Fully-3D PET image reconstruction using scanner-independent, adaptive projection data and highly rotation-symmetric voxel assemblies

Setup and characterisation according to NEMA NU 4 of thephenoPET scanner, a PET system dedicated for plant sciences

Objective.ThephenoPET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber. Here, we present the setup ofphenoPET, its data processing and image reconstruction together with its performance.Approach.The performance characterization follows the national electrical manufacturers association (NEMA) standard for small animal PET systems with a number of adoptions due to the vertical oriented bore of a PET for plant sciences. In addition temperature stability and spatial resolution with a hot rod phantom are addressed.Main results.The spatial resolution for a22Na point source at a radial distance of 5 mm to the center of the field-of-view (FOV) is 1.45 mm, 0.82 mm and 1.88 mm with filtered back projection in radial, tangential and axial direction, respectively. A hot rod phantom with18F gives a spatial resolution of up to 1.6 mm. The peak noise-equivalent count rates are 550 kcps @ 35.08 MBq, 308 kcps @ 33 MBq and 45 kcps @ 40.60 MBq for the mouse, rat and monkey size scatter phantoms, respectively. The scatter fractions for these phantoms are 12.63%, 22.64% and 55.90%. We observe a peak sensitivity of up to 3.6% and a total sensitivity of up toSA,tot= 2.17%. For the NEMA image quality phantom we observe a uniformity of %STD= 4.22% with ordinary Poisson maximum likelihood expectation-maximization with 52 iterations. Here, recovery coefficients of 0.12, 0.64, 0.89, 0.93 and 0.91 for 1 mm, 2 mm, 3 mm, 4 mm and 5 mm rods are obtained and spill-over ratios of 0.08 and 0.14 for the water-filled and air-filled inserts, respectively.Significance.ThephenoPET and its laboratory are now in routine operation for the administration of [11C]CO2and non-invasive measurement of transport and allocation of11C-labelled photoassimilates in plants.

GABAA receptor availability relates to emotion-induced BOLD responses in the medial prefrontal cortex: simultaneous fMRI/PET with [11C]flumazenil

Introduction

The fMRI BOLD response to emotional stimuli highlighting the role of the medial prefrontal cortex (MPFC) has been thoroughly investigated. Recently, the relationship between emotion processing and GABA levels has been studied using MPFC proton magnetic resonance spectroscopy (1H-MRS). However, the role of GABAA receptors in the MPFC during emotion processing remains unexplored.

Methods

Using [11C]flumazenil PET, we investigated the relationship between the binding potential of GABAA receptors and emotion processing as measured using simultaneous fMRI BOLD. We hypothesized a correlation between the percent signal change in the BOLD signal and the binding potential of GABAA receptors in the MPFC. In a combined simultaneous fMRI and [11C]flumazenil-PET study, we analyzed the data from 15 healthy subjects using visual emotional stimuli. Our task comprised two types of emotional processing: passive viewing and appraisal. Following the administration of a bolus plus infusion protocol, PET and fMRI data were simultaneously acquired in a hybrid 3 T MR-BrainPET.

Results

We found a differential correlation of BOLD percent signal change with [11C]flumazenil binding potential in the MPFC. Specifically, [11C]flumazenil binding potential in the ventromedial prefrontal cortex (vMPFC) correlated with passive viewing of emotionally valenced pictures. In contrast, the [11C]flumazenil binding potential and the BOLD signal induced by picture appraisal did show a correlation in the paracingulate gyrus.

Conclusion

Our data deliver first evidence for a relationship between MPFC GABAA receptors and emotion processing in the same region. Moreover, we observed that GABAA receptors appear to play different roles in emotion processing in the vMPFC (passive viewing) and paracingulate gyrus (appraisal).

ProTheRaMon - a GATE simulation framework for proton therapy range monitoring using PET imaging

OBJECTIVE

This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland.

APPROACH

The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user's needs and specific settings of a given proton therapy facility and PET scanner design.

