Objective The study was performed to evaluate cartilage within the knee following a first-time patellar dislocation, using elevated MRI-based T1ρ relaxation times as an indicator of low proteoglycan concentration. The hypothesis is that MRI-based T1ρ relaxation times for patellofemoral and tibiofemoral cartilage are significantly longer for knees being treated for patellar dislocation than for healthy control knees. Design Twenty-one subjects being treated for a first-time, unilateral dislocation of the patella and 16 healthy controls participated in MRI-based T1ρ relaxation time mapping. Mean relaxation times were quantified for patellofemoral and tibiofemoral regions for injured knees, the contralateral knees, and healthy controls. T1ρ values for each region were compared between the 3 groups with generalized estimating equations. Linear regressions were also performed to correlate T1ρ relaxation times with time from injury. Results The knees with a disloction had longer T1ρ relaxation times than the contralateral knees and control group at the medial patella and longer relaxation times than the control group at the lateral tibia ( P < 0.05). T1ρ relaxation times at the medial patella also decreased with time from injury (r2 = 0.21, P = 0.037). Conclusions Compositional changes to cartilage on the medial patella are related to traumatic impact during a dislocation. Potential exists for cartilage properties at the medial patella to improve with time. Cartilage degradation at the lateral tibia is not directly related to traumatic impact. The current baseline data are a starting point to characterize the pathway from a first-time dislocation to progressive cartilage degradation and osteoarthritis.
This study aims to determine if baseline T1ρ and T2 will predict cartilage morphological lesion progression in the patellofemoral joint (PFJ) and patient‐reported outcomes at 2‐year after anterior cruciate ligament (ACL) reconstruction (ACLR). Thirty‐nine ACL‐injured patients were studied at baseline and two‐year after ACLR. 3 T MR T1ρ and T2 images and Knee Injury and Osteoarthritis Outcome Score (KOOS) were acquired at both time points. Voxel‐based relaxometry (VBR) technique was used to detect local cartilage abnormalities. Patients were divided into progression and non‐progression groups based on changes of the whole‐organ magnetic resonance imaging scoring (WORMS) grading of cartilage in PFJ from baseline to 2‐year, and into lower (more pain) and higher (less pain) KOOS pain groups based on 2‐year KOOS pain scores, separately. Voxel‐based analyses of covariance were used to compare T1ρ and T2 values at baseline between the defined groups. Using VBR analysis, the progression group at 2‐year showed higher T1ρ and T2 compared with the non‐progression group at baseline, with the medial femoral condyle showing the largest areas with significant differences. At two‐year, 56% of patients were able to recover with respect to KOOS pain. The lower KOOS pain group at 2‐year showed significantly elevated T1ρ and T2 in the patella at baseline compared with the higher KOOS pain group. In conclusion, baseline T1ρ and T2 mapping, combined with VBR analysis, may help identify ACLR patients at high risk of developing progressive PFJ cartilage lesions and worse clinical symptoms 2‐year after surgery.
Purpose
Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross‐sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions.
Methods
A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects.
Results
The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar.
Conclusions
The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large‐scale patient studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.