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Sun H, Huang Y, Hu D, Hong X, Salimi Y, Lv W, Chen H, Zaidi H, Wu H, Lu L. Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET. EJNMMI Phys 2024; 11:66. [PMID: 39028439 PMCID: PMC11264498 DOI: 10.1186/s40658-024-00666-8] [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: 12/26/2023] [Accepted: 07/04/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain. METHODS Clinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference. RESULTS CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results. CONCLUSIONS CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.
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Affiliation(s)
- Hao Sun
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Yanchao Huang
- Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital Southern Medical University, Guangzhou, 510515, China
| | - Debin Hu
- Department of Medical Engineering, Nanfang Hospital Southern Medical University, Guangzhou, 510515, China
| | - Xiaotong Hong
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Wenbing Lv
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, 650091, China
| | - Hongwen Chen
- Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital Southern Medical University, Guangzhou, 510515, China
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Hubing Wu
- Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital Southern Medical University, Guangzhou, 510515, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Pazhou Lab, Guangzhou, 510330, China.
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Li S, Zhu Y, Spencer BA, Wang G. Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:4075-4089. [PMID: 38941203 DOI: 10.1109/tip.2024.3418347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray CT scan. It combines the already existing X-ray CT image with a 511 keV γ -ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized framework has been developed for reconstructing gCT image but this method has not fully exploited the potential of prior knowledge. Use of deep neural networks may explore the power of deep learning in this application. However, common approaches require a large database for training, which is impractical for a new imaging method like PET-enabled DECT. Here, we propose a single-subject method by using neural-network representation as a deep coefficient prior to improving gCT image reconstruction without population-based pre-training. The resulting optimization problem becomes the tomographic estimation of nonlinear neural-network parameters from gCT projection data. This complicated problem can be efficiently solved by utilizing the optimization transfer strategy with quadratic surrogates. Each iteration of the proposed neural optimization transfer algorithm includes: PET activity image update; gCT image update; and least-square neural-network learning in the gCT image domain. This algorithm is guaranteed to monotonically increase the data likelihood. Results from computer simulation, real phantom data and real patient data have demonstrated that the proposed method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.
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Chen X, Zhao Y, Court LE, Wang H, Pan T, Phan J, Wang X, Ding Y, Yang J. SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy. Comput Med Imaging Graph 2024; 113:102353. [PMID: 38387114 DOI: 10.1016/j.compmedimag.2024.102353] [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: 08/07/2023] [Revised: 12/14/2023] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images. To enable anatomy compensation, we expand input channels of the CT generator by including a body mask and introduce a truncation loss between sCT and real CT. The body mask for each patient was automatically created from the simulation CT scans and transformed to daily MR images by rigid registration as another input for our SC-GAN in addition to the MR images. The truncation loss was constructed by implementing either an auto-segmentor or an edge detector to penalize the difference in body outlines between sCT and real CT. The experimental results show that our SC-GAN achieved much improved accuracy of sCT generation in both truncated and untruncated regions compared to the original cycleGAN and conditional GAN methods.
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Affiliation(s)
- Xinru Chen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
| | - Yao Zhao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - He Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tinsu Pan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Jack Phan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xin Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Ding
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
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uz Zaman M, Fatima N. Artificial Intelligence (AI) in Nuclear Medicine: Is a Friend Not Foe. World J Nucl Med 2024; 23:1-2. [PMID: 38595844 PMCID: PMC11001459 DOI: 10.1055/s-0043-1777698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Affiliation(s)
- Maseeh uz Zaman
- Department of Radiology, Aga Khan University Hospital, Karachi, Pakistan
| | - Nosheen Fatima
- Department of Radiology, Aga Khan University Hospital, Karachi, Pakistan
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Li X, Johnson JM, Strigel RM, Bancroft LCH, Hurley SA, Estakhraji SIZ, Kumar M, Fowler AM, McMillan AB. Attenuation correction and truncation completion for breast PET/MR imaging using deep learning. Phys Med Biol 2024; 69:045031. [PMID: 38252969 DOI: 10.1088/1361-6560/ad2126] [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: 05/22/2023] [Accepted: 01/22/2024] [Indexed: 01/24/2024]
Abstract
Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.
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Affiliation(s)
- Xue Li
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States of America
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Jacob M Johnson
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Roberta M Strigel
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Samuel A Hurley
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - S Iman Zare Estakhraji
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Manoj Kumar
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- ICTR Graduate Program in Clinical Investigation, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Amy M Fowler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
| | - Alan B McMillan
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States of America
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
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Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 2024; 92:103046. [PMID: 38052145 DOI: 10.1016/j.media.2023.103046] [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: 06/05/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Affiliation(s)
- Sanuwani Dayarathna
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
| | | | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom
| | - Munawar Hayat
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Zhaolin Chen
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia
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Izadi S, Shiri I, F Uribe C, Geramifar P, Zaidi H, Rahmim A, Hamarneh G. Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks. Z Med Phys 2024:S0939-3889(24)00002-3. [PMID: 38302292 DOI: 10.1016/j.zemedi.2024.01.002] [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: 05/11/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 02/03/2024]
Abstract
In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.
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Affiliation(s)
- Saeed Izadi
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Carlos F Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark; University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada.
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Fallahpoor M, Chakraborty S, Pradhan B, Faust O, Barua PD, Chegeni H, Acharya R. Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107880. [PMID: 37924769 DOI: 10.1016/j.cmpb.2023.107880] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.
