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Allahabadi S, Yazdi AA, Weissman AC, Meeker ZD, Yanke AB, Cole BJ. Sport-specific Differences in Cartilage Treatment. Sports Med Arthrosc Rev 2024; 32:68-74. [PMID: 38978200 DOI: 10.1097/jsa.0000000000000393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Articular cartilage defects in the knee are common in athletes who have a variety of loading demands across the knee. Athletes of different sports may have different baseline risk of injury. The most studied sports in terms of prevalence and treatment of cartilage injuries include soccer (football), American football, and basketball. At this time, the authors do not specifically treat patients by their sport; however, return to sports timing may be earlier in sports with fewer demands on the knee based on the rehabilitation protocol. If conservative management is unsuccessful, the authors typically perform a staging arthroscopy with chondroplasty, followed by osteochondral allograft transplantation with possible additional concomitant procedures, such as osteotomies or meniscal transplants. Athletes in a variety of sports and at high levels of competition can successfully return to sports with the appropriate considerations and treatment.
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Rubin EB, Schmidt AM, Koff MF, Kogan F, Gao K, Majumdar S, Potter H, Gold GE. Advanced MRI Approaches for Evaluating Common Lower Extremity Injuries in Basketball Players: Current and Emerging Techniques. J Magn Reson Imaging 2024; 59:1902-1913. [PMID: 37854004 DOI: 10.1002/jmri.29019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023] Open
Abstract
Magnetic resonance imaging (MRI) can provide accurate and non-invasive diagnoses of lower extremity injuries in athletes. Sport-related injuries commonly occur in and around the knee and can affect the articular cartilage, patellar tendon, hamstring muscles, and bone. Sports medicine physicians utilize MRI to evaluate and diagnose injury, track recovery, estimate return to sport timelines, and assess the risk of recurrent injury. This article reviews the current literature and describes novel developments of quantitative MRI tools that can further advance our understanding of sports injury diagnosis, prevention, and treatment while minimizing injury risk and rehabilitation time. Innovative approaches for enhancing the early diagnosis and treatment of musculoskeletal injuries in basketball players span a spectrum of techniques. These encompass the utilization of T2, T1ρ, and T2* quantitative MRI, along with dGEMRIC and Na-MRI to assess articular cartilage injuries, 3D-Ultrashort echo time MRI for patellar tendon injuries, diffusion tensor imaging for acute myotendinous injuries, and sagittal short tau inversion recovery and axial long-axis T1-weighted, and 3D Cube sequences for bone stress imaging. Future studies should further refine and validate these MR-based quantitative techniques while exploring the lifelong cumulative impact of basketball on players' knees. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Elka B Rubin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Andrew M Schmidt
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Matthew F Koff
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Kenneth Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Hollis Potter
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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3
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Nosrat C, Gao KT, Bhattacharjee R, Pedoia V, Koff MF, Gold GE, Potter HG, Majumdar S. Multiparametric MRI of Knees in Collegiate Basketball Players: Associations With Morphological Abnormalities and Functional Deficits. Orthop J Sports Med 2023; 11:23259671231216490. [PMID: 38107843 PMCID: PMC10722938 DOI: 10.1177/23259671231216490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 12/19/2023] Open
Abstract
Background Rates of cartilage degeneration in asymptomatic elite basketball players are significantly higher compared with the general population due to excessive loads on the knee. Compositional quantitative magnetic resonance imaging (qMRI) techniques can identify local biochemical changes of macromolecules observed in cartilage degeneration. Purpose/Hypothesis The purpose of this study was to utilize multiparametric qMRI to (1) quantify how T1ρ and T2 relaxation times differ based on the presence of anatomic abnormalities and (2) correlate T1ρ and T2 with self-reported functional deficits. It was hypothesized that prolonged relaxation times will be associated with knees with MRI-graded abnormalities and knees belonging to basketball players with greater self-reported functional deficits. Study Design Cross-sectional study; Level of evidence, 3. Methods A total of 75 knees from National Collegiate Athletic Association Division I basketball players (40 female, 35 male) were included in this multicenter study. All players completed the Knee injury and Osteoarthritis Outcome Score (KOOS) and had bilateral knee MRI scans taken. T1ρ and T2 were calculated on a voxel-by-voxel basis. The cartilage surfaces were segmented into 6 compartments: lateral femoral condyle, lateral tibia, medial femoral condyle, medial tibia (MT), patella (PAT), and trochlea (TRO). Lesions from the MRI scans were graded for imaging abnormalities, and statistical parametric mapping was performed to study cross-sectional differences based on MRI scan grading of anatomic knee abnormalities. Pearson partial correlations between relaxation times and KOOS subscore values were computed, obtaining r value statistical parametric mappings and P value clusters. Results Knees without patellar tendinosis displayed significantly higher T1ρ in the PAT compared with those with patellar tendinosis (average percentage difference, 10.4%; P = .02). Significant prolongation of T1ρ was observed in the MT, TRO, and PAT of knees without compared with those with quadriceps tendinosis (average percentage difference, 12.7%, 13.3%, and 13.4%, respectively; P ≤ .05). A weak correlation was found between the KOOS-Symptoms subscale values and T1ρ/T2. Conclusion Certain tissues that bear the brunt of impact developed tendinosis but spared cartilage degeneration. Whereas participants reported minimal functional deficits, their high-impact activities resulted in structural damage that may lead to osteoarthritis after their collegiate careers.
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Affiliation(s)
- Cameron Nosrat
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Kenneth T. Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Matthew F. Koff
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Hollis G. Potter
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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4
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Gao KT, Xie E, Chen V, Iriondo C, Calivà F, Souza RB, Majumdar S, Pedoia V. Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis. Arthritis Rheumatol 2023; 75:1958-1968. [PMID: 37262347 DOI: 10.1002/art.42623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/24/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness. METHODS A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration. RESULTS Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears. CONCLUSION This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.
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Affiliation(s)
- Kenneth T Gao
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Emily Xie
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Vincent Chen
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Claudia Iriondo
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Francesco Calivà
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Richard B Souza
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco and Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, United States
| | - Sharmila Majumdar
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Valentina Pedoia
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
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5
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Trovato B, Petrigna L, Sortino M, Roggio F, Musumeci G. The influence of different sports on cartilage adaptations: A systematic review. Heliyon 2023; 9:e14136. [PMID: 36923870 PMCID: PMC10009456 DOI: 10.1016/j.heliyon.2023.e14136] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/13/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Molecular composition and structural adaptation are changes in the cartilage tissue after different stimuli. Sports activities with different loads at different angles, speeds, and intensities can modify the molecular composition of the articular cartilage, hence it is crucial to understand the molecular adaptations and structural modifications generated by sports practice and this review aims to synthesize the current evidence on this topic. A systematic search until July 2022 was performed on the database Medline, Pubmed, Scopus, and Web of Science with a collection of 62,198. After the screening process, the included articles were analyzed narratively. Thirty-one studies have been included in the analysis. From the results emerged that running, swimming, ballet and handball were not correlated with detrimental structural or molecular cartilage adaptation; instead, soccer, volleyball, basketball, weightlifting, climbing, and rowing showed signs of cartilage alteration and molecular adaptation that could be early predictive degeneration's signs. From the included studies it came to light that the regions more interested in morphological cartilage changes were the knee in athletes from different disciplines. In conclusion, different sports induce different cartilage modifications both at a molecular and structural level and it is important to know the risks correlated to sports to implement preventive strategies.
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Affiliation(s)
- Bruno Trovato
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy
| | - Luca Petrigna
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy
| | - Martina Sortino
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy
| | - Federico Roggio
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy.,Sport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Via Giovanni Pascoli 6, Palermo, 90144, Italy
| | - Giuseppe Musumeci
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy.,Research Center on Motor Activities (CRAM), University of Catania, Via S. Sofia n°97, 95123, Catania, Italy.,Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, 19122, PA, United States
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Tolpadi AA, Bharadwaj U, Gao KT, Bhattacharjee R, Gassert FG, Luitjens J, Giesler P, Morshuis JN, Fischer P, Hein M, Baumgartner CF, Razumov A, Dylov D, van Lohuizen Q, Fransen SJ, Zhang X, Tibrewala R, de Moura HL, Liu K, Zibetti MVW, Regatte R, Majumdar S, Pedoia V. K2S Challenge: From Undersampled K-Space to Automatic Segmentation. Bioengineering (Basel) 2023; 10:bioengineering10020267. [PMID: 36829761 PMCID: PMC9952400 DOI: 10.3390/bioengineering10020267] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/01/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.
