Device for Assessing Knee Joint Dynamics During Magnetic Resonance Imaging.

BACKGROUND: Knee assessment with and without load using magnetic resonance imaging (MRI) can provide information on knee joint dynamics and improve the diagnosis of knee joint diseases. Performing such studies on a routine MRI-scanner require a load-exerting device during scanning. There is a need for more studies on developing loading devices and evaluating their clinical potential. PURPOSE: Design and develop a portable and easy-to-use axial loading device to evaluate the knee joint dynamics during the MRI study. STUDY TYPE: Prospective study. SUBJECTS: Nine healthy subjects. FIELD STRENGTH/SEQUENCE: A 0.25 T standing-open MRI and 3.0 T MRI. PD-T2 -weighted FSE, 3D-fast-spoiled-gradient-echo, FS-PD, and CartiGram sequences. ASSESSMENT: Design and development of loading device, calibration of loads, MR safety assessment (using projectile angular displacement, torque, and temperature tests). Scoring system for ease of doing. Qualitative (by radiologist) and quantitative (using structural similarity index measure [SSIM]) image-artifact assessment. Evaluation of repeatability, comparison with various standing stances load, and loading effect on knee MR parameters (tibiofemoral bone gap [TFBG], femoral cartilage thickness [FCT], tibial cartilage thickness [TCT], femoral cartilage T2 -value [FCT2], and tibia cartilage T2 -value [TCT2]). The relative percentage change (RPC) in parameters due to the device load was computed. STATISTICAL TEST: Pearson's correlation coefficient (r). RESULTS: The developed device is conditional-MR safe (details in the manuscript and supplementary materials), 15 × 15 × 45 cm3 dimension, and <3 kg. The ease of using the device was 4.9/5. The device introduced no visible image artifacts, and SSIM of 0.9889 ± 0.0153 was observed. The TFBG intraobserver variability (absolute difference) was <0.1 mm. Interobserver variability of all regions of interest was <0.1 mm. The load exerted by the device was close to the load during standing on both legs in 0.25 T scanner with r > 0.9. Loading resulted in RPC of 1.5%-11.0%, 7.9%-8.5%, and -1.5% to 13.0% in the TFBG, FCT, and TCT, respectively. FCT2 and TCT2 were reduced in range of 1.5-2.7 msec and 0.5-2.3 msec due to load. DATA CONCLUSION: The proposed device is conditionally MR safe, low cost (material cost < INR 6000), portable, and effective in loading the knee joint with up to 50% of body weight. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Model for in-vivo estimation of stiffness of tibiofemoral joint using MR imaging and FEM analysis.

BACKGROUND: Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject's tissue material properties are inaccessible. PURPOSE: The current study's purpose was to develop a methodology to estimate the subject-specific stiffness of the tibiofemoral joint using finite-element-analysis (FEA) and MRI data of knee joint with and without load. METHODS: In this study, six Magnetic Resonance Imaging (MRI) datasets were acquired from 3 healthy volunteers with axially loaded and unloaded knee joint. The strain was computed from the tibiofemoral bone gap difference (ΔmBGFT) using the knee MR images with and without load. The knee FEM study was conducted using a subject-specific knee joint 3D-model and various soft-tissue stiffness values (1 to 50 MPa) to develop subject-specific stiffness versus strain models. RESULTS: Less than 1.02% absolute convergence error was observed during the simulation. Subject-specific combined stiffness of weight-bearing tibiofemoral soft-tissue was estimated with mean values as 2.40 ± 0.17 MPa. Intra-subject variability has been observed during the repeat scan in 3 subjects as 0.27, 0.12, and 0.15 MPa, respectively. All subject-specific stiffness-strain relationship data was fitted well with power function (R2 = 0.997). CONCLUSION: The current study proposed a generalized mathematical model and a methodology to estimate subject-specific stiffness of the tibiofemoral joint for FEM analysis. Such a method might enhance the efficacy of FEM in implant design optimization and biomechanics for subject-specific studies. Trial registration The institutional ethics committee (IEC), Indian Institute of Technology, Delhi, India, approved the study on 20th September 2017, with reference number P-019; it was a pilot study, no clinical trail registration was recommended.

A semi-automatic framework based upon quantitative analysis of MR-images for classification of femur cartilage into asymptomatic, early OA, and advanced-OA groups.

To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2-mapping, 2D-WearMap generation, and subregion analysis were performed semi-automatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computer-aided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion.

PURPOSE: The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA). METHODS: In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements. RESULTS: DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA). CONCLUSION: In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.