MAIN RESULTS

ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized.

SIGNIFICANCE

We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github.

Evaluation of STIR Library Adapted for PET Scanners with Non-Cylindrical Geometry

Software for Tomographic Image Reconstruction (STIR) is an open source C++ library used to reconstruct single photon emission tomography and positron emission tomography (PET) data. STIR has an experimental scanner geometry modelling feature to accurately model detector placement. In this study, we test and improve this new feature using several types of data: Monte Carlo simulations and measured phantom data acquired from a dedicated brain PET prototype scanner. The results show that the new geometry class applied to non-cylindrical PET scanners improved spatial resolution, uniformity, and image contrast. These are directly observed in the reconstructions of small features in the test quality phantom. Overall, we conclude that the revised "BlocksOnCylindrical" class will be a valuable addition to the next STIR software release with adjustments of existing features (Single Scatter Simulation, forward projection, attenuation corrections) to "BlocksOnCylindrical".

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

Bolus infusion scheme for the adjustment of steady state [11C]Flumazenil levels in the grey matter and in the blood plasma for neuroreceptor imaging

The use of hybrid PET/MR imaging facilitates the simultaneous investigation of challenge-related changes in ligand binding to neuroreceptors using PET, while concurrently measuring neuroactivation or blood flow with MRI. Having attained a steady state of the PET radiotracer using a bolus-infusion protocol, it is possible to observe alterations in ligand neuroreceptor binding through changes in distribution volumes. Here, we present an iterative procedure for establishing an administration scheme to obtain steady state [¹¹C]flumazenil concentrations in grey matter in the human brain. In order to achieve a steady state in the shortest possible time, the bolus infusion ratio from a previous examination was adapted to fit the subsequent examination. 17 male volunteers were included in the study. Boli and infusions with different weightings were given to the subjects and were characterised by kbol values from 74 ​min down to 42 ​min. Metabolite analysis was used to ascertain the value of unmetabolised flumazenil in the plasma, and PET imaging was used to assess its binding in the grey matter. The flumazenil time-activity curves (TACs) in the brain were decomposed into activity contributions from pure grey and white matter and analysed for 12 ​vol of interest (VOIs). The curves highlighted a large variability in metabolic rates between the subjects, with kbol ​= ​54.3 ​min being a reliable value to provide flumazenil equilibrium conditions in the majority of the VOIs and cases. The distribution volume of flumazenil in all 12 VOIs was determined.

Evaluation of quantitative, efficient image reconstruction for VersaPET, a compact PET system

PURPOSE

Previously we developed a high-resolution positron emission tomography (PET) system-VersaPET-characterized by a block geometry with relatively large axial and transaxial interblock gaps and a compact geometry susceptible to parallax blurring effects. In this work, we report the qualitative and quantitative evaluation of a graphic processing unit (GPU)-accelerated maximum-likelihood by expectation-maximization (MLEM) image reconstruction framework for VersaPET which features accurate system geometry and projection space point-spread-function (PSF) modeling.

METHODS

We combined the ray-tracing module from software for tomographic image reconstruction (STIR), an open-source PET image reconstruction package, with VersaPET's exact block geometry for the geometric system matrix. Point-spread-function modeling of crystal penetration and scattering was achieved by a custom Monte-Carlo simulation for projection space blurring in all dimensions. We also parallelized the reconstruction in GPU taking advantage of the system's symmetry for PSF computation. To investigate the effects of PSF width, we generated and studied multiple kernels between one that reflects the true LYSO density in the MC simulation and another that reflects geometry only (no PSF). GATE simulations of hot and cold-sphere phantoms with spheres of different sizes, real microDerenzo phantom, and human blood vessel data were used to characterize the quantitative and qualitative performances of the reconstruction.

