List-mode Format Research Articles

Activity standardization of 99mTc by digitizer-based 4πce(LS)-γ and 4πce(PC)-γ coincidence counting

An aqueous solution of 99mTc was standardized by means of 4πce(LS)-γ and 4πce(PC)-γ coincidence counting. The activity concentration was obtained by efficiency extrapolation relying on four different variation techniques. In both setups, fast digitizers were used for data acquisition. Detected events were recorded in list mode format, and their coincidence relationship was analyzed in the subsequent offline analysis. The combined result was used to participate in an international comparison using the Transfer Instrument of the International Reference System.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks

BackgroundSingle-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for alpha-particle radiopharmaceutical therapies (alpha-RPTs). However, quantitative SPECT for alpha-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.MethodsGiven the low-count setting, it is important that the reconstruction method extracts the maximal possible information from each detected photon. Processing data over multiple energy windows and in list-mode (LM) format provide mechanisms to achieve that objective. Towards this goal, we propose a list-mode multi energy window (LM-MEW) ordered-subsets expectation–maximization-based SPECT reconstruction method that uses data from multiple energy windows in LM format and include the energy attribute of each detected photon. For computational efficiency, we developed a multi-GPU-based implementation of this method. The method was evaluated using 2-D SPECT simulation studies in a single-scatter setting conducted in the context of imaging [^{223}Ra]RaCl{_2}, an FDA-approved RPT for metastatic prostate cancer.ResultsThe proposed method yielded improved performance on the task of estimating activity uptake within known regions of interest in comparison to approaches that use a single energy window or use binned data. The improved performance was observed in terms of both accuracy and precision and for different sizes of the region of interest.ConclusionsResults of our studies show that the use of multiple energy windows and processing data in LM format with the proposed LM-MEW method led to improved quantification performance in low-count SPECT of isotopes with multiple emission peaks. These results motivate further development and validation of the LM-MEW method for such imaging applications, including for alpha-RPT SPECT.

Optimization of the Algorithm for the Implementation of Point Spread Function in the 3D-OSEM Reconstruction Algorithm Based on the List-Mode Micro PET Data

Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of PET images to degrade. It is possible to reduce this PVE and achieve better image quality by measuring and modeling the point spread function (PSF) and then accounting for it inside the reconstruction algorithm. In this work, we examined the response characteristics of the MetisTM PET/CT system by acquiring 22Na point source at different locations in the field of view (FOV) of the scanner and reconstructing with small pixel size for images to obtain their radial, tangential, and axial full-width half maximum (FWHM). An image-based model of the PSF model was then obtained by fitting asymmetric two-dimensional Gaussians on the 22Na images. This PSF model determined by FWHM in three directions was integrated into a three-dimensional ordered subsets expectation maximization (3D-OSEM) algorithm based on a list-mode format to form a new PSF-OSEM algorithm. We used both algorithms to reconstruct point source, Derenzo phantom, and mouse PET images and performed qualitative and quantitative analyses. In the point source study, the PSF-OSEM algorithm reduced the FWHM of the point source PET image in three directions to about 0.67 mm, and in the phantom study, the PET image reconstructed by the PSF-OSEM algorithm had better visual effects. At the same time, the quantitative analysis results of the Derenzo phantom were better than the original 3D-OSEM algorithm. In the mouse experiment, the results of qualitative and quantitative analyses showed that the imaging quality of PSF-OSEM algorithm was better than that of 3D-OSEM algorithm. Our results show that adding the PSF model to the 3D-OSEM algorithm in the MetisTM PET/CT system helps to improve the resolution of the image and satisfy the qualitative and quantitative analysis criteria.

Phantom-based acquisition time and image reconstruction parameter optimisation for oncologic FDG PET/CT examinations using a digital system

BackgroundNew-generation silicon-photomultiplier (SiPM)-based PET/CT systems exhibit an improved lesion detectability and image quality due to a higher detector sensitivity. Consequently, the acquisition time can be reduced while maintaining diagnostic quality. The aim of this study was to determine the lowest 18F-FDG PET acquisition time without loss of diagnostic information and to optimise image reconstruction parameters (image reconstruction algorithm, number of iterations, voxel size, Gaussian filter) by phantom imaging. Moreover, patient data are evaluated to confirm the phantom results.MethodsThree phantoms were used: a soft-tissue tumour phantom, a bone-lung tumour phantom, and a resolution phantom. Phantom conditions (lesion sizes from 6.5 mm to 28.8 mm in diameter, lesion activity concentration of 15 kBq/mL, and signal-to-background ratio of 5:1) were derived from patient data. PET data were acquired on an SiPM-based Biograph Vision PET/CT system for 10 min in list-mode format and resampled into time frames from 30 to 300 s in 30-s increments to simulate different acquisition times. Different image reconstructions with varying iterations, voxel sizes, and Gaussian filters were probed. Contrast-to-noise-ratio (CNR), maximum, and peak signal were evaluated using the 10-min acquisition time image as reference. A threshold CNR value ≥ 5 and a maximum (peak) deviation of ± 20% were considered acceptable. 20 patient data sets were evaluated regarding lesion quantification as well as agreement and correlation between reduced and full acquisition time standard uptake values (assessed by Pearson correlation coefficient, intraclass correlation coefficient, Bland–Altman analyses, and Krippendorff’s alpha).ResultsAn acquisition time of 60 s per bed position yielded acceptable detectability and quantification results for clinically relevant phantom lesions ≥ 9.7 mm in diameter using OSEM-TOF or OSEM-TOF+PSF image reconstruction, a 4-mm Gaussian filter, and a 1.65 × 1.65 x 2.00-mm3 or 3.30 × 3.30 x 3.00-mm3 voxel size. Correlation and agreement of patient lesion quantification between full and reduced acquisition times were excellent.ConclusionA threefold reduction in acquisition time is possible. Patients might benefit from more comfortable examinations or reduced radiation exposure, if instead of the acquisition time the applied activity is reduced.

Deep learning\u2013based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance

PurposeThis work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.MethodsClinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (− 3 to + 3) grading scheme.ResultsThe highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland–Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively.ConclusionThe noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.

Reducing Radiation Exposure to Paediatric Patients Undergoing [18F]FDG-PET/CT Imaging

PurposeTo investigate the possibility of reducing the injected activity for whole-body [18F]FDG-PET/CT studies of paediatric oncology patients and to assess the usefulness of time-of-flight (TOF) acquisition on PET image quality at reduced count levels.ProceduresTwenty-nine paediatric oncology patients (12F/17M, 3–18 years old (median age 13y), weight 45±20 kg, BMI 19±4 kg/m2), who underwent routine whole-body PET/CT examinations on a Siemens Biograph mCT TrueV system with TOF capability (555ps) were included in this study. The mean injected activity was 156 ± 45 MBq (3.8 ± 0.8 kg/MBq) and scaled to patient weight. The raw data was collected in listmode (LM) format and pre-processed to simulate reduced levels of [18F]FDG activity (75, 50, 35, 20 and 10% of the original counts) by randomly removing events from the original LM data. All data were reconstructed using the vendor-specific e7-tools with standard OSEM only, with OSEM plus resolution recovery (PSF). The reconstructions were repeated with added TOF (TOF) and PSF+TOF. The benefit of TOF together with the reduced count levels was evaluated by calculating the gains in signal-to-noise ratio (SNR) in the liver and contrast-to-noise ratio (CNR) in all PET-positive lesions before and after TOF employed at every simulated reduced count level. Finally, the PSF+TOF images at 50, 75 and 100% of counts were evaluated clinically on a 5-point scale by three nuclear medicine physicians.ResultsThe visual inspection of the reconstructed images did not reveal significant differences in image quality between 75 and 100% count levels for PSF+TOF. The improvements in SNR and CNR were the greatest for TOF reconstruction and PSF combined. Both SNR and CNR gains did increase linearly with the patients BMI for both OSEM only and PSF reconstruction. These benefits were observed until reducing the counts to 50 and 35% for SNR and CNR, respectively.ConclusionsThe benefit of using TOF was noticeable when using 50% or greater of the counts when evaluating the CNR and SNR. For [18F]FDG-PET/CT, whole-body paediatric imaging the injected activity can be reduced to 75% of the original dose without compromising PET image quality.

Task-based assessment of binned and list-mode SPECT systems.

