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Multi-sequence MRIs can be necessary for reliable diagnosis in clinicalpractice due to the complimentary information within sequences. However,redundant information exists across sequences, which interferes with miningefficient representations by modern machine learning or deep learning models.To handle various clinical scenarios, we propose a sequence-to-sequencegeneration framework (Seq2Seq) for imaging-differentiation representationlearning. In this study, not only do we propose arbitrary 3D/4D sequencegeneration within one model to generate any specified target sequence, but alsowe are able to rank the importance of each sequence based on a new metricestimating the difficulty of a sequence being generated. Furthermore, we alsoexploit the generation inability of the model to extract regions that containunique information for each sequence. We conduct extensive experiments usingthree datasets including a toy dataset of 20,000 simulated subjects, a brainMRI dataset of 1,251 subjects, and a breast MRI dataset of 2,101 subjects, todemonstrate that (1) our proposed Seq2Seq is efficient and lightweight forcomplex clinical datasets and can achieve excellent image quality; (2)top-ranking sequences can be used to replace complete sequences withnon-inferior performance; (3) combining MRI with our imaging-differentiationmap leads to better performance in clinical tasks such as glioblastoma MGMTpromoter methylation status prediction and breast cancer pathological completeresponse status prediction. Our code is available athttps://github.com/fiy2W/mri_seq2seq.

Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI