Bi-component dictionary matching for MR fingerprinting for efficient quantification of fat fraction and water T₁ in...

To propose an efficient bi-component MR fingerprinting (MRF) fitting method using a Variable Projection (VARPRO) strategy, applied to the quantification of fat fraction (FF) and water T1 ( $T{1}_{H_{2}0}$ ) in skeletal muscle tissues.

The MRF signals were analyzed in a two-step process by comparing them to the elements of separate water and fat dictionaries (bi-component dictionary matching). First, each pair of water and fat dictionary elements was fitted to the acquired signal to determine an optimal FF that was used to merge the fingerprints in a combined water/fat dictionary. Second, standard dictionary matching was applied to the combined dictionary for determining the remaining parameters. A clustering method was implemented to further accelerate the fitting. Accuracy, precision, and matching time of this approach were evaluated on both numerical and in vivo datasets, and compared to the reference dictionary-matching approach that includes FF as a dictionary parameter.

In numerical phantoms, all MRF parameters showed high correlation with ground truth for the reference and the bi-component method (R²  > 0.98). In vivo, the estimated parameters from the proposed method were highly correlated with those from the reference approach (R²  > 0.997). The bi-component method achieved an acceleration factor of up to 360 compared to the reference dictionary matching.

The proposed bi-component fitting approach enables a significant acceleration of the reconstruction of MRF parameter maps for fat-water imaging, while maintaining comparable precision and accuracy to the reference on FF and $T{1}_{H_{2}0}$ estimation.