IEEE Trans Med Imaging. 2024 Mar;43(3):1006-1017. doi: 10.1109/TMI.2023.3327295. Epub 2024 Mar 5.
Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.
胎儿磁共振成像（MRI）受到胎儿运动和母体呼吸的影响。虽然快速 MRI 序列允许无伪影地获取单个 2D 切片，但在获取空间相邻切片时，运动经常发生。因此，对于重建胎儿脑 3D MRI，每个切片的运动校正都是至关重要的。在本文中，我们提出了一种新的多任务学习框架，采用由粗到精的策略，共同学习运动校正的位姿估计参数和胎儿 MRI 中每个切片的组织分割图。特别是，我们设计了一种基于回归的分割损失，作为深度监督，以学习对位姿估计和分割更具解剖意义的特征。在粗阶段，类似于 U-Net 的网络学习两个任务共享的特征。在细化阶段，为了充分利用解剖信息，从粗分割构建的有符号距离图被引入到两个任务的特征学习中。最后，有符号距离图的迭代合并进一步逐步提高了回归和分割的性能。跨两个不同扫描仪和成像协议采集的不同胎儿数据集的交叉验证实验结果表明，与最先进的方法相比，该方法在减少位姿估计误差和获得更好的组织分割结果方面是有效的。
