Does the dGEMRIC Index Recover 3 Years After Surgical FAI Correction and an Initial dGEMRIC Decrease at 1-Year Follow-up? A Controlled Prospective Study

Meier, Malin; Scheuber, Sybille; Hanke, Markus S.; Haefeli, Pascal C; Ruckli, Adrian C.; Liechti, Emanuel F; Gerber, Nicholas; Lerch, Till; Tannast, Moritz; Siebenrock, Klaus A.; Steppacher, Simon D.; Schmaranzer, Florian

doi:10.1177/03635465231167854

Cited by 3 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 In addition, standard 2D MRI is limited to detection of advanced cartilage damage while biochemically sensitive MRI such as delayed gadolinium enhanced MRI of cartilage (dGEMRIC) can be used as a more sensitive and quantitative cartilage biomarker, thereby guiding the surgeon in the question of whether hip joint preserving or replacement surgery should be performed. 3,4 In daily clinical practice, a detailed morphologic 3D analysis of hip cartilage and labrum as well as the assessment of dGEMRIC indices is not routinely feasible due to the laborious task of manual segmentation. Several attempts have been made to perform this task for the hip cartilage automatically, including 3D deformable models 5 and multitemplate based label fusion techniques 6 .…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Automatic 3D Segmentation of Hip Cartilage and Labrum from Direct Hip MR Arthrography

Meier,

Helfenstein,

Boschung

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients. Institutional routine protocol included a 3D T1 mapping sequence, which was used for manual segmentation of hip cartilage and labrum. 80 hips were used for training and the remaining 20 for testing. Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. Inter-rater agreement of manual segmentation served as benchmark for automatic segmentation performance. 100 patients were included (mean age 30 ± 10 years, 64% female patients). Mean DSC for cartilage was 0.92 ± 0.02 (95% confidence interval [CI] 0.92–0.93) and 0.83 ± 0.04 (0.81–0.85) for labrum and comparable (p = 0.232 and 0.297, respectively) to inter-rater agreement of manual segmentation: DSC cartilage 0.93 ± 0.04 (0.92–0.95); DSC labrum 0.82 ± 0.05 (0.80–0.85). When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88–0.90) and 0.71 ± 0.04 (0.69–0.73) for cartilage and labrum, respectively. The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Automatic 3D Segmentation of Hip Cartilage and Labrum from Direct Hip MR Arthrography

Meier,

Helfenstein,

Boschung

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Systematic review of techniques in cartilage imaging

Buchanan,

Markhardt

2024

Journal of Cartilage & Joint Preservation

View full text Add to dashboard Cite

Femoral head decentration on hip MRI: comparison between imaging planes, methods of contrast administration, and hip deformities

Schmaranzer,

Becker,

Heimann

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objectives To compare the prevalence of femoral head decentration (FHD) on different MR imaging planes in patients undergoing direct/indirect hip MR arthrography (MRA) with asymptomatic controls and to evaluate its association with osseous deformities. Methods IRB-approved retrospective single-center study of symptomatic hips undergoing direct or indirect hip MRA at 3 T. Asymptomatic participants underwent non-contrast hip MRI at 3 T. FHD was defined as a continuous fluid layer between the acetabulum and femoral head and assessed on axial, sagittal and radial images. The association of intra-articular/intra-venous contrast agents and the prevalence of FHD was evaluated. The association of FHD with osseous deformities and joint damage was assessed using multiple logistic regression analysis. Results Three-hundred ninety-four patients (447 hips, mean age 31 ± 9 years, 247 females) were included and compared to 43 asymptomatic controls (43 hips, mean age 31 ± 6 years, 26 females). FHD was most prevalent on radial images and more frequent in symptomatic hips (30% versus 2%, p < 0.001). FHD prevalence was not associated with the presence/absence of intra-articular contrast agents (30% versus 22%, OR = 1.5 (95% CI 0.9–2.5), p = 0.125). FHD was associated with hip dysplasia (OR = 6.1 (3.3–11.1), p < 0.001), excessive femoral torsion (OR = 3.0 (1.3–6.8), p = 0.010), and severe cartilage damage (OR = 3.6 (2.0–6.7), p < 0.001). Conclusion While rare in asymptomatic patients, femoral head decentration in symptomatic patients is associated with osseous deformities predisposing to hip instability, as well as with extensive cartilage damage. Critical relevance statement Decentration of the femoral head on radial MRA may be interpreted as a sign of hip instability in symptomatic hips without extensive cartilage defects. Its presence could unmask hip instability and yield promise in surgical decision-making. Key Points The best method of identifying femoral head decentration is radial MRI. The presence/absence of intra-articular contrast is not associated with femoral head decentration. Femoral head decentration is associated with hip deformities predisposing to hip instability. Graphical Abstract

show abstract

Does the dGEMRIC Index Recover 3 Years After Surgical FAI Correction and an Initial dGEMRIC Decrease at 1-Year Follow-up? A Controlled Prospective Study

Cited by 3 publications

References 37 publications

A Deep Learning Approach for Automatic 3D Segmentation of Hip Cartilage and Labrum from Direct Hip MR Arthrography

A Deep Learning Approach for Automatic 3D Segmentation of Hip Cartilage and Labrum from Direct Hip MR Arthrography

Systematic review of techniques in cartilage imaging

Femoral head decentration on hip MRI: comparison between imaging planes, methods of contrast administration, and hip deformities

Contact Info

Product

Resources

About