论文标题
运动补偿自我监督的深度学习，以高度加速3D Ultrastort Echo时间肺MRI
Motion Compensated Self Supervised Deep Learning for Highly Accelerated 3D Ultrashort Echo Time Pulmonary MRI
论文作者
论文摘要
目的：研究运动补偿，自我监督，基于模型的深度学习（MBDL）作为重建自由呼吸的一种方法，3D肺Ultrashort Echo Echo Time（UTE）采集。 理论和方法：开发了一个自我监督的额外维度MBDL架构（XD-MBDL），结合了呼吸状态以重建单个高质量的3D图像。通过从低分辨率运动（XD-GRASP）迭代重建中估算运动场，将非刚性的基于GPU的运动场纳入了该体系结构中。在具有和没有对比度的肺UTE数据集上评估了运动补偿的XD-MBDL，并与不考虑呼吸运动的自我监督MBDL的受约束重建和变体进行了比较。 结果：使用XD-MBDL重建的图像表明，相对于自我监督的MBDL方法，通过明显的SNR，CNR和视觉评估来衡量的图像质量改善，这些方法无法说明动态呼吸道状态，XD-GRASP和最近提出的运动补偿迭代迭代迭代术语策略（IMOCO）。此外，XD-MBDL相对于XD-Grasp和Imoco缩短了重建时间。 结论：开发了一种方法来允许自我监督的MBDL结合多个呼吸状态以重建单个图像。该方法与基于GPU的图像注册结合使用，以进一步提高重建质量。这种方法显示出令人鼓舞的结果，从3D肺UTE获取中重建用户选择的呼吸阶段。
Purpose: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D Pulmonary ultrashort echo time (UTE) acquisitions. Theory and Methods: A self-supervised eXtra Dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid, GPU based motion fields were incorporated into this architecture by estimating motion fields from a low resolution motion resolved (XD-GRASP) iterative reconstruction. Motion Compensated XD-MBDL was evaluated on lung UTE datasets with and without contrast and was compared to constrained reconstructions and variants of self-supervised MBDL that do not consider respiratory motion. Results: Images reconstructed using XD-MBDL demonstrate improved image quality as measured by apparent SNR, CNR and visual assessment relative to self-supervised MBDL approaches that do not account for dynamic respiratory states, XD-GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction time relative to both XD-GRASP and iMoCo. Conclusion: A method was developed to allow self-supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with GPU-based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user-selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.
