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Zhao X, Jakobsson V, Tao Y, Zhao T, Wang J, Khong PL, Chen X, Zhang J. Targeted Radionuclide Therapy in Glioblastoma. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 39042829 DOI: 10.1021/acsami.4c07850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Despite the development of various novel therapies, glioblastoma (GBM) remains a devastating disease, with a median survival of less than 15 months. Recently, targeted radionuclide therapy has shown significant progress in treating solid tumors, with the approval of Lutathera for neuroendocrine tumors and Pluvicto for prostate cancer by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This achievement has shed light on the potential of targeted radionuclide therapy for other solid tumors, including GBM. This review presents the current status of targeted radionuclide therapy in GBM, highlighting the commonly used therapeutic radionuclides emitting alpha, beta particles, and Auger electrons that could induce potent molecular and cellular damage to treat GBM. We then explore a range of targeting vectors, including small molecules, peptides, and antibodies, which selectively target antigen-expressing tumor cells with minimal or no binding to healthy tissues. Considering that radiopharmaceuticals for GBM are often administered locoregionally to bypass the blood-brain barrier (BBB), we review prominent delivery methods such as convection-enhanced delivery, local implantation, and stereotactic injections. Finally, we address the challenges of this therapeutic approach for GBM and propose potential solutions.
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Affiliation(s)
- Xiaobin Zhao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Vivianne Jakobsson
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Yucen Tao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Tianzhi Zhao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Jingyan Wang
- Xiamen University, School of Public Health, Xiang'an South Road, Xiamen 361102, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Xiaoyuan Chen
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Departments of Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Jingjing Zhang
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
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Moraitis A, Küper A, Tran-Gia J, Eberlein U, Chen Y, Seifert R, Shi K, Kim M, Herrmann K, Fragoso Costa P, Kersting D. Future Perspectives of Artificial Intelligence in Bone Marrow Dosimetry and Individualized Radioligand Therapy. Semin Nucl Med 2024; 54:460-469. [PMID: 39013673 DOI: 10.1053/j.semnuclmed.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
Radioligand therapy is an emerging and effective treatment option for various types of malignancies, but may be intricately linked to hematological side effects such as anemia, lymphopenia or thrombocytopenia. The safety and efficacy of novel theranostic agents, targeting increasingly complex targets, can be well served by comprehensive dosimetry. However, optimization in patient management and patient selection based on risk-factors predicting adverse events and built upon reliable dose-response relations is still an open demand. In this context, artificial intelligence methods, especially machine learning and deep learning algorithms, may play a crucial role. This review provides an overview of upcoming opportunities for integrating artificial intelligence methods into the field of dosimetry in nuclear medicine by improving bone marrow and blood dosimetry accuracy, enabling early identification of potential hematological risk-factors, and allowing for adaptive treatment planning. It will further exemplify inspirational success stories from neighboring disciplines that may be translated to nuclear medicine practices, and will provide conceptual suggestions for future directions. In the future, we expect artificial intelligence-assisted (predictive) dosimetry combined with clinical parameters to pave the way towards truly personalized theranostics in radioligand therapy.
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Affiliation(s)
- Alexandros Moraitis
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| | - Alina Küper
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Uta Eberlein
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Yizhou Chen
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Robert Seifert
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Moon Kim
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pedro Fragoso Costa
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - David Kersting
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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Wang Y, Liu Y, Bai Y, Zhou Q, Xu S, Pang X. A generalization performance study on the boosting radiotherapy dose calculation engine based on super-resolution. Z Med Phys 2024; 34:208-217. [PMID: 36631314 DOI: 10.1016/j.zemedi.2022.10.006] [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: 12/24/2021] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution-based deep learning model for fast and accurate dose prediction in clinical practice. METHOD A Multi-stage Dose Super-Resolution Network (MDSR Net) architecture with sparse masks module and multi-stage progressive dose distribution restoration method were developed to predict high-resolution dose distribution using low-resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U-Net and cubic interpolation methods using Dose-volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. RESULTS The prediction errors of MDSR were 0.06-0.84% of Dmean indices, and the gamma passing rate was 83.1-91.0% on the benchmark testing dataset, and 0.02-1.03% and 71.3-90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U-Net and interpolation methods (p < 0.05). The mean errors of the MDSR model decreased (monotonously by 0.03-0.004%) with dose and increased (by 0.01-0.73%) with the dose gradient. There was no correlation between prediction errors and the CT values. CONCLUSION The proposed MDSR model achieved good agreement with the baseline high-resolution dose distribution, with small prediction errors for DVH indices and high gamma passing rate for both seen and unseen sites, indicating a robust and generalizable dose prediction model. The model can provide fast and accurate high-resolution dose distribution for clinical dose calculation, particularly for the routine practice of OLART.
