Compressed sensing magnetization-prepared 2 rapid gradient-echo for accelerated hip cartilage T1 mapping and deep learning-based 3D segmentation of hip cartilage and labrum.

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Original abstract

Background Comprehensive assessment of hip cartilage morphology and composition is essential for surgical decision-making in femoroacetabular impingement and hip dysplasia. Magnetization-prepared 2 rapid gradient-echo (MP2RAGE) enables T1 mapping and segmentation but requires long acquisition times, limiting clinical adoption. To validate a compressed sensing (CS) MP2RAGE protocol against the standard sequence for hip cartilage T1 mapping and automated 3D segmentation of hip cartilage and labrum.Methods This retrospective study included 18 patients who underwent direct MR arthrography with both standard MP2RAGE (7 min 32 s) and CS-MP2RAGE (5 min). Automated segmentation of cartilage and labrum was performed using a 3D U-Net deep learning model to extract global and regional (anterior, superior, posterior) hip cartilage T1 values and 3D parameters of hip cartilage and labrum. Morphological damage was scored semi-quantitatively on clinical proton-density weighted MRI. Agreement was assessed using paired t-tests, Bland-Altman analysis, Dice similarity coefficients, and Spearman correlations.Results The mean difference in overall T1 values was 24.5 ms (4.2%, p < 0.001) with excellent correlation (rs = 0.983). Both sequences showed strong inverse correlations with morphological damage scores (CS: rs = -0.78; standard: rs = -0.76). Relative differences for cartilage volume, thickness, area, and surface area ranged from -6.9% to -0.6%. Labral volume, area, and joint surface contribution ranged from -2.6% to 0.4%. All morphological parameters showed excellent correlation (rs > 0.96).Conclusions Compressed sensing MP2RAGE enables 5-min acquisition with strong agreement for cartilage T1 and automated morphological assessment compared with conventional MP2RAGE. Further prospective validation is needed before broader clinical implementation.

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