Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by modern machine learning or deep learning models. To handle various clinical scenarios, we propose a sequence-to-sequence generation framework (Seq2Seq) for imaging-differentiation representation learning. In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence. We conduct extensive experiments using three datasets including a toy dataset of 20,000 simulated subjects, a brain MRI dataset of 1,251 subjects, and a breast MRI dataset of 2,101 subjects, to demonstrate that (1) our proposed Seq2Seq is efficient and lightweight for complex clinical datasets and can achieve excellent image quality; (2) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (3) combining MRI with our imaging-differentiation map leads to better performance in clinical tasks such as glioblastoma MGMT promoter methylation status prediction and breast cancer pathological complete response status prediction. Our code is available at https://github.com/fiy2W/mri_seq2seq.

翻译：多序列MRI在临床实践中可能对可靠诊断至关重要，因为不同序列之间包含互补信息。然而，序列之间也存在冗余信息，这会干扰现代机器学习或深度学习模型挖掘高效表征的能力。为应对各种临床场景，我们提出了一种序列到序列生成框架（Seq2Seq），用于影像差异化表示学习。在本研究中，我们不仅提出了在单个模型内进行任意3D/4D序列生成以产生任何指定的目标序列，还能基于衡量序列生成难度的新指标对各序列的重要性进行排序。此外，我们还利用模型生成能力的局限性来提取每个序列包含独特信息的区域。我们使用三个数据集进行了大量实验，包括一个包含20,000个模拟受试者的玩具数据集、一个包含1,251个受试者的脑部MRI数据集以及一个包含2,101个受试者的乳腺MRI数据集，结果表明：（1）我们提出的Seq2Seq方法对复杂临床数据集高效且轻量，能够实现优异的图像质量；（2）排名靠前的序列可用于替代完整序列，且性能非劣效；（3）将MRI与我们的影像差异化图相结合，在胶质母细胞瘤MGMT启动子甲基化状态预测和乳腺癌病理完全缓解状态预测等临床任务中取得了更优性能。我们的代码可在https://github.com/fiy2W/mri_seq2seq获取。