| Computer # |
| 2249.
 | 65 | Automated pipeline for creating personalized biomechanical knee models and computing personalized cartilage pressures during gait Kathryn R Marusich1, Allison Clouthier2, Carmichael F Ong3, Anna Bartsch4, Feliks Kogan5, Garry E Gold5, Akshay S Chaudhari5, and Anthony A Gatti5
1Department of Mechanical Engineering, Stanford University, Stanford, CA, United States, 2School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada, 3Department of Bioengineering, Stanford University, Stanford, CA, United States, 4Department of Orthopedics and Traumatology, University Hospital Basel, Basel, Switzerland, 5Department of Radiology, Stanford University, Stanford, CA, United States Keywords: Whole Joint, Cartilage, Cartilage, Biomechanics, Bone, Segmentation, Personalized Motivation: Automated analysis of MRI and biomechanics data can provide personalized information about cartilage pressures. Goal(s): Our goal was to develop an automated pipeline to create a personalized biomechanical knee joint model from MRI data, to simulate personalized knee mechanics during gait in comparison to knee mechanics of a generic knee joint geometry.
Approach: Bone and cartilage geometry was automatically segmented from knee MRI scans via deep learning. Gait simulations were performed on musculoskeletal models with personalized and generic knee models.
Results: Personalizing knee joint geometries affected cartilage pressure distributions in the joint but maintained peak cartilage pressures and contact forces.
Impact: Biomechanical models personalized with MRI data enable understanding of how bone geometry influences cartilage pressures during gait, which may lead to better tailoring and evaluation of interventions. |
| 2250.
 | 66 | In Vivo Reproducibility of T2 and T1rho Relaxation Times in Multiple Coils and Sequences Yael Vainberg1, Anthony Gatti1, Anoosha Pai S.1, and Feliks Kogan1
1Radiology, Stanford University, Stanford, CA, United States Keywords: Osteoarthritis, Cartilage, Reproducibility, coils Motivation: T2 and T1rho reproducibility is essential for evaluating the small changes that occur during early osteoarthritis that are predictive of disease progression. Goal(s): To evaluate the reproducibility of T2 and T1rho relaxation times in multiple coils and with multiple imaging sequences. Approach: We studied the reproducibility of T1rho and T2 relaxation times with a MAPSS and qDESS sequence in a Transmit-Receive knee coil and two different flexible extremity coil arrays. Results: Intra-coil CVs of less than 5% (QIBA Goal) in qDESS T2 measurements with all coils tested, MAPSS T2 measurements with both flexible-coil-arrays and MAPSS T1rho measurements only with the Air coil. Impact: Preliminary findings suggest that flexible, receive-only coil arrays show similar or improved reproducibility for evaluating cartilage T2 and T1rho relaxation times compared to standard T/R knee coils. |
| 2251.
 | 67 | In vivo T1 and T2 mapping of human knee at 0.05 Tesla Shiqi Yang1,2, Shi Su1,2, Ye Ding1,2, Yujiao Zhao1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China Keywords: Cartilage, Low-Field MRI, T2 mapping, T1 mapping Motivation: To study knee tissue properties at Ultra-low-field (ULF) for sequence design/optimization, enabling ULF MRI for wide-ranging applications. Goal(s): To measure the T2 and T1 values of various tissues in vivo human knee at 0.05T. Approach: Multiple TE and TR measurements were conducted on a home-built and RF shielding-free 0.05T MRI scanner for T2 and T1 mapping through pixel-wise numerical fitting. Results: Phantom results exhibited a linear relationship between the Gadolinium concentration and relaxation rate, demonstrating the feasibility of mapping procedure. Additionally, the T2 and T1 values of cartilage, tendon, fat, and skeletal muscle in human knee were estimated and reported. Impact: The
estimated Gadolinium T2 and T1 relaxivities at 0.05 Tesla are significantly
higher than those at 1.5T and 3.0T, guiding the usage of contrast agent dose.
