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Fraioli F, Albert N, Boellaard R, Galazzo IB, Brendel M, Buvat I, Castellaro M, Cecchin D, Fernandez PA, Guedj E, Hammers A, Kaplar Z, Morbelli S, Papp L, Shi K, Tolboom N, Traub-Weidinger T, Verger A, Van Weehaeghe D, Yakushev I, Barthel H. Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging. Eur J Nucl Med Mol Imaging 2024; 51:1007-1011. [PMID: 38097746 DOI: 10.1007/s00259-023-06553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Affiliation(s)
- Francesco Fraioli
- Institute of Nuclear Medicine, University College London Hospitals, 5Th Floor UCH, 235 Euston Rd, London, NW1 2BU, UK.
| | - Nathalie Albert
- Department of Nuclear Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | | | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Irene Buvat
- Institut Curie - Inserm Laboratory of Translational Imaging in Oncology, Paris, France
| | - Marco Castellaro
- Department of Information Engineering, University-Hospital of Padova, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University-Hospital of Padova, Padua, Italy
| | - Pablo Aguiar Fernandez
- CIMUS, Universidade Santiago de Compostela & Nuclear Medicine Dept, Univ. Hospital IDIS, Santiago de Compostela, Spain
| | - Eric Guedj
- Département de Médecine Nucléaire, Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Hôpital de La Timone, CERIMED, Marseille, France
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London St Thomas' Hospital, London, SE1 7EH, UK
| | - Zoltan Kaplar
- Institute of Nuclear Medicine, University College London Hospitals, 5Th Floor UCH, 235 Euston Rd, London, NW1 2BU, UK
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute E Della Scienza Di Torino, University of Turin, Turin, Italy
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kuangyu Shi
- Lab for Artificial Intelligence and Translational Theranostic, Dept. of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, Utrecht University Medical Center, Utrecht, The Netherlands
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, IADI, INSERM U1254, Nancy, France
| | - Donatienne Van Weehaeghe
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Centre, Leipzig, Germany
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Galve P, Arias-Valcayo F, Villa-Abaunza A, Ibáñez P, Udías JM. UMC-PET: a fast and flexible Monte Carlo PET simulator. Phys Med Biol 2024; 69:035018. [PMID: 38198727 DOI: 10.1088/1361-6560/ad1cf9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 01/10/2024] [Indexed: 01/12/2024]
Abstract
Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.
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Affiliation(s)
- Pablo Galve
- Grupo de Física Nuclear, EMFTEL & IPARCOS, Universidad Complutense de Madrid, CEI Moncloa, 28040 Madrid, Spain
- Université Paris Cité, Inserm, PARCC, F-75015 Paris, France
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Fernando Arias-Valcayo
- Grupo de Física Nuclear, EMFTEL & IPARCOS, Universidad Complutense de Madrid, CEI Moncloa, 28040 Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Amaia Villa-Abaunza
- Grupo de Física Nuclear, EMFTEL & IPARCOS, Universidad Complutense de Madrid, CEI Moncloa, 28040 Madrid, Spain
| | - Paula Ibáñez
- Grupo de Física Nuclear, EMFTEL & IPARCOS, Universidad Complutense de Madrid, CEI Moncloa, 28040 Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - José Manuel Udías
- Grupo de Física Nuclear, EMFTEL & IPARCOS, Universidad Complutense de Madrid, CEI Moncloa, 28040 Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
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Lee H, Cheon BW, Feld JW, Grogg K, Perl J, Ramos-Méndez JA, Faddegon B, Min CH, Paganetti H, Schuemann J. TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems. Phys Med Biol 2023; 68:10.1088/1361-6560/acc565. [PMID: 36930985 PMCID: PMC10164408 DOI: 10.1088/1361-6560/acc565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/17/2023] [Indexed: 03/19/2023]
Abstract
Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.