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Affiliation(s)
- Yewei Wang
- Department of Radiation Physics, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China
| | - Yaoying Liu
- School of Physics, Beihang University, Beijing, China; Manteia Technologies Co, Ltd, Xiamen, Fujian, China; Department of Radiation Oncology, PLA General Hospital, Beijing, China
| | - Yanlin Bai
- Department of Radiation Physics, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China
| | - Qichao Zhou
- Manteia Technologies Co, Ltd, Xiamen, Fujian, China
| | - Shouping Xu
- Department of Radiation Oncology, PLA General Hospital, Beijing, China
| | - Xueying Pang
- Department of Oncology, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China.
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Mansouri Z, Salimi Y, Akhavanallaf A, Shiri I, Teixeira EPA, Hou X, Beauregard JM, Rahmim A, Zaidi H. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [ 177Lu]Lu-DOTATATE radiopharmaceutical therapy. Eur J Nucl Med Mol Imaging 2024; 51:1516-1529. [PMID: 38267686 PMCID: PMC11043201 DOI: 10.1007/s00259-024-06618-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Eliluane Pirazzo Andrade Teixeira
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Xinchi Hou
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Mathieu Beauregard
- Cancer Research Centre and Department of Radiology and Nuclear Medicine, Université Laval, Quebec City, QC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
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Tzanis E, Stratakis J, Myronakis M, Damilakis J. A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT. Phys Med 2024; 117:103195. [PMID: 38048731 DOI: 10.1016/j.ejmp.2023.103195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023] Open
Abstract
PURPOSE To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Stratakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - Marios Myronakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece.
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Jia Y, Li Z, Akhavanallaf A, Fessler JA, Dewaraja YK. 90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework. EJNMMI Phys 2023; 10:82. [PMID: 38091168 PMCID: PMC10719178 DOI: 10.1186/s40658-023-00598-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
PURPOSE 90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy-efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods. METHODS Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT. RESULTS When testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100). CONCLUSION Our deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current "gold-standard" MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-RE.
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Affiliation(s)
- Yixuan Jia
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA.
| | - Zongyu Li
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA
| | | | - Jeffrey A Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
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Brosch-Lenz JF, Delker A, Schmidt F, Tran-Gia J. On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies. Nuklearmedizin 2023; 62:379-388. [PMID: 37827503 DOI: 10.1055/a-2179-6872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.
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Affiliation(s)
| | - Astrid Delker
- Department of Nuclear Medicine, LMU University Hospital, Munich, Germany
| | - Fabian Schmidt
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tuebingen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany
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Myronakis M, Stratakis J, Damilakis J. Rapid estimation of patient-specific organ doses using a deep learning network. Med Phys 2023; 50:7236-7244. [PMID: 36918360 DOI: 10.1002/mp.16356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/23/2023] [Accepted: 02/26/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on time-consuming Monte Carlo methods or/and generalized anthropomorphic phantoms. PURPOSE We proposed a proof-of-concept rapid workflow based on deep learning networks to estimate organ doses for individuals following thorax Computed Tomography (CT) examinations. METHODS CT scan data from 95 individuals undergoing thorax CT examinations were used. Monte Carlo simulations were performed and three-dimensional (3D) dose distributions for each patient were obtained. A fully connected sequential deep learning network model was constructed and trained for each organ considered in this study. Water-equivalent diameter (WED), scan length, and tube current were the independent variables. Organ doses for heart, lungs, esophagus, and bones were calculated from the Monte Carlo 3D distribution and used to train the deep learning networks. Organ dose predictions from each network were evaluated using an independent data set of 19 patients. RESULTS The trained networks provided organ dose predictions within a second. There was very good agreement between the deep learning network predictions and reference organ dose values calculated from Monte Carlo simulations. The average difference was -1.5% for heart, -1.6% for esophagus, -1.0% for lungs, and -0.4% for bones in the 95 patients dataset, and -5.1%, 4.3%, 0.9%, and 1.4% respectively in the 19 patients test dataset. CONCLUSIONS The proposed workflow demonstrated that patient-specific organ-doses can be estimated in nearly real-time using deep learning networks. The workflow can be readily implemented and requires a small set of representative data for training.
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Affiliation(s)
- Marios Myronakis
- Department of Medical Physics, School of Medicine, University of Crete, Iraklion, Greece
| | - John Stratakis
- Department of Medical Physics, School of Medicine, University of Crete, Iraklion, Greece
- Medical Physics Department, University Hospital of Crete, Iraklion, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, Iraklion, Greece
- Medical Physics Department, University Hospital of Crete, Iraklion, Greece
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Gustafsson J, Taprogge J. Future trends for patient-specific dosimetry methodology in molecular radiotherapy. Phys Med 2023; 115:103165. [PMID: 37880071 DOI: 10.1016/j.ejmp.2023.103165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/03/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023] Open
Abstract
Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.
