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A generalized reconstruction framework for unconventional PET systems.

ABSTRACT: Quantitative estimation of the radionuclide activity concentration in positron emission tomography (PET) requires precise modeling of PET physics. The authors are focused on designing unconventional PET geometries for specific applications. This work reports the creation of a generalized reconstruction framework, capable of reconstructing tomographic PET data for systems that use right cuboidal detector elements positioned at arbitrary geometry using a regular Cartesian grid of image voxels.The authors report on a variety of design choices and optimization for the creation of the generalized framework. The image reconstruction algorithm is maximum likelihood-expectation-maximization. System geometry can be specified using a simple script. Given the geometry, a symmetry seeking algorithm finds existing symmetry in the geometry with respect to the image grid to improve the memory usage/speed. Normalization is approached from a geometry independent perspective. The system matrix is computed using the Siddon's algorithm and subcrystal approach. The program is parallelized through open multiprocessing and message passing interface libraries. A wide variety of systems can be modeled using the framework. This is made possible by modeling the underlying physics and data correction, while generalizing the geometry dependent features.Application of the framework for three novel PET systems, each designed for a specific application, is presented to demonstrate the robustness of the framework in modeling PET systems of unconventional geometry. Three PET systems of unconventional geometry are studied. (1) Virtual-pinhole half-ring insert integrated into Biograph-40: although the insert device improves image quality over conventional whole-body scanner, the image quality varies depending on the position of the insert and the object. (2) Virtual-pinhole flat-panel insert integrated into Biograph-40: preliminary results from an investigation into a modular flat-panel insert are presented. (3) Plant PET system: a reconfigurable PET system for imaging plants, with resolution of greater than 3.3 mm, is shown. Using the automated symmetry seeking algorithm, the authors achieved a compression ratio of the storage and memory requirement by a factor of approximately 50 for the half-ring and flat-panel systems. For plant PET system, the compression ratio is approximately five. The ratio depends on the level of symmetry that exists in different geometries.This work brings the field closer to arbitrary geometry reconstruction. A generalized reconstruction framework can be used to validate multiple hypotheses and the effort required to investigate each system is reduced. Memory usage/speed can be improved with certain optimizations.

SUBMITTER: Mathews AJ

PROVIDER: S-EPMC4506302 | biostudies-other | 2015 Aug

REPOSITORIES: biostudies-other

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A generalized reconstruction framework for unconventional PET systems.

Mathews Aswin John AJ Li Ke K Komarov Sergey S Wang Qiang Q Ravindranath Bosky B O'Sullivan Joseph A JA Tai Yuan-Chuan YC

Medical physics 20150801 8

<h4>Purpose</h4>Quantitative estimation of the radionuclide activity concentration in positron emission tomography (PET) requires precise modeling of PET physics. The authors are focused on designing unconventional PET geometries for specific applications. This work reports the creation of a generalized reconstruction framework, capable of reconstructing tomographic PET data for systems that use right cuboidal detector elements positioned at arbitrary geometry using a regular Cartesian grid of i ...[more]

PMID: 26233187

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Project description:BackgroundPositron-emission tomography (PET) simulators are frequently used for development and performance evaluation of segmentation methods or quantitative uptake metrics. To date, most PET simulation tools are based on Monte Carlo simulations, which are computationally demanding. Other analytical simulation tools lack the implementation of time of flight (TOF) or resolution modelling (RM). In this study, a fast and easy-to-use PET simulation-reconstruction package, SiMulAtion and ReconsTruction (SMART)-PET, is developed and validated, which includes both TOF and RM. SMART-PET, its documentation and instructions to calibrate the tool to a specific PET/CT system are available on Zenodo. SMART-PET allows the fast generation of 3D PET images. As input, it requires one image representing the activity distribution and one representing the corresponding CT image/attenuation map. It allows the user to adjust different parameters, such as reconstruction settings (TOF/RM), noise level or scan duration. Furthermore, a random spatial shift can be included, representing patient repositioning. To evaluate the tool, simulated images were compared with real scan data of the NEMA NU 2 image quality phantom. The scan was acquired as a 60-min list-mode scan and reconstructed with and without TOF and/or RM. For every reconstruction setting, ten statistically equivalent images, representing 30, 60, 120 and 300 s scan duration, were generated. Simulated and real-scan data were compared regarding coefficient of variation in the phantom background and activity recovery coefficients (RCs) of the spheres. Furthermore, standard deviation images of each of the ten statistically equivalent images were compared.ResultsSMART-PET produces images comparable to actual phantom data. The image characteristics of simulated and real PET images varied in similar ways as function of reconstruction protocols and noise levels. The change in image noise with variation of simulated TOF settings followed the theoretically expected behaviour. RC as function of sphere size agreed within 0.3-11% between simulated and actual phantom data.ConclusionsSMART-PET allows for rapid and easy simulation of PET data. The user can change various acquisition and reconstruction settings (including RM and TOF) and noise levels. The images obtained show similar image characteristics as those seen in actual phantom data.

Project description:ObjectiveSimultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.MethodThe NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3-7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction.ResultsThe range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRCmean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRCmax, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRCpeak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively.ConclusionsFor two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

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A generalized reconstruction framework for unconventional PET systems.

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A generalized reconstruction framework for unconventional PET systems.

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