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Affiliation(s)
- Aniket A. Tolpadi
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Correspondence:
| | - Upasana Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kenneth T. Gao
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Felix G. Gassert
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Johanna Luitjens
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Paula Giesler
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jan Nikolas Morshuis
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | - Paul Fischer
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | - Matthias Hein
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | | | - Artem Razumov
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Dmitry Dylov
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Quintin van Lohuizen
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Stefan J. Fransen
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Xiaoxia Zhang
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Radhika Tibrewala
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector Lise de Moura
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Kangning Liu
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V. W. Zibetti
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder Regatte
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
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Lombardi AF, Guma M, Chung CB, Chang EY, Du J, Ma YJ. Ultrashort echo time magnetic resonance imaging of the osteochondral junction. NMR IN BIOMEDICINE 2023; 36:e4843. [PMID: 36264245 PMCID: PMC9845195 DOI: 10.1002/nbm.4843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/20/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Osteoarthritis is a common chronic degenerative disease that causes pain and disability with increasing incidence worldwide. The osteochondral junction is a dynamic region of the joint that is associated with the early development and progression of osteoarthritis. Despite the substantial advances achieved in the imaging of cartilage and application to osteoarthritis in recent years, the osteochondral junction has received limited attention. This is primarily related to technical limitations encountered with conventional MR sequences that are relatively insensitive to short T2 tissues and the rapid signal decay that characterizes these tissues. MR sequences with ultrashort echo time (UTE) are of great interest because they can provide images of high resolution and contrast in this region. Here, we briefly review the anatomy and function of cartilage, focusing on the osteochondral junction. We also review basic concepts and recent applications of UTE MR sequences focusing on the osteochondral junction.
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Affiliation(s)
- Alecio F. Lombardi
- Department of Radiology, University of California San Diego, CA, United States
- Research Service, Veterans Affairs San Diego Healthcare System, CA, United States
| | - Monica Guma
- Research Service, Veterans Affairs San Diego Healthcare System, CA, United States
- Department of Medicine, University of California San Diego, CA, United States
| | - Christine B. Chung
- Department of Radiology, University of California San Diego, CA, United States
- Research Service, Veterans Affairs San Diego Healthcare System, CA, United States
| | - Eric Y. Chang
- Department of Radiology, University of California San Diego, CA, United States
- Research Service, Veterans Affairs San Diego Healthcare System, CA, United States
| | - Jiang Du
- Department of Radiology, University of California San Diego, CA, United States
| | - Ya-Jun Ma
- Department of Radiology, University of California San Diego, CA, United States
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8
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Gao KT, Pedoia V, Young KA, Kogan F, Koff MF, Gold GE, Potter HG, Majumdar S. Multiparametric MRI characterization of knee articular cartilage and subchondral bone shape in collegiate basketball players. J Orthop Res 2021; 39:1512-1522. [PMID: 32910520 PMCID: PMC8359246 DOI: 10.1002/jor.24851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/31/2020] [Accepted: 09/02/2020] [Indexed: 02/04/2023]
Abstract
Magnetic resonance imaging (MRI) is commonly used to evaluate the morphology of the knee in athletes with high-knee impact; however, complex repeated loading of the joint can lead to biochemical and structural degeneration that occurs before visible morphological changes. In this study, we utilized multiparametric quantitative MRI to compare morphology and composition of articular cartilage and subchondral bone shape between young athletes with high-knee impact (basketball players; n = 40) and non-knee impact (swimmers; n = 25). We implemented voxel-based relaxometry to register all cases to a single reference space and performed a localized compositional analysis of T 1ρ - and T 2 -relaxation times on a voxel-by-voxel basis. Additionally, statistical shape modeling was employed to extract differences in subchondral bone shape between the two groups. Evaluation of cartilage composition demonstrated a significant prolongation of relaxation times in the medial femoral and tibial compartments and in the posterolateral femur of basketball players in comparison to relaxation times in the same cartilage compartments of swimmers. The compositional analysis also showed depth-dependent differences with prolongation of the superficial layer in basketball players. For subchondral bone shape, three total modes were found to be significantly different between groups and related to the relative sizes of the tibial plateaus, intercondylar eminences, and the curvature and concavity of the patellar lateral facet. In summary, this study identified several characteristics associated with a high-knee impact which may expand our understanding of local degenerative patterns in this population.
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Affiliation(s)
- Kenneth T. Gao
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Valentina Pedoia
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Feliks Kogan
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Matthew F. Koff
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Garry E. Gold
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Hollis G. Potter
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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