An off-the-grid approach to multi-compartment magnetic resonance fingerprinting

TL;DR

This paper introduces an off-the-grid numerical method with neural network acceleration for multi-compartment magnetic resonance fingerprinting, enabling accurate tissue separation and property estimation without prior knowledge of tissue types.

Contribution

It presents a novel off-the-grid sparse approximation approach combined with neural network-based Bloch response modeling for improved MRF tissue compartment analysis.

Findings

Effective separation of tissue compartments demonstrated on simulated data.

Accurate quantitative NMR property estimation in in-vivo brain data.

Outperforms baseline multicompartment MRF methods.

Abstract

We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements. The number of tissues, their types or quantitative properties are not a-priori known, but the image is assumed to be composed of sparse compartments with linearly mixed Bloch magnetisation responses within voxels. Fine-grid discretisation of the multi-dimensional NMR properties creates large and highly coherent MRF dictionaries that can challenge scalability and precision of the numerical methods for (discrete) sparse approximation. To overcome these issues, we propose an off-the-grid approach equipped with an extended notion of the sparse group lasso regularisation for sparse approximation using continuous (non-discretised) Bloch response models. Further, the nonlinear and non-analytical Bloch responses are approximated by a neural network, enabling efficient back-propagation of the gradients through the proposed algorithm. Tested on simulated and in-vivo healthy brain MRF data, we demonstrate effectiveness of the proposed scheme compared to the baseline multicompartment MRF methods.