Accelerated in vivo cardiac diffusion-tensor MRI using residual deep learning–based denoising in participants with obesity

Purpose: To develop and assess a residual deep learning algorithm to accelerate in vivo cardiac diffusion-tensor MRI (DT-MRI) by reducing the number of averages while preserving image quality and DT-MRI parameters.

Materials and Methods: In this prospective study, a denoising convolutional neural network (DnCNN) for DT-MRI was developed; a total of 26 participants, including 20 without obesity (body mass index [BMI], 30 kg/m2; mean age, 28 years 6 3 [standard deviation]; 11 women) and six with obesity (BMI 30 kg/m2; mean age, 48 years 6 11; five women), were recruited from June 19, 2019, to July 29, 2020. DT-MRI data were constructed at four averages (4Av), two averages (2Av), and one average (1Av) without and with the application of the DnCNN (4AvDnCNN, 2AvDnCNN, 1AvDnCNN). All data were compared against the reference DT-MRI data constructed at eight averages (8Av). Image quality, characterized by using the signal-to-noise ratio (SNR) and structural similarity index (SSIM), and the DT-MRI parameters of mean diffusivity (MD), fractional anisotropy (FA), and helix angle transmurality (HAT) were quantified.

Results: No differences were found in image quality or DT-MRI parameters between the accelerated 4AvDnCNN DT-MRI and the reference 8Av DT-MRI data for the SNR (29.1 6 2.7 vs 30.5 6 2.9), SSIM (0.97 6 0.01), MD (1.3 μm2/msec 6 0.1 vs 1.31 μm2/msec 6 0.11), FA (0.32 6 0.05 vs 0.30 6 0.04), or HAT (1.10°/% 6 0.13 vs 1.11°/% 6 0.09). The relationship of a higher MD and lower FA and HAT in individuals with obesity compared with individuals without obesity in reference 8Av DT-MRI measurements was retained in 4AvDnCNN and 2AvDnCNN DT-MRI measurements but was not retained in 4Av or 2Av DT-MRI measurements.

Conclusion: Cardiac DT-MRI can be performed at an at least twofold-accelerated rate by using DnCNN to preserve image quality and DT-MRI parameter quantification.