Moreover, T2 and T1 mapping of musculoskeletal tissues can
facilitate sequence design/optimization. |
| 2252.
 | 68 | Machine Learning Assisted Prediction of Cartilage Proteoglycan Content Using MR Fingerprinting Ville Kantola1, Olli Nykänen2,3, Victor Casula1,4, Ville-Pauli Karjalainen1, Mikko Nissi2, and Miika Nieminen1,4,5
1Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland, 2University of Eastern Finland, Kuopio, Finland, 3Oulu University Hospital, Oulu, Finland, 4Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland, 5Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland Keywords: Cartilage, Cartilage, MRF Motivation: Early signs of cartilage degeneration include changes in proteoglycan content, which cannot be diagnosed using standard clinical imaging tools. Goal(s):
Prediction of cartilage proteoglycan content from quantitative MR fingerprinting data at 3T. Approach:
Gaussian process regression (GPR) models were trained to predict optical density of safranin-O stained cartilage sections, representing proteoglycan content, from MRF data on a voxel-by-voxel basis. Results: The trained GPR models reached very high accuracy (mean correlation of 0.81 with a respective NRMSE of 11.7%) and had clearly enhanced performance when compared to linear models. Impact: Non-invasive prediction of
proteoglycan content in cartilage using MR fingerprinting at clinical field
strength is feasible, holding promise for direct clinical imaging of cartilage
composition in the future.
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| 2253.
 | 69 | Learned Variable Flip-Angles to Improve Bi-Exponential 3D T2 and T1rho Mapping on the Knee Cartilage Marcelo Victor Wust Zibetti1,2, Hector Lise De Moura1,2, Anmol Monga1,2, Mahesh B. Keerthivasan3, and Ravinder R. Regatte1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Siemens Medical Solutions, Malvern, PA, United States Keywords: Cartilage, Quantitative Imaging Motivation: Bi-exponential T2 and T1rho mapping of the knee cartilage can potentially improve early detection of knee osteoarthritis. Goal(s): Scan time is usually long and SNR is low with standard methods. We plan to improve these aspects with a machine-learned pulse sequence. Approach: We use a machine learning approach, called optimized variable flip-angles (OVFA) on magnetization-prepared gradient-echo (MPGRE) sequences to improve bi-exponential T2 and T1rho mapping on the knee cartilage. Results: We observed an improvement of ~50% in SNR and a reduction of acquisition time by almost 2X when compared to standard MAPSS, typically used for quantitative T1rho and T2 mapping. Impact: This study shows that the learned pulse sequence, named MPGRE-OVFA, can
obtain similar bi-exponential T2 and T1rho mapping values as MAPSS, but it is 2
times faster and has 50% more SNR, potentially improving early detection of
osteoarthritis. |
| 2254.
 | 70 | Robust Fitting Methods for Knee Cartilage T1ρ Quantification with Varied SNR – Preliminary results from a Multi-vendor Multi-site study Zhiyuan Zhang1,2,3, Jeehun Kim1,2,4, Richard Latery1,2, Carl Scherman Winalski1,2,5, Jing Liu6, Thomas Link6, Qi Peng7, Leslie Ying8,9, Peter Hardy10, and Xiaojuan Li1,2,5
1Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 2Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States, 3Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 4Department Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States, 5Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, 6Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 7Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, United States, 8Department of Biomedical Engineering, University at Buffalo, Buffalo, OH, United States, 9Department of Electrical Engineering, University at Buffalo, Buffalo, NY, United States, 10Department of Radiology, University of Kentucky, Lexington, KY, United States Keywords: Cartilage, MSK Motivation: High-resolution T1ρ mapping is desired for improved sensitivity to small lesions and less partial volume averaging effects. However, the low image SNR can induce bias in T1ρ quantification. Goal(s): To develop and evaluate advanced fitting methods of high-resolution T1ρ mapping in a multi-site multi-vendor setting. Approach: High-resolution T1ρ mapping in volunteers were collected with a harmonized protocol from three sites and three MR platforms. Data were fitted with nonlinear least-squares (NLS) fitting, noise-corrected NLS fitting (NCNLS), and maximum-likelihood estimation (MLE). Results: :NLS overestimated T1ρ while NCNLS and MLE reduced the overestimation. The results were consistent across all sites Impact: Advanced fitting
methods can reduce fitting errors induced by low SNR for high-resolution T1ρ
mapping, which may provide improved diagnosis of osteoarthritis. |
| 2255.
