Objective: 3D joint space width (JSW) mapping quantifies structural narrowing in knee osteoarthritis (OA), but the effect of surface mesh resolution on measurement reliability and precision remains unclear. This study assessed how mesh resolution influences 3D JSW measurements, whether effects differ by metric and OA severity, and which resolutions best preserve OA group-level discrimination and subject-level agreement.
Methods: Knees with medial knee OA (KL grades 0–4) were analyzed. For each knee, 3D JSW maps were generated on a 0.1 mm reference mesh and seven coarser meshes (0.3, 0.5, 0.7, 1.0, 2.0, 3.0, and 4.0 mm). Minimum and median JSW (minJSW, medJSW) were calculated within a standardized medial region and compared to the reference. Systematic error, agreement (Bland–Altman), and subject-level interchangeability were evaluated across resolutions for the overall cohort and early versus advanced OA subgroups. Absolute-error thresholds were set at 0.2 mm (sensitivity) and 0.5 mm (clinical significance). Mesh-by-severity interactions were analyzed using mixed-effects models, and group-level discrimination was assessed at each resolution.
Results: Seventy-two knees from 54 patients (mean age 65 ± 10.4 years; 48.6% advanced OA) were included. The 0.1 mm reference mesh had excellent geometric fidelity (RMS deviation 0.0011 ± 0.0010 mm) and high vertex density (median 118,300 vertices), which dropped to 14,848 at 0.3 mm, 5033 at 0.5 mm, and 72 at 4.0 mm. From 0.1 to 4.0 mm, medJSW increased from 4.07 ± 0.77 mm to 5.14 ± 0.96 mm and minJSW from 1.98 ± 0.81 to 2.34 ± 0.81 mm, converging at 0.3 mm. Subject-level agreement for medJSW declined beyond 0.3 mm: at 0.5 mm and coarser meshes, ≥ 44.4% of knees exceeded the 0.2 mm error threshold; at 0.7 mm and coarser meshes, ≥ 12.5% exceeded the 0.5 mm threshold. In contrast, minJSW remained within the 0.5 mm clinical threshold through 1.0 mm. Early OA showed a larger absolute medJSW inflation (p = 0.023), whereas advanced OA had greater relative minJSW error (p < 0.05). Group-level OA discrimination remained preserved at all resolutions (p < 0.001), while subject-level agreement deteriorated at coarser meshes.
Conclusions: Mesh resolution significantly influences 3D JSW measurements, with effects modulated by selected metric, disease stage, and level of analysis. Using a rigorous 0.1-mm reference mesh, specific thresholds were established: a 0.3-mm mesh provided the most consistent performance across all conditions; a 0.5-mm mesh may be acceptable for medJSW under a less stringent error tolerance; and up to 1.0 mm sufficed only for identifying the narrowest point (minJSW). At coarser resolutions, baseline JSW overestimation can be large enough to alter perceived structural severity of medial compartment narrowing, carrying risks for clinical misinterpretation and treatment decisions.
| [1] |
N. A. Segal, M. C. Nevitt, M. Morales Aquino, et al., “Improved Responsiveness to Change in Joint Space Width Over 24-Month Follow-Up: Comparison of 3D JSW on Weight-Bearing CT vs 2D JSW on Radiographs in the MOST Study,” Osteoarthritis and Cartilage 31, no. 3 (2023): 406–413.
|
| [2] |
T. D. Turmezei, S. B. Low, S. Rupret, et al., “Quantitative Three-Dimensional Assessment of Knee Joint Space Width From Weight-Bearing CT,” Radiology 299, no. 3 (2021): 649–659.
|
| [3] |
M. Dório, D. J. Hunter, J. E. Collins, et al., “Association of Baseline and Change in Tibial and Femoral Cartilage Thickness and Development of Widespread Full-Thickness Cartilage Loss in Knee Osteoarthritis–Data From the Osteoarthritis Initiative,” Osteoarthritis and Cartilage 28, no. 6 (2020): 811–818.
|
| [4] |
M. P. Jansen, S. C. Mastbergen, F. Eckstein, R. J. Van Heerwaarden, S. Spruijt, and F. P. J. G. Lafeber, “Comparison Between 2D Radiographic Weight-Bearing Joint Space Width and 3D MRI Non-Weight-Bearing Cartilage Thickness Measures in the Knee Using Non-Weight-Bearing 2D and 3D CT as an Intermediary,” Therapeutic Advances in Chronic Disease 12 (2021): 20406223211037868.
|
| [5] |
C. K. Kwoh, H. Guehring, A. Aydemir, M. J. Hannon, F. Eckstein, and M. C. Hochberg, “Predicting Knee Replacement in Participants Eligible for Disease-Modifying Osteoarthritis Drug Treatment With Structural Endpoints,” Osteoarthritis and Cartilage 28, no. 6 (2020): 782–791.
|
| [6] |
A. Varaschin, H. S. Gill, S. Zaffagnini, et al., “Personalised High Tibial Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping,” Applied Sciences 14, no. 19 (2024): 9033.
|
| [7] |
M. J. Richard, G. H. Lo, J. B. Driban, et al., “Knee Cartilage Change on Magnetic Resonance Imaging: Should We Lump or Split Topographical Regions? A 2-Year Study of Data From the Osteoarthritis Initiative,” Clinical Anatomy 37, no. 2 (2024): 210–217.
|
| [8] |
C. M. Parsons, A. Judge, R. Meyer, et al., “Determining Individual Trajectories of Joint Space Loss: Improved Statistical Methods for Monitoring Knee Osteoarthritis Disease Progression,” Osteoarthritis and Cartilage 29, no. 1 (2021): 59–67.
|
| [9] |
B. Fritz, J. Fritz, S. F. Fucentese, C. W. A. Pfirrmann, and R. Sutter, “Three-Dimensional Analysis for Quantification of Knee Joint Space Width With Weight-Bearing CT: Comparison With Non-Weight-Bearing CT and Weight-Bearing Radiography,” Osteoarthritis and Cartilage 30, no. 5 (2022): 671–680.
|
| [10] |
H. Mezlini-Gharsallah, R. Youssef, S. Uk, J. D. Laredo, and C. Chappard, “Three-Dimensional Mapping of the Joint Space for the Diagnosis of Knee Osteoarthritis Based on High Resolution Computed Tomography: Comparison With Radiographic, Outerbridge, and Meniscal Classifications,” Journal of Orthopaedic Research 36, no. 9 (2018): 2380–2391.
|
| [11] |
W. Wang, T. Jiang, J. Zhang, et al., “Subchondral Bone Expansion in Advanced Knee Osteoarthritis: Relation With Radiographic Severity and Role in Surgical Decision-Making,” Osteoarthritis and Cartilage Open 6, no. 2 (2024): 100461.
|
| [12] |
T. D. Turmezei, G. M. Treece, A. H. Gee, et al., “Quantitative 3D Imaging Parameters Improve Prediction of Hip Osteoarthritis Outcome,” Scientific Reports 10, no. 1 (2020): 4127.
|
| [13] |
K. Alencar Mendes De Carvalho, A. Behrens, V. Mallavarapu, et al., “Automated Three-Dimensional Distance and Coverage Mapping of Hallux Valgus: A Case-Control Study,” Journal of Foot & Ankle 16, no. 1 (2022): 41–45.
|
| [14] |
K. S. Stok, A. J. Burghardt, S. Boutroy, et al., “Consensus Approach for 3D Joint Space Width of Metacarpophalangeal Joints of Rheumatoid Arthritis Patients Using High-Resolution Peripheral Quantitative Computed Tomography,” Quantitative Imaging in Medicine and Surgery 10, no. 2 (2020): 314–325.
|
| [15] |
P. Honigmann, M. Keller, N. Devaux-Voumard, F. M. Thieringer, and D. Sutter, “Distance Mapping in Three-Dimensional Virtual Surgical Planning in Hand, Wrist and Forearm Surgery: A Tool to Avoid Mistakes,” International Journal of Computer Assisted Radiology and Surgery 18 (2022): 565–574.
|
| [16] |
J. Shiota, D. Momma, Y. Matsui, N. Inoue, E. Kondo, and N. Iwasaki, “Changes in Wrist Joint Contact Area Following Radial Shortening Osteotomy for Kienböck's Disease,” Scientific Reports 12, no. 1 (2022): 4001.
|
| [17] |
N. A. M. Zain, R. Daud, N. S. Kamarrudin, K. R. Ahmad, and N. H. A. Aziz, “Mesh Refinement for Cortical and Trabecular Bone Finite Element Modeling: A Review,” IOP Conference Series: Materials Science and Engineering 991, no. 1 (2020): 012134.
|
| [18] |
A. Brett, M. A. Bowes, and P. G. Conaghan, “Comparison of 3D Quantitative Osteoarthritis Imaging Biomarkers From Paired CT and MR Images: Data From the IMI-APPROACH Study,” BMC Musculoskeletal Disorders 24, no. 1 (2023): 76.
|
| [19] |
N. A. Segal, J. Bergin, A. Kern, C. Findlay, and D. D. Anderson, “Test–Retest Reliability of Tibiofemoral Joint Space Width Measurements Made Using a Low-Dose Standing CT Scanner,” Skeletal Radiology 46, no. 2 (2017): 217–222.
|
| [20] |
R. J. Lisonbee, A. C. Peterson, and A. L. Lenz, “Correspondence Model-Based Approach for Evaluating Static and Dynamic Joint Distance Measurements,” Computer Methods in Biomechanics and Biomedical Engineering 20 (2025): 1–16.
|
| [21] |
W. M. Chen, Y. H. Cai, Y. Yu, X. Geng, and X. Ma, “Optimal Mesh Criteria in Finite Element Modeling of Human Foot: The Dependence for Multiple Model Outputs on Mesh Density and Loading Boundary Conditions,” Journal of Mechanics in Medicine and Biology 21, no. 9 (2021): 2140034.
|
| [22] |
G. Treece and A. Gee, “Cortical Bone Mapping: Measurement and Statistical Analysis of Localised Skeletal Changes,” Current Osteoporosis Reports 16, no. 5 (2018): 617–625.
|
| [23] |
C. Phombut, S. Rooppakhun, and B. Sindhupakorn, “Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur,” Applied Sciences 11, no. 3 (2021): 1052.
|
| [24] |
T. Inai, T. Takabayashi, S. Watanabe, et al., “A New Method for Estimating Three-Dimensional Movement of the Patella Using a Surface Mapping Method and Computed Tomography,” Heliyon 6, no. 8 (2020): e04729.
|
| [25] |
A. H. M. Hudzari, A. K. Nasution, A. H. Abdullah, M. R. A. Kadir, and M. H. Ramlee, “Mesh Convergence Analysis of Three-Dimensional Tibial Bone Model: A Finite Element Method,” AIP Conference Proceedings 3056 (2025): 070003.
|
| [26] |
V. Grosser, “Berufskrankheit Gonarthrose (BK 2112): Kausalitätsbeurteilung,” Trauma Und Berufskrankheit 20, no. S2 (2018): 108–114.
|
| [27] |
D. Hayashi, F. W. Roemer, M. Jarraya, and A. Guermazi, “Imaging of Osteoarthritis,” in Medical Radiology, ed. H.-U. Kauczor, P. M. Parizel, W. C. G. Peh, J. J. Lu, and L. W. Brady (Springer Berlin Heidelberg, 2025), https://doi.org/10.1007/174_2024_516.
|
| [28] |
M. D. Kothari, K. G. Rabe, D. D. Anderson, et al., “The Relationship of Three-Dimensional Joint Space Width on Weight-Bearing CT With Pain and Physical Function,” Journal of Orthopaedic Research 38, no. 6 (2020): 1333–1339.
|
| [29] |
C. R. Smith, K. Won Choi, D. Negrut, and D. G. Thelen, “Efficient Computation of Cartilage Contact Pressures Within Dynamic Simulations of Movement,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6, no. 5 (2018): 491–498.
|
| [30] |
L. Yu, J. Jia, K. Lakshminarayanan, Y. Li, Y. Gan, and Y. Yao, “A Finite Element Analysis of the Carpal Arch With Various Locations of Carpal Tunnel Release,” Frontiers in Surgery 10 (2023): 1134129, https://doi.org/10.3389/fsurg.2023.1134129.
|
| [31] |
M. Elbashti, P. Molinero-Mourelle, A. Aswehlee, et al., “Effect of Triangular Mesh Resolution on the Geometrical Trueness of Segmented CBCT Maxillofacial Data Into STL Format,” Journal of Dentistry 138 (2023): 104722.
|
| [32] |
C. Ratzlaff, E. L. Ashbeck, A. Guermazi, F. W. Roemer, J. Duryea, and C. K. Kwoh, “A Quantitative Metric for Knee Osteoarthritis: Reference Values of Joint Space Loss,” Osteoarthritis and Cartilage 26, no. 9 (2018): 1215–1224.
|
| [33] |
A. E. Anderson, B. J. Ellis, S. A. Maas, and J. A. Weiss, “Effects of Idealized Joint Geometry on Finite Element Predictions of Cartilage Contact Stresses in the Hip,” Journal of Biomechanics 43, no. 7 (2010): 1351–1357.
|
| [34] |
B. Schmutz, M. E. Wullschleger, and M. A. Schuetz, “The Effect of CT Slice Spacing on the Geometry of 3D Models,” in Proceedings of the 6th Australasian Biomechanics Conference (The University of Auckland, 2007), 93–94.
|
| [35] |
E. Gryska, P. Fredrikson, K. Libberecht, C. Stor Swinkels, P. Axelsson, and A. Björkman, “An Exploratory Study of the Impact of CT Slice Thickness and Inter-Rater Variability on Anatomical Accuracy of Malunited Distal Radius Models and Surgical Guides for Corrective Osteotomy,” PLoS One 19, no. 10 (2024): e0311805.
|
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