Improving parametric estimation in the brain from multispin-echo sequences using a fusion bootstrap moves solver.

Purpose: To simultaneously estimate the field (along with the T ) in the brain with multispin-echo (MSE) sequences and dictionary matching.

Methods: T mapping provides clinically relevant information such as in the assessment of brain degenerative diseases. It is commonly obtained with MSE sequences, and accuracy can be further improved by matching the MSE signal to a precomputed dictionary of echo-modulation curves. For additional T quantification, transmit field knowledge is also required. Preliminary work has shown that although simultaneous brain estimation along with T is possible, it presents a bimodal distribution with the main peak coinciding with the true value. By taking advantage of this, the maps are expected to be spatially smooth by applying an iterative method that takes into account each pixel neighborhood known as the fusion bootstrap moves solver (FBMS). The effect of the FBMS on accuracy and piecewise smoothness is investigated and different spatial regularization levels are compared. Total variation regularization was used for both and T simultaneous estimation because of its simplicity as an initial proof-of-concept; future work could explore non edge-preserving regularization independently for .

Results: Improvements in accuracy (up to 45.37% and 16.81% error decrease) and recovery of spatially homogeneous maps are shown in simulations and in vivo 3.0T brain data, respectively.

Conclusion: Accurate estimated values can be obtained from widely available MSE sequences while jointly estimating T maps with the use of echo-modulation curve matching and FBMS at no further cost.