In SPECT, list-mode (LM) format allows storing data at higher precision compared to binned data. There is significant interest in investigating whether this higher precision translates to improved performance on clinical tasks. Towards this goal, in this study, we quantitatively investigated whether processing data in LM format, and in particular, the energy attribute of the detected photon, provides improved performance on the task of absolute quantification of region-of-interest (ROI) uptake in comparison to processing the data in binned format. We conducted this evaluation study using a DaTscan brain SPECT acquisition protocol, conducted in the context of imaging patients with Parkinson's disease. This study was conducted with a synthetic phantom. A signal-known exactly/background-known-statistically (SKE/BKS) setup was considered. An ordered-subset expectation-maximization algorithm was used to reconstruct images from data acquired in LM format, including the scatter-window data, and including the energy attribute of each LM event. Using a realistic 2-D SPECT system simulation, quantification tasks were performed on the reconstructed images. The results demonstrated improved quantification performance when LM data was used compared to binning the attributes in all the conducted evaluation studies. Overall, we observed that LM data, including the energy attribute, yielded improved performance on absolute quantification tasks compared to binned data.

Effect of data conserving respiratory motion compensation on left ventricular functional parameters assessed in gated myocardial perfusion SPECT

BackgroundRespiratory motion compromises image quality in myocardial perfusion (MP) single-photon emission computed tomography (SPECT) imaging and may affect analysis of left ventricular (LV) functional parameters, including phase analysis-quantified mechanical dyssynchrony parameters. In this paper, we investigate the performance of two algorithms, respiratory blur modeling (RBM) and joint motion-compensated (JMC) ordered-subsets expectation maximization (OSEM), and the effects of motion compensation on cardiac-gated MP-SPECT studies.MethodsImage acquisitions were carried out with a dual-detector SPECT/CT system in list-mode format. A cardiac phantom was imaged as stationary and under respiratory motion. The images were reconstructed with OSEM, RBM-OSEM, and JMC-OSEM algorithms, and compared in terms of mean squared error (MSE). Subsequently, MP-SPECT data of 19 patients were binned into dual-gated (respiratory and cardiac gating) projection images. The images of the patients were analyzed with Quantitative Gated SPECT (QGS) 2012 program (Cedars-Sinai Medical Center, USA). The parameters of interest were LV volumes, ejection fraction, wall motion, wall thickening, phase analysis, and perfusion parameters.ResultsIn phantom experiment, compared to the stationary OSEM reconstruction, the MSE values for OSEM, RBM-OSEM, and JMC-OSEM were 8.5406·10−5,2.7190·10−5, and 2.0795·10−5, respectively. In the analysis of LV function, use of JMC had a small but statistically significant (p < 0.05) effect on several parameters: it increased LV volumes and standard deviation of phase angle histogram, and it decreased ejection fraction, global wall motion, and lateral, septal, and apical perfusion.ConclusionsCompared to standard OSEM algorithm, RBM-OSEM and JMC-OSEM both improve image quality under motion. Motion compensation has a minor effect on LV functional parameters.

Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution

The potential to perform attenuation and scatter compensation (ASC) in single-photon emission computed tomography (SPECT) without a separate transmission scan is highly significant. In this context, attenuation in SPECT is primarily due to Compton scattering, where the probability of Compton scatter is proportional to the attenuation coefficient of the tissue and the energy of the scattered photon and the scattering angle are related. Based on this premise, we investigated whether the SPECT emission data, including the scatter-window data, acquired in list-mode (LM) format and including the energy information, can be used to estimate the attenuation map. For this purpose, we propose a Fisher-information-based method that yields the Cramér–Rao bound (CRB) for the task of jointly estimating the activity and attenuation distribution using only the SPECT emission data. In the process, a path-based formalism to process the LM SPECT emission data, including the scattered-photon data, is proposed. The Fisher information method was implemented on NVIDIA graphics processing units (GPUs) for acceleration. The method was applied to quantify the information content of SPECT LM emission data, which contains up to first-order scattered events, in a simulated SPECT system with parameters modeling a clinical system using realistic computational studies with 2D digital synthetic and anthropomorphic phantoms. Experiments with anthropomorphic phantoms simulated myocardial perfusion and dopamine transporter (DaT)-Scan SPECT studies. The method was also applied to LM data containing up to second-order scatter for a synthetic phantom. The results show that the CRB obtained for the attenuation and activity coefficients was typically much lower than the true value of these coefficients. An increase in the number of detected photons yielded lower CRB for both the attenuation and activity coefficients. Further, we observed that systems with better energy resolution yielded a lower CRB for the attenuation coefficient. Overall, the results provide evidence that LM SPECT emission data, including the scatter-window data, contains information to jointly estimate the activity and attenuation coefficients.

A LIST-MODE OSEM-BASED ATTENUATION AND SCATTER COMPENSATION METHOD FOR SPECT.