RESULTS

Reconstruction with an accurate system geometry effectively improved image quality compared to STIR (version 3.0) which assumes an idealized system geometry. Reconstructions of GATE-simulated hot-sphere phantom data showed that all PSF kernels achieved superior performance in contrast recovery and bias reduction compared to using no PSF, but may introduce edge artifact and lumped background noise pattern depending on the width of PSF kernels. Cold-sphere phantom simulation results also indicated improvement in contrast recovery and quantification with PSF modeling (compared to no PSF) for 5 and 10 mm cold spheres. Real microDerenzo phantom images with the PSF kernel that reflects the true LYSO density showed degraded resolving power of small sectors that could be resolved more clearly by underestimated PSF kernels, which is consistent with recent literature despite differences in scanner geometries and in approaches to system model estimation. The human vessel results resemble those of the hot-sphere phantom simulation with the PSF kernel that reflects the true LYSO density achieving the highest peak in the time activity curve (TAC) and similar lumped noise pattern.

CONCLUSIONS

We fully evaluated a practical MLEM reconstruction framework that we developed for VersaPET in terms of qualitative and quantitative performance. Different PSF kernels may be adopted for improving the results of specific imaging tasks but the underlying reasons for the variation in optimal kernel for the real and simulation studies requires further study.

Resolution modeling in projection space using a factorized multi-block detector response function for PET image reconstruction

Positron emission tomography (PET) images usually suffer from limited resolution and statistical uncertainties. However, a technique known as resolution modeling (RM) can be used to improve image quality by accurately modeling the system's detection process within the iterative reconstruction. In this study, we present an accurate RM method in projection space based on a simulated multi-block detector response function (DRF) and evaluate it on the Siemens hybrid MR-BrainPET system. The DRF is obtained using GATE simulations that consider nearly all the possible annihilation photons from the field-of-view (FOV). Intrinsically, the multi-block DRF allows the block crosstalk to be modeled. The RM blurring kernel is further generated by factorizing the blurring matrix of one line-of-response (LOR) into two independent detector responses, which can then be addressed with the DRF. Such a kernel is shift-variant in 4D projection space without any distance or angle compression, and is integrated into the image reconstruction for the BrainPET insert with single instruction multiple data (SIMD) and multi-thread support. Evaluation of simulations and measured data demonstrate that the reconstruction with RM yields significantly improved resolutions and reduced mean squared error (MSE) values at different locations of the FOV, compared with reconstruction without RM. Furthermore, the shift-variant RM kernel models the varying blurring intensity for different LORs due to the depth-of-interaction (DOI) dependencies, thus avoiding severe edge artifacts in the images. Additionally, compared to RM in single-block mode, the multi-block mode shows significantly improved resolution and edge recovery at locations beyond 10 cm from the center of BrainPET insert in the transverse plane. However, the differences have been observed to be low for patient data between single-block and multi-block mode RM, due to the brain size and location as well as the geometry of the BrainPET insert. In conclusion, the RM method proposed in this study can yield better reconstructed images in terms of resolution and MSE value, compared to conventional reconstruction without RM.

An automatic algorithm to exploit the symmetries of the system response matrix in positron emission tomography iterative reconstruction

Positron emission tomography (PET) iterative 3D reconstruction is a very computational demanding task. One of the main issues of the iterative reconstruction concerns the management of the system response matrix (SRM). The SRM models the relationship between the projection and the voxel space and its memory footprint can easily exceed hundreds of GB. Moreover, in order to make the reconstruction fast enough not to hinder its practical application, the SRM must be stored in the random access memory of the workstation used for the reconstruction. This issue is normally solved by implementing efficient storage schemes and by reducing the number of redundant patterns in the SRM through symmetries. However, finding a sufficient number of symmetries is often non-trivial and is typically performed using dedicated solutions that cannot be exported to different detectors and geometries. In this paper, an automatic approach to reduce the memory footprint of a pre-computed SRM is described. The proposed approach was named symmetry search algorithm (SSA) and consists in an algorithm that searches for some of the redundant patterns present in the SRM, leading to its lossy compression. This approach was built to detect translations, reflections and coordinates swap in voxel space. Therefore, it is particularly well suited for those scanners where some of the rotational symmetries are broken, e.g. small animal scanner where the modules are arranged in a polygonal ring made of few elements, and dual head planar PET systems. In order to validate this approach, the SSA is applied to the SRM of a preclinical scanner (the IRIS PET/CT). The data acquired by the scanner were reconstructed with a dedicated maximum likelihood estimation maximization algorithm with both the uncompressed and the compressed SRMs. The results achieved show that the information lost due to the SSA compression is negligible. Compression factors up to 52 when using the SSA together with manually inserted symmetries and up to 204 when using the SSA alone, can be obtained for the IRIS SRM. These results come without significant differences in the values and in the main quality metrics of the reconstructed images, i.e. spatial resolution and noise. Although the compression factors depend on the system considered, the SSA is applicable to any SRM and therefore it can be considered a general tool to reduce the footprint of a pre-computed SRM.

