Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
Med Image Anal. 2023 Apr;85:102742. doi: 10.1016/j.media.2023.102742. Epub 2023 Jan 13.
Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of derived diffusion scalars that are eventually utilized for quantification of tissue microstructure. Here, we propose two novel loss functions, called microstructural loss and spherical variance loss, to explicitly consider the quality of both the predicted DMRI data and derived diffusion scalars. We apply these loss functions to the prediction of multi-shell data and enhancement of angular resolution. Evaluation based on infant and adult DMRI data indicates that both microstructural loss and spherical variance loss improve the quality of derived diffusion scalars.
深度学习预测弥散磁共振成像（DMRI）数据依赖于有效损失函数的利用。现有的损失函数通常是在不考虑最终用于量化组织微观结构的衍生扩散标量质量的情况下，对预测和目标 DMRI 数据的信号差异进行测量。在这里，我们提出了两种新的损失函数，称为微观结构损失和球形方差损失，以明确考虑预测的 DMRI 数据和衍生扩散标量的质量。我们将这些损失函数应用于多壳数据的预测和角分辨率的增强。基于婴儿和成人 DMRI 数据的评估表明，微观结构损失和球形方差损失都能提高衍生扩散标量的质量。
