Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure

Zhao, Peng; Lu, Yu; Xu, Yuhong; Li, Yongzhe; Cheng, Bowen; Ni, Ming; Chen, Zhi; Xi, Peng; Xie, Qiang; Wang, Shi‐Cun; Xu, Xiaobin

doi:10.1088/1361-6560/ac58dd

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…Despite the lack of GPU-accelerated simulations for either SimSET or GATE, there are GPU-based tools that have recently been developed for various nuclear medicine imaging applications with promising improvements in computation times: the programs gPET [29] and UMC-PET [30] (Galve 2024) provide efficient MC simulations for PET, and ARCHER-NM [31] (Peng 2022) enables radiation dose calculations in organs of PET/CT patients. These tools have so far only been evaluated in phantom studies and validated again other MC-based software, but might provide alternatives where the use of SimSET or GATE is not specifically required.…”

Section: Discussionmentioning

confidence: 99%

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Bayerlein,

Swarnakar,

Selfridge

et al. 2024

Biomed. Phys. Eng. Express

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Objective. This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden—even when executed on modern multi-core computing servers. Cloud computing allows simulation tasks to be highly parallelized and considerably accelerated. Approach. We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between 10 5 and 2 · 10 10 simulated decays. We deployed two popular MC simulation frameworks—SimSET and GATE—within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service. Main results. Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 109 decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs. Significance. Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably improve processing times and overall workflow efficiency, with future research exploring additional enhancements through optimized configurations and computational methods.

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Section: Discussionmentioning

confidence: 99%

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Bayerlein,

Swarnakar,

Selfridge

et al. 2024

Biomed. Phys. Eng. Express

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“…Region of interests were drawn manually based on CT images by medical physicists, and with the help of Deep Viewer (Wisdom Tech, Hefei, China), a validated deep-learning-based organ segmentation software (Peng et al 2020(Peng et al , 2022. 17 non-sexual organs for each patient were drawn, including muscle, bladder, brain, breast, gall bladder, heart, kidney, liver, lung, pancreas, parotid, intestine, bone, spinal cord, spleen, stomach and thyroid.…”

Section: Methodsmentioning

confidence: 99%

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations

Jia,

Xue,

et al. 2024

Phys. Med. Biol.

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Purpose: To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed to 18F-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children. Methods: Data of 29 Chinese pediatric patients undergoing whole-body 18F-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities. Absorbed radiation dose for both CT and PET component were calculated using Monte Carlo simulations. Regression models were fitted to time integrated activity coefficient (TIAC) and organ absorbed dose for each patient.Results: TIACs of all the organs in compartment model and the organ dose for 12 organs were correlated with patients’ weight. Young children have significantly large uptake in brain compared to adults. The distinctions of anatomical and biological characteristics between Chinese and Caucasian children contribute to variations in the absorbed dose of 18F-FDG PET scans. PET contributed more in organ dose than CT did in most organs, especially in brain and bladder. The average effective dose (± SD) was 4.48 mSv (± 1.12 mSv), 7.04 mSv (± 2.49 mSv) and 11.52 mSv (± 3.20 mSv) from CT, PET and their sum respectively. PET contributed 1.57 times higher than CT.Conclusions: To the best of our knowledge, this work represents the first attempt to estimate patient-specific radiation doses from PET/CT for Chinese pediatric patients. TIACs derived from our methodology in both age groups exhibited significant differences from the that reported in ICRP 128. Our methodology offers valuable approach not only for estimating pharmacokinetic characteristics and patient-specific doses in pediatric patients undergoing 18F-FDG studies but also for other cohorts with similar characteristics.

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“…were consistent with those of the pCT images except for the HU values. Using the RP and RD les optimized based on the pCT images, the dose recalculations were implemented by a GPU-accelerated Monte Carlo code, ArcherQA, previously developed by our group [31,32]. ArcherQA integrates the GPU acceleration function to quickly and accurately calculate the dose distribution based on CT images.…”

Section: Delineation and Dose Calculationmentioning

confidence: 99%

Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy

Chang

Liang

Yang

et al. 2022

Preprint

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Objective: Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy. Methods: In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain DoseCBCT, DosepCT, DosedpCT and DosesCT. Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually. Results: The global gamma pass rate of DosesCT and DosedpCT was 99.66% ± 0.34%, while that of DoseCBCT and DosedpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on DosedpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT. Conclusions: For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.

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Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure

Cited by 10 publications

References 39 publications

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations

Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy

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