TRIMAGE: A dedicated trimodality (PET/MR/EEG) imaging tool for schizophrenia

Simultaneous PET/MR/EEG (Positron Emission Tomography - Magnetic Resonance - Electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here within the framework of the European Union Project TRIMAGE. The trimodal, cost-effective PET/MR/EEG imaging tool makes use of cutting edge technology both in PET and in MR fields. A novel type of magnet (1.5T, non-cryogenic) has been built together with a PET scanner that makes use of the most advanced photodetectors (i.e., SiPM matrices), scintillators matrices (LYSO) and digital electronics. The combined PET/MR/EEG system is dedicated to brain imaging and has an inner diameter of 260 mm and an axial Field-of-View of 160 mm. It enables the acquisition and assessment of molecular metabolic information with high spatial and temporal resolution in a given brain simultaneously. The dopaminergic system and the glutamatergic system in schizophrenic patients are investigated via PET, the same physiological/pathophysiological conditions with regard to functional connectivity, via fMRI, and its electrophysiological signature via EEG. In addition to basic neuroscience questions addressing neurovascular-metabolic coupling, this new methodology lays the foundation for individual physiological and pathological fingerprints for a wide research field addressing healthy aging, gender effects, plasticity and different psychiatric and neurological diseases. The preliminary performances of two components of the imaging tool (PET and MR) are discussed. Initial results of the search of possible candidates for suitable schizophrenia biomarkers are also presented as obtained with PET/MR systems available to the collaboration.

PET performance evaluation of MADPET4: a small animal PET insert for a 7 T MRI scanner

MADPET4 is the first small animal PET insert with two layers of individually read out crystals in combination with silicon photomultiplier technology. It has a novel detector arrangement, in which all crystals face the center of field of view transaxially. In this work, the PET performance of MADPET4 was evaluated and compared to other preclinical PET scanners using the NEMA NU 4 measurements, followed by imaging a mouse-size hot-rod resolution phantom and two in vivo simultaneous PET/MRI scans in a 7 T MRI scanner. The insert had a peak sensitivity of 0.49%, using an energy threshold of 350 keV. A uniform transaxial resolution was obtained up to 15 mm radial offset from the axial center, using filtered back-projection with single-slice rebinning. The measured average radial and tangential resolutions (FWHM) were 1.38 mm and 1.39 mm, respectively. The 1.2 mm rods were separable in the hot-rod phantom using an iterative image reconstruction algorithm. The scatter fraction was 7.3% and peak noise equivalent count rate was 15.5 kcps at 65.1 MBq of activity. The FDG uptake in a mouse heart and brain were visible in the two in vivo simultaneous PET/MRI scans without applying image corrections. In conclusion, the insert demonstrated a good overall performance and can be used for small animal multi-modal research applications.

Simultaneous trimodal PET-MR-EEG imaging: Do EEG caps generate artefacts in PET images?

