Treffer: GPU-based list-mode TOF PET image reconstruction with complete correction techniques.
Title:
GPU-based list-mode TOF PET image reconstruction with complete correction techniques.
Authors:
Yuan Z; School of Physics and Astronomy, Beijing Normal University, Beijing, China., Zhan F; The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.; Wuxi School of Medicine, Jiangnan University, Wuxi, China., Lu H; School of Artificial Intelligence, Beijing Normal University, Beijing, China.; Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia., Hou Y; School of Physics and Astronomy, Beijing Normal University, Beijing, China., Liao R; School of Physics and Astronomy, Beijing Normal University, Beijing, China., Li C; School of Physics and Astronomy, Beijing Normal University, Beijing, China., Jiang J; School of Physics and Astronomy, Beijing Normal University, Beijing, China.
Source:
Medical physics [Med Phys] 2026 Jan; Vol. 53 (1), pp. e70216.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE
Imprint Name(s):
Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
MeSH Terms:
References:
Gallagher B, Ansari A, Atkins H, et al., Radiopharmaceuticals XXVII. 18F‐labeled 2‐deoxy‐2‐fluoro‐d‐glucose as a radiopharmaceutical for measuring regional myocardial glucose metabolism in vivo: Tissue distribution and imaging studies in animals. J Nucl Med. 1977;18:990‐996.
Kuhl D, Phelps M, Hoffman E, Robinson Jr G, MacDonald N. Initial clinical experience with 18F‐2‐fluoro‐2‐deoxy‐d‐glucose for determination of local cerebral glucose utilization by emission computed tomography. Acta Neurol Scand Suppl. 1977;64:192‐193.
Yang M, Yan P, Huang H, Li J. Filtered back projection reconstruction research based on Gaussian in PET images. in 2012 International Conference on Audio, Language and Image Processing. IEEE; 2012:360‐364.
Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1:113‐122.
Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr. 1984;8:306‐316.
Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994;13:601‐609.
Reader AJ, Erlandsson K, Flower MA, Ott RJ. Fast accurate iterative reconstruction for low‐statistics positron volume imaging. Phys Med Biol. 1998;43:835.
Teimoorisichani M, Goertzen AL. A cube‐based dual‐GPU list‐mode reconstruction algorithm for PET imaging. IEEE Trans Radiat Plasma Med Sci. 2021;6:463‐474.
Lewellen TK. Time‐of‐flight PET, in Semin. nucl. med. 1998;28:268‐275.
Conti M. Why is TOF PET reconstruction a more robust method in the presence of inconsistent data?. Phys Med Biol. 2010;56:155.
Karp JS, Surti S, Daube‐Witherspoon ME, Muehllehner G. Benefit of time‐of‐flight in PET: Experimental and clinical results. J Nucl Med. 2008;49:462‐470.
Conti M, Bendriem B, Casey M, et al., First experimental results of time‐of‐flight reconstruction on an LSO PET scanner. Phys Med Biol. 2005;50:4507.
Jin X, Chan C, Mulnix T, et al., List‐mode reconstruction for the Biograph mCT with physics modeling and event‐by‐event motion correction. Phys Med Biol. 2013;58:5567.
Nikulin P, Maus J, Hofheinz F, Lougovski A, Van den Hoff J. Time efficient scatter correction for time‐of‐flight PET: the immediate scatter approximation. Phys Med Biol. 2019;64:075005.
Guide D. Cuda C++ programming guide, NVIDIA, July (2020).
Luebke D. CUDA: Scalable parallel programming for high‐performance scientific computing. in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; 2008:836–838.
Cui J‐y, Pratx G, Prevrhal S, Levin CS. Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA. Med Phys. 2011;38:6775‐6786.
Kim KS, Ye JC. Fully 3D iterative scatter‐corrected OSEM for HRRT PET using a GPU. Phys Med Biol. 2011;56:4991.
Liu J, Ren N, Zeng T, et al. The software system of a dedicated brain PET scanner using dual‐ended readout detectors with high‐DOI resolution. IEEE Trans Radiat Plasma Med Sci. 2024;8:655‐663.
Markiewicz PJ, Ehrhardt MJ, Erlandsson K, et al. NiftyPET: A high‐throughput software platform for high quantitative accuracy and precision PET imaging and analysis. Neuroinformatics. 2018;16:95‐115.
Schramm G, Thielemans K. PARALLELPROJ—An open‐source framework for fast calculation of projections in tomography. Front Nucl Med. 2024;3:1324562.
Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: Software for tomographic image reconstruction release 2. Phys Med Biol. 2012;57:867.
Komarov B, Maa‐Hacquoil H, Poladyan H, et al. Transition to GPU‐based reconstruction for clinical organ‐targeted PET scanner. Phys Med Biol. 2025;70:055001.
Polson LA, Fedrigo R, Li C, et al. Pytomography: A python library for medical image reconstruction. SoftwareX. 2025;29:102020.
Iatrou M, Ross S, Manjeshwar R, Stearns C. A fully 3D iterative image reconstruction algorithm incorporating data corrections. in IEEE Symposium Conference Record Nuclear Science 2004. vol 4. IEEE; 2004:2493–2497.
Tamal M, Reader AJ, Markiewicz P, Julyan PJ, Hastings DL. Noise properties of four strategies for incorporation of scatter and attenuation information in PET reconstruction using the EM‐ML algorithm. IEEE Trans Nucl Sci. 2006;53:2778‐2786.
Qi J, Leahy RM, Cherry SR, Chatziioannou A, Farquhar TH. High‐resolution 3D Bayesian image reconstruction using the microPET small‐animal scanner. Phys Med Biol. 1998;43:1001.
Lougovski A, Hofheinz F, Maus J, Schramm G, Will E, Van den Hoff J. A volume of intersection approach for on‐the‐fly system matrix calculation in 3D PET image reconstruction. Phys Med Biol. 2014;59:561.
Filipović M, Comtat C, Stute S. Time‐of‐flight (TOF) implementation for PET reconstruction in practice. Phys Med Biol. 2019;64:23NT01.
Badawi R, Marsden P. Developments in component‐based normalization for 3D PET. Phys Med Biol. 1999;44:571.
Panin V, Chen M, Michel C. Simultaneous update iterative algorithm for variance reduction on random coincidences in PET. in 2007 IEEE Nuclear Science Symposium Conference Record. vol 4. IEEE; 2007:2807‐2811.
Watson C. Extension of single scatter simulation to scatter correction of time‐of‐flight PET. IEEE Trans Nucl Sci. 2007;54:1679‐1686.
Klein O, Nishina Y. Über die Streuung von Strahlung durch freie Elektronen nach der neuen relativistischen Quantendynamik von Dirac. Z Phys. 1929;52:853‐868.
Bal H, Kiser JW, Conti M, Bowen SL. Comparison of maximum likelihood and conventional PET scatter scaling methods for 18F‐FDG and 68Ga‐DOTATATE PET/CT. Med Phys. 2021;48:4218‐4228.
Defrise M, Salvo K, Rezaei A, Nuyts J, Panin V, Casey M. ML estimation of the scatter scaling in TOF PET. in 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE; 2014:1–5.
Bal H, Panin VY, Aykac M, Conti M. ML background scale factors estimation for prompt gamma tracers in PETCT imaging. in 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE; 2018:1–4.
Jan S, Santin G, Strul D, et al. GATE: A simulation toolkit for PET and SPECT. Phys Med Biol. 2004;49:4543.
Sarrut D, Bardiès M, Boussion N, et al. A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. Med Phys. 2014;41:064301.
Sarrut D, Bała M, Bardiès M, et al. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol. 2021;66:10TR03.
Arias‐Valcayo F, Galve P, Herraiz JL, Vaquero J, Desco M, Udías JM. Reconstruction of multi‐animal PET acquisitions with anisotropically variant PSF. Biomed Phys Eng Express. 2023;9:065018.
Rapisarda E, Bettinardi V, Thielemans K, Gilardi MC. Image‐based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET. Phys Med Biol. 2010;55:4131‐4151.
Accorsi R, Adam L‐E, Werner ME, Karp JS. Implementation of a single scatter simulation algorithm for 3D PET: Application to emission and transmission scanning. in 2002 IEEE Nuclear Science Symposium Conference Record. vol 2. IEEE; 2002:816‐820.
Accorsi R, Adam L‐E, Werner ME, Karp JS. Optimization of a fully 3D single scatter simulation algorithm for 3D PET. Phys Med Biol. 2004;49:2577.
Amanatides J, Woo A. A fast voxel traversal algorithm for ray tracing. in Eurographics. vol 87. 1987:3–10.