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Affiliation(s)
- Hoyeon Lee
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Bo-Wi Cheon
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Gangwon-do 26493, Republic of Korea
| | - Joseph W Feld
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Kira Grogg
- The Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Joseph Perl
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025 United States of America
| | - José A Ramos-Méndez
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115 United States of America
| | - Bruce Faddegon
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115 United States of America
| | - Chul Hee Min
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Gangwon-do 26493, Republic of Korea
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
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Rajagopal A, Natsuaki Y, Wangerin K, Hamdi M, An H, Sunderland JJ, Laforest R, Kinahan PE, Larson PEZ, Hope TA. Synthetic PET via Domain Translation of 3-D MRI. IEEE Trans Radiat Plasma Med Sci 2023; 7:333-343. [PMID: 37396797 PMCID: PMC10311993 DOI: 10.1109/trpms.2022.3223275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
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.
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Affiliation(s)
- Abhejit Rajagopal
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Yutaka Natsuaki
- Department of Radiation Oncology, University of New Mexico, Albuquerque, NM 87131 USA
| | | | - Mahdjoub Hamdi
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Hongyu An
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - John J Sunderland
- Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA
| | - Richard Laforest
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Paul E Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98195 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
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Bourgeat P, Doré V, Burnham SC, Benzinger T, Tosun D, Li S, Goyal M, LaMontagne P, Jin L, Rowe CC, Weiner MW, Morris JC, Masters CL, Fripp J, Villemagne VL. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3. Neuroimage 2022; 262:119527. [PMID: 35917917 PMCID: PMC9550562 DOI: 10.1016/j.neuroimage.2022.119527] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/11/2022] [Accepted: 07/28/2022] [Indexed: 10/31/2022] Open
Abstract
INTRODUCTION The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. METHODS All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. RESULTS The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. CONCLUSIONS FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.
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Affiliation(s)
| | - Vincent Doré
- CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
| | | | | | - Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Shenpeng Li
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Manu Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, USA
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López-González FJ, Costoya-Sánchez A, Paredes-Pacheco J, Moscoso A, Silva-Rodríguez J, Aguiar P. Impact of spill-in counts from off-target regions on [ 18F]Flortaucipir PET quantification. Neuroimage 2022; 259:119396. [PMID: 35753593 DOI: 10.1016/j.neuroimage.2022.119396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/23/2022] [Accepted: 06/15/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND [18F]Flortaucipir (FTP) PET quantification is usually hindered by spill-in counts from off-target binding (OFF) regions. The present work aims to provide an in-depth analysis of the impact of OFF in FTP PET quantification, as well as to identify optimal partial volume correction (PVC) strategies to minimize this problem. METHODS 309 amyloid-beta (Aβ) negative cognitively normal subjects were included in the study. Additionally, 510 realistic FTP images with different levels of OFF were generated using Monte Carlo simulation (MC). Images were corrected for PVC using both a simple two-compartment and a multi-region method including OFF regions. FTP standardized uptake value ratio (SUVR) was quantified in Braak Areas (BA), the hippocampus (which was not included in Braak I/II) and different OFF regions (caudate, putamen, pallidum, thalamus, choroid plexus (ChPlex), cerebellar white matter (cerebWM), hemispheric white matter (hemisWM) and cerebrospinal fluid (CSF)) using the lower portion of the cerebellum as a reference region. The correlations between OFF and cortical SUVRs were studied both in real and in simulated PET images, with and without PVC. RESULTS In-vivo, we found correlations between all OFF and target regions, especially strong for the hemisWM (slope>0.63, R2>0.4). All the correlations were attenuated but remained significant after applying PVC, except for the ChPlex. In MC simulations, the hemisWM and CSF were the main contributors to PVE in all BA (slopes 0.15-0.26 and 0.13-0.21 respectively). The hemisWM (slope=0.2), as well as the ChPlex (slope=0.02), influenced SUVRs in the hippocampus. The CerebWM was negatively correlated with all target regions (slope<-0.02, R2>0.8). While no other correlations between OFF and target regions were found, hemisWM was correlated with all OFF regions but the cerebWM (slopes 0.06-0.33). HemisWM correlations attenuated (slopes<0.06) when applying two-compartment PVC, but the hippocampus-ChPlex and the cerebWM correlations required more complex PVC with dedicated compartments for these regions. In-vivo, PVC removed a notably higher fraction of the correlation between OFF regions found to be affected by PVE in the simulation studies and BA (≈50%) than for OFF regions not affected by PVE (16%). CONCLUSION HemisWM is the main driver of spill-in effects in FTP PET, affecting both target regions and the rest of OFF regions. PVC successfully reduces PVE, even when using a simple two-compartment method. Despite PVC, non-zero correlations were still observed between target and OFF regions in vivo, which suggests the existence of biological or tracer-related contributions to these correlations.
