PDF(Pubmed)
Abstract：
Head motion correction is an essential component of brain PET imaging, in which even motion of small magnitude can greatly degrade image quality and introduce artifacts. Building upon previous work, we propose a new head motion correction framework taking fast reconstructions as input. The main characteristics of the proposed method are: (i) the adoption of a high-resolution short-frame fast reconstruction workflow; (ii) the development of a novel encoder for PET data representation extraction; and (iii) the implementation of data augmentation techniques. Ablation studies are conducted to assess the individual contributions of each of these design choices. Furthermore, multi-subject studies are conducted on an 18F-FPEB dataset, and the method performance is qualitatively and quantitatively evaluated by MOLAR reconstruction study and corresponding brain Region of Interest (ROI) Standard Uptake Values (SUV) evaluation. Additionally, we also compared our method with a conventional intensity-based registration method. Our results demonstrate that the proposed method outperforms other methods on all subjects, and can accurately estimate motion for subjects out of the training set. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_fast_recon_miccai2023.
摘要：
头部运动校正是脑PET成像的重要组成部分，其中即使是小幅度的运动也会极大地降低图像质量并引入伪影。在以前工作的基础上，我们提出了一个新的头部运动校正框架，以快速重建为输入。所提出的方法的主要特征是:(i)采用高分辨率短帧快速重建工作流程;(ii)开发用于PET数据表示提取的新型编码器;以及(iii)实现数据增强技术。进行消融研究以评估这些设计选择中的每一个的个体贡献。此外，多学科研究是在18F-FPEB数据集上进行的，通过MOLAR重建研究和相应的大脑感兴趣区域（ROI）标准摄取值（SUV）评估，对方法性能进行了定性和定量评估。此外,我们还将我们的方法与传统的基于强度的配准方法进行了比较。我们的结果表明，该方法在所有主题上都优于其他方法，并且可以准确地估计出训练集之外的受试者的运动。所有代码均可在GitHub上公开获得：https://github.com/OnofreyLab/dl-hmc_fast_recon_miccai2023。
