Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment,European Journal of Nuclear Medicine and Molecular Imaging

当前位置： X-MOL 学术 › Eur. J. Nucl. Med. Mol. Imaging › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment
European Journal of Nuclear Medicine and Molecular Imaging ( IF 8.6 ) Pub Date : 2024-08-13 , DOI: 10.1007/s00259-024-06872-x
Yihuan Lu ₁ , Fei Kang ₂ , Duo Zhang ₁ , Yue Li ₁ , Hao Liu ₁ , Chen Sun ₁ , Hao Zeng ₁ , Lei Shi ₁ , Yumo Zhao ₁ , Jing Wang ₂

Affiliation

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 SUV_max and SUV_mean 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_max 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.

中文翻译：

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) 显着影响胸腹部 PET/CT 成像的图像质量。本研究引入了一种统一的数据驱动的呼吸运动校正（uRMC）方法，利用深度学习神经网络来解决 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。

方法

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 [ ¹⁸ F]FDG PET/CT 扫描。验证集由 99 名还佩戴呼吸监测设备 (VSM) 的患者组成。 638 名患者的剩余数据用于训练 uRMC 中使用的神经网络。 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，以及 (3) PET-CT 对齐。采用三种方法在 906 个病灶内计算 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)。估计主要器官的 RM 大小。

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 值和改进 PET-CT 对齐来增强诊断能力。与 NMC 相比，uRMC 在 906 个病变中的 SUV _max和 SUV_平均值分别增加了 10% 和 17%。亚组分析显示，uRMC 的中小型病变中 SUV 显着增加。基于 VSM 的方法和数据驱动的 uRMC 方法之间存在微小差异，两种方法之间的 SUV _max具有统计边际显着性或不显着性。该研究观察到主要器官的运动幅度不同，通常范围为 10 至 20 毫米。