 | 71 | The influence of fat-suppression on T2 values and texture features in articular cartilage Pavol Szomolanyi1,2, Vladimir Juras1, Stefan Toegel3, Markus Schreiner3, Veronika Janacova1, Didier Laurent4, Franziska Saxer4, Rahel Heule5, Oliver Bieri6, Esther Raithel7, Christoph Fuchssteiner8, Wolfgang Weninger8, Reinhard Windhager3, and Siegfried Trattnig1,9,10,11
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 2Department of Imaging Methods, Institute of Measurement Science, Bratislava, Slovakia, 3Department of Orthopedics and Trauma Surgery, Medical University of Vienna, Vienna, Austria, 4Department of translational Medicine, Novartis Biomedical Research, Basel, Switzerland, 5Center for MR Research, University Children's Hospital, Zurich, Switzerland, 6Division of Radiological Physics, Department of Radiology, University of Basel Hospital, Basel, Switzerland, 7Siemens Healthcare AG, Forchheim, Germany, 8Center for Anatomy and Cell Biology, Division of Anatomy, Medical University of Vienna, Vienna, Austria, 9CD Laboratory for MR Imaging Biomarkers (BIOMAK), Vienna, Austria, 10Austrian Cluster for Tissue Regeneration, Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Vienna, Austria, 11Institute for Clinical Molecular MRI in the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria Keywords: Cartilage, Cartilage Motivation: If fat signal is not properly suppressed, it can lead to errors in the T2 calculations in human articular cartilage. Goal(s): This work aimed to quantify the influence of fat suppression on T2 values as well as texture features extracted from T2 maps. Approach: Ten donors were scanned in 3T MRI with DESS, T2-TESS and CPMG with and without fat suppression. Results: The results of this study showed the importance of using fat suppression while acquiring T2 maps. The influence of fat suppression was substantially greater for CPMG-T2-mapping compared to TESS-T2-mapping. Impact: If fat
signal is not properly suppressed, it can lead to errors in the T2 calculations
in human articular cartilage which can have a significant impact on
longitudinal clinical trials. |
| 2256.
 | 72 | Repeatability of Quantitative T1, T2 and T1ρ Mapping of Knee Cartilage with 3D-MR Fingerprinting Xiaoxia Zhang1,2, Hector L.de Moura1,2, Anmol Monga1,2, Marcelo V.W. Zibetti1,2, Richard Kijowski1,2, and Ravinder R. Regatte1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States Keywords: Cartilage, Cartilage Motivation: 3D-MRF sequence for simultaneous multi-parametric mapping has the potential to provide a more time-efficient comprehensive evaluation of the knee cartilage. Goal(s): However, evaluation of 3D-MRF repeatability of knee cartilage is limited. Approach: 3D-MRF and conventional sequences for knee cartilage were acquired four times on fourteen healthy subjects. Multi-parametric maps were computed and repeatability was evaluated. Results: High inter- and intra-subject repeatabilities were found using the 3D-MRF sequence over seven days with good agreement to conventional sequences. Impact: The 3D-MRF sequence showed high T1, T2, and T1ρ repeatability on knee cartilage on different days and good agreement with conventional methods. |
| 2257.