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Affiliation(s)
| | - Jan Taprogge
- National Radiotherapy Trials Quality Assurance (RTTQA) Group, Joint Department of Physics, Royal Marsden NHSFT, Downs Road, Sutton SM2 5PT, United Kingdom; The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, United Kingdom
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11
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Bahonar BM, Changizi V, Ebrahiminia A, Baradaran S. Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS). Phys Eng Sci Med 2023; 46:1071-1080. [PMID: 37245194 PMCID: PMC10225119 DOI: 10.1007/s13246-023-01276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 05/05/2023] [Indexed: 05/29/2023]
Abstract
In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46 mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.
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Affiliation(s)
- Bahareh Moradmand Bahonar
- Department of Radiology and Radiotherapy Technology, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Changizi
- Department of Radiology and Radiotherapy Technology, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ali Ebrahiminia
- Department of Medical Physics, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
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12
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Scarinci I, Valente M, Pérez P. A machine learning-based model for a dose point kernel calculation. EJNMMI Phys 2023; 10:41. [PMID: 37358735 DOI: 10.1186/s40658-023-00560-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/13/2023] [Indexed: 06/27/2023] Open
Abstract
PURPOSE Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. METHODS DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with [Formula: see text]Y. RESULTS The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than [Formula: see text] in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than [Formula: see text] were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. CONCLUSION An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.
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Affiliation(s)
- Ignacio Scarinci
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina
| | - Mauro Valente
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina.
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina.
- Centro de Excelencia en Física e Ingeniería en Salud (CFIS) & Departamento de Ciencias Físicas, Universidad de la Frontera, Avenida Francisco Salazar 01145, 4811230, Temuco, Cautín, Chile.
| | - Pedro Pérez
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina
- Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n, 5000, Córdoba, Argentina
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13
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Currie GM, Rohren EM. Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks. Semin Nucl Med 2023; 53:457-466. [PMID: 36379728 DOI: 10.1053/j.semnuclmed.2022.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/14/2022]
Abstract
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment with scope to adjust dosing for optimal target and non-target tissue doses is consistent with striving for improved the outcomes of precision medicine. The combination of artificial intelligence and production of digital twins could provide an avenue for an individualised therapy doses and enhanced outcomes in theranostics. While there are barriers to overcome, the maturity of individual technologies (i.e. radiation dosimetry, artificial intelligence, theranostics and digital twins) places these approaches within reach.
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Affiliation(s)
- Geoffrey M Currie
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX.
| | - Eric M Rohren
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX
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14
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Kim KM, Lee MS, Suh MS, Cheon GJ, Lee JS. Voxel-Based Internal Dosimetry for 177Lu-Labeled Radiopharmaceutical Therapy Using Deep Residual Learning. Nucl Med Mol Imaging 2023; 57:94-102. [PMID: 36998593 PMCID: PMC10043146 DOI: 10.1007/s13139-022-00769-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/28/2022] [Accepted: 08/05/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose In this study, we propose a deep learning (DL)-based voxel-based dosimetry method in which dose maps acquired using the multiple voxel S-value (VSV) approach were used for residual learning. Methods Twenty-two SPECT/CT datasets from seven patients who underwent 177Lu-DOTATATE treatment were used in this study. The dose maps generated from Monte Carlo (MC) simulations were used as the reference approach and target images for network training. The multiple VSV approach was used for residual learning and compared with dose maps generated from deep learning. The conventional 3D U-Net network was modified for residual learning. The absorbed doses in the organs were calculated as the mass-weighted average of the volume of interest (VOI). Results The DL approach provided a slightly more accurate estimation than the multiple-VSV approach, but the results were not statistically significant. The single-VSV approach yielded a relatively inaccurate estimation. No significant difference was noted between the multiple VSV and DL approach on the dose maps. However, this difference was prominent in the error maps. The multiple VSV and DL approach showed a similar correlation. In contrast, the multiple VSV approach underestimated doses in the low-dose range, but it accounted for the underestimation when the DL approach was applied. Conclusion Dose estimation using the deep learning-based approach was approximately equal to that in the MC simulation. Accordingly, the proposed deep learning network is useful for accurate and fast dosimetry after radiation therapy using 177Lu-labeled radiopharmaceuticals.