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Affiliation(s)
- Maryam Fallahpoor
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Prabal Datta Barua
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | | | - Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
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Lee JS, Lee MS. Advancements in Positron Emission Tomography Detectors: From Silicon Photomultiplier Technology to Artificial Intelligence Applications. PET Clin 2024; 19:1-24. [PMID: 37802675 DOI: 10.1016/j.cpet.2023.06.003] [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] [Indexed: 10/08/2023]
Abstract
This review article focuses on PET detector technology, which is the most crucial factor in determining PET image quality. The article highlights the desired properties of PET detectors, including high detection efficiency, spatial resolution, energy resolution, and timing resolution. Recent advancements in PET detectors to improve these properties are also discussed, including the use of silicon photomultiplier technology, advancements in depth-of-interaction and time-of-flight PET detectors, and the use of artificial intelligence for detector development. The article provides an overview of PET detector technology and its recent advancements, which can significantly enhance PET image quality.
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Affiliation(s)
- Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul 03080, South Korea; Brightonix Imaging Inc., Seoul 04782, South Korea
| | - Min Sun Lee
- Environmental Radioactivity Assessment Team, Nuclear Emergency & Environmental Protection Division, Korea Atomic Energy Research Institute, Daejeon 34057, South Korea.
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Shiri I, Salimi Y, Maghsudi M, Jenabi E, Harsini S, Razeghi B, Mostafaei S, Hajianfar G, Sanaat A, Jafari E, Samimi R, Khateri M, Sheikhzadeh P, Geramifar P, Dadgar H, Bitrafan Rajabi A, Assadi M, Bénard F, Vafaei Sadr A, Voloshynovskiy S, Mainta I, Uribe C, Rahmim A, Zaidi H. Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement. Eur J Nucl Med Mol Imaging 2023; 51:40-53. [PMID: 37682303 PMCID: PMC10684636 DOI: 10.1007/s00259-023-06418-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
PURPOSE Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sara Harsini
- BC Cancer Research Institute, Vancouver, BC, Canada
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Esmail Jafari
- The Persian Gulf Nuclear Medicine Research Center, Department of Nuclear Medicine, Molecular Imaging, and Theranostics, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Rezvan Samimi
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Peyman Sheikhzadeh
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habibollah Dadgar
- Cancer Research Center, Razavi Hospital, Imam Reza International University, Mashhad, Iran
| | - Ahmad Bitrafan Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Department of Nuclear Medicine, Molecular Imaging, and Theranostics, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - François Bénard
- BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, 17033, USA
| | | | - Ismini Mainta
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Carlos Uribe
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neuro Center, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Shiri I, Salimi Y, Hervier E, Pezzoni A, Sanaat A, Mostafaei S, Rahmim A, Mainta I, Zaidi H. Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance. Clin Nucl Med 2023; 48:1035-1046. [PMID: 37883015 PMCID: PMC10662584 DOI: 10.1097/rlu.0000000000004912] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/22/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance. METHODS The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18 F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18 F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions. RESULTS Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUV mean , SUV max , and SUV peak , respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction. CONCLUSION We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18 F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.
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Affiliation(s)
- Isaac Shiri
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Yazdan Salimi
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
| | - Elsa Hervier
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
| | - Agathe Pezzoni
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
| | - Amirhossein Sanaat
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Ismini Mainta
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
| | - Habib Zaidi
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva
- Geneva University Neuro Center, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Chen X, Liu C. Deep-learning-based methods of attenuation correction for SPECT and PET. J Nucl Cardiol 2023; 30:1859-1878. [PMID: 35680755 DOI: 10.1007/s12350-022-03007-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/02/2022] [Indexed: 10/18/2022]
Abstract
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT, 06520, USA.
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Sun H, Wang F, Yang Y, Hong X, Xu W, Wang S, Mok GSP, Lu L. Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network. Eur J Nucl Med Mol Imaging 2023; 50:3630-3646. [PMID: 37474736 DOI: 10.1007/s00259-023-06343-9] [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: 12/16/2022] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. METHODS We recruited 60 subjects for rest-only scans and 14 subjects for rest-stress scans, all of whom underwent [13N]ammonia cardiac PET/CT examinations to acquire static and dynamic frames with both 3D NAC and CT-based AC (CTAC) PET images. We developed a 3D pix2pix deep learning AC (DLAC) framework via a U-net + ResNet-based generator and a convolutional neural network-based discriminator. Paired static or dynamic NAC and CTAC PET images from 60 rest-only subjects were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) training, respectively. The pre-trained S-DLAC network was then fine-tuned by paired dynamic NAC and CTAC PET frames of 60 rest-only subjects to derive an improved D-DLAC-FT for dynamic PET images. The 14 rest-stress subjects were used as an internal testing dataset and separately tested on different network models without training. The proposed methods were evaluated using visual quality and quantitative metrics. RESULTS The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods were consistent with clinical CTAC in terms of various images and quantitative metrics. The S-DLAC (slope = 0.9423, R2 = 0.947) showed a higher correlation with the reference static CTAC as compared to static NAC (slope = 0.0992, R2 = 0.654). D-DLAC-FT yielded lower myocardial blood flow (MBF) errors in the whole left ventricular myocardium than D-DLAC, but with no significant difference, both for the 60 rest-state subjects (6.63 ± 5.05% vs. 7.00 ± 6.84%, p = 0.7593) and the 14 stress-state subjects (1.97 ± 2.28% vs. 3.21 ± 3.89%, p = 0.8595). CONCLUSION The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods achieve comparable performance with clinical CTAC. Transfer learning shows promising potential for dynamic MP PET.
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Affiliation(s)
- Hao Sun
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Fanghu Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuling Yang
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Xiaotong Hong
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China
| | - Weiping Xu
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shuxia Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, China.
- Pazhou Lab, Guangzhou, 510330, China.
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Kim H, Li Z, Son J, Fessler JA, Dewaraja YK, Chun SY. Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2961-2973. [PMID: 37104110 PMCID: PMC10593395 DOI: 10.1109/tmi.2023.3270868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100× shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for 177Lu SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing.
