Integrated motion correction and dictionary learning for free-breathing myocardial T 1 mapping.

Purpose: To develop and evaluate an integrated motion correction and dictionary learning (MoDic) technique to accelerate data acquisition for myocardial T-1 mapping with improved accuracy. Methods: MoDic integrates motion correction with dictionary learning-based reconstruction. A random undersampling scheme was implemented for slice-interleaved T-1 mapping sequence to allow prospective undersampled data acquisition. Phantom experiments were performed to evaluate the effect of reconstruction on T-1 measurement. In vivo T-1 mappings were acquired in 8 healthy subjects using 6 different acceleration approaches: uniform or randomly undersampled k-space data with reduction factors (R) of 2, 3, and 4. Uniform undersampled data were reconstructed with SENSE, and randomly undersampled k-space data were reconstructed using dictionary learning, compressed sensing SENSE, and MoDic methods. Three expert readers subjectively evaluated the quality of T-1 maps using a 4-point scoring system. The agreement between T-1 values was assessed by Bland-Altman analysis. Results: In the phantom study, the accuracy of T-1 measurements improved with increasing reduction factors (-31 +/- 35 ms, -13 +/- 18 ms, and -5 +/- 11 ms for reduction factor (R) = 2 to 4, respectively). The image quality of in vivo T-1 maps assessed by subjective scoring using MoDic was similar to that of SENSE at R = 2 (P = .61) but improved at R = 3 and 4 (P < .01). The scores of dictionary learning (2.98 +/- 0.71, 2.91 +/- 0.60, and 2.67 +/- 0.71 for R = 2 to 4) and CS-SENSE (3.32 +/- 0.42, 3.05 +/- 0.43, and 2.53 +/- 0.43) were lower than those of MoDic (3.48 +/- 0.46, 3.38 +/- 0.52, and 2.9 +/- 0.60) for all reduction factors (P < .05 for all). Conclusion: The MoDic method accelerates data acquisition for myocardial T-1 mapping with improved T-1 measurement accuracy.