Borges, Pedro; (2023) Physics-Based Image Synthesis for MRI Sequence Standardisation. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive medical imaging modality adept at showcasing soft-tissue contrast and well-suited to imaging most body parts. However, MRI is overwhelmingly used to produce qualitative images whose individual voxel values carry no diagnostic value. Instead, information is primarily derived from analysing the contrast between regions of interest. Challenges persist when it comes to downstream analyses predicated on using images acquired under different conditions. The first is that models are prone to lack generalisability to domains which they were not made privy to during training, and the second is the lack of standardisation when extracting biomarkers, as models typically cannot divorce perceived contrast from the true underlying anatomy. This thesis addresses the generalisability and standardisation problem by designing self-supervised segmentation networks that are cognizant of the physics underpinning the acquisition process. These networks are trained using simulated MR images boasting a wealth of contrasts, thus enabling a breadth of generalisability and granting them the ability to innately account for and standardise MR images, regardless of the sequence parameters used to acquire them. This is followed by iterating over the initial designs, enhancing generalisability and robustness and reducing the pre-processing time by modifying various aspects of the training pipeline. Further, uncertainty modelling is incorporated into the models to allow for additional levels of safety and introspection. Additionally, we demonstrate that despite their simulation-based training, our models generalise to real-world data, and so too does their internal modelling of the interplay between contrast and sequence parameters. Lastly, an unsupervised, heteromodal framework for translating typical qualitative images into quantitative tissue maps is proposed, the first of its kind. The hope is that the work contained herein will benefit the standardisation community and that its concepts will be translated into a greater variety of sequences and body part images.

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