深度学习在 PET/MR 中定量和定性 PET 方面的应用:尚未满足的技术和临床需求。

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-08-01 Epub Date: 2024-08-21 DOI:10.1007/s10334-024-01199-y
Jaewon Yang, Asim Afaq, Robert Sibley, Alan McMilan, Ali Pirasteh
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引用次数: 0

摘要

我们旨在概述深度学习(DL)应用于 PET/MR 定量和定性 PET 的技术和临床未满足需求,重点关注衰减校正、图像增强、运动校正、动力学建模和模拟数据生成。(1) 由于数据短缺和技术限制,基于 DL 的衰减校正(DLAC)仍是儿科全身 PET/MR 和肺部特定 DLAC 的有限探索领域。(2) 基于 DL 的图像增强近似于以高分辨率 MR 为先验的 MR 引导的正则化重建,在提高 PET 图像质量方面已显示出前景。然而,其临床价值尚未在各种放射性核素中得到全面评估,而且由于运动伪影,在头部以外的应用可能会带来挑战。(3) 基于 DL 的运动校正的稳健训练需要成对的运动破坏和运动校正 PET/MR 数据。然而,这些数据对很少见。(4) 基于 DL 的方法可以解决动态 PET 的局限性,如扫描持续时间长可能导致患者不适和运动,从而提供新的研究机会。(5) 使用拟人数字模型进行蒙特卡洛模拟可提供大量数据集,解决临床数据不足的问题。以上总结的技术/临床挑战和潜在解决方案可为研究界提供研究机会,促进 DL 解决方案的临床转化。
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Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

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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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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