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Abstract #0787

Accelerated In Vivo Cardiac Diffusion Tensor MRI with Residual Deep Learning based Denoising in Lean and Obese Subjects

Kellie Phipps1, Robert Eder1, Sam Allen Michelhaugh2, Aferdita Spahillari2, Maaike van den Boomen1,3,4, Joan Kim1, Shestruma Parajuli1, Timothy G Reese3,5, Choukri Mekkaoui3,5, David Sosnovik1,3,6, Denise Gee7,8, Ravi Shah1,6, and Christopher Nguyen1,3,6
1Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, United States, 2Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States, 3Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Department of Radiology, University Medical Center Groningen, Groningen, Netherlands, 5Department of Radiology, Harvard Medical School, Boston, MA, United States, 6Department of Medicine, Harvard Medical School, Boston, MA, United States, 7Weight Center, Massachusetts General Hospital, Boston, MA, United States, 8Department of Surgery, Harvard Medical School, Boston, MA, United States

In vivo cardiac DT-MRI allows for imaging of the underlying myocardial fiber orientations but is hindered by clinically infeasible scan times. We developed and tested a residual deep learning denoising algorithm, DnCNN-54, on cardiac DT-MRI scans with fewer averages (4, 2, and 1) than the conventional 8-average 30 minute scan. We demonstrated a 2-fold acceleration can be achieved after DnCNN-54 is applied to 4 average dataset compared with the reference 8-average scan that preserves signal to noise ratio and cardiac DT-MRI parameter quantification. This 2-fold acceleration via DnCNN-54 denoising also maintained cardiac DT-MRI mean differences between obese and lean subjects.

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