TrustMRI: Trustworthy and Robust Magnetic Resonance Image Reconstruction with Uncertainty Modelling and Deep Learning

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Magnetic resonance image (MRI) is the leading diagnostic modality for a wide range of exams due to the lack of ionising radiation and its ability to probe various aspects of the physiology. The use of MRI in UK has seen a large increase in recent years and with the technological advances and an ageing population, this demand is likely to continue to increase year-on-year. However unfortunately the physics of MRI data acquisition process makes it inherently slow, and the sustained increase in demand for MRI and its reduced reliability have also led to patients' longer waits and repeated procedures. It is therefore essential that society finds new ways to improve and optimise towards efficient MR imaging workflows. Recently, artificial intelligence (AI) techniques have opened the possibility to accelerate the MRI acquisition process considerably and have enabled progress beyond the limitations of conventional reconstruction methods. However, there is still a lack of consideration of their trustworthiness and failure management on unseen cases, which limits their translational potential in clinical practice. With the increasing development of deep learning-based techniques for MRI reconstruction, awareness about trustworthiness and uncertainty over deep learning reconstructed scans are becoming necessary and are also critical for downstream diagnostic decision-makings.This project aims to tackle the critical and growing problem of AI trustworthiness for AI-enabled MRI reconstruction. The proposed research will integrate and advance state-of-the-art research in machine learning and medical imaging. It will develop novel Bayesian deep learning approaches to quantify uncertainty for model-driven MRI reconstruction, build original failure prediction mechanisms to evaluate uncertainty, and investigate advanced test-time uncertainty reduction techniques for handling out-of-distribution data. This will conduce to creation of a streamlined pipeline to foster the common uncertainty practices in deep learning-based MRI reconstruction. It will also be evaluated on two clinical applications of accelerated pathological brain MRI and motion-corrupted cardiac MRI reconstruction. The confluence of the development in AI-enabled MRI reconstruction and its translational need opens exciting possibilities that we propose to investigate in this project.