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

Abstract

Purpose To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE. Methods To reconstruct T1ρ 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. T1ρ quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bi-lateral scans of three healthy volunteers. Result T1ρ maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T1ρ errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T1ρ reported from previous studies. Conclusion The proposed method can significantly accelerate the acquisition process of T1ρ quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.

Cartilage T 1ρ and T 2 relaxation times: longitudinal reproducibility and variations using different coils, MR systems and sites

“…In addition, the relatively low resolution of T 1ρ and T 2 images (0.6 mm in plane with 4 mm slices) may introduce bias to T 1ρ and T 2 values due to the partial volume effect. Advanced acceleration techniques can be applied in the future to obtain T 1ρ and T 2 images with higher resolutions within clinically acceptable acquisition time (27). The reproducibility was evaluated only in healthy controls and should be evaluated in OA subjects in future studies.…”

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

“…It has been shown that sparse reconstructions can be used for faster and yet accurate quantitative sodium imaging of knee cartilage at 7T, opening the door for sodium imaging-based quantitative assessment of osteoarthritis 170 . Quantification of T 1ρ similarly holds promise for characterization of osteoarthritis, and acceleration of T 1ρ mapping in the knee has been demonstrated in early feasibility studies 171,172 . Accelerated dynamic functional metabolic imaging of phosphocreatine kinetics after calf muscle exercise has also been shown for 3D 31 P spectroscopic imaging at 7T 173 .…”

Rapid compositional mapping of knee cartilage with compressed sensing MRI

“…a. This sampling pattern was used previously . Since many relaxation‐weighted images have to be captured to reconstruct relaxation maps, the acquisition process is repeated several times.…”

“…The reconstruction problem can be formulated as: where x is a vector that represents the reconstructed set of relaxation‐weighted images, with its original size of , y is a vector that represents the captured k ‐space data for all relaxation‐weighted images, its original size is k y × k z × c × p , where c is the number of receive coils, and the matrix E represents the encoding matrix mapping x to y , containing coil sensitivities (when parallel imaging and CS is jointly used, Fourier transforms, and sampling pattern . Many CS methods for knee cartilage use the joint parallel imaging and CS approach to achieve higher undersampling rates, as shown previously . The use of squared l 2 ‐norm, or Euclidean norm, , is quite common either because it is related to the usual assumption of Gaussian noise, or because it leads to a more tractable mathematical problem.…”

“…The Levenberg–Marquardt algorithm is the most commonly used for the fitting part of the quantitative mapping. It has been used for T 1ρ mapping and for T 1 and T 2 mapping with monoexponential models. The conjugate gradient Steihaug's trust‐region algorithm is used for monoexponential and for biexponential T 1ρ mapping.…”