National Electrical Manufacturers Association. NEMA NU‐2 Standards Publication NU‐2‐2007: Performance Measurements of Positron Emission Tomography, Standard, National Electrical Manufacturers Association, Rosslyn, VA, 2007.
Ziegler S, Jakoby BW, Braun H, Paulus DH, Quick HH. NEMA image quality phantom measurements and attenuation correction in integrated PET/MR hybrid imaging. EJNMMI Phys. 2015;2:18.
Raczyński L, Wińlicki W, Klimaszewski K, et al. 3D TOF‐PET image reconstruction using total variation regularization. Physica Medica. 2020;80:230‐242.
José Santo R, Salomon A, WAM de Jong H, Stute S, Merlin T, Beijst C. openSSS: an open‐source implementation of scatter estimation for 3D TOF‐PET. EJNMMI Phys. 2025;12:1‐15.
Bharkhada D, Panin V, Conti M, Daube‐Witherspoon ME, Matej S, Karp JS. Listmode reconstruction for biograph vision PET/CT scanner. in 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE; 2019:1‐6.
Joseph PM. An improved algorithm for reprojecting rays through pixel images. IEEE Trans Med Imaging. 1982;1:192‐196.
Oliver JF, Rafecas M. Modelling random coincidences in positron emission tomography by using singles and prompts: A comparison study. PloS One. 2016;11:e0162096.
Daube‐Witherspoon ME, Moore SC, Karp JS. Randoms estimation for long axial field‐of‐view PET. IEEE Trans Radiat Plasma Med Sci. 2025;1‐1.
Hou Y, Zhan F, Liu J, et al. Cycle‐constrained adversarial denoising convolutional network for PET image denoising: Multi‐dimensional validation on large datasets with reader study and real low‐dose data. Med Image Anal. 2026;107:103826.
Kuhl D, Phelps M, Hoffman E, Robinson Jr G, MacDonald N. Initial clinical experience with 18F‐2‐fluoro‐2‐deoxy‐d‐glucose for determination of local cerebral glucose utilization by emission computed tomography. Acta Neurol Scand Suppl. 1977;64:192‐193.
Yang M, Yan P, Huang H, Li J. Filtered back projection reconstruction research based on Gaussian in PET images. in 2012 International Conference on Audio, Language and Image Processing. IEEE; 2012:360‐364.
Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1:113‐122.
Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr. 1984;8:306‐316.
Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994;13:601‐609.
Reader AJ, Erlandsson K, Flower MA, Ott RJ. Fast accurate iterative reconstruction for low‐statistics positron volume imaging. Phys Med Biol. 1998;43:835.
Teimoorisichani M, Goertzen AL. A cube‐based dual‐GPU list‐mode reconstruction algorithm for PET imaging. IEEE Trans Radiat Plasma Med Sci. 2021;6:463‐474.
Lewellen TK. Time‐of‐flight PET, in Semin. nucl. med. 1998;28:268‐275.
Conti M. Why is TOF PET reconstruction a more robust method in the presence of inconsistent data?. Phys Med Biol. 2010;56:155.
Karp JS, Surti S, Daube‐Witherspoon ME, Muehllehner G. Benefit of time‐of‐flight in PET: Experimental and clinical results. J Nucl Med. 2008;49:462‐470.
Conti M, Bendriem B, Casey M, et al., First experimental results of time‐of‐flight reconstruction on an LSO PET scanner. Phys Med Biol. 2005;50:4507.
Jin X, Chan C, Mulnix T, et al., List‐mode reconstruction for the Biograph mCT with physics modeling and event‐by‐event motion correction. Phys Med Biol. 2013;58:5567.
Nikulin P, Maus J, Hofheinz F, Lougovski A, Van den Hoff J. Time efficient scatter correction for time‐of‐flight PET: the immediate scatter approximation. Phys Med Biol. 2019;64:075005.
Guide D. Cuda C++ programming guide, NVIDIA, July (2020).
Luebke D. CUDA: Scalable parallel programming for high‐performance scientific computing. in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; 2008:836–838.
Cui J‐y, Pratx G, Prevrhal S, Levin CS. Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA. Med Phys. 2011;38:6775‐6786.
Kim KS, Ye JC. Fully 3D iterative scatter‐corrected OSEM for HRRT PET using a GPU. Phys Med Biol. 2011;56:4991.
Liu J, Ren N, Zeng T, et al. The software system of a dedicated brain PET scanner using dual‐ended readout detectors with high‐DOI resolution. IEEE Trans Radiat Plasma Med Sci. 2024;8:655‐663.
