FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.

Objective.Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently.Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation.Main results.According to the NEMA IQ phantom simulation, FAST-PET's simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures.Significance.FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

An automatic pipeline for PET/MRI attenuation correction validation in the brain.

Purpose: PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches. Methods: The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework. The synthetic lesion insertion tool is used to insert simulated spherical, and brain regions of interest (ROI) into the PET projection space and reconstructed with four different PET MRAC techniques, while FreeSurfer is used to generate brain ROIs from T1 weighted MRI image. Using a cohort of 11 patients' brain PET dataset, the quantitative accuracy of four MRAC(s), which are: DIXON AC, DIXONbone AC, UTE AC, and Deep learning trained with DIXON AC, named DL-DIXON AC, were compared to the PET-based CT attenuation correction (PET CTAC). MRAC to CTAC activity bias in spherical lesions and brain ROIs were reconstructed with and without background activity and compared to the original PET images. Results: The proposed pipeline provides accurate and consistent results for inserted spherical lesions and brain ROIs inserted with and without considering the background activity and following the same MRAC to CTAC pattern as the original brain PET images. As expected, the DIXON AC showed the highest bias; the second was for the UTE, then the DIXONBone, and the DL-DIXON with the lowest bias. For simulated ROIs inserted in the background activity, DIXON showed a -4.65% MRAC to CTAC bias, 0.06% for the DIXONbone, -1.70% for the UTE, and - 0.23% for the DL-DIXON. For lesion ROIs inserted without background activity, DIXON showed a -5.21%, -1% for the DIXONbone, -2.55% for the UTE, and - 0.52 for the DL-DIXON. For MRAC to CTAC bias calculated using the same 16 FreeSurfer brain ROIs in the original brain PET reconstructed images, a 6.87% was observed for the DIXON, -1.83% for DIXON bone, -3.01% for the UTE, and - 0.17% for the DL-DIXON. Conclusion: The proposed pipeline provides accurate and consistent results for synthetic spherical lesions and brain ROIs inserted with and without considering the background activity; hence a new attenuation correction approach can be evaluated without using measured PET emission data.

An automatic pipeline for PET/MRI attenuation correction validation in the brain.

PURPOSE: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity. METHODS: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images. RESULTS: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs. CONCLUSION: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

Synthetic PET via Domain Translation of 3-D MRI.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

Phase 1 Evaluation of 11C-CS1P1 to Assess Safety and Dosimetry in Human Participants.

This study evaluated the safety, dosimetry, and characteristics of 3-((2-fluoro-4-(5-(2'-methyl-2-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-1,2,4-oxadiazol-3-yl)benzyl)(methyl-11C)amino)propanoic acid (11C-CS1P1), a radiotracer targeting sphingosine-1-phosphate receptor (S1PR) 1 (S1PR1). S1PR1 is of clinical interest because of its role in multiple sclerosis (and other conditions), with an expanding class of S1PR modulators approved for relapsing multiple sclerosis. 11C-CS1P1 binds S1PR1 with high specificity and has shown promise in animal models of inflammatory diseases. Methods: 11C-CS1P1 was injected into 5 male and 6 female healthy participants. Ten participants were imaged with PET using a multipass whole-body continuous-bed-motion acquisition, and one had dedicated head and neck PET and MRI. Participants were continuously monitored for safety events. Organ time-activity curve data were collected, integrated, and normalized to the injected activity. Organ radiation doses and effective dose were computed using the adult male and female models in OLINDA, version 2.2. SUV images were evaluated for qualitative biodistribution. Results: No adverse events were observed after the dose, including no bradycardia. The liver was the critical organ from dosimetry analysis (mean ± SD: female, 23.12 ± 5.19 µSv/MBq; male, 21.06 ± 1.63 µSv/MBq). The whole-body effective dose (as defined by International Commission on Radiological Protection publication 103) was 4.18 ± 0.30 µSv/MBq in women and 3.54 ± 0.14 µSv/MBq in men. Using a maximum delivered dose of 740 MBq (20 mCi), the effective dose for women would be 3.1 mSv (0.31 rem), with a liver dose of 17.1 mSv (1.7 rem); the effective dose for men would be 2.6 mSv (0.26 rem), with a liver dose of 15.6 mSv (1.56 rem). Brain uptake was seen predominantly in gray matter and correlated with regional S1PR1 RNA expression (r = 0.84). Conclusion: These results support the safety of 11C-CS1P1 for evaluation of inflammation in human clinical populations. Dosimetry permits repeated measures in the same participants. Brain uptake correlates well with known target topography.

Performance comparison of a dedicated total breast PET system with a clinical whole-body PET system: a simulation study.

