Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI

Luyi Han, Tao Tan, Tianyu Zhang, Yunzhi Huang, Xin Wang, Yuan Gao, Jonas Teuwen, Ritse Mann

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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 learning-based 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 1251 subjects, and a breast MRI dataset of 2101 subjects, to demonstrate that (1) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (2) 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.

Original languageEnglish
Article number103044
JournalMedical Image Analysis
Volume92
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Imaging differentiation
  • MRI synthesis
  • Multi-sequence MRI

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