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Affiliation(s)
- Keon Min Kim
- Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, 03080 South Korea
- Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, 03080 South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 South Korea
| | - Min Sun Lee
- Environmental Radioactivity Assessment Team, Nuclear Emergency & Environmental Protection Division, Korea Atomic Energy Research Institute, Daejeon, 34057 Korea
| | - Min Seok Suh
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, 03080 South Korea
| | - Gi Jeong Cheon
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, 03080 South Korea
| | - Jae Sung Lee
- Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, 03080 South Korea
- Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, 03080 South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, 03080 South Korea
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15
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Pogue JA, Cardenas CE, Cao Y, Popple RA, Soike M, Boggs DH, Stanley DN, Harms J. Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning. Front Oncol 2023; 13:1130119. [PMID: 36845685 PMCID: PMC9950631 DOI: 10.3389/fonc.2023.1130119] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Background Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart. Methods Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked via adherence to planning objectives, a comparison of DVH and quality indices against the clinical Edge plans, and qualitative reviews by two board-certified radiation oncologists. Results 85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage (p = 0.01), significantly decreased heart V1.5Gy (p< 0.001), and increased contralateral breast V5Gy, skin D0.01cc, and RTOG conformity index (p = 0.03, p = 0.03, and p = 0.01, respectively). However, only the reduction in heart dose was significant after correcting for multiple testing. Physicist-selected plans were deemed clinically acceptable without modification for 75% and 90% of plans by physicians A and B, respectively. Physicians A and B scored at least one automatically generated plan as clinically acceptable for 100% and 95% of planning intents, respectively. Conclusions Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.
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Affiliation(s)
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Yanan Cao
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Richard A. Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Michael Soike
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Drexell Hunter Boggs
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dennis N. Stanley
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Joseph Harms
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
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16
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Scarinci I, Valente M, Pérez P. A Machine Learning based model for a Dose Point Kernel calculation. RESEARCH SQUARE 2023:rs.3.rs-2419706. [PMID: 36711517 PMCID: PMC9882689 DOI: 10.21203/rs.3.rs-2419706/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
PURPOSE Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study shows applications of machine learning to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. METHODS DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Three machine learning (ML) algorithms were trained using the MC DPKs. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the ML sDPK approach was applied to a patient-specific case calculating the dose voxel kernels (DVK) for a hepatic radioembolization treatment with \(^{90}\)Y. RESULTS The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than \(10%\) in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than \(7 %\) were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. CONCLUSION An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required remarkable short computation times.
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Affiliation(s)
- Ignacio Scarinci
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina
| | - Mauro Valente
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina.,Centro de Excelencia en Física e Ingeniería en Salud (CFIS) & Departamento de Ciencias Físicas, Universidad de la Frontera, Avenida Francisco Salazar 01145, Temuco, 4811230, Cautín, Chile.,Corresponding author(s).
| | - Pedro Pérez
- Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.,Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina
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17
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Gupta RV, Kalra MK, Ebrahimian S, Kaviani P, Primak A, Bizzo B, Dreyer KJ. Complex Relationship Between Artificial Intelligence and CT Radiation Dose. Acad Radiol 2022; 29:1709-1719. [PMID: 34836775 DOI: 10.1016/j.acra.2021.10.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/15/2021] [Accepted: 10/17/2021] [Indexed: 12/22/2022]
Abstract
Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.
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Affiliation(s)
- Reya V Gupta
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Parisa Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Andrew Primak
- Siemens Medical Solutions USA Inc, Malvern, Pennsylvania
| | - Bernardo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| | - Keith J Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
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18
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Sarrut D, Arbor N, Baudier T, Borys D, Etxebeste A, Fuchs H, Gajewski J, Grevillot L, Jan S, Kagadis GC, Kang HG, Kirov A, Kochebina O, Krzemien W, Lomax A, Papadimitroulas P, Pommranz C, Roncali E, Rucinski A, Winterhalter C, Maigne L. The OpenGATE ecosystem for Monte Carlo simulation in medical physics. Phys Med Biol 2022; 67:10.1088/1361-6560/ac8c83. [PMID: 36001985 PMCID: PMC11149651 DOI: 10.1088/1361-6560/ac8c83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022]
Abstract
This paper reviews the ecosystem of GATE, an open-source Monte Carlo toolkit for medical physics. Based on the shoulders of Geant4, the principal modules (geometry, physics, scorers) are described with brief descriptions of some key concepts (Volume, Actors, Digitizer). The main source code repositories are detailed together with the automated compilation and tests processes (Continuous Integration). We then described how the OpenGATE collaboration managed the collaborative development of about one hundred developers during almost 20 years. The impact of GATE on medical physics and cancer research is then summarized, and examples of a few key applications are given. Finally, future development perspectives are indicated.
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Affiliation(s)
- David Sarrut
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Nicolas Arbor
- Université de Strasbourg, IPHC, CNRS, UMR7178, F-67037 Strasbourg, France
| | - Thomas Baudier
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Damian Borys
- Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Ane Etxebeste
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Hermann Fuchs
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Vienna, Währinger Gürtel 18-20, A-1090 Wien, Austria
| | - Jan Gajewski
- Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland
| | | | - Sébastien Jan
- Université Paris-Saclay, Inserm, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), F-91401 Orsay, France
| | - George C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
| | - Han Gyu Kang
- National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Assen Kirov
- Memorial Sloan Kettering Cancer, New York, NY 10021, United States of America
| | - Olga Kochebina
- Université Paris-Saclay, Inserm, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), F-91401 Orsay, France
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Lojasiewicza 11, 30-348 Krakow, Poland
- Centre for Theranostics, Jagiellonian University, Kopernika 40 St, 31 501 Krakow, Poland
| | - Antony Lomax
- Center for Proton Therapy, PSI, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | | | - Christian Pommranz
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany
- Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - Emilie Roncali
- University of California Davis, Departments of Biomedical Engineering and Radiology, Davis, CA 95616, United States of America
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland
| | - Carla Winterhalter
- Center for Proton Therapy, PSI, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - Lydia Maigne
- Université Clermont Auvergne, Laboratoire de Physique de Clermont, CNRS, UMR 6533, F-63178 Aubière, France
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Organ-based estimation and minimization of clinician's X-ray dose. Int J Comput Assist Radiol Surg 2022; 17:2357-2364. [PMID: 35877018 DOI: 10.1007/s11548-022-02710-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/24/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Hybrid surgeries, allowing real-time visualization of patient inner anatomy, are possible through the use of intraoperative X-ray imaging. However, the intensive use of X-rays can have undesired consequences for the clinicians or the patient in the operating room (OR). METHODS In this paper, we provide a tool to visualize the X-rays and to optimally place protective shields in the hybrid operating room to reduce the clinician's dose according to their most sensitive body parts. We first acquire measurements in a hybrid operating room with dosimeters placed at different locations on a mannequin simulating a clinician. We demonstrate that a small displacement of a protective shield has significant consequences on the dose received by a clinician. Then, we reproduce the scene virtually and use Monte Carlo simulations to estimate the dose received by the clinician. Finally, we optimally place protective shields with a Nelder-Mead-based numerical optimization algorithm. RESULTS The results show a high sensitivity of the clinician's dose to protective shield placement. Numerical optimization of the shields' placement can help to reduce the dose and show a decrease between 79 and 89% of the exposition when comparing no external shield protection and our optimal external shield position. CONCLUSION Our work can help to raise awareness of the risks induced by X-rays during intraoperative surgery and reduce the dose received by the clinicians. In future work, our approach can be linked with human pose estimation algorithms to trace surgeons' moves, estimate dynamically the dose and summarize it in a surgical report, giving the dose for important organs.
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20
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Tzanis E, Damilakis J. A novel methodology to train and deploy a machine learning model for personalized dose assessment in head CT. Eur Radiol 2022; 32:6418-6426. [PMID: 35384458 DOI: 10.1007/s00330-022-08756-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/28/2022] [Accepted: 03/19/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To propose a machine learning-based methodology for the creation of radiation dose maps and the prediction of patient-specific organ/tissue doses associated with head CT examinations. METHODS CT data were collected retrospectively for 343 patients who underwent standard head CT examinations. Patient-specific Monte Carlo (MC) simulations were performed to determine the radiation dose distribution to patients' organs/tissues. The collected CT images and the MC-produced dose maps were processed and used for the training of the deep neural network (DNN) model. For the training and validation processes, data from 231 and 112 head CT examinations, respectively, were used. Furthermore, a software tool was developed to produce dose maps from head CT images using the trained DNN model and to automatically calculate the dose to the brain and cranial bones. RESULTS The mean (range) percentage differences between the doses predicted from the DNN model and those provided by MC simulations for the brain, eye lenses, and cranial bones were 4.5% (0-17.7%), 5.7% (0.2-19.0%), and 5.2% (0.1-18.9%), respectively. The graphical user interface of the software offers a user-friendly way for radiation dose/risk assessment. The implementation of the DNN allowed for a 97% reduction in the computational time needed for the dose estimations. CONCLUSIONS A novel methodology that allows users to develop a DNN model for patient-specific CT dose prediction was developed and implemented. The approach demonstrated herein allows accurate and fast radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be used in everyday clinical practice. KEY POINTS • The methodology presented herein allows fast and accurate radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be implemented in everyday clinical practice. • The scripts developed in the current study will allow users to train models for the acquisition protocols of their CT scanners, generate dose maps, estimate the doses to the brain and cranial bones, and estimate the lifetime attributable risk of radiation-induced brain cancer.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, 71003, Heraklion, Crete, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, 71003, Heraklion, Crete, Greece.
