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 Boomen^1,3,4, Joan Kim¹, Shestruma Parajuli¹, Timothy G Reese^3,5, Choukri Mekkaoui^3,5, David Sosnovik^1,3,6, Denise Gee^7,8, Ravi Shah^1,6, and Christopher Nguyen^1,3,6

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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