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Affiliation(s)
- Hanvit Kim
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon, South Korea
- Department of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
| | - Zongyu Li
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Jiye Son
- Interdisciplinary Program for Bioengineering, Seoul National University (SNU), Seoul, South Korea. This work was done when she was with the School of Electrical and Computer Engineering (ECE), UNIST
| | - Jeffrey A. Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Yuni K. Dewaraja
- Dewaraja is with the Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Se Young Chun
- Department of ECE, INMC & IPAI, SNU, Seoul, South Korea
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Krokos G, MacKewn J, Dunn J, Marsden P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys 2023; 10:52. [PMID: 37695384 PMCID: PMC10495310 DOI: 10.1186/s40658-023-00569-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
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Affiliation(s)
- Georgios Krokos
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Jane MacKewn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
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Rich JM, Bhardwaj LN, Shah A, Gangal K, Rapaka MS, Oberai AA, Fields BKK, Matcuk GR, Duddalwar VA. Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis. FRONTIERS IN RADIOLOGY 2023; 3:1241651. [PMID: 37614529 PMCID: PMC10442705 DOI: 10.3389/fradi.2023.1241651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023]
Abstract
Introduction Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
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Affiliation(s)
- Joseph M. Rich
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Lokesh N. Bhardwaj
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Aman Shah
- Department of Applied Biostatistics and Epidemiology, University of Southern California, Los Angeles, CA, United States
| | - Krish Gangal
- Bridge UnderGrad Science Summer Research Program, Irvington High School, Fremont, CA, United States
| | - Mohitha S. Rapaka
- Department of Biology, University of Texas at Austin, Austin, TX, United States
| | - Assad A. Oberai
- Department of Aerospace and Mechanical Engineering Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Brandon K. K. Fields
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - George R. Matcuk
- Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Vinay A. Duddalwar
- Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
- Department of Radiology, USC Radiomics Laboratory, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Park J, Kang SK, Hwang D, Choi H, Ha S, Seo JM, Eo JS, Lee JS. Automatic Lung Cancer Segmentation in [ 18F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl Med Mol Imaging 2023; 57:86-93. [PMID: 36998591 PMCID: PMC10043063 DOI: 10.1007/s13139-022-00745-7] [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: 09/23/2021] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 10/18/2022] Open
Abstract
Purpose Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [18F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [18F]FDG PET/CT. Methods The whole-body [18F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. Results The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. Conclusion The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [18F]FDG PET/CT.
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Affiliation(s)
- Junyoung Park
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seung Kwan Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
| | - Donghwi Hwang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St Mary’s Hospital, The Catholic University of Korea, Seoul, 06591 Korea
| | - Jong Mo Seo
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
| | - Jae Seon Eo
- Department of Nuclear Medicine, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308 Korea
| | - Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 Korea
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18
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Duan H, Iagaru A. Neuroendocrine Tumor Diagnosis: PET/MR Imaging. PET Clin 2023; 18:259-266. [PMID: 36707370 DOI: 10.1016/j.cpet.2022.11.008] [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] [Indexed: 01/26/2023]
Abstract
Imaging plays a critical role in the diagnosis and management of neuroendocrine tumors (NETs). The initial workup of the primary tumor, including its characterization, local and distant staging, defines subsequent treatment decisions. Functional imaging using hybrid systems, such as PET combined with computed tomography, has become the gold standard. As NETs majorly arise from the gastrointestinal system and metastasize primarily to the liver, simultaneous PET and MR imaging with its high soft tissue contrast might be a valuable clinical one-stop-shop whole-body imaging tool. This review presents the current status and challenges of PET/MR imaging for diagnosis of NETs.
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Affiliation(s)
- Heying Duan
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA
| | - Andrei Iagaru
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA.
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19
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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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20
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Kwon K, Hwang D, Oh D, Kim JH, Yoo J, Lee JS, Lee WW. CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning. EJNMMI Phys 2023; 10:20. [PMID: 36947267 PMCID: PMC10033819 DOI: 10.1186/s40658-023-00536-9] [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: 10/21/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023] Open
Abstract
PURPOSE Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid. METHODS Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification. RESULTS The synthetic μ-map demonstrated a strong correlation (R2 = 0.972) and minimum error (mean square error = 0.936 × 10-4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of - 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29). CONCLUSION CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning.
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Affiliation(s)
- Kyounghyoun Kwon
- Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dongkyu Oh
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ji Hye Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Jihyung Yoo
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Won Woo Lee
- Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
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21
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Shi L, Zhang J, Toyonaga T, Shao D, Onofrey JA, Lu Y. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Phys Med Biol 2023; 68. [PMID: 36584395 DOI: 10.1088/1361-6560/acaf49] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However,λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predictingμ-DL fromλ-MLAA andμ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies.Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data.Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures.Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.
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Affiliation(s)
- Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China
| | - John A Onofrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.,Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Department of Urology, Yale University, New Haven, CT, United States of America
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
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22
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Liu H, Wu J, Shi L, Liu Y, Miller E, Sinusas A, Liu YH, Liu C. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. J Nucl Cardiol 2022; 29:2881-2892. [PMID: 34671940 DOI: 10.1007/s12350-021-02817-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/09/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. METHODS In the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. RESULTS The PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and - 1.3% ± 8.0% and - 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. CONCLUSION We developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.
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Affiliation(s)
- Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA.