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21
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Alfuraih AA. Simulation of Gamma-Ray Transmission Buildup Factors for Stratified Spherical Layers. Dose Response 2022; 20:15593258211068625. [PMID: 35197813 PMCID: PMC8859677 DOI: 10.1177/15593258211070911] [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] [Indexed: 11/18/2022]
Abstract
Deterministic particle transport codes usually take into account scattered photons with correct attenuation laws and application of buildup factor to incident beam. Transmission buildup factors for adipose, bone, muscle, and skin human tissues, as well as for various combinations of these media for point isotropic photon source with energies of .15, 1.5 and 15 MeV, for different thickness of layers, were carried out using Geant4 (version 10.5) simulation toolkit. Also, we performed the analysis of existing multilayered shield fitting models (Lin and Jiang, Kalos, Burke and Beck) of buildup factor and the proposition of a new model. We found that the model combining those of Burke and Beck, for low atomic number (Z) followed by high Z materials and Kalos 1 for high Z followed by low Z materials, accurately reproduces simulation results with approximated deviation of 3 ± 3%, 2 ± 2%, and 3 ± 2% for 2, 3, and 4 layers, respectively. Since buildup factors are the key parameter for point kernel calculations, a correct study can be of great interest to the large community of radiation physicists, in general, and to medical imaging and radiotreatment physicists, especially.
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Affiliation(s)
- Abdulrahman A. Alfuraih
- Department of Radiological Sciences, College of Applied Medical Science, King Saud University, Riyadh, Saudi Arabia
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22
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Danieli R, Milano A, Gallo S, Veronese I, Lascialfari A, Indovina L, Botta F, Ferrari M, Cicchetti A, Raspanti D, Cremonesi M. Personalized Dosimetry in Targeted Radiation Therapy: A Look to Methods, Tools and Critical Aspects. J Pers Med 2022; 12:205. [PMID: 35207693 PMCID: PMC8874397 DOI: 10.3390/jpm12020205] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 12/10/2022] Open
Abstract
Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in external beam radiation therapy (EBRT). The purpose of this paper is to provide a thorough introduction to the field of personalized dosimetry in TRT, explaining its rationale in the context of optimization and describing the currently available methodologies. After listing the main therapies currently employed, the clinical workflow for the absorbed dose calculation is described, based on works of the most experienced authors in the literature and recent guidelines. Moreover, the widespread software packages for internal dosimetry are presented and critical aspects discussed. Overall, a selection of the most important and recent articles about this topic is provided.
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Affiliation(s)
- Rachele Danieli
- Dipartimento di Fisica, Università degli Studi di Pavia, Via Bassi 6, 27100 Pavia, Italy;
| | - Alessia Milano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168 Roma, Italy;
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italy
| | - Salvatore Gallo
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy; (S.G.); (I.V.)
- INFN Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
| | - Ivan Veronese
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy; (S.G.); (I.V.)
- INFN Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
| | - Alessandro Lascialfari
- INFN-Pavia Unit, Department of Physics, University of Pavia, Via Bassi 6, 27100 Pavia, Italy;
| | - Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168 Roma, Italy;
| | - Francesca Botta
- Medical Physics Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy; (F.B.); (M.F.)
| | - Mahila Ferrari
- Medical Physics Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy; (F.B.); (M.F.)
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy;
| | - Davide Raspanti
- Temasinergie S.p.A., Via Marcello Malpighi 120, 48018 Faenza, Italy;
| | - Marta Cremonesi
- Radiation Research Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy;
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23
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Kennedy J, Chicheportiche A, Keidar Z. Quantitative SPECT/CT for dosimetry of peptide receptor radionuclide therapy. Semin Nucl Med 2021; 52:229-242. [PMID: 34911637 DOI: 10.1053/j.semnuclmed.2021.11.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Neuroendocrine tumors (NETs) are uncommon malignancies of increasing incidence and prevalence. As these slow growing tumors usually overexpress somatostatin receptors (SSTRs), the use of 68Ga-DOTA-peptides (gallium-68 chelated with dodecane tetra-acetic acid to somatostatin), which bind to the SSTRs, allows for PET based imaging and selection of patients for peptide receptor radionuclide therapy (PRRT). PRRT with radiolabeled somatostatin analogues such as 177Lu-DOTATATE (lutetium-177-[DOTA,Tyr3]-octreotate), is mainly used for the treatment of metastatic or inoperable NETs. However, PRRT is generally administered at a fixed injected activity in order not to exceed dose limits in critical organs, which is suboptimal given the variability in radiopharmaceutical uptake among patients. Advances in SPECT (single photon emission computed tomography) imaging enable the absolute quantitative measure of the true radiopharmaceutical distribution providing for PRRT dosimetry in each patient. Personalized PRRT based on patient-specific dosimetry could improve therapeutic efficacy by optimizing effective tumor absorbed dose while limiting treatment related radiotoxicity.
