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/m; mean age, 28 years ± 3 [standard deviation]; 11 women) and six with obesity (BMI ≥ 30 kg/m; mean age, 48 years ± 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 (4Av, 2Av, 1Av).