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
| | - Jing Wu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, China
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yaqiang Liu
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China
- Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Edward Miller
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
| | - Albert Sinusas
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
- Department of Biomedical Imaging and Radiological Sciences, National Yangming Jiaotong University, Taipei, Taiwan
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06520, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
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23
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Detection and localization of hyperfunctioning parathyroid glands on [ 18F]fluorocholine PET/ CT using deep learning - model performance and comparison to human experts. Radiol Oncol 2022; 56:440-452. [PMID: 36503715 PMCID: PMC9784363 DOI: 10.2478/raon-2022-0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In the setting of primary hyperparathyroidism (PHPT), [18F]fluorocholine PET/CT (FCH-PET) has excellent diagnostic performance, with experienced practitioners achieving 97.7% accuracy in localising hyperfunctioning parathyroid tissue (HPTT). Due to the relative triviality of the task for human readers, we explored the performance of deep learning (DL) methods for HPTT detection and localisation on FCH-PET images in the setting of PHPT. PATIENTS AND METHODS We used a dataset of 93 subjects with PHPT imaged using FCH-PET, of which 74 subjects had visible HPTT while 19 controls had no visible HPTT on FCH-PET. A conventional Resnet10 as well as a novel mPETResnet10 DL model were trained and tested to detect (present, not present) and localise (upper left, lower left, upper right or lower right) HPTT. Our mPETResnet10 architecture also contained a region-of-interest masking algorithm that we evaluated qualitatively in order to try to explain the model's decision process. RESULTS The models detected the presence of HPTT with an accuracy of 83% and determined the quadrant of HPTT with an accuracy of 74%. The DL methods performed statistically worse (p < 0.001) in both tasks compared to human readers, who localise HPTT with the accuracy of 97.7%. The produced region-of-interest mask, while not showing a consistent added value in the qualitative evaluation of model's decision process, had correctly identified the foreground PET signal. CONCLUSIONS Our experiment is the first reported use of DL analysis of FCH-PET in PHPT. We have shown that it is possible to utilize DL methods with FCH-PET to detect and localize HPTT. Given our small dataset of 93 subjects, results are nevertheless promising for further research.
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24
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Ahangari S, Beck Olin A, Kinggård Federspiel M, Jakoby B, Andersen TL, Hansen AE, Fischer BM, Littrup Andersen F. A deep learning-based whole-body solution for PET/MRI attenuation correction. EJNMMI Phys 2022; 9:55. [PMID: 35978211 PMCID: PMC9385907 DOI: 10.1186/s40658-022-00486-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.
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Affiliation(s)
- Sahar Ahangari
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.
| | - Anders Beck Olin
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Thomas Lund Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Diagnostic Radiology, Rigshospitalet, Copenhagen, Denmark
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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25
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Shao W, Leung KH, Xu J, Coughlin JM, Pomper MG, Du Y. Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging. Diagnostics (Basel) 2022; 12:1945. [PMID: 36010295 PMCID: PMC9406894 DOI: 10.3390/diagnostics12081945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 11/16/2022] Open
Abstract
While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson's disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.
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Affiliation(s)
- Wenyi Shao
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Kevin H. Leung
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jingyan Xu
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jennifer M. Coughlin
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Martin G. Pomper
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yong Du
- The Russell H. Morgan Department of Radiology and Radiational Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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26
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Sari H, Teimoorisichani M, Mingels C, Alberts I, Panin V, Bharkhada D, Xue S, Prenosil G, Shi K, Conti M, Rominger A. Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners. Eur J Nucl Med Mol Imaging 2022; 49:4490-4502. [PMID: 35852557 PMCID: PMC9606046 DOI: 10.1007/s00259-022-05909-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 07/10/2022] [Indexed: 12/19/2022]
Abstract
Purpose Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. Methods Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. Results No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. Conclusion We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05909-3.
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Affiliation(s)
- Hasan Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland.
| | | | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | | | - Song Xue
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - George Prenosil
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
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27
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Visvikis D, Lambin P, Beuschau Mauridsen K, Hustinx R, Lassmann M, Rischpler C, Shi K, Pruim J. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging 2022; 49:4452-4463. [PMID: 35809090 PMCID: PMC9606092 DOI: 10.1007/s00259-022-05891-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/25/2022] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.
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Affiliation(s)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands
| | - Kim Beuschau Mauridsen
- Center of Functionally Integrative Neuroscience and MindLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Roland Hustinx
- GIGA-CRC in Vivo Imaging, University of Liège, GIGA, Avenue de l'Hôpital 11, 4000, Liege, Belgium
| | - Michael Lassmann
- Klinik Und Poliklinik Für Nuklearmedizin, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jan Pruim
- Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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28
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Toyonaga T, Shao D, Shi L, Zhang J, Revilla EM, Menard D, Ankrah J, Hirata K, Chen MK, Onofrey JA, Lu Y. Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. Eur J Nucl Med Mol Imaging 2022; 49:3086-3097. [PMID: 35277742 PMCID: PMC10725742 DOI: 10.1007/s00259-022-05748-2] [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: 08/20/2021] [Accepted: 02/25/2022] [Indexed: 11/04/2022]
Abstract
A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). METHODS Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. RESULTS µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine. CONCLUSIONS The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.
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Affiliation(s)
- Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | | | | | - Kenji Hirata
- Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Yale New Haven Hospital, New Haven, CT, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Urology, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
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29
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Leynes AP, Ahn S, Wangerin KA, Kaushik SS, Wiesinger F, Hope TA, Larson PEZ. Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:678-689. [PMID: 38223528 PMCID: PMC10785227 DOI: 10.1109/trpms.2021.3118325] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.
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Affiliation(s)
- Andrew P Leynes
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
- UC Berkeley-UC San Francisco Joint Graduate Program in Bioengineering, University of California at Berkeley, Berkeley, CA 94720 USA
| | - Sangtae Ahn
- Biology and Physics Department, GE Research, Niskayuna, NY 12309 USA
| | | | - Sandeep S Kaushik
- MR Applications Science Laboratory Europe, GE Healthcare, 80807 Munich, Germany
- Department of Computer Science, Technical University of Munich, 80333 Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, 8057 Zurich, Switzerland
| | - Florian Wiesinger
- MR Applications Science Laboratory Europe, GE Healthcare, 80807 Munich, Germany
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA, USA
- Department of Radiology, San Francisco VA Medical Center, San Francisco, CA 94121 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
- UC Berkeley-UC San Francisco Joint Graduate Program in Bioengineering, University of California at Berkeley, Berkeley, CA 94720 USA
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30
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Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. Eur J Nucl Med Mol Imaging 2022; 49:3046-3060. [PMID: 35169887 PMCID: PMC9253078 DOI: 10.1007/s00259-022-05718-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/06/2022] [Indexed: 12/22/2022]
Abstract
PURPOSE Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. RESULTS For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Huidong Xie
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China
| | | | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Visage Imaging, Inc, San Diego, CA, USA
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Imaging and Radiological Sciences, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Edward J Miller
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA.