 | 73 | Mapping Hip Cartilage Over Time: A Pipeline for Longitudinal Analysis of Quantitative MRI Using Radial Sections Batool Abbas1,2, Guido Gerig3, and Riccardo Lattanzi1
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3New York University Tandon School of Engineering, New York, NY, United States Keywords: Cartilage, Osteoarthritis Motivation: Scientific literature on osteoarthritis of the hip is very sparse, especially compared to the knee, because MRI assessment of the hip is incredibly challenging. Goal(s): Our goal was to determine clinical feasibility for cartilage assessment of the hip captured through radial imaging planes. Approach: We acquired 2D radial images of the hip joint at three different time points and assessed them using standard image processing techniques. Results: We demonstrate that 2D radial imaging is a constructive approach for mitigating partial volume artifacts and the resulting images can be effectively processed for quantitative and qualitative analyses of the hip joint cartilage. Impact: We demonstrate a processing pipeline for analysis of hip cartilage longitudinally acquired through radial imaging planes. The proposed methods could facilitate the clinical translation of quantitative radial imaging for assessment of the hip cartilage for pre-symptomatic indicators of disease. |
| 2258.
 | 74 | T2* Cartilage Mapping in Early Axial Spondyloarthritis: Diagnostic Accuracy and Correlation with Clinical Characteristics and Sacroiliitis Hongjie Huang1,2, Yuyang Zhang1, Feifei Zhuang1, Xi Liu1, Keyi Wu1, Feng Wang1, Xiance Zhao3, and Dairong Cao1,2,4
1Department of Radiology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China, 2Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China, 3Philips Healthcare, Shanghai, China, 4Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China Keywords: Osteoarthritis, Bone Motivation: Cartilage degradation has been recognized as an early and crucial feature in axSpA. Goal(s): The objectives of the present study were (1) to determine the performance of T2* cartilage mapping in diagnosing and assessing disease activity in early axSpA, (2) to investigate the interaction of cartilage damage with clinical characteristics and sacroiliitis. Approach: Sacroiliac joints cartilage T2* values were higher in patients with early axSpA compared to controls without axSpA. Results: The combination of T2* values and ‘positive MRI’ improve diagnostic efficiency of axSpA. Sacroiliac joints cartilage damage correlates with age, disease activity, acute sacroiliitis, and structural damage. Impact: T2*
relaxation time may be a promising imaging biomarker for diagnosing and
differentiating disease activity in early axSpA. T2* mapping could
be a recommended addition to routine MRI protocol of SIJs. |
| 2259.
 | 75 | T2 MR Fingerprinting in global and focal knee cartilage Diana Sitarcikova1, Martijn A. Cloos2, Veronika Janacova1, Barbara Hristoska1, Malina Gologan3, Siegfried Trattnig3,4,5,6, and Vladimir Juras3
1Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria, 2Centre for Advanced Imaging, University of Queensland, Queensland, Australia, 3Medical University Vienna, Vienna, Austria, 4CD Laboratory for MR Imaging Biomarkers (BIOMAK), Vienna, Austria, 5Austrian Cluster for Tissue Regeneration, Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Vienna, Austria, 6Institute for Clinical Molecular MRI in the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria Keywords: Cartilage, Osteoarthritis, T2 mapping, MR Value, MSK, Relaxometry Motivation: Simultaneous measurement of multiple parameters during single acquisition saves measurement and post-processing time with reduced motion artifacts. Goal(s): To evaluate and compare T2 mapping of global and focal knee articular cartilage via MR fingerprinting and conventional CPMG sequences. Approach: Volunteer and patient knees were scanned with protocol including MRF and CPMG T2 mapping. The two T2 mapping methods were compared in global cartilage, focal cartilage lesions and morphologically normal appearing cartilage segments. Results: The average bias between the two methods was 17.09 ± 6.3 ms, and correlation was moderate to very high in global cartilage and high in focal cartilage. Impact: T2 mapping
with MR fingerprinting is reliable in global and focal articular knee cartilage
in terms of segment bulk T2 value assessment when compared to conventional
method. Future study will concentrate on longitudinal change in T2 during
patient follow-up. |
| 2260.
