Foundation Models in Multi-sequence MRI Analysis

PhD Qualifying Examination


Title: "Foundation Models in Multi-sequence MRI Analysis"

by

Mr. Zelin QIU


Abstract:

Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in modern 
radiology, valued for its exceptional soft-tissue contrast and lack of 
ionizing radiation. A single MRI examination typically yields multiple 
sequences, each sensitive to a distinct set of tissue properties. Therefore, 
accurate interpretation requires integrating complementary information from 
these sequences, and their heterogeneity prolongs reading time and contributes 
to the increasing workload of radiologists. Deep learning has demonstrated 
superior performance in MRI analysis across various clinical applications, 
facilitating more accurate diagnostics, enabling personalized treatment 
regimens, uncovering biological insights, and optimizing healthcare delivery 
efficiency. Nevertheless, the acquisition of specialized training data still 
faces significant obstacles due to the high cost of annotation. Foundation 
models have recently emerged as a promising approach to mitigate these 
challenges and are adaptable to numerous clinical tasks, as they learn 
generalizable representations by pre-training on extensive, diverse MRI 
datasets. In this survey, we present a comprehensive review of recent advances 
in foundation models for multi-sequence MRI analysis. We review the benchmarks 
and evaluation metrics employed to assess these models. Then we organize 
existing methods via a taxonomy of self-supervised pre-training paradigms and 
discuss the advanced strategies tailored to the multi-sequence setting. 
Finally, we identify the core challenges in the field and outline promising 
future research directions towards a more generalizable, interpretable, and 
collaborative framework.


Date:                   Thursday, 10 September 2026

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Dr. Hao Chen (Supervisor)
                        Dr. Long Chen (Chairperson)
                        Dr. Sehi L'Yi