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Bayerlein R, Swarnakar V, Selfridge A, Spencer BA, Nardo L, Badawi RD. Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging. Biomed Phys Eng Express 2024; 10:045053. [PMID: 38876087 DOI: 10.1088/2057-1976/ad5847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 06/14/2024] [Indexed: 06/16/2024]
Abstract
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 between105and2·1010simulated 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 109decays 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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Affiliation(s)
- Reimund Bayerlein
- Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America
- Department of Radiology, University of California Davis, Davis, CA, United States of America
| | - Vivek Swarnakar
- Department of Radiology, University of California Davis, Davis, CA, United States of America
| | - Aaron Selfridge
- Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America
| | - Benjamin A Spencer
- Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America
- Department of Radiology, University of California Davis, Davis, CA, United States of America
| | - Lorenzo Nardo
- Department of Radiology, University of California Davis, Davis, CA, United States of America
| | - Ramsey D Badawi
- Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America
- Department of Radiology, University of California Davis, Davis, CA, United States of America
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Huh Y, Shrestha UM, Gullberg GT, Seo Y. Monte Carlo Simulation and Reconstruction: Assessment of Myocardial Perfusion Imaging of Tracer Dynamics With Cardiac Motion Due to Deformation and Respiration Using Gamma Camera With Continuous Acquisition. Front Cardiovasc Med 2022; 9:871967. [PMID: 35911544 PMCID: PMC9326051 DOI: 10.3389/fcvm.2022.871967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/16/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) is routinely used for stress testing in nuclear medicine. Recently, our group extended its potential going from 3D visual qualitative image analysis to 4D spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration and the estimated myocardial blood flow (MBF) and coronary flow reserve (CFR). However, the quality of reconstructed image is compromised due to cardiac deformation and respiration. The work presented here develops an algorithm that reconstructs the dynamic sequence of separate respiratory and cardiac phases and evaluates the algorithm with data simulated with a Monte Carlo simulation for the continuous image acquisition and processing with a slowly rotating SPECT camera. Methods A clinically realistic Monte Carlo (MC) simulation is developed using the 4D Extended Cardiac Torso (XCAT) digital phantom with respiratory and cardiac motion to model continuous data acquisition of dynamic cardiac SPECT with slowly rotating gamma cameras by incorporating deformation and displacement of the myocardium due to cardiac and respiratory motion. We extended our previously developed 4D maximum-likelihood expectation-maximization (MLEM) reconstruction algorithm for a data set binned from a continuous list mode (LM) simulation with cardiac and respiratory information. Our spatiotemporal image reconstruction uses splines to explicitly model the temporal change of the tracer for each cardiac and respiratory gate that delineates the myocardial spatial position as the tracer washes in and out. Unlike in a fully list-mode data acquisition and reconstruction the accumulated photons are binned over a specific but very short time interval corresponding to each cardiac and respiratory gate. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it continuously deforms. These results are then compared with the conventional 4D spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. Mean Stabilized Activity (MSA), signal to noise ratio (SNR) and Bias for the myocardium activities for three different target-to-background ratios (TBRs) are evaluated. Dynamic quantitative indices such as wash-in (K1) and wash-out (k2) rates at each gate were also estimated. Results The MSA and SNR are higher with higher TBRs while biases were improved with higher TBRs to less than 10%. The correlation between exhalation-inhalation sequence with the ground truth during respiratory cycle was excellent. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Estimated values of K1 and k2 were also consistent with the ground truth. Conclusion The continuous image acquisition for dynamic scan using conventional two-head gamma cameras can provide valuable information for MPI. Our study demonstrated the viability of using a continuous image acquisition method on a widely used clinical two-head SPECT system. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Precise implementation of reconstruction algorithms, better segmentation techniques by generating images of different tissue types and background activity would improve the feasibility of the method in real clinical environment.
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Affiliation(s)
- Yoonsuk Huh
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Uttam M. Shrestha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Grant T. Gullberg
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Nuclear Engineering, University of California, Berkeley, Berkeley, CA, United States
- *Correspondence: Youngho Seo,
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Sarrut D, Bała M, Bardiès M, Bert J, Chauvin M, Chatzipapas K, Dupont M, Etxebeste A, M Fanchon L, Jan S, Kayal G, S Kirov A, Kowalski P, Krzemien W, Labour J, Lenz M, Loudos G, Mehadji B, Ménard L, Morel C, Papadimitroulas P, Rafecas M, Salvadori J, Seiter D, Stockhoff M, Testa E, Trigila C, Pietrzyk U, Vandenberghe S, Verdier MA, Visvikis D, Ziemons K, Zvolský M, Roncali E. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol 2021; 66:10.1088/1361-6560/abf276. [PMID: 33770774 PMCID: PMC10549966 DOI: 10.1088/1361-6560/abf276] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- David Sarrut
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | | | - Manuel Bardiès
- Cancer Research Institute of Montpellier, U1194 INSERM/ICM/Montpellier University, 208 Av des Apothicaires, F-34298 Montpellier cedex 5, France
| | - Julien Bert
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Maxime Chauvin
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | | | | | - Ane Etxebeste
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Louise M Fanchon
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Sébastien Jan
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, F-91401, Orsay, France
| | - Gunjan Kayal
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Paweł Kowalski
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Joey Labour
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Mirjam Lenz
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | - George Loudos
- Bioemission Technology Solutions (BIOEMTECH), Alexandras Av. 116, Athens, Greece
| | | | - Laurent Ménard
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | | | | | - Magdalena Rafecas
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Julien Salvadori
- Department of Nuclear Medicine and Nancyclotep molecular imaging platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
| | - Daniel Seiter
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, United States of America
| | - Mariele Stockhoff
- Medical Image and Signal Processing (MEDISIP), Ghent University, Ghent, Belgium
| | - Etienne Testa
- Univ. Lyon, Univ. Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, F-69622, Villeurbanne, France
| | - Carlotta Trigila
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
| | - Uwe Pietrzyk
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | | | - Marc-Antoine Verdier
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | - Dimitris Visvikis
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Karl Ziemons
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
| | - Milan Zvolský
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
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Frezza A, Joachim-Paquet C, Chauvin M, Després P. Validation of irtGPUMCD, a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy. Phys Med 2020; 73:95-104. [PMID: 32334403 DOI: 10.1016/j.ejmp.2020.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/07/2020] [Accepted: 04/12/2020] [Indexed: 11/28/2022] Open
Abstract
PURPOSE Monte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy. METHODS The female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on 177Lu and including 99mTc and 131I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (108 photons), with gamma branching ratios of ICRP 107 publication. The 177Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency. RESULTS The relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 108 photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.
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Affiliation(s)
- Andrea Frezza
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Charles Joachim-Paquet
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Maxime Chauvin
- CRCT, UMR 1037, Inserm, Université Toulouse III Paul Sabatier, Toulouse, France
| | - Philippe Després
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada; Department of Radiation Oncology and Research Center of CHU de Québec - Université Laval, Quebec City, QC G1R 2J6, Canada.
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Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images. EJNMMI Phys 2018; 5:1. [PMID: 29302810 PMCID: PMC5754277 DOI: 10.1186/s40658-017-0201-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 12/07/2017] [Indexed: 02/05/2023] Open
Abstract
Background Full Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (1283 or 2563). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC). Result The performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 1283 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM. Conclusion The GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of 177Lu-DOTATATE treatments revealed clearly improved resolution and contrast.
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