Abstract

Motivation: Bi-exponential T2 and T1rho mapping of the knee cartilage can potentially improve early detection of knee osteoarthritis. Goal(s): Scan time is usually long and SNR is low with standard methods. We plan to improve these aspects with a machine-learned pulse sequence. Approach: We use a machine learning approach, called optimized variable flip-angles (OVFA) on magnetization-prepared gradient-echo (MPGRE) sequences to improve bi-exponential T2 and T1rho mapping on the knee cartilage. Results: We observed an improvement of ~50% in SNR and a reduction of acquisition time by almost 2X when compared to standard MAPSS, typically used for quantitative T1rho and T2 mapping. Impact: This study shows that the learned pulse sequence, named MPGRE-OVFA, can obtain similar bi-exponential T2 and T1rho mapping values as MAPSS, but it is 2 times faster and has 50% more SNR, potentially improving early detection of osteoarthritis.