Integrated motion correction and dictionary learning for free‐breathing myocardial T1 mapping. Issue 4 (27th November 2018)

• Record Type: Journal Article
• Authors: Zhu, Yanjie; Kang, Jinkyu; Duan, Chong; Nezafat, Maryam; Neisius, Ulf; Jang, Jihye; Nezafat, Reza
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 4(2019)
• Page Start: 2644
• Page End: 2654
• Publication Date: 2018-11-27
• Subjects: compressed sensing -- dictionary learning -- motion correction -- myocardial T1 mapping; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27579
• Languages: English
• ISSNs: 0740-3194

Abstract

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.