Trimodal simultaneous acquisition of positron emission tomography (PET), magnetic resonance imaging (MRI), and electroencephalography (EEG) has become feasible due to the development of hybrid PET-MR scanners. To capture the temporal dynamics of neuronal activation on a millisecond-by-millisecond basis, an EEG system is appended to the quantitative high resolution PET-MR imaging modality already established in our institute. One of the major difficulties associated with the development of simultaneous trimodal acquisition is that the components traditionally used in each modality can cause interferences in its counterpart. The mutual interferences of MRI components and PET components on PET and MR images, and the influence of EEG electrodes on functional MRI images have been studied and reported on. Building on this, this study aims to investigate the influence of the EEG cap on the quality and quantification of PET images acquired during simultaneous PET-MR measurements. A preliminary transmission scan study on the ECAT HR+ scanner, using an Iida phantom, showed visible attenuation effect due to the EEG cap. The BrainPET-MR emission images of the Iida phantom with [¹⁸F]Fluordeoxyglucose, as well as of human subjects with the EEG cap, did not show significant effects of the EEG cap, even though the applied attenuation correction did not take into account the attenuation of the EEG cap itself.

A generalized reconstruction framework for unconventional PET systems

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Distributed MLEM: an iterative tomographic image reconstruction algorithm for distributed memory architectures

The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach.

MR-based PET motion correction procedure for simultaneous MR-PET neuroimaging of human brain

Positron Emission Tomography (PET) images are prone to motion artefacts due to the long acquisition time of PET measurements. Recently, simultaneous magnetic resonance imaging (MRI) and PET have become available in the first generation of Hybrid MR-PET scanners. In this work, the elimination of artefacts due to head motion in PET neuroimages is achieved by a new approach utilising MR-based motion tracking in combination with PET list mode data motion correction for simultaneous MR-PET acquisitions. The method comprises accurate MR-based motion measurements, an intra-frame motion minimising and reconstruction time reducing temporal framing algorithm, and a list mode based PET reconstruction which utilises the Ordinary Poisson Algorithm and avoids axial and transaxial compression. Compared to images uncorrected for motion, an increased image quality is shown in phantom as well as in vivo images. In vivo motion corrected images show an evident increase of contrast at the basal ganglia and a good visibility of uptake in tiny structures such as superior colliculi.

Multimodal imaging utilising integrated MR-PET for human brain tumour assessment

OBJECTIVES

The development of integrated magnetic resonance (MR)-positron emission tomography (PET) hybrid imaging opens up new horizons for imaging in neuro-oncology. In cerebral gliomas the definition of tumour extent may be difficult to ascertain using standard MR imaging (MRI) only. The differentiation of post-therapeutic scar tissue, tumour rests and tumour recurrence is challenging. The relationship to structures such as the pyramidal tract to the tumour mass influences the therapeutic neurosurgical approach.

METHODS

The diagnostic information may be enriched by sophisticated MR techniques such as diffusion tensor imaging (DTI), multiple-volume proton MR spectroscopic imaging (MRSI) and functional MRI (fMRI). Metabolic imaging with PET, especially using amino acid tracers such as (18)F-fluoroethyl-L-tyrosine (FET) or (11)C-L-methionine (MET) will indicate tumour extent and response to treatment.

RESULTS

The new technologies comprising MR-PET hybrid systems have the advantage of providing comprehensive answers by a one-stop-job of 40-50 min. The combined approach provides data of different modalities using the same iso-centre, resulting in optimal spatial and temporal realignment. All images are acquired exactly under the same physiological conditions.

CONCLUSIONS

We describe the imaging protocol in detail and provide patient examples for the different imaging modalities such as FET-PET, standard structural imaging (T1-weighted, T2-weighted, T1-weighted contrast agent enhanced), DTI, MRSI and fMRI.

KEY POINTS

Hybrid MR-PET opens up new horizons in neuroimaging. Hybrid MR-PET allows brain tumour assessment in one stop. Hybrid MR-PET allows simultaneous acquisition of structural, functional and molecular images.

High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid

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.

Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration

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.