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Abstract #3991

Clinically Feasible Whole Knee MR T1ρ and T2 Mapping in Under 3 Minutes with Accelerated Imaging and Automated Analysis

Ahmed Tahseen Minhaz1,2, Richard Lartey1,2, Zhiyuan Zhang1,2, Jee Hun Kim1,2, Mingrui Yang1,2, Jiasen Zhang1,3, Joseph Mo3, Weihong Guo3, Naveen Subhas1,4, Carl S. Winalski1,2,4, and Xiaojuan Li1,2,4
1Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 2Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States, 3Department of Mathematics, Case Western Reserve University, Cleveland, OH, United States, 4Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Analysis/Processing, Osteoarthritis, AI, Accelerated Imaging

Motivation: Long MRI acquisition times limit T1ρ/T2 mapping for assessing cartilage health, necessitating faster and reliable quantification methods.

Goal(s): This study evaluates the use of accelerated qMRI and deep learning-based segmentation for reliable T1ρ/T2 mapping without high-resolution morphological images.

Approach: A pretrained segmentation model, with and without transfer learning, was used to segment six cartilage and meniscal regions from GRAPPA2-MAPSS and compressed sensing accelerated (CS-AF8)-MAPSS echo images. These segmentations, overlaid on maps, provided T1ρ/T2 quantification.

Results: CS-AF8, with transfer learning, achieved comparable segmentation performance and T1ρ/T2 quantification to the standard MAPSS with DESS segmentation, although it showed higher variation in T1ρ/T2 values.

Impact: This research offers a clinically feasible accelerated qMRI and deep learning-based approach for faster and accurate qMRI-based cartilage assessment, allowing T1ρ and T2 mapping in under 3 minutes.

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Keywords