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31
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Tamam MO, Tamam MC. Artificial intelligence technologies in nuclear medicine. World J Radiol 2022; 14:151-154. [PMID: 35978976 PMCID: PMC9258309 DOI: 10.4329/wjr.v14.i6.151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/20/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence plays a crucial role in developing precision medicine in nuclear medicine. Artificial intelligence refers to a field of computer science aimed at imitating the performance of tasks typically requiring human intelligence. From machine learning to generative adversarial networks, artificial intelligence automized the workflow of medical imaging. In this mini-review, we encapsulate artificial intelligence models and their use in nuclear medicine imaging workflow.
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Affiliation(s)
- Muge Oner Tamam
- Department of Nuclear Medicine, Prof. Dr. Cemil Tascioglu City Hospital, İstanbul 34381, Turkey
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32
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Renner A, Rausch I, Cal Gonzalez J, Laistler E, Moser E, Jochimsen T, Sattler T, Sabri O, Beyer T, Figl M, Birkfellner W, Sattler B. Technical Note: A PET/MR coil with an integrated, orbiting 511 keV transmission source for PET/MR imaging validated in an animal study. Med Phys 2022; 49:2366-2372. [PMID: 35224747 PMCID: PMC9310742 DOI: 10.1002/mp.15586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 11/11/2022] Open
Abstract
Background Purpose Methods Results Conclusion
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Affiliation(s)
- Andreas Renner
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
- Department of Radiation Oncology Medical University Vienna Austria
| | - Ivo Rausch
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Jacobo Cal Gonzalez
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Elmar Laistler
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Thies Jochimsen
- Department of Nuclear Medicine University Hospital Leipzig Germany
| | - Tatjana Sattler
- Clinic for Ruminants and Swine University of Leipzig Germany
| | - Osama Sabri
- Department of Nuclear Medicine University Hospital Leipzig Germany
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Michael Figl
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Wolfgang Birkfellner
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Bernhard Sattler
- Department of Nuclear Medicine University Hospital Leipzig Germany
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33
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Kim JS, Son HJ, Oh M, Lee DY, Kim HW, Oh J. 60 Years of Achievements by KSNM in Neuroimaging Research. Nucl Med Mol Imaging 2022; 56:3-16. [PMID: 35186156 PMCID: PMC8828843 DOI: 10.1007/s13139-021-00727-1] [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: 07/22/2021] [Revised: 11/01/2021] [Accepted: 12/07/2021] [Indexed: 02/03/2023] Open
Abstract
Nuclear medicine neuroimaging is able to show functional and molecular biologic abnormalities in various neuropsychiatric diseases. Therefore, it has played important roles in the clinical diagnosis and in research on the normal and pathological states of the brain. More than 400 outstanding studies have been conducted by Korean researchers over the past 60 years. In the 1990s, when multiheaded single-photon emission computed tomography (SPECT) scanners were first introduced in South Korea, stroke research using brain perfusion SPECT was conducted. With the spread of positron emission tomography (PET) scanners in the 2000s, research on the clinical usefulness of PET and the evaluation of pathophysiology in various diseases such as epilepsy, brain tumors, degenerative brain diseases, and other neuropsychiatric diseases were actively conducted using [18F]FDG and various neuroreceptor tracers. In the 2010s, with the clinical application of new radiopharmaceuticals for amyloid and tau imaging, research demonstrating the clinical usefulness of PET imaging and the pathophysiology of dementia has increased rapidly. It is expected that the role of nuclear medicine will expand with the development of new radiopharmaceuticals and analysis technologies, along with the application of artificial intelligence for early and differential diagnosis, and the development of therapeutic agents for degenerative brain diseases.
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Affiliation(s)
- Jae Seung Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hye Joo Son
- Department of Nuclear Medicine, Dankook University College of Medicine, Cheonan, Republic of Korea
| | - Minyoung Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Dong Yun Lee
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hae Won Kim
- Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
| | - Jungsu Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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34
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Minoshima S, Cross D. Application of artificial intelligence in brain molecular imaging. Ann Nucl Med 2022; 36:103-110. [PMID: 35028878 DOI: 10.1007/s12149-021-01697-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 12/22/2022]
Abstract
Initial development of artificial Intelligence (AI) and machine learning (ML) dates back to the mid-twentieth century. A growing awareness of the potential for AI, as well as increases in computational resources, research, and investment are rapidly advancing AI applications to medical imaging and, specifically, brain molecular imaging. AI/ML can improve imaging operations and decision making, and potentially perform tasks that are not readily possible by physicians, such as predicting disease prognosis, and identifying latent relationships from multi-modal clinical information. The number of applications of image-based AI algorithms, such as convolutional neural network (CNN), is increasing rapidly. The applications for brain molecular imaging (MI) include image denoising, PET and PET/MRI attenuation correction, image segmentation and lesion detection, parametric image formation, and the detection/diagnosis of Alzheimer's disease and other brain disorders. When effectively used, AI will likely improve the quality of patient care, instead of replacing radiologists. A regulatory framework is being developed to facilitate AI adaptation for medical imaging.
