United Imaging Healthcare, No. 2258 Chengbei Road, Shanghai, 201807, China.
Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China.
Eur J Nucl Med Mol Imaging. 2024 Dec;52(1):62-73. doi: 10.1007/s00259-024-06872-x. Epub 2024 Aug 13.
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.
In a retrospective study, 737 patients underwent [F]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.
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 SUV and SUV 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 SUV 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.
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.
呼吸运动（RM）显著影响胸腹部 PET/CT 成像的图像质量。本研究引入了一种基于数据驱动的呼吸运动校正（uRMC）方法，利用深度学习神经网络，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 配准不良。
在一项回顾性研究中，737 名患者使用 uMI Panorama PET/CT 扫描仪进行了[F]FDG PET/CT 扫描。其中 99 名患者还配备了呼吸监测设备（VSM），形成验证集。其余 638 名患者的数据用于训练用于 uRMC 的神经网络。uRMC 主要由三个关键组件组成：（1）数据驱动的呼吸信号提取，（2）衰减图生成，和（3）PET-CT 对齐。对于三种方法，即数据驱动的 uRMC（提出的）、基于 VSM 的 uRMC 和无运动校正的 OSEM（NMC），在 906 个病变内计算了 SUV 指标。估计了主要器官的 RM 幅度。
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 配准，提高了诊断能力。与 NMC 相比，uRMC 在 906 个病变中 SUV 和 SUV 的值分别增加了 10%和 17%。亚组分析显示，uRMC 中小和中等大小的病变 SUV 显著增加。基于 VSM 的 uRMC 方法和数据驱动的 uRMC 方法之间存在细微差异，两种方法之间的 SUV 值存在统计学边际显著或不显著。研究观察到主要器官的运动幅度变化较大，通常在 10 到 20 毫米之间。
已经开发、验证和评估了用于 PET/CT 呼吸运动的基于数据的解决方案。据我们所知，这是第一个补偿 PET 内运动模糊、由 PET-CT 配准不良引起的衰减不匹配伪影以及 PET 和 CT 之间配准不良的统一解决方案。
