Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notice that there are significant relevances among these tasks and this procedure artificially cuts off these potential connections, which may lead to losing clinically important information for the final diagnosis. To involve these potential relations for further performance improvement, a sequential multi-task joint learning network model is proposed to train a combined end-to-end pipeline in a differentiable way, aiming at exploring the mutual influence among those tasks simultaneously. Our design consists of three cascaded modules: 1) deep sampling pattern learning module optimizes the $k$-space sampling pattern with predetermined sampling rate; 2) deep reconstruction module is dedicated to reconstructing MR images from the undersampled data using the learned sampling pattern; 3) deep segmentation module encodes MR images reconstructed from the previous module to segment the interested tissues. The proposed model retrieves the latently interactive and cyclic relations among those tasks, from which each task will be mutually beneficial. The proposed framework is verified on MRB dataset, which achieves superior performance on other SOTA methods in terms of both reconstruction and segmentation.
翻译:磁共振成像(MRI)的获取、重建和分离通常在磁共振工作流程的常规做法中独立处理,很容易注意到这些任务之间有着重大关联性,而这一程序人为地切断了这些潜在连接,可能导致临床上的重要信息丢失,从而导致最终诊断;为了涉及这些潜在关系,以进一步改进性能,建议采用一个连续的多任务联合学习网络模型,以不同的方式对端对端混合管道进行培训,目的是同时探索这些任务之间的相互影响。我们的设计由三个级联模块组成:(1)深层抽样模式学习模块以预定的采样率优化美元-空间采样模式;(2)深层重建模块致力于利用所学的采样模式,从未得到充分采样的数据中重建MR图像;(3)深层分解模块将从以前的模块中重建的MR图像编码为从以前的模块到感兴趣的组织之间的部分。拟议的模型将上述任务之间的潜在互动和周期性关系检索出来,每项任务都将相互受益。拟议的框架在MRB数据集上得到核实,该模块在重建的其他区段方法和部分方法上均取得优性表现。