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Affiliation(s)
- John Kennedy
- Department of Nuclear Medicine, Rambam Health Care Campus, Haifa, Israel; B. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
| | - Alexandre Chicheportiche
- Department of Nuclear Medicine and Biophysics, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Zohar Keidar
- Department of Nuclear Medicine, Rambam Health Care Campus, Haifa, Israel; B. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
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24
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Li Z, Fessler JA, Mikell JK, Wilderman SJ, Dewaraja YK. DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single-photon emission computerized tomography imaging resolution. Med Phys 2021; 49:1216-1230. [PMID: 34882821 PMCID: PMC10041998 DOI: 10.1002/mp.15397] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 11/18/2021] [Accepted: 11/18/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be suboptimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images. METHODS We trained our CNN using MC-generated dose-rate maps that directly corresponded to the true activity maps in virtual patient phantoms. Residual learning was applied such that our CNN learned only the difference between the true dose-rate map and DVK dose-rate map with density scaling. Our CNN consists of a 3D depth feature extractor followed by a 2D U-Net, where the input was 11 slices (3.3 cm) of a given Lu-177 SPECT/CT image and density map, and the output was the dose-rate map corresponding to the center slice. The CNN was trained with nine virtual patient phantoms and tested on five different phantoms plus 42 SPECT/CT scans of patients who underwent Lu-177 DOTATATE therapy. RESULTS When testing on virtual patient phantoms, the lesion/organ mean dose-rate error and the normalized root mean square error (NRMSE) relative to the ground truth of the CNN method was consistently lower than DVK and MC, when applied to SPECT images. Compared to DVK/MC, the average improvement for the CNN in mean dose-rate error was 55%/53% and 66%/56%; and in NRMSE was 18%/17% and 10%/11% for lesion and kidney regions, respectively. Line profiles and dose-volume histograms demonstrated compensation for SPECT resolution effects in the CNN-generated dose-rate maps. The ensemble noise standard deviation, determined from multiple Poisson realizations, was improved by 21%/27% compared to DVK/MC. In patients, potential improvements from CNN dose-rate maps compared to DVK/MC were illustrated qualitatively, due to the absence of ground truth. The trained residual CNN took about 30 s on a single GPU (Tesla V100) to generate a 512 × 512 × 130 dose-rate map for a patient. CONCLUSION The proposed residual CNN, trained using phantoms generated from patient images, has potential for real-time patient-specific dosimetry in clinical treatment planning due to its demonstrated improvement in accuracy, resolution, noise, and speed over the DVK/MC approaches.
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Affiliation(s)
- Zongyu Li
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeffrey A Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
| | - Justin K Mikell
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Scott J Wilderman
- Department of Nuclear Engineering and Radiologic Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
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25
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Lassmann M, Eberlein U, Gear J, Konijnenberg M, Kunikowska J. Dosimetry for Radiopharmaceutical Therapy: The European Perspective. J Nucl Med 2021; 62:73S-79S. [PMID: 34857624 DOI: 10.2967/jnumed.121.262754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/13/2021] [Indexed: 11/16/2022] Open
Abstract
This review presents efforts in Europe over the last few years with respect to standardization of quantitative imaging and dosimetry and comprises the results of several European research projects on practices regarding radiopharmaceutical therapies (RPTs). Because the European Union has regulatory requirements concerning dosimetry in RPTs, the European Association of Nuclear Medicine released a position paper in 2021 on the use of dosimetry under these requirements. The importance of radiobiology for RPTs is elucidated in another position paper by the European Association of Nuclear Medicine. Furthermore, how dosimetry interacts with clinical requirements is described, with several clinical examples. In the future, more efforts need to be undertaken to increase teaching and standardization efforts and to incorporate radiobiology for further individualizing patient treatment, with the aim of improving the outcome and safety of RPTs.
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Affiliation(s)
- Michael Lassmann
- Department of Nuclear Medicine, University of Würzburg, Würzburg, Germany
| | - Uta Eberlein
- Department of Nuclear Medicine, University of Würzburg, Würzburg, Germany;
| | - Jonathan Gear
- Joint Department of Physics, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, London, United Kingdom
| | - Mark Konijnenberg
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; and
| | - Jolanta Kunikowska
- Nuclear Medicine Department, Medical University of Warsaw, Warsaw, Poland
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26
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Brosch-Lenz J, Yousefirizi F, Zukotynski K, Beauregard JM, Gaudet V, Saboury B, Rahmim A, Uribe C. Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies. PET Clin 2021; 16:627-641. [PMID: 34537133 DOI: 10.1016/j.cpet.2021.06.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.
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Affiliation(s)
- Julia Brosch-Lenz
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada
| | - Katherine Zukotynski
- Department of Medicine and Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Jean-Mathieu Beauregard
- Department of Radiology and Nuclear Medicine, Cancer Research Centre, Université Laval, 2325 Rue de l'Université, Québec City, Quebec G1V 0A6, Canada; Department of Medical Imaging, Research Center (Oncology Axis), CHU de Québec - Université Laval, 2325 Rue de l'Université, Québec City, Quebec G1V 0A6, Canada
| | - Vincent Gaudet
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 11th Floor, 2775 Laurel St, Vancouver, British Columbia V5Z 1M9, Canada; Department of Physics, University of British Columbia, 325 - 6224 Agricultural Road, Vancouver, British Columbia V6T 1Z1, Canada
| | - Carlos Uribe
- Department of Radiology, University of British Columbia, 11th Floor, 2775 Laurel St, Vancouver, British Columbia V5Z 1M9, Canada; Department of Functional Imaging, BC Cancer, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
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27
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Douglass MJJ, Keal JA. DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing. Phys Med 2021; 89:306-316. [PMID: 34492498 DOI: 10.1016/j.ejmp.2021.08.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/03/2021] [Accepted: 08/27/2021] [Indexed: 12/23/2022] Open
Abstract
Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac. In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated 'ground-truth' masks using an optical path-tracing engine to 'fake' mega-voltage EPID images. The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm. DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.