Reliable attenuation and scatter compensation (ASC) is a pre-requisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based version of this method using an ordered subsets expectation maximization (OSEM) algorithm. The method was objectively evaluated using realistic simulation studies on the task of estimating uptake in the striatal regions of the brain in a 2-D dopamine transporter (DaT)-scan SPECT study. We observed that inclusion of data from the scatter window and using list-mode data yielded improved quantification compared to using data only from the photopeak window or using binned data. These results motivate further development of list-mode-based ASC methods that include scatter-window data for SPECT.

Standardisation of 85Sr with digital anticoincidence counting and half-life determination of the 514 keV level of 85Rb

The activity of a 85Sr solution was determined by means of fully digital 4πβ(LS)-γ anticoincidence counting. The measurements were carried out in a custom-built, combined TDCR / 4πβ(LS)-γ coincidence system, utilising a commercially available CAEN N6751C digitizer. The analysis of the experimental data, collected in list-mode format, was performed off-line by using the SoftKAM computer code developed at PTB. The data were also used to determine the half-life of the 514 keV level of 85Rb which was found to be (1020.2 ± 6.0) ns.

Compton scatter tomography in TOF-PET

Scatter coincidences contain hidden information about the activity distribution on the positron emission tomography (PET) imaging system. However, in conventional reconstruction, the scattered data cause the blurring of images and thus are estimated and subtracted from detected coincidences. List mode format provides a new aspect to use time of flight (TOF) and energy information of each coincidence in the reconstruction process. In this study, a novel approach is proposed to reconstruct activity distribution using the scattered data in the PET system. For each single scattering coincidence, a scattering angle can be determined by the recorded energy of the detected photons, and then possible locations of scattering can be calculated based on the scattering angle. Geometry equations show that these sites lie on two arcs in 2D mode or the surface of a prolate spheroid in 3D mode, passing through the pair of detector elements. The proposed method uses a novel and flexible technique to estimate source origin locations from the possible scattering locations, using the TOF information. Evaluations were based on a Monte-Carlo simulation of uniform and non-uniform phantoms at different resolutions of time and detector energy. The results show that although the energy uncertainties deteriorate the image spatial resolution in the proposed method, the time resolution has more impact on image quality than the energy resolution. With progress of the TOF system, the reconstruction using the scattered data can be used in a complementary manner, or to improve image quality in the next generation of PET systems.

Fast and accurate generation method of PSF-based system matrix for PET reconstruction**Supported by National Natural Science Foundation of China (81301348) and China Postdoctoral Science Foundation (2015M570154)

This work investigates the positional single photon incidence response (P-SPIR) to provide an accurate point spread function (PSF)-contained system matrix and its incorporation within the image reconstruction framework. Based on the Geant4 Application for Emission Tomography (GATE) simulation, P-SPIR theory takes both incidence angle and incidence position of the gamma photon into account during crystal subdivision, instead of only taking the former into account, as in single photon incidence response (SPIR). The response distribution obtained in this fashion was validated using Monte Carlo simulations. In addition, two-block penetration and normalization of the response probability are introduced to improve the accuracy of the PSF. With the incorporation of the PSF, the homogenization model is then analyzed to calculate the spread distribution of each line-of-response (LOR). A primate PET scanner, Eplus-260, developed by the Institute of High Energy Physics, Chinese Academy of Sciences (IHEP), was employed to evaluate the proposed method. The reconstructed images indicate that the P-SPIR method can effectively mitigate the depth-of-interaction (DOI) effect, especially at the peripheral area of field-of-view (FOV). Furthermore, the method can be applied to PET scanners with any other structures and list-mode data format with high flexibility and efficiency.

Linear models to perform treaty verification tasks for enhanced information security

Linear mathematical models were applied to binary-discrimination tasks relevant to arms control verification measurements in which a host party wishes to convince a monitoring party that an item is or is not treaty accountable. These models process data in list-mode format and can compensate for the presence of variability in the source, such as uncertain object orientation and location. The Hotelling observer applies an optimal set of weights to binned detector data, yielding a test statistic that is thresholded to make a decision. The channelized Hotelling observer applies a channelizing matrix to the vectorized data, resulting in a lower dimensional vector available to the monitor to make decisions. We demonstrate how incorporating additional terms in this channelizing-matrix optimization offers benefits for treaty verification. We present two methods to increase shared information and trust between the host and monitor. The first method penalizes individual channel performance in order to maximize the information available to the monitor while maintaining optimal performance. Second, we present a method that penalizes predefined sensitive information while maintaining the capability to discriminate between binary choices. Data used in this study was generated using Monte Carlo simulations for fission neutrons, accomplished with the GEANT4 toolkit. Custom models for plutonium inspection objects were measured in simulation by a radiation imaging system. Model performance was evaluated and presented using the area under the receiver operating characteristic curve.

O33. The value of different reconstruction algorithms for quantification of FDG PET brain imaging

Introduction Reconstruction influences the quantitative results in PET imaging. The aim of this study was the evaluation of different image reconstruction parameters and their impact on quantification for 18 F-FDG PET of the brain. The reconstruction parameters studied were the number of iterations, smoothing levels (relaxation parameter lambda), and the use of time of flight (TOF) information. In addition, we investigated the effect of signal-to-noise ratio on each of these reconstructed brain images, as well as the effect of scan duration on image quality. Materials and Methods A Philips® Gemini-TF Big bore PET/CT was used for acquiring data of a 3D Hoffman Brain phantom. Data was acquired for 25 minutes in list mode format after injection of 40 MBq FDG, and reconstructed with a voxel size of 2 × 2 × 2 mm 3 using two different iterative reconstruction algorithms: LOR-RAMLA and BLOB-OS. The number of iterations and subsets was varied successive from 3/33 (vendor default) to 30/33, acquisition scan duration from 1 to 25 minutes, lambda was selected as smooth (0.7) and normal (1.0), and TOF was switched on and off for BLOB-OS. The impact on image quality was analyzed in 15 cortical and subcortical brain regions (volumes of interest, VOIs) and for grey and white matter. Results Contrast increased for all regions of the brain and for grey matter/white matter (GM/WM) ratio if the number of iterations increased. Image convergence was reached after fifteen iterations for all different algorithms. When varying the smoothing filter it was found that lambda 1.0 resulted in a faster convergence than 0.7. The coefficient of variation (COV) for all VOIs showed BLOB-OS with TOF to be superior to the other algorithms. The COV results for different scan durations showed only a minimal improvement after 5 minutes in high-activity regions (GM), and after 10 minutes in low-activity regions (WM). Conclusion Based on phantom data 18 F-FDG brain imaging for 10 minutes and reconstructed with the BLOB-OS algorithm including TOF information with 15 iterations is optimal on the Philips Gemini TF Big bore PET/CT. Further analyses is planned using patient data to verify if these findings remain valid in a clinical setting.

Development of an ideal observer that incorporates nuisance parameters and processes list-mode data.

Observer models were developed to process data in list-mode format in order to perform binary discrimination tasks for use in an arms-control-treaty context. Data used in this study was generated using GEANT4 Monte Carlo simulations for photons using custom models of plutonium inspection objects and a radiation imaging system. Observer model performance was evaluated and presented using the area under the receiver operating characteristic curve. The ideal observer was studied under both signal-known-exactly conditions and in the presence of unknowns such as object orientation and absolute count-rate variability; when these additional sources of randomness were present, their incorporation into the observer yielded superior performance.

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

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

Retrospective data-driven respiratory gating for PET using TOF information.

Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.

Rapid workflow of mMR PET list-mode data processing using CUDA.

The purpose of this work is to provide a software solution which would enable very fast list-mode data processing offered by the massively parallel graphics processor units (GPU) while dealing efficiently with the common bottleneck of data transfers between hard disk storage, host (CPU) memory and device (GPU) memory. This software is dedicated to the PETLINK™ list-mode data format which is used by the Siemens’ hybrid Biograph Molecular MR (mMR) scanner. This software is developed as part of our platform for mMR 3D and 4D PET image reconstruction and will be freely accessible to the community. The aim is to perform real time processing (i.e. without perceivable delay) of the list-mode data during data transfers from hard disk to the CPU memory. The output of the processing includes: (1) The count-rate data (head curve: prompts, randoms and singles per second). (2) Plot of the variation of the centre of mass due to kinetics and motion (crude motion detection and quantification). (3) Projection movies (sagittal and coronal) for visual inspection for motion and data quality. (4) Crystal fan-sums for any time frame (used for randoms noise reduction). (5) Singles rate for each detector bucket (used for dead-time correction during normalisation). (6) Static and dynamic sinograms of span-1 and span-11. This workflow enables list-mode data processing synchronously with data transfer from disk to CPU memory. It opens a way for fast creation of multiple bootstrap realisations and multiple image reconstructions for a single dataset providing valuable insight into distributions of any statistic used in the development of robust image bio-markers. Also, the proposed workflow is advantageous for list-mode image reconstruction.

A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

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.