Efficient spatial regularisation of dictionary matching using discrete Markov random fields

Motivation: Dictionary matching, used extensively in quantitative MRI, is resistant to standard approaches to spatial regularisation due to its discrete nature. Goal(s): Demonstrate the feasibilty of efficient spatial regularisation of parameter maps produced via dictionary matching, in this work using total variation (TV) regularisation. Approach: Spatially regularised dictionary matching was formulated as an optimisation on a discrete Markov random field, and the result optimisation problem solved using a primal-dual strategy, with the efficient iterative solver FastPD. Results: TV regularisation improved apparent quality of parameter maps in phantoms and in vivo.Impact: The proposed technique offers a means to improve the quality of parameter maps from any quantitative framework employing dictionary matching, covering a wide range of possible anatomies and clinical applications.