 | 76 | ROI based Multi-parameter Quantitative Network(RMQ-Net) with Uncertainty-awareness for Quantitative UTE MRI Study of Cartilage Xing Lu1, Kevin Du1, Yajun Ma1, Jiyo Athertya1, Bhavsimran Singh Malhi1, Eric Y Chang1,2, Susan V Bukata1,3, and Christine Chung1,2
1Department of Radiology, University of California, San Diego, San Diego, CA, United States, 2Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States, 3Department of Orthopaedic Surgery, University of California San Diego, San Diego, CA, United States Keywords: Cartilage, MSK Motivation: Quantitative MRI (qMRI) studies of cartilage regions need both regional segmentation and pixel-wise fitting analysis, which can be time-consuming and subject to inter-individual variability. Goal(s): To design a deep neural network for simultaneous qMRI mapping and accurate tissue segmentation. Approach: By leveraging different scan sequences, we proposed a RMQ-net with Uncertainty-awareness(UA) module, or UA-QMR-net. A majority-voting strategy was applied for robust cartilage segmentation and accelerated qMRI analysis. Results: The results demonstrated that the UA-RMQ-net achieved higher performance than the original RMQ-net for both UTE-T1 and UTE-T1r analyses of articular cartilage. Impact: By leveraging
information from different scan sequences, the proposed UA-RMQ-net could obtain
higher performance for accelerated qMRI analysis. |
| 2261.
 | 77 | Quantitative Ultrashort Echo Time Double Echo Steady State (qUTE-DESS) for T1, T2, and Diffusivity Mapping of Human Cartilage Hyungseok Jang1, Soo Hyun Shin1, Dina Moazamian1, Yajun Ma1, Jiang Du1,2,3, Christine B. Chung1,4, and Eric Y. Chang1,4
1Radiology, University of California, San Diego, San Diego, CA, United States, 2Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States, 3Bioengineering, University of California, San Diego, San Diego, CA, United States, 4Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States Keywords: Cartilage, Cartilage, UTE, DESS, UTE-DESS, Diffusion, T1, T2 Motivation: A comprehensive validation of parameter mapping for T1, T2, and ADC based on qUTE-DESS has not been conducted yet. Goal(s): To investigate the feasibility and accuracy of qUTE-DESS for estimating T1, T2, and ADC parameters in human patellar cartilage and to compare these results with conventional MR techniques. Approach: The study used a qUTE-DESS sequence with variable flip angles and gradient moment adjustments. The cartilage sample was imaged using this approach and conventional imaging sequences, and the acquired data were processed using signal fitting. Results: Significant correlations exist between the parameters estimated by qUTE-DESS and those from conventional sequences. Impact: This study pioneers the comprehensive validation of T1, T2, and ADC parameter mapping using qUTE-DESS for knee joint imaging. Its findings offer a promising avenue to enhance the assessment of short T2 tissues and advance clinical applications in musculoskeletal diagnostics. |
| 2262.
 | 78 | Healthy Knee Cartilage T2 Relaxation properties at 0.55T using Radial TSE approach: A comparison with 3.0T MAPSS Rupsa Bhattacharjee1, Fei Han2, Pan Su2, Yang Yang1, Thomas M Link1, and Sharmila Majumdar1
1Department of Radiology & Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, CA, United States, 2Siemens Medical Solutions USA Inc., Malvern, PA, United States Keywords: Cartilage, Low-Field MRI Motivation: Baseline healthy-knee-cartilage reference comparisons across 0.55T and 3.0T can be utilized for comparing healthy to diseased cartilage in early-to-moderate OA at 0.55T. Goal(s): To compute and compare T2-baseline relaxation time measurements of healthy knee cartilage obtained at 0.55T using Radial-TSE approach with 3.0T using MAPSS, paired with DL-based cartilage segmentation. Approach: Phantom and Healthy Knee-cartilage-compartmental T2 relaxation values are compared using Exp2 and Sepg3 approaches with Radial TSE at 0.55T and 3.0T MAPSS. Results: We demonstrated healthy knee cartilage reference T2-values at 0.55T using Radial-TSE. Average T2-increase from 3.0T to 0.55T yields a wider range for detecting voxel-by-voxel granular changes of the cartilage. Impact: Baseline healthy-knee-cartilage reference Radial-TSE-T2 at 0.55T and T2-MAPSS-3.0T comparisons can be utilized for assessing healthy and diseased cartilage in early-to-moderate OA at 0.55T. Research findings at 3.0T could be translated at low-field economic scanners with a wider range of early-detection. |
| 2263.