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Affiliation(s)
- Francisco J López-González
- Molecular Imaging Group, Department of Radiology, Faculty of Medicine and Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela (USC), Campus Vida, Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit (UIM), Centro de Investigaciones Médico-Sanitarias (CIMES), General Foundation of the University of Málaga (Fguma), Málaga, Spain
| | - Alejandro Costoya-Sánchez
- Molecular Imaging Group, Department of Radiology, Faculty of Medicine and Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela (USC), Campus Vida, Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department and Molecular Imaging Group, University Hospital CHUS-IDIS, Travesía da Choupana s/n, Santiago de Compostela, 15706, Spain
| | - José Paredes-Pacheco
- Molecular Imaging Group, Department of Radiology, Faculty of Medicine and Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela (USC), Campus Vida, Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit (UIM), Centro de Investigaciones Médico-Sanitarias (CIMES), General Foundation of the University of Málaga (Fguma), Málaga, Spain
| | - Alexis Moscoso
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, and The Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department and Molecular Imaging Group, University Hospital CHUS-IDIS, Travesía da Choupana s/n, Santiago de Compostela, 15706, Spain; Movement Disorders Unit, Clinical Neurology and Neurophysiology Department, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain.
| | - Pablo Aguiar
- Molecular Imaging Group, Department of Radiology, Faculty of Medicine and Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela (USC), Campus Vida, Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department and Molecular Imaging Group, University Hospital CHUS-IDIS, Travesía da Choupana s/n, Santiago de Compostela, 15706, Spain
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Pain CD, Egan GF, Chen Z. Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement. Eur J Nucl Med Mol Imaging 2022; 49:3098-3118. [PMID: 35312031 PMCID: PMC9250483 DOI: 10.1007/s00259-022-05746-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/25/2022] [Indexed: 12/21/2022]
Abstract
Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.
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Affiliation(s)
- Cameron Dennis Pain
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia.
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Department of Data Science and AI, Monash University, Melbourne, Australia
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Peter J. Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences. Philos Trans A Math Phys Eng Sci 2021; 379:20200190. [PMID: 34218676 DOI: 10.1098/rsta.2020.0190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/11/2021] [Indexed: 06/13/2023]
Abstract
A software-based workflow is proposed for managing the execution of simulation and image reconstruction for SPECT, PET, CBCT, MRI, BLI and FMI packages in single and multimodal biomedical imaging applications. The workflow is composed of a Bash script, the purpose of which is to provide an interface to the user, and to organize data flow between dedicated programs for simulation and reconstruction. The currently incorporated simulation programs comprise GATE for Monte Carlo simulation of SPECT, PET and CBCT, SpinScenario for simulating MRI, and Lipros for Monte Carlo simulation of BLI and FMI. Currently incorporated image reconstruction programs include CASToR for SPECT and PET as well as RTK for CBCT. MetaImage (mhd) standard is used for voxelized phantom and image data format. Meshlab project (mlp) containers incorporating polygon meshes and point clouds defined by the Stanford triangle format (ply) are employed to represent anatomical structures for optical simulation, and to represent tumour cell inserts. A number of auxiliary programs have been developed for data transformation and adaptive parameter assignment. The software workflow uses fully automatic distribution to, and consolidation from, any number of Linux workstations and CPU cores. Example data are presented for clinical SPECT, PET and MRI systems using the Mida head phantom and for preclinical X-ray, PET and BLI systems employing the Digimouse phantom. The presented method unifies and simplifies multimodal simulation setup and image reconstruction management and might be of value for synergistic image research. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Jörg Peter
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Im Neuenheimer Feld, 280, 69120 Heidelberg, Germany
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