Accelerating T1ρ 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 bilateral scans of three healthy volunteers.

Results: 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.

Keywords: T1ρ mapping; cartilage imaging; compressed sensing; iterative support detection; joint sensitivity estimation; principal component analysis.

Fig. 1

The pulse diagram of the MAPSS (magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots) T_1ρ quantification pulse sequence.

Fig. 2

Maps of support in x-PCA space. (a): True support. (b): Detected (known) support T. (c): Unknown support Δ which excludes T. (d): Detected support T_c that is correct. (e): Detected support T_f that is wrong. (f) Missed support. Support is shown in white. An acceleration factor of 3 is used.

Fig. 3

The post processing process: T_1ρ-weighted images were rigidly registered to the images with the shortest TSL using VTK CISG Registration Toolkit. The T_1ρ maps were constructed by the Levenberg-Marquardt mono-exponential fitting algorithm that fits the image intensity pixel to pixel. The cartilage were segmented semi-automatically into six compartments (LFC: lateral femoral condyle; LT: lateral tibia; MFC: medial femoral condyle; MT: medial tibia; Pat: patella; T: trochlea) based on edge detection and Bezier splines. All segments were manually corrected to avoid erroneous inclusion of synovial fluid or other surrounding tissues.

Fig. 4

The reconstruction results of the in vivo human knee using k-t LAISD with acceleration factors of 3 and 3.5. Different slices from a volunteer are shown. Error images were scaled accordingly to better reveal the difference. Normalized root mean square errors are shown on the left top corner of each error image.

Fig. 5

Averaged signal-intensity curves as a function of TSLs for all six ROI compartments. The curves represent the reconstructions from full acquisition and reduced acquisition using our technique with acceleration factors of 3 and 3.5. Both curves are consistent with that from full scan for all compartments.

Fig. 6

T_1ρ maps overlaid on the knee cartilage images. Maps from acceleration factors of 3 and 3.5 are compared with those from the full scan as the reference. Two different slices are shown to include all six compartments. The T_1ρ values obtained from the accelerated scans are consistent with those from the full scan.

Fig. 7

Top: The mean T_1ρ values of all six cartilage compartments of a volunteer from fully sampled, k-t LAISD with JSENSE, and k-t LAISD, k-t ISD and k-t FOCUSS with conventional SENSE. Bottom: Corresponding percentage errors of the T_1ρ values. It can be seen that the reconstructions from k-t LAISD with JSENSE achieves the lowest error percentage compared with the other three methods.

Fig. 8

Coil sensitivity maps of a single coil estimated using different methods. Regions with low signal intensity (background and bone) are removed for better visualization. (a): Conventional SENSE with composite images without smoothing. (b): Conventional SENSE with composite images with smoothing. (c): Conventional SENSE with the low resolution images without smoothing. (d): Conventional SENSE with the low resolution images with smoothing. (e): JSENSE.

Fig. 9

Top: The mean T_1ρ values of all six datasets from fully sampled image and images with R = 3 and 3.5. Results from all six compartments are shown. Bottom: Corresponding percentage errors of the mean T_1ρ values. It can be seen that the reconstructions from accelerated scans have errors below 1% in all compartments. The results with R = 3.5 show larger variations than those with R = 3.

Fig. 10

Left: The linear regression plot of T_1ρ values for fully sampled reference, and accelerated reconstructions with R = 3 and 3.5. It shows that the T1ρ values from the accelerated reconstructions strongly correlate with those from reference (R²=0.9963 for 3× acceleration and R²=0.9938 for 3.5× acceleration). Right: The Bland-Altman plot of the T_1ρ values difference vs. the average T_1ρ values for R = 3 (Bias = −0.1055, 95% limits of agreement [−0.610, 0.420]) and R = 3.5 (Bias = −0.129, 95% limits of agreement [−0.695, 0.437]).