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Affiliation(s)
- Michael John James Douglass
- School of Physical Sciences, University of Adelaide, Adelaide 5005, South Australia, Australia; Department of Medical Physics, Royal Adelaide Hospital, Adelaide 5000, South Australia, Australia.
| | - James Alan Keal
- School of Physical Sciences, University of Adelaide, Adelaide 5005, South Australia, Australia
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28
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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29
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Cheng Z, Wen J, Huang G, Yan J. Applications of artificial intelligence in nuclear medicine image generation. Quant Imaging Med Surg 2021; 11:2792-2822. [PMID: 34079744 PMCID: PMC8107336 DOI: 10.21037/qims-20-1078] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 02/14/2021] [Indexed: 12/12/2022]
Abstract
Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed.
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Affiliation(s)
- Zhibiao Cheng
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Junhai Wen
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Gang Huang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Jianhua Yan
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
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30
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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: 49] [Impact Index Per Article: 16.3] [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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Zaidi H, El Naqa I. Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques. Annu Rev Biomed Eng 2021; 23:249-276. [PMID: 33797938 DOI: 10.1146/annurev-bioeng-082420-020343] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.
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Affiliation(s)
- Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211 Geneva, Switzerland; .,Geneva Neuroscience Centre, University of Geneva, 1205 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, 9700 RB Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, DK-5000 Odense, Denmark
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida 33612, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Oncology, McGill University, Montreal, Quebec H3A 1G5, Canada
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Boursalie O, Samavi R, Doyle TE, Koff DA. Using Medical Imaging Effective Dose in Deep Learning Models: Estimation and Evaluation. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3029038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 2021; 83:122-137. [DOI: 10.1016/j.ejmp.2021.03.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/18/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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Mok GSP, Dewaraja YK. Recent advances in voxel-based targeted radionuclide therapy dosimetry. Quant Imaging Med Surg 2021; 11:483-489. [PMID: 33532249 PMCID: PMC7779928 DOI: 10.21037/qims-20-1006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/27/2020] [Indexed: 02/04/2023]
Affiliation(s)
- Greta S. P. Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Yuni K. Dewaraja
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, USA
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Whole-body voxel-based internal dosimetry using deep learning. Eur J Nucl Med Mol Imaging 2020; 48:670-682. [PMID: 32875430 PMCID: PMC8036208 DOI: 10.1007/s00259-020-05013-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 08/23/2020] [Indexed: 12/20/2022]
Abstract
Purpose In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms. Methods We extended the voxel-scale MIRD approach from single S-value kernel to specific S-value kernels corresponding to patient-specific anatomy to construct 3D dose maps using hybrid emission/transmission image sets. In this context, we employed a Deep Neural Network (DNN) to predict the distribution of deposited energy, representing specific S-values, from a single source in the center of a 3D kernel composed of human body geometry. The training dataset consists of density maps obtained from CT images and the reference voxelwise S-values generated using Monte Carlo simulations. Accordingly, specific S-value kernels are inferred from the trained model and whole-body dose maps constructed in a manner analogous to the voxel-based MIRD formalism, i.e., convolving specific voxel S-values with the activity map. The dose map predicted using the DNN was compared with the reference generated using MC simulations and two MIRD-based methods, including Single and Multiple S-Values (SSV and MSV) and Olinda/EXM software package. Results The predicted specific voxel S-value kernels exhibited good agreement with the MC-based kernels serving as reference with a mean relative absolute error (MRAE) of 4.5 ± 1.8 (%). Bland and Altman analysis showed the lowest dose bias (2.6%) and smallest variance (CI: − 6.6, + 1.3) for DNN. The MRAE of estimated absorbed dose between DNN, MSV, and SSV with respect to the MC simulation reference were 2.6%, 3%, and 49%, respectively. In organ-level dosimetry, the MRAE between the proposed method and MSV, SSV, and Olinda/EXM were 5.1%, 21.8%, and 23.5%, respectively. Conclusion The proposed DNN-based WB internal dosimetry exhibited comparable performance to the direct Monte Carlo approach while overcoming the limitations of conventional dosimetry techniques in nuclear medicine.
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