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

TrustMRI：利用不确定性建模和深度学习进行可靠且鲁棒的磁共振图像重建

• 批准号: EP/X039277/1
• 负责人: Chen Qin
• 金额: $ 61.92万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039277%2F1
• 关键词: TrustMRI; Trustworthy; Robust; Magnetic; Resonance

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.

磁共振成像（MRI）由于缺乏电离辐射和探测生理各方面的能力，是广泛检查的主要诊断方式。近年来，磁共振成像的使用在英国有了很大的增长，随着技术的进步和人口的老龄化，这种需求可能会继续逐年增加。然而，不幸的是，MRI数据采集过程的物理特性使其固有的缓慢，并且对MRI需求的持续增加及其可靠性的降低也导致患者等待时间更长和重复程序。因此，社会必须找到新的方法来改进和优化高效的磁共振成像工作流程。最近，人工智能（AI）技术已经开启了大大加快MRI采集过程的可能性，并且已经超越了传统重建方法的局限性。然而，目前仍缺乏对其可靠性的考虑和对未见病例的失败管理，这限制了其在临床实践中的转化潜力。随着基于深度学习的MRI重建技术的不断发展，对深度学习重建扫描的可信度和不确定性的认识变得越来越必要，对下游诊断决策也至关重要。该项目旨在解决人工智能核磁共振重建中日益严重的人工智能可信度问题。拟议的研究将整合和推进机器学习和医学成像方面的最新研究。它将开发新的贝叶斯深度学习方法来量化模型驱动的MRI重建的不确定性，建立原始的故障预测机制来评估不确定性，并研究先进的测试时间不确定性减少技术来处理分布外数据。这将有助于建立一个流线型的管道，以促进基于深度学习的MRI重建中常见的不确定性实践。本文还将对加速病理脑MRI和运动损坏心脏MRI重建的两种临床应用进行评估。人工智能核磁共振重建的发展及其转化需求的融合打开了令人兴奋的可能性，我们建议在这个项目中进行研究。