This paper presents a novel PET geometry for breast cancer imaging. The scanner consists of a 'stadium' (a rectangle with two semi-circles on opposite sides) shaped ring, along with anterior and posterior panels to provide high sensitivity and high spatial resolution for an imaging field-of-view (FOV) that include both breasts, mediastinum and axilla. We simulated this total-breast PET system using GATE and reconstructed the coincidence events using a GPU-based list-mode image reconstruction implementing maximum likelihood expectation-maximization (ML-EM) algorithm. The rear-panel is made up of a single layer of LSO crystals (3.2 × 3.2 × 20 mm3each), while the 'stadium'-shaped elongated ring and the anterior panel are made with dual-layered LSO crystals (1.6 × 1.6 × 6 mm3each). The energy resolution and coincidence resolving time of all detectors are assumed to be 12% and 250 ps full-width-at-half-maximum, respectively. Various sized simulated lesions (4, 5, 6 mm) having 4:1, 5:1, and 6:1 lesion-to-background radioactivity concentration ratios, mimicking different biological uptakes, were strategically located throughout a volumetric torso phantom. We compared system sensitivity and lesion detectability of the dedicated total-breast PET system to a state-of-the-art clinical whole-body PET scanner. The mean sensitivity of the total-breast PET system is 3.21 times greater than that of a whole-body PET scanner in the breast regions. The total-breast PET system also provides better contrast-recovery coefficients for lesions of all sizes and lesion-to-background ratios in the breast when compared to a reference clinical whole-body PET scanner. Receiver operating characteristics (ROC) study shows the area under the ROC curve is 0.948 and 0.924 for the total-breast system and the whole-body PET scanner, respectively, in the detection of 4 mm diameter lesions with 4:1 lesion-to-background ratio. This study demonstrates our novel geometry can provide an imaging FOV larger than conventional PEM systems to simultaneously image both breasts, chest wall and axillae with significantly improved lesion detectability in the breasts when compared to a whole-body PET scanner.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion.

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.

G4DARI: Geant4/GATE based Monte Carlo simulation interface for dosimetry calculation in radiotherapy.

Monte Carlo (MC) simulation is widely recognized as an important technique to study the physics of particle interactions in nuclear medicine and radiation therapy. There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and Geant4. However, such codes made the physics easier but the programming remains a tedious task even for physicists familiar with computer programming. In this paper we report the development of a new interface GEANT4 Dose And Radiation Interactions (G4DARI) based on GEANT4 for absorbed dose calculation and for particle tracking in humans, small animals and complex phantoms. The calculation of the absorbed dose is performed based on 3D CT human or animal images in DICOM format, from images of phantoms or from solid volumes which can be made from any pure or composite material to be specified by its molecular formula. G4DARI offers menus to the user and tabs to be filled with values or chemical formulas. The interface is described and as application, we show results obtained in a lung tumor in a digital mouse irradiated with seven energy beams, and in a patient with glioblastoma irradiated with five photon beams. In conclusion, G4DARI can be easily used by any researcher without the need to be familiar with computer programming, and it will be freely available as an application package.

Comparison between X-rays spectra and their effective energies in small animal CT tomographic imaging and dosimetry.

Small animal CT imaging and dosimetry usually rely on X-ray radiation produced by X-ray tubes. These X-rays typically cover a large energy range. In this study, we compared poly-energetic X-ray spectra against estimated equivalent (effective) mono-energetic beams with the same number of simulated photons for small animal CT imaging and dosimetry applications. Two poly-energetic X-ray spectra were generated from a tungsten anode at 50 and 120 kVp. The corresponding effective mono-energetic beams were established as 36 keV for the 50 kVp spectrum and 49.5 keV for the 120 kVp spectrum. To assess imaging applications, we investigated the spatial resolution by a tungsten wire, and the contrast-to-noise ratio in a reference phantom and in a realistic mouse phantom. For dosimetry investigation, we calculated the absorbed dose in a segmented digital mouse atlas in the skin, fat, heart and bone tissues. Differences of 2.1 and 2.6% in spatial resolution were respectively obtained between the 50 and 120 kVp poly-energetic spectra and their respective 36 and 49.5 keV mono-energetic beams. The differences in contrast-to-noise ratio between the poly-energetic 50 kVp spectrum and its corresponding mono-energetic 36 keV beam for air, fat, brain and bone were respectively -2.9, -0.2, 11.2 and -4.8%, and similarly between the 120 kVp and its effective energy 49.5 keV: -11.3, -20.2, -4.2 and -13.5%. Concerning the absorbed dose, for the lower X-ray beam energies, 50 kVp against 36 keV, the poly-energetic radiation doses were higher than the mono-energetic doses. Instead, for the higher X-ray beam energies, 120 kVp and 49.5 keV, the absorbed dose to the bones and lungs were higher for the mono-energetic 49.5 keV. The intensity and energy of the X-ray beam spectrum have an impact on both imaging and dosimetry in small animal studies. Simulations with mono-energetic beams should take into account these differences in order to study biological effects or to be compared to experimental data.