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Affiliation(s)
- Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East #1A071, Salt Lake City, UT, 84132, USA.
| | - Donna Cross
- Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East #1A071, Salt Lake City, UT, 84132, USA
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35
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Omidvari N, Cheng L, Leung EK, Abdelhafez YG, Badawi RD, Ma T, Qi J, Cherry SR. Lutetium background radiation in total-body PET-A simulation study on opportunities and challenges in PET attenuation correction. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2022; 2:963067. [PMID: 36172601 PMCID: PMC9513593 DOI: 10.3389/fnume.2022.963067] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The current generation of total-body positron emission tomography (PET) scanners offer significant sensitivity increase with an extended axial imaging extent. With the large volume of lutetium-based scintillation crystals that are used as detector elements in these scanners, there is an increased flux of background radiation originating from 176Lu decay in the crystals and higher sensitivity for detecting it. Combined with the ability of scanning the entire body in a single bed position, this allows more effective utilization of the lutetium background as a transmission source for estimating 511 keV attenuation coefficients. In this study, utilization of the lutetium background radiation for attenuation correction in total-body PET was studied using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner, with particular focus on ultralow-dose PET scans that are now made possible with these scanners. Effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification were studied. Furthermore, quantification accuracy of lutetium-based attenuation correction was compared for a 20-min scan of the whole body on the uEXPLORER, a one-meter-long, and a conventional 24-cm-long scanner. Quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan, the mean bias in all analyzed organs of interest were within a ±10% range compared to ground-truth activity maps. Quantification was affected in certain organs, when scan duration was reduced to 5 min or a reduced acceptance angle of 17° was used. Analysis of the Compton scattered events suggests that implementing a scatter correction method for the transmission data will be required, and increasing the energy threshold from 250 keV to 290 keV can reduce the computational costs and data rates, with negligible effects on PET quantification. Finally, the current results can serve as groundwork for transferring lutetium-based attenuation correction into research and clinical practice.
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Affiliation(s)
- Negar Omidvari
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States,CORRESPONDENCE: Negar Omidvari,
| | - Li Cheng
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Edwin K. Leung
- Department of Radiology, University of California, Davis, Davis, CA, United States,United Imaging Healthcare America Inc., Houston, TX, United States
| | - Yasser G. Abdelhafez
- Department of Radiology, University of California, Davis, Davis, CA, United States,Nuclear Medicine Unit, South Egypt Cancer Institute, Assiut University, Asyut, Egypt
| | - Ramsey D. Badawi
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States,Department of Radiology, University of California, Davis, Davis, CA, United States
| | - Tianyu Ma
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Simon R. Cherry
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States,Department of Radiology, University of California, Davis, Davis, CA, United States
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36
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Hwang D, Kang SK, Kim KY, Choi H, Lee JS. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur J Nucl Med Mol Imaging 2021; 49:1833-1842. [PMID: 34882262 DOI: 10.1007/s00259-021-05637-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/24/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET. METHODS One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNNNAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. RESULTS The error of the attenuation correction factors estimated using μ-CT and μ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). CONCLUSION The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.
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Affiliation(s)
- Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Seung Kwan Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Kyeong Yun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
- Brightonix Imaging Inc., Seoul, South Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea.
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37
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Lee JS, Kim KM, Choi Y, Kim HJ. A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea. Nucl Med Mol Imaging 2021; 55:265-284. [PMID: 34868376 DOI: 10.1007/s13139-021-00721-7] [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: 07/19/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 10/19/2022] Open
Abstract
We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.
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Affiliation(s)
- Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Kyeong Min Kim
- Department of Isotopic Drug Development, Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
| | - Yong Choi
- Department of Electronic Engineering, Sogang University, Seoul, Korea
| | - Hee-Joung Kim
- Department of Radiological Science, Yonsei University, Wonju, Korea
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38
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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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39
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Yin T, Obi T. Generation of attenuation correction factors from time-of-flight PET emission data using high-resolution residual U-net. Biomed Phys Eng Express 2021; 7. [PMID: 34438372 DOI: 10.1088/2057-1976/ac21aa] [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: 03/30/2021] [Accepted: 08/26/2021] [Indexed: 11/12/2022]
Abstract
Attenuation correction of annihilation photons is essential in PET image reconstruction for providing accurate quantitative activity maps. In the absence of an aligned CT device to obtain attenuation information, we propose the high-resolution residual U-net (HRU-Net) to extract attenuation correction factors (ACF) directly from time-of-flight (TOF) PET emission data. HRU-Net is built upon the U-Net encoding-decoding architecture and it utilizes four blocks of modified residual connections in each stage. In each residual block, concatenation is performed to incorporate input and output feature vectors. In addition, flexible and efficient elements of convolutional neural network (CNN) such as dilated convolutions, pre-activation order of a batch normalization (BN) layer, a rectified linear unit (ReLU) layer and a convolution layer, and residual connections are utilized to extract high resolution features. To illustrate the effectiveness of the proposed method, HRU-Net estimated ACF, attenuation maps and activity maps are compared with maximum likelihood ACF (MLACF) algorithm, U-Net, and HC-Net. An ablation study is conducted using non-TOF and TOF sinograms as inputs of networks. The experimental results show that HRU-Net with TOF projections as inputs leads to normalized root mean square error (NRMSE) of 4.84% ± 1.58%, outperforming MLACF, U-Net and HC-Net with NRMSE of 47.82% ± 13.62%, 6.92% ± 1.94%, and 7.99% ± 2.49%, respectively.