Markiewicz PJ, Ehrhardt MJ, Erlandsson K, et al. NiftyPET: A high‐throughput software platform for high quantitative accuracy and precision PET imaging and analysis. Neuroinformatics. 2018;16:95‐115.
Schramm G, Thielemans K. PARALLELPROJ—An open‐source framework for fast calculation of projections in tomography. Front Nucl Med. 2024;3:1324562.
Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: Software for tomographic image reconstruction release 2. Phys Med Biol. 2012;57:867.
Komarov B, Maa‐Hacquoil H, Poladyan H, et al. Transition to GPU‐based reconstruction for clinical organ‐targeted PET scanner. Phys Med Biol. 2025;70:055001.
Polson LA, Fedrigo R, Li C, et al. Pytomography: A python library for medical image reconstruction. SoftwareX. 2025;29:102020.
Iatrou M, Ross S, Manjeshwar R, Stearns C. A fully 3D iterative image reconstruction algorithm incorporating data corrections. in IEEE Symposium Conference Record Nuclear Science 2004. vol 4. IEEE; 2004:2493–2497.
Tamal M, Reader AJ, Markiewicz P, Julyan PJ, Hastings DL. Noise properties of four strategies for incorporation of scatter and attenuation information in PET reconstruction using the EM‐ML algorithm. IEEE Trans Nucl Sci. 2006;53:2778‐2786.
Qi J, Leahy RM, Cherry SR, Chatziioannou A, Farquhar TH. High‐resolution 3D Bayesian image reconstruction using the microPET small‐animal scanner. Phys Med Biol. 1998;43:1001.
Lougovski A, Hofheinz F, Maus J, Schramm G, Will E, Van den Hoff J. A volume of intersection approach for on‐the‐fly system matrix calculation in 3D PET image reconstruction. Phys Med Biol. 2014;59:561.
Filipović M, Comtat C, Stute S. Time‐of‐flight (TOF) implementation for PET reconstruction in practice. Phys Med Biol. 2019;64:23NT01.
Badawi R, Marsden P. Developments in component‐based normalization for 3D PET. Phys Med Biol. 1999;44:571.
Panin V, Chen M, Michel C. Simultaneous update iterative algorithm for variance reduction on random coincidences in PET. in 2007 IEEE Nuclear Science Symposium Conference Record. vol 4. IEEE; 2007:2807‐2811.
Watson C. Extension of single scatter simulation to scatter correction of time‐of‐flight PET. IEEE Trans Nucl Sci. 2007;54:1679‐1686.
Klein O, Nishina Y. Über die Streuung von Strahlung durch freie Elektronen nach der neuen relativistischen Quantendynamik von Dirac. Z Phys. 1929;52:853‐868.
Bal H, Kiser JW, Conti M, Bowen SL. Comparison of maximum likelihood and conventional PET scatter scaling methods for 18F‐FDG and 68Ga‐DOTATATE PET/CT. Med Phys. 2021;48:4218‐4228.
Defrise M, Salvo K, Rezaei A, Nuyts J, Panin V, Casey M. ML estimation of the scatter scaling in TOF PET. in 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE; 2014:1–5.
Bal H, Panin VY, Aykac M, Conti M. ML background scale factors estimation for prompt gamma tracers in PETCT imaging. in 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE; 2018:1–4.
Jan S, Santin G, Strul D, et al. GATE: A simulation toolkit for PET and SPECT. Phys Med Biol. 2004;49:4543.
Sarrut D, Bardiès M, Boussion N, et al. A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. Med Phys. 2014;41:064301.
Sarrut D, Bała M, Bardiès M, et al. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol. 2021;66:10TR03.
Arias‐Valcayo F, Galve P, Herraiz JL, Vaquero J, Desco M, Udías JM. Reconstruction of multi‐animal PET acquisitions with anisotropically variant PSF. Biomed Phys Eng Express. 2023;9:065018.
Rapisarda E, Bettinardi V, Thielemans K, Gilardi MC. Image‐based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET. Phys Med Biol. 2010;55:4131‐4151.
Accorsi R, Adam L‐E, Werner ME, Karp JS. Implementation of a single scatter simulation algorithm for 3D PET: Application to emission and transmission scanning. in 2002 IEEE Nuclear Science Symposium Conference Record. vol 2. IEEE; 2002:816‐820.
