Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Phys Med Biol. 2021 May 20;66(11). doi: 10.1088/1361-6560/abfc8f.
We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-bodyF-fluorodeoxyglucose (F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUVwas increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.
我们提出了一种基于深度学习的数据驱动呼吸相位匹配门控 PET 衰减校正（AC）方法，该方法不需要门控 CT。所提出的方法是一个多步骤的过程，包括数据驱动的呼吸门控、使用衰减和活动最大似然重建（MLAA）算法的门控衰减图估计，以及使用卷积神经网络（CNN）增强门控衰减图。通过 CNN 增强的门控 MLAA 衰减图可实现门控 PET 图像的相位匹配 AC。我们对门控 PET 图像进行了非刚性配准，以生成无运动的 PET 图像。我们通过对 80 个肿瘤全身 F-氟脱氧葡萄糖（F-FDG）PET/CT 扫描数据进行基于 3D 补丁的学习来训练 CNN，并将其应用于覆盖下肺和上肝的七个区域 PET/CT 扫描中。我们研究了不利用 CT 进行 PET 呼吸相位匹配 AC 对肿瘤大小和标准摄取值（SUV）评估以及 PET 图像质量（%STD）的影响。使用所提出的方法生成的校正后的门控和无运动的 PET 图像具有更清晰的器官边界和更好的噪声特性，优于传统的门控和非门控 PET 图像。在相位失配的基于 CT 的 AC 中观察到的香蕉伪影在提出的方法中没有观察到。通过采用所提出的方法，与运动大于 5 毫米的肿瘤相比，肿瘤的大小减小了 12.3%，SUV 增加了 13.3%，肝脏摄取的%STD 降低了 11.1%。基于深度学习的数据驱动呼吸相位匹配 AC 方法提高了 PET 图像质量并减少了运动伪影。
