Model-based multi-parameter mapping

• URL: http://arxiv.org/abs/2102.01604v1
• Date: Tue, 2 Feb 2021 17:00:11 GMT
• Title: Model-based multi-parameter mapping
• Authors: Yael Balbastre, Mikael Brudfors, Michela Azzarito, Christian Lambert, Martina F. Callaghan, John Ashburner
• Abstract summary: Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. Estimations often assume noise subsets of data to solve for different quantities in isolation. Instead, a generative model can be formulated and inverted to jointly recover parameter estimates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, extracting quantitative parameters such as the longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*), or magnetisation-transfer saturation (MTsat) involves inverting a highly non-linear function. Estimations often assume noise-free measurements and use subsets of the data to solve for different quantities in isolation, with error propagating through each computation. Instead, a probabilistic generative model of the entire dataset can be formulated and inverted to jointly recover parameter estimates with a well-defined probabilistic meaning (e.g., maximum likelihood or maximum a posteriori). In practice, iterative methods must be used but convergence is difficult due to the non-convexity of the log-likelihood; yet, we show that it can be achieved thanks to a novel approximate Hessian and, with it, reliable parameter estimates obtained. Here, we demonstrate the utility of this flexible framework in the context of the popular multi-parameter mapping framework and further show how to incorporate a denoising prior and predict posterior uncertainty. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.

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