe an ordinary Poisson list-mode expectation maximization (OP-LMEM) algorithm with a sinogram-based scatter correction method based on the single scatter simulation (SSS) technique and a random correction method based on the variance-reduced delayed-coincidence technique. We also describe a practical approximate scatter and random-estimation approach for dynamic PET studies based on a time-averaged scatter and random estimate followed by scaling according to the global numbers of true coincidences and randoms for each temporal frame. The quantitative accuracy achieved using OP-LMEM was compared to that obtained using the histogram-mode 3D ordinary Poisson ordered subset expectation maximization (3D-OP) algorithm with similar scatter and random correction methods, and they showed excellent agreement. The accuracy of the approximated scatter and random estimates was tested by comparing time activity curves (TACs) as well as the spatial scatter distribution from dynamic non-human primate studies obtained from the conventional (frame-based) approach and those obtained from the approximate approach. An excellent agreement was found, and the time required for the calculation of scatter and random estimates in the dynamic studies became much less dependent on the number of frames (we achieved a nearly four times faster performance on the scatter and random estimates by applying the proposed method). The precision of the scatter fraction was also demonstrated for the conventional and the approximate approach using phantom studies. This work was supported by the Canadian Institute of Health Research, a TRIUMF Life Science Grant, the Natural Sciences and Engineering Research Council of Canada UFA (V Sossi) and the Michael Smith Foundation for Health Research Scholarship (V Sossi).
A 4D Hyperspherical Interpretation of q-Space
Hosseinbor, A. Pasha; Chung, Moo K.; Wu, Yu-Chien; Bendlin, Barbara B.; Alexander, Andrew L.
2015-01-01
3D q-space can be viewed as the surface of a 4D hypersphere. In this paper, we seek to develop a 4D hyperspherical interpretation of q-space by projecting it onto a hypersphere and subsequently modeling the q-space signal via 4D hyperspherical harmonics (HSH). Using this orthonormal basis, we derive several well-established q-space indices and numerically estimate the diffusion orientation distribution function (dODF). We also derive the integral transform describing the relationship between the diffusion signal and propagator on a hypersphere. Most importantly, we will demonstrate that for hybrid diffusion imaging (HYDI) acquisitions low order linear expansion of the HSH basis is sufficient to characterize diffusion in neural tissue. In fact, the HSH basis achieves comparable signal and better dODF reconstructions than other well-established methods, such as Bessel Fourier orientation reconstruction (BFOR), using fewer fitting parameters. All in all, this work provides a new way of looking at q-space. PMID:25624043
Brain tissue segmentation in 4D CT using voxel classification
NASA Astrophysics Data System (ADS)
van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.
2012-02-01
A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.
Founding Gravitation in 4D Euclidean Space-Time Geometry
Winkler, Franz-Guenter
2010-11-24
The Euclidean interpretation of special relativity which has been suggested by the author is a formulation of special relativity in ordinary 4D Euclidean space-time geometry. The natural and geometrically intuitive generalization of this view involves variations of the speed of light (depending on location and direction) and a Euclidean principle of general covariance. In this article, a gravitation model by Jan Broekaert, which implements a view of relativity theory in the spirit of Lorentz and Poincare, is reconstructed and shown to fulfill the principles of the Euclidean approach after an appropriate reinterpretation.
Los Alamos National Laboratory 4D Database
Atencio, Julian J.
2014-05-02
4D is an integrated development platform - a single product comprised of the components you need to create and distribute professional applications. You get a graphical design environment, SQL database, a programming language, integrated PHP execution, HTTP server, application server, executable generator, and much more. 4D offers multi-platform development and deployment, meaning whatever you create on a Mac can be used on Windows, and vice-versa. Beyond productive development, 4D is renowned for its great flexibility in maintenance and modification of existing applications, and its extreme ease of implementation in its numerous deployment options. Your professional application can be put into production more quickly, at a lower cost, and will always be instantly scalable. 4D makes it easy, whether you're looking to create a classic desktop application, a client-server system, a distributed solution for Web or mobile clients - or all of the above!
Computing Myocardial Motion in 4D Echocardiography
Mukherjee, Ryan; Sprouse, Chad; Pinheiro, Aurélio; Abraham, Theodore; Burlina, Philippe
2012-01-01
4D (3D spatial+time) echocardiography is gaining widespread acceptance at clinical institutions for its high temporal resolution and relatively low cost. We describe a novel method for computing dense 3D myocardial motion with high accuracy. The method is based on a classical variational optical flow technique, but exploits modern developments in optical flow research to utilize the full capabilities of 4D echocardiography. Using a variety of metrics, we present an in-depth performance evaluation of the method on synthetic, phantom, and intraoperative 4D Transesophageal Echocardiographic (TEE) data. When compared with state-of-the-art optical flow and speckle tracking techniques currently found in 4D echocardiography, the method we present shows notable improvements in error. We believe the performance improvements shown can have a positive impact when the method is used as input for various applications, such as strain computation, biomechanical modeling, or automated diagnostics. PMID:22677256
NASA Astrophysics Data System (ADS)
Bergshoeff, Eric A.; Fernández-Melgarejo, J. J.; Rosseel, Jan; Townsend, Paul K.
2012-04-01
We construct a four-dimensional (4D) gauge theory that propagates, unitarily, the five polarization modes of a massive spin-2 particle. These modes are described by a "dual" graviton gauge potential and the Lagrangian is 4th-order in derivatives. As the construction mimics that of 3D "new massive gravity", we call this 4D model (linearized) "new massive dual gravity". We analyse its massless limit, and discuss similarities to the Eddington-Schrödinger model.
4D flow mri post-processing strategies for neuropathologies
NASA Astrophysics Data System (ADS)
Schrauben, Eric Mathew
double-gated flow acquisition and reconstruction scheme demonstrates respiratory-induced changes in internal jugular vein flow. Finally, a semi-automated intracranial vessel segmentation and flow parameter measurement software tool for fast and consistent 4D flow post-processing analysis is developed, validated, and exhibited an in-vivo.
Helical 4D CT and Comparison with Cine 4D CT
NASA Astrophysics Data System (ADS)
Pan, Tinsu
4D CT was one of the most important developments in radiation oncology in the last decade. Its early development in single slice CT and commercialization in multi-slice CT has radically changed our practice in radiation treatment of lung cancer, and has enabled the stereotactic radiosurgery of early stage lung cancer. In this chapter, we will document the history of 4D CT development, detail the data sufficiency condition governing the 4D CT data collection; present the design of the commercial helical 4D CTs from Philips and Siemens; compare the differences between the helical 4D CT and the GE cine 4D CT in data acquisition, slice thickness, acquisition time and work flow; review the respiratory monitoring devices; and understand the causes of image artifacts in 4D CT.