Model-based multi-parameter mapping

TL;DR

This paper introduces a probabilistic model-based approach for multi-parameter MRI mapping that improves accuracy and noise robustness, enables uncertainty estimation, and leverages GPU acceleration for efficient computation.

Contribution

It proposes a novel probabilistic generative model for multi-parameter MRI mapping, with a second order optimization method and the ability to incorporate priors and estimate uncertainty.

Findings

More accurate parameter maps from multi-parameter MRI data

Reduced noise in the estimated maps using total variation prior

Efficient GPU-accelerated implementation

Abstract

Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.