Magnetic resonance imaging · 2026
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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Hirt Julien, Roshardt Jose, Meier Malin Kristin, Ruckli Adrian Cyril, Lerch Till Dominic, Tannast Moritz, Steppacher Simon Damian, Bonanno Gabriele, Piredda Gian Franco, Jung Bernd, Schmaranzer Florian
Аннотация
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.
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.