 | 79 | Graph Analysis of MRI-Derived Radiomics in Articular Knee Cartilage: Differentiating Healthy from Osteoarthritic Joints Dominik Vilimek1, Veronika Janacova2,3, Radana Vilimkova Kahankova1, Pavla Hanzlikova4,5, Jindrich Brablik1, Michaela Pomaki4,5, Siegfried Trattnig2,3,6,7, Radek Martinek1, and Vladimir Juras2
1Department of Cybernetics and Biomedical Engineering, VSB Technical University of Ostrava, Ostrava, Czech Republic, 2Department of Biomedical Imaging and Image-guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria, 3CD Laboratory fo MR Imaging Biomarkers (BIOMAK), Vienna, Austria, 4Department of Imaging Methods, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic, 5Department of Imaging Method, University Hospital Ostrava, Ostrava, Czech Republic, 6Austrian Cluster for Tissue Regeneration, Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Vienna, Austria, 7Institute for Clinical Molecular MRI in the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria Keywords: Cartilage, Radiomics Motivation: Our study examines the utility of graph-based analyses in revealing the interplay of radiomic features in knee osteoarthritis (OA), specifically to discover patterns that are hidden in traditional analyses. Goal(s): To differentiate radiomic profiles of healthy individuals from OA patients using graph-based methodologies and identify key features associated with OA progression. Approach: We analyze feature interconnections within knee joint compartments using MRI-based radiomics and cosine similarity graphs to evaluate features from 20 subjects. Results: Clustering coefficients and path lengths within the graphs revealed a distinct, pathology-driven convergence of radiomic features in OA patients compared to controls. Impact: The graph analysis
revealed a convergence of radiomic features in OA, potentially contributing to
a better understanding of the disease and therefore opening the path to novel
analysis strategies. |
| 2264.
 | 80 | Knee Cartilage T2 Associates with Foot and Ankle Posture Ashley Anne Williams1,2, Jade He1,2, and Constance R Chu1,2
1Orthopaedic Surgery, Stanford Univesity, Stanford, CA, United States, 2Joint Preservation Center, Palo Alto Veterans Healthcare System, Palo Alto, CA, United States Keywords: Cartilage, Cartilage, ankle, kinematics, eversion Motivation: Flat-footedness and pronated ankle posture have been observed in patients with knee osteoarthritis. Goal(s): This works seeks to determine if compositional changes to cartilage assessed from T2 relaxation times are associated with foot and ankle posture. Approach: Foot center of pressure, ankle eversion and tibial rotation were compared to tibiofemoral cartilage T2 in 24 participants with healthy knees. Results: Lower medial femoral cartilage T2 correlated to more medial foot center of pressure and greater ankle eversion (R=-0.71, p<0.0005; R=-0.54, p=0.006; respectively). Higher lateral tibial T2 correlated to more internal tibial rotation (R=-0.52, p=0.009). Impact: Correlation of foot and ankle posture to
cartilage composition at the knee suggest that gait or shoe interventions to appropriately
alter weight distribution at the foot and ankle could be utilized to benefit to
knee cartilage health. |