Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

IF 8.6 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yihuan Lu, Fei Kang, Duo Zhang, Yue Li, Hao Liu, Chen Sun, Hao Zeng, Lei Shi, Yumo Zhao, Jing Wang
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引用次数: 0

Abstract

Purpose: Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment.

Methods: In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated.

Results: uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20 mm.

Conclusion: A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

Abstract Image

PET/CT 中的深度学习辅助呼吸运动补偿:解决运动引起的分辨率损失、衰减校正伪影和 PET-CT 错位。
目的呼吸运动(RM)严重影响胸腹部 PET/CT 成像的图像质量。本研究利用深度学习神经网络引入了一种统一的数据驱动呼吸运动校正(uRMC)方法,以解决RM引起的所有主要问题,即PET分辨率损失、衰减校正伪影和PET-CT错位:在一项回顾性研究中,737 名患者使用 uMI Panorama PET/CT 扫描仪接受了[18F]FDG PET/CT 扫描。其中,99 名患者同时配有呼吸监测装置(VSM),构成验证集。638 名患者的其余数据用于训练 uRMC 中使用的神经网络。uRMC 主要由三个关键部分组成:(1)数据驱动的呼吸信号提取;(2)衰减图生成;(3)PET-CT 配对。通过三种方法计算了 906 个病灶的 SUV 指标,即数据驱动的 uRMC(建议)、基于 VSM 的 uRMC 和无运动校正的 OSEM(NMC)。结果:uRMC 通过揭示以前未检测到的病变、锐化病变轮廓、增加 SUV 值和改善 PET-CT 对位,提高了诊断能力。与 NMC 相比,uRMC 使 906 个病灶的 SUVmax 和 SUVmean 分别增加了 10%和 17%。分组分析显示,uRMC 使中小型病变的 SUV 值明显增加。基于 VSM 的 uRMC 方法和数据驱动的 uRMC 方法之间存在微小差异,两种方法的 SUVmax 在统计学上不显著或微不足道。研究观察到主要器官的运动幅度各不相同,通常在 10 到 20 毫米之间:针对 PET/CT 中呼吸运动的数据驱动解决方案已经开发、验证和评估。据我们所知,这是第一个统一的解决方案,可以补偿 PET 内的运动模糊、PET-CT 错位造成的衰减失配伪影以及 PET 和 CT 之间的错位。
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来源期刊
CiteScore
15.60
自引率
9.90%
发文量
392
审稿时长
3 months
期刊介绍: The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.
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