Accelerating t_1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE

Purpose To accelerate T_1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE. Methods To reconstruct T_1ρ images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the reconstruction process alternates iteratively among local support detection in the domain of principal component analysis, compressed sensing reconstruction of the image sequence, and sensitivity estimation with JSENSE. T_1ρ quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bilateral scans of three healthy volunteers. Results T_1ρ maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T_1ρ errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T_1ρ reported from previous studies. Conclusion The proposed method can significantly accelerate the acquisition process of T_1ρ quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.