Integrated motion correction and dictionary learning for free-breathing myocardial T1 mapping

Abstract

Purpose: To develop and evaluate an integrated motion correction and dictionary learning (MoDic) technique to accelerate data acquisition for myocardial T₁ mapping with improved accuracy.

Methods: MoDic integrates motion correction with dictionary learning-based reconstruction. A random undersampling scheme was implemented for slice-interleaved T₁ mapping sequence to allow prospective undersampled data acquisition. Phantom experiments were performed to evaluate the effect of reconstruction on T₁ measurement. In vivo T₁ 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 T₁ maps using a 4-point scoring system. The agreement between T₁ values was assessed by Bland-Altman analysis.

Results: In the phantom study, the accuracy of T₁ 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 T₁ 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 T₁ mapping with improved T₁ measurement accuracy.

Keywords: compressed sensing; dictionary learning; motion correction; myocardial T1 mapping.