Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK.
Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK.
Med Image Anal. 2021 Feb;68:101941. doi: 10.1016/j.media.2020.101941. Epub 2020 Dec 17.
Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.
运动伪影是磁共振成像（MRI）中的一个核心问题。本研究旨在解决如何从高度欠采样的 MRI 数据中获得更高质量、无运动伪影的超高分辨率重建问题。在这项工作中，我们首次提出了一种变分多任务框架，该框架可以将 MRI 中的三个相关任务（重建、配准和超分辨率）结合在一起。我们的框架将一组受运动干扰的多个欠采样 MR 采集作为一个新的多任务优化模型，该模型由一个 L 保真度项组成，允许在任务之间共享表示，以及超分辨率基础和超弹性变形，以模拟生物组织的行为。我们证明，这种组合比顺序模型和其他双任务方法有显著的改进。与最先进的方法相比，我们的结果能够显示精细的细节，并补偿运动伪影，生成清晰且具有高度纹理的图像，同时保持低 CPU 时间。我们的改进在临床评估和统计分析中都得到了评估。