Accorsi R, Adam L‐E, Werner ME, Karp JS. Optimization of a fully 3D single scatter simulation algorithm for 3D PET. Phys Med Biol. 2004;49:2577.
Amanatides J, Woo A. A fast voxel traversal algorithm for ray tracing. in Eurographics. vol 87. 1987:3–10.
National Electrical Manufacturers Association. NEMA NU‐2 Standards Publication NU‐2‐2007: Performance Measurements of Positron Emission Tomography, Standard, National Electrical Manufacturers Association, Rosslyn, VA, 2007.
Ziegler S, Jakoby BW, Braun H, Paulus DH, Quick HH. NEMA image quality phantom measurements and attenuation correction in integrated PET/MR hybrid imaging. EJNMMI Phys. 2015;2:18.
Raczyński L, Wińlicki W, Klimaszewski K, et al. 3D TOF‐PET image reconstruction using total variation regularization. Physica Medica. 2020;80:230‐242.
José Santo R, Salomon A, WAM de Jong H, Stute S, Merlin T, Beijst C. openSSS: an open‐source implementation of scatter estimation for 3D TOF‐PET. EJNMMI Phys. 2025;12:1‐15.
Bharkhada D, Panin V, Conti M, Daube‐Witherspoon ME, Matej S, Karp JS. Listmode reconstruction for biograph vision PET/CT scanner. in 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE; 2019:1‐6.
Joseph PM. An improved algorithm for reprojecting rays through pixel images. IEEE Trans Med Imaging. 1982;1:192‐196.
Oliver JF, Rafecas M. Modelling random coincidences in positron emission tomography by using singles and prompts: A comparison study. PloS One. 2016;11:e0162096.
Daube‐Witherspoon ME, Moore SC, Karp JS. Randoms estimation for long axial field‐of‐view PET. IEEE Trans Radiat Plasma Med Sci. 2025;1‐1.
Hou Y, Zhan F, Liu J, et al. Cycle‐constrained adversarial denoising convolutional network for PET image denoising: Multi‐dimensional validation on large datasets with reader study and real low‐dose data. Med Image Anal. 2026;107:103826.
Grant Information:
12475336 National Natural Science Foundation of China; 20230484413 Beijing Nova Program
Contributed Indexing:
Keywords: compute unified device architecture; image reconstruction; noise correction; positron emission tomography
Entry Date(s):
Date Created: 20251221 Date Completed: 20251221 Latest Revision: 20260120
Update Code:
20260120
DOI:
10.1002/mp.70216
PMID:
41423580
Database:
MEDLINE
Weitere Informationen
Background: Positron emission tomography (PET) is essential for the early diagnosis of cancer, neurological disorders, and cardiovascular diseases. However, achieving high-quality PET images remains challenging due to the complex physical factors involved and the trade-off between reconstruction accuracy and computational efficiency.
Purpose: This study aimed to develop QuanTOF, a GPU-accelerated PET reconstruction framework integrating comprehensive physical corrections and advanced modeling to enhance image quality while maintaining clinical practicality.
Methods: QuanTOF employs a GPU-accelerated Bayesian penalized-likelihood reconstruction algorithm with time-of-flight (TOF) and point spread function (PSF) modeling. It incorporates full corrections for attenuation, normalization, random coincidences, and scatter coincidences. A memory-efficient TOF single scatter simulation (SSS) algorithm enabled on-the-fly scatter correction without storing full TOF sinograms. Validation included Monte Carlo simulations, clinical phantom experiments, and blinded reader studies using a Siemens Biograph Vision PET/CT scanner.
Results: In phantom studies, QuanTOF achieved uniformity comparable to commercial systems and resolved 2.4 mm hot rods in Derenzo phantoms, with peak-to-valley ratios of 2.61 (3.2 mm rods) and 1.29 (2.4 mm rods). Scatter correction time was reduced to INLINEMATH s, two orders of magnitude faster than existing methods. Clinicians rated QuanTOF images significantly higher (average score: 3.90 vs. 2.15 for clinical images) in reader studies. Reconstruction times remained clinically acceptable (e.g., 2.78 s for NEMA phantom scatter correction) CONCLUSIONS: QuanTOF balances accuracy and efficiency through GPU-optimized physics modeling and memory-efficient algorithms. It delivers high-resolution PET images with diagnostic confidence, demonstrating potential for clinical oncology, neurology, and cardiology applications.
(© 2025 American Association of Physicists in Medicine.)