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Yang J, Afaq A, Sibley R, McMilan A, Pirasteh A. Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01199-y. [PMID: 39167304 DOI: 10.1007/s10334-024-01199-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 08/06/2024] [Accepted: 08/08/2024] [Indexed: 08/23/2024]
Abstract
We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.
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Affiliation(s)
- Jaewon Yang
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA.
| | - Asim Afaq
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Robert Sibley
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Alan McMilan
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, USA
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Arabi H, Zaidi H. Recent Advances in Positron Emission Tomography/Magnetic Resonance Imaging Technology. Magn Reson Imaging Clin N Am 2023; 31:503-515. [PMID: 37741638 DOI: 10.1016/j.mric.2023.06.002] [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: 09/25/2023]
Abstract
More than a decade has passed since the clinical deployment of the first commercial whole-body hybrid PET/MR scanner in the clinic. The major advantages and limitations of this technology have been investigated from technical and medical perspectives. Despite the remarkable advantages associated with hybrid PET/MR imaging, such as reduced radiation dose and fully simultaneous functional and structural imaging, this technology faced major challenges in terms of mutual interference between MRI and PET components, in addition to the complexity of achieving quantitative imaging owing to the intricate MRI-guided attenuation correction in PET/MRI. In this review, the latest technical developments in PET/MRI technology as well as the state-of-the-art solutions to the major challenges of quantitative PET/MR imaging are discussed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva CH-1205, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense 500, Denmark.
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Chen S, Eldeniz C, Fraum TJ, Ludwig DR, Gan W, Liu J, Kamilov US, Yang D, Gach HM, An H. Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system. Med Phys 2023; 50:6163-6176. [PMID: 37184305 DOI: 10.1002/mp.16469] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. PURPOSE We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. METHODS Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. RESULTS Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p < 0.0001) and smaller RMSEs (p < 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring > 8 out of 10), followed by P2P200 (scoring > 5 out of 10), and then motion-uncorrected (scoring < 3 out of 10) in sharpness, contrast, and artifact-freeness. CONCLUSIONS We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.
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Affiliation(s)
- Sihao Chen
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tyler J Fraum
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Daniel R Ludwig
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Weijie Gan
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jiaming Liu
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ulugbek S Kamilov
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Deshan Yang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - H Michael Gach
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Hongyu An
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
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