Multi-sequence MRI is valuable in clinical settings for reliable diagnosis
and treatment prognosis, but some sequences may be unusable or missing for
various reasons. To address this issue, MRI synthesis is a potential solution.
Recent deep learning-based methods have achieved good performance in combining
multiple available sequences for missing sequence synthesis. Despite their
success, these methods lack the ability to quantify the contributions of
different input sequences and estimate the quality of generated images, making
it hard to be practical. Hence, we propose an explainable task-specific
synthesis network, which adapts weights automatically for specific sequence
generation tasks and provides interpretability and reliability from two sides:
(1) visualize the contribution of each input sequence in the fusion stage by a
trainable task-specific weighted average module; (2) highlight the area the
network tried to refine during synthesizing by a task-specific attention
module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and
results on arbitrary sequence synthesis indicate that the proposed method
achieves better performance than the state-of-the-art methods. Our code is
available at https://github.com/fiy2W/mri_seq2seq.

本文提出一种可解释的任务特定综合网络，通过训练可调节的任务特定加权平均模块和任务特定注意模块，可视化每个输入序列的贡献并突出网络尝试在合成过程中完善的区域。在 BraTS2021 数据集上的实验证明了该方法在任意序列合成方面具有优异的性能，是当前最先进方法的一步之遥。