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Affiliation(s)
- Tuo Yin
- Department of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Takashi Obi
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
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40
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Arabi H, Zaidi H. MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers. Magn Reson Med 2021; 87:686-701. [PMID: 34480771 PMCID: PMC9292636 DOI: 10.1002/mrm.29003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/14/2021] [Accepted: 08/21/2021] [Indexed: 12/22/2022]
Abstract
Purpose We compare the performance of three commonly used MRI‐guided attenuation correction approaches in torso PET/MRI, namely segmentation‐, atlas‐, and deep learning‐based algorithms. Methods Twenty‐five co‐registered torso 18F‐FDG PET/CT and PET/MR images were enrolled. PET attenuation maps were generated from in‐phase Dixon MRI using a three‐tissue class segmentation‐based approach (soft‐tissue, lung, and background air), voxel‐wise weighting atlas‐based approach, and a residual convolutional neural network. The bias in standardized uptake value (SUV) was calculated for each approach considering CT‐based attenuation corrected PET images as reference. In addition to the overall performance assessment of these approaches, the primary focus of this work was on recognizing the origins of potential outliers, notably body truncation, metal‐artifacts, abnormal anatomy, and small malignant lesions in the lungs. Results The deep learning approach outperformed both atlas‐ and segmentation‐based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation‐based method with up to 20% SUV bias in bony structures and the atlas‐based method with 9% bias in the lung. The deep learning‐based method exhibited superior performance. Yet, in case of sever truncation and metallic‐artifacts in the input MRI, this approach was outperformed by the atlas‐based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods. Conclusion The deep learning‐based method provides promising outcome for synthetic CT generation from MRI. However, metal‐artifact and body truncation should be specifically addressed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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41
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Schaart DR, Schramm G, Nuyts J, Surti S. Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:598-618. [PMID: 34553105 PMCID: PMC8454900 DOI: 10.1109/trpms.2021.3084539] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.
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Affiliation(s)
- Dennis R Schaart
- Section Medical Physics & Technology, Radiation Science and Technology Department, Delft University of Technology, 2629 JB Delft, The Netherlands
| | - Georg Schramm
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
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Abstract
PET/MR imaging is in routine clinical use and is at least as effective as PET/CT for oncologic and neurologic studies with advantages with certain PET radiopharmaceuticals and applications. In addition, whole body PET/MR imaging substantially reduces radiation dosages compared with PET/CT which is particularly relevant to pediatric and young adult population. For cancer imaging, assessment of hepatic, pelvic, and soft-tissue malignancies may benefit from PET/MR imaging. For neurologic imaging, volumetric brain MR imaging can detect regional volume loss relevant to cognitive impairment and epilepsy. In addition, the single-bed position acquisition enables dynamic brain PET imaging without extending the total study length which has the potential to enhance the diagnostic information from PET.
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Affiliation(s)
- Farshad Moradi
- Department of Radiology, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA.
| | - Andrei Iagaru
- Department of Radiology, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA 94305, USA
| | - Jonathan McConathy
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, JT 773, Birmingham, AL 35249, USA
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43
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Li S, Wang G. Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200204. [PMID: 34218670 PMCID: PMC8255948 DOI: 10.1098/rsta.2020.0204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/04/2021] [Indexed: 06/13/2023]
Abstract
Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy γ-ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Siqi Li
- University of California Davis Medical Center, Department of Radiology, Saramento, CA, USA
| | - Guobao Wang
- University of California Davis Medical Center, Department of Radiology, Saramento, CA, USA
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44
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Mostafapour S, Gholamiankhah F, Dadgar H, Arabi H, Zaidi H. Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain. Clin Nucl Med 2021; 46:609-615. [PMID: 33661195 DOI: 10.1097/rlu.0000000000003585] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning. METHODS Whole-body 68Ga-PSMA PET images of 399 subjects were used to train a residual deep learning model, taking PET non-attenuation-corrected images (PET-nonAC) as input and CT-based attenuation-corrected PET images (PET-CTAC) as target (reference). Forty-six whole-body 68Ga-PSMA PET images were used as an independent validation dataset. For validation, synthetic deep learning-based attenuation-corrected PET images were assessed considering the corresponding PET-CTAC images as reference. The evaluation metrics included the mean absolute error (MAE) of the SUV, peak signal-to-noise ratio, and structural similarity index (SSIM) in the whole body, as well as in different regions of the body, namely, head and neck, chest, and abdomen and pelvis. RESULTS The deep learning-guided direct attenuation and scatter correction produced images of comparable visual quality to PET-CTAC images. It achieved an MAE, relative error (RE%), SSIM, and peak signal-to-noise ratio of 0.91 ± 0.29 (SUV), -2.46% ± 10.10%, 0.973 ± 0.034, and 48.171 ± 2.964, respectively, within whole-body images of the independent external validation dataset. The largest RE% was observed in the head and neck region (-5.62% ± 11.73%), although this region exhibited the highest value of SSIM metric (0.982 ± 0.024). The MAE (SUV) and RE% within the different regions of the body were less than 2.0% and 6%, respectively, indicating acceptable performance of the deep learning model. CONCLUSIONS This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning with clinically tolerable errors. The technique has the potential of performing attenuation correction on stand-alone PET or PET/MRI systems.
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Affiliation(s)
- Samaneh Mostafapour
- From the Department of Radiology Technology, Faculty of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad
| | - Faeze Gholamiankhah
- Department of Medical Physics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd
| | - Habibollah Dadgar
- Cancer Research Center, Razavi Hospital, Imam Reza International University, Mashhad, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva 4
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Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network. ELECTRONICS 2021. [DOI: 10.3390/electronics10151836] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.
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Murata T, Yokota H, Yamato R, Horikoshi T, Tsuneda M, Kurosawa R, Hashimoto T, Ota J, Sawada K, Iimori T, Masuda Y, Mori Y, Suyari H, Uno T. Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography. Med Phys 2021; 48:4177-4190. [PMID: 34061380 DOI: 10.1002/mp.15016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). METHODS In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. RESULTS U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. CONCLUSION New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.
