Spatially regularized T1 estimation from variable flip angles MRI

Authors


Abstract

Purpose:

To develop efficient algorithms for fast voxel-by-voxel quantification of tissue longitudinal relaxation time (T1) from variable flip angles magnetic resonance images (MRI) to reduce voxel-level noise without blurring tissue edges.

Methods:

T1 estimations regularized by total variation (TV) and quadratic penalty are developed to measure T1 from fast variable flip angles MRI and to reduce voxel-level noise without decreasing the accuracy of the estimates. First, a quadratic surrogate for a log likelihood cost function of T1 estimation is derived based upon the majorization principle, and then the TV-regularized surrogate function is optimized by the fast iterative shrinkage thresholding algorithm. A fast optimization algorithm for the quadratically regularized T1 estimation is also presented. The proposed methods are evaluated by the simulated and experimental MR data.

Results:

The means of the T1 values in the simulated brain data estimated by the conventional, TV-regularized, and quadratically regularized methods have less than 3% error from the true T1 in both GM and WM tissues with image noise up to 9%. The relative standard deviations (SDs) of the T1 values estimated by the conventional method are more than 12% and 15% when the images have 7% and 9% noise, respectively. In comparison, the TV-regularized and quadratically regularized methods are able to suppress the relative SDs of the estimated T1 to be less than 2% and 3%, respectively, regardless of the image noise level. However, the quadratically regularized method tends to overblur the edges compared to the TV-regularized method.

Conclusions:

The spatially regularized methods improve quality of T1 estimation from multiflip angles MRI. Quantification of dynamic contrast-enhanced MRI can benefit from the high quality measurement of native T1.

Ancillary