Accelerating Cardiac Diffusion Tensor Imaging With a U‐Net Based Model: Toward Single Breath‐Hold.

Background: In vivo cardiac diffusion tensor imaging (cDTI) characterizes myocardial microstructure. Despite its potential clinical impact, considerable technical challenges exist due to the inherent low signal‐to‐noise ratio. Purpose: To reduce scan time toward one breath‐hold by reconstructing diffusion tensors for in vivo cDTI with a fitting‐free deep learning approach. Study type: Retrospective. Population: A total of 197 healthy controls, 547 cardiac patients. Field strength/sequence: A 3 T, diffusion‐weighted stimulated echo acquisition mode single‐shot echo‐planar imaging sequence. Assessment: A U‐Net was trained to reconstruct the diffusion tensor elements of the reference results from reduced datasets that could be acquired in 5, 3 or 1 breath‐hold(s) (BH) per slice. Fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and sheetlet angle (E2A) were calculated and compared to the same measures when using a conventional linear‐least‐square (LLS) tensor fit with the same reduced datasets. A conventional LLS tensor fit with all available data (12 ± 2.0 [mean ± sd] breath‐holds) was used as the reference baseline. Statistical tests: Wilcoxon signed rank/rank sum and Kruskal–Wallis tests. Statistical significance threshold was set at P = 0.05. Intersubject measures are quoted as median [interquartile range]. Results: For global mean or median results, both the LLS and U‐Net methods with reduced datasets present a bias for some of the results. For both LLS and U‐Net, there is a small but significant difference from the reference results except for LLS: MD 5BH (P = 0.38) and MD 3BH (P = 0.09). When considering direct pixel‐wise errors the U‐Net model outperformed significantly the LLS tensor fit for reduced datasets that can be acquired in three or just one breath‐hold for all parameters. Data conclusion: Diffusion tensor prediction with a trained U‐Net is a promising approach to minimize the number of breath‐holds needed in clinical cDTI studies. Evidence Level: 4 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]