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Affiliation(s)
- Taisuke Murata
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan
| | - Ryuhei Yamato
- Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Takuro Horikoshi
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Masato Tsuneda
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan
| | - Ryuna Kurosawa
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Takuma Hashimoto
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Joji Ota
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Koichi Sawada
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Takashi Iimori
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Yoshitada Masuda
- Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan
| | - Yasukuni Mori
- Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Hiroki Suyari
- Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan
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Kang SK, An HJ, Jin H, Kim JI, Chie EK, Park JM, Lee JS. Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy. Biomed Eng Lett 2021; 11:263-271. [PMID: 34350052 DOI: 10.1007/s13534-021-00195-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/01/2021] [Accepted: 06/11/2021] [Indexed: 12/22/2022] Open
Abstract
Although MR-guided radiotherapy (MRgRT) is advancing rapidly, generating accurate synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural networks require large dataset of precisely co-registered CT and MRI pairs that are difficult to obtain due to respiration and peristalsis. Here, we propose a method to generate sCT based on deep learning training with weakly paired CT and MR images acquired from an MRgRT system using a cycle-consistent GAN (CycleGAN) framework that allows the unpaired image-to-image translation in abdomen and thorax. Data from 90 cancer patients who underwent MRgRT were retrospectively used. CT images of the patients were aligned to the corresponding MR images using deformable registration, and the deformed CT (dCT) and MRI pairs were used for network training and testing. The 2.5D CycleGAN was constructed to generate sCT from the MRI input. To improve the sCT generation performance, a perceptual loss that explores the discrepancy between high-dimensional representations of images extracted from a well-trained classifier was incorporated into the CycleGAN. The CycleGAN with perceptual loss outperformed the U-net in terms of errors and similarities between sCT and dCT, and dose estimation for treatment planning of thorax, and abdomen. The sCT generated using CycleGAN produced virtually identical dose distribution maps and dose-volume histograms compared to dCT. CycleGAN with perceptual loss outperformed U-net in sCT generation when trained with weakly paired dCT-MRI for MRgRT. The proposed method will be useful to increase the treatment accuracy of MR-only or MR-guided adaptive radiotherapy. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-021-00195-8.
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Affiliation(s)
- Seung Kwan Kang
- Department of Biomedical Sciences and 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 College of Medicine, Seoul, 03080 South Korea
| | - Hyun Joon An
- Department of Radiation Oncology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea
| | - Hyeongmin Jin
- Department of Radiation Oncology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea
| | - Jung-In Kim
- Department of Radiation Oncology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 South Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 South Korea
| | - Jong Min Park
- Department of Radiation Oncology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 South Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences and Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 South Korea.,Department of Nuclear Medicine, Seoul National University Hospital, Seoul, 03080 South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 South Korea
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48
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Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med 2021; 35:691-701. [PMID: 33811600 DOI: 10.1007/s12149-021-01611-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/17/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVES Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomography (TCT) images from non-AC (NAC) PET images using a convolutional neural network (CNN) with a huge dataset of various radiotracers for brain PET imaging. METHODS The proposed framework is comprised of three steps: (1) NAC PET image generation, (2) synthetic TCT generation using CNN, and (3) PET image reconstruction. We trained the CNN by combining the mixed image dataset of six radiotracers to avoid overfitting, including [18F]FDG, [18F]BCPP-EF, [11C]Racropride, [11C]PIB, [11C]DPA-713, and [11C]PBB3. We used 1261 brain NAC PET and TCT images (1091 for training and 70 for testing). We did not include [11C]Methionine subjects in the training dataset, but included them in the testing dataset. RESULTS The image quality of the synthetic TCT images obtained using the CNN trained on the mixed dataset of six radiotracers was superior to those obtained using the CNN trained on the split dataset generated from each radiotracer. In the [18F]FDG study, the mean relative PET biases of the emission-segmented AC (ESAC) and deep AC were 8.46 ± 5.24 and - 5.69 ± 4.97, respectively. The deep AC PET and TCT AC PET images exhibited excellent correlation for all seven radiotracers (R2 = 0.912-0.982). CONCLUSION These results indicate that our proposed deep AC framework can be leveraged to provide quantitatively superior PET images when using the CNN trained on the mixed dataset of PET tracers than when using the CNN trained on the split dataset which means specific for each tracer.
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Affiliation(s)
- Fumio Hashimoto
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan.
| | - Masanori Ito
- Global Strategic Challenge Center, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan.
| | - Kibo Ote
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan
| | - Takashi Isobe
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan
| | - Hiroyuki Okada
- Global Strategic Challenge Center, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan
- Hamamatsu Medical Imaging Center, Hamamatsu Medical Photonics Foundation, Hamamatsu, 434-8601, Japan
| | - Yasuomi Ouchi
- Department of Biofunctional Imaging, Preeminent Medical Photonics Education and Research Center, Hamamatsu University School of Medicine, Hamamatsu, 431-3192, Japan
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Jha AK, Mithun S, Rangarajan V, Wee L, Dekker A. Emerging role of artificial intelligence in nuclear medicine. Nucl Med Commun 2021; 42:592-601. [PMID: 33660696 DOI: 10.1097/mnm.0000000000001381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
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Schaefferkoetter J, Yan J, Moon S, Chan R, Ortega C, Metser U, Berlin A, Veit-Haibach P. Deep learning for whole-body medical image generation. Eur J Nucl Med Mol Imaging 2021; 48:3817-3826. [PMID: 34021779 DOI: 10.1007/s00259-021-05413-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation. PURPOSE This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types. MATERIALS AND METHODS This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation. RESULTS A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions. CONCLUSION The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data.
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Affiliation(s)
- Joshua Schaefferkoetter
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA.
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada.
| | - Jianhua Yan
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Sangkyu Moon
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Rosanna Chan
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
| | - Alejandro Berlin
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Cancer Centre, Mount Sinai Hospital and Women's College Hospital, University of Toronto, University Health Network, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada
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