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Abstract #0901

Automatic estimation of T1, T2, T1ρ and fat fraction in calf muscles for patients with diabetic peripheral neuropathy

Radhika Tibrewala1,2,3, Azadeh Sharafi4, Jill T Shah1, Smita Rao5, Ravinder R Regatte1,2,3, and Ryan Brown1,2,3
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, United States, 4Medical College of Wisconsin, Milwaukee, WI, United States, 5Department of Physical Therapy, New York University, New York, NY, United States

Synopsis

Keywords: Muscle, Diabetes

Motivation: Quantitative MRI is used for muscle parameter mapping, but single-parameter techniques and manual muscle segmentations take long.

Goal(s): Develop an automatic processing pipeline to generate parameter maps from 3D MRF and fat fraction images to extract quantitative biomarkers.

Approach: 3D MRF and fat fraction images were acquired on patients with diabetic peripheral neuropathy, deep-learning methods and post-processing were used to generate muscle masks and parameter maps before and after exercise intervention.

Results: Automatic muscle segmentation and 3D MRF are able to generate quantitative fat fraction, T1, T2, T1ρ volumetric maps within muscle ROIs for tracking changes in patients before and after exercise intervention.

Impact: Both the 3D MRF sequence and automatic muscle extraction help reduce acquisition and post-processing time, allowing faster assessment of treatment response in diabetic patients.

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