T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study

Axel Honn\'e; Christiane Kuhl; Daniel Truhn; Marc von der St\"uck; Philipp Schad; Robert Siepmann; Simon Westfechtel; Sven Nebelung; Teresa Lemainque

REVIEW 3 major objections 6 minor 34 references

T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read T2 texture variance detects cartilage loading where mean T2 fails

desk verdict Plausible new signal in five cadaver knees, but the 'more sensitive' claim is not statistically supported as written; worth peer review with a required formal comparison test. read the letter →

arxiv 2508.15309 v1 pith:BZ5PKRMC submitted 2025-08-21 physics.med-ph

Teresa Lemainque , Philipp Schad , Simon Westfechtel , Marc von der Stück , Robert Siepmann , Axel Honné , Christiane Kuhl , Daniel Truhn

show 1 more author

Sven Nebelung

This is my paper · ORCID

classification physics.med-ph

keywords stressMRIcartilagemeniscectomyT2mappingradiomicsGLCMtextureanalysiskneeosteoarthritisquantitative

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This study asks whether quantitative MRI can see how knee cartilage responds to mechanical loading and to loss of the meniscus. Using five human cadaveric knee joints in an MR-compatible loading device, the authors scanned cartilage with T2 mapping under unloaded, neutrally loaded, and 10° varus-loaded conditions, before and after arthroscopic meniscectomy. They report that average T2 relaxation time did not change significantly with pressure or with meniscectomy, but one gray-level co-occurrence matrix texture feature, variance, decreased significantly with increasing load in the medial femur and, in part, in the medial tibia. If this result is right, stress MRI studies should analyze the texture of T2 maps, not just the mean, to detect early mechanical cartilage changes after meniscectomy.

What carries the argument

The argument runs on two paired mechanisms. The experimental one is an MR-compatible pneumatic loading device that applies reproducible compressive force (3 bar, 0.74 kN) to a cadaveric knee in neutral or 10° varus alignment, so that the same cartilage can be imaged unloaded, neutrally loaded, and varus-loaded; joint space width measurements confirm the load took effect. The analytical one is the gray-level co-occurrence matrix (GLCM), a table counting how often pairs of neighboring voxel intensities occur in the segmented cartilage T2 map. From that table the study computes four texture features—variance, homogeneity, contrast, energy—of which variance (the spread of neighboring intensity-p

What would settle it

Ask two or more independent readers to re-segment the same T2 maps and recompute GLCM variance for UL, LN, and LV; if the significant medial-femur variance differences disappear once segmentation variability is included, the load response is an artifact of boundary placement rather than cartilage texture. A simpler mechanical check would be a numerical phantom with fixed mean T2 and known voxel rearrangement: variance must change in the direction the study predicts.

Watch

Extended reading notes

Core claim

In this cadaveric stress MRI study, the central finding is that the spatial texture of cartilage T2 maps carries load-response information that the regional mean T2 does not. Under controlled 3-bar compressive loading (0.74 kN) in neutral or 10° varus alignment, medial joint space width narrowed significantly across all pressure states and after meniscectomy, confirming that load was actually applied. Mean T2 in the medial femur and medial tibia showed no significant dependence on pressure or meniscectomy. In the same T2 maps, the GLCM feature variance decreased significantly in the medial femur from unloaded to neutrally loaded to varus-loaded (UL vs LN p=0.042; UL vs LV p<0.001; LN vs LV p

Load-bearing premise

Manual segmentation of the medial femoral and tibial cartilage plates is accurate and consistent across all loading and meniscectomy states, because texture features are sensitive to exactly where the boundaries are drawn and the study does not report inter-reader variability.

Editorial extensions

If this is right

Stress MRI protocols that rely on mean T2 alone will miss an acute load response that texture analysis can see; adding GLCM variance is a low-cost post-processing step.
In the medial femur, GLCM variance separated all three pressure states, so it could serve as a quantitative marker of loading intensity in cadaveric or controlled in-vivo studies.
Meniscectomy did not significantly shift mean T2 or texture features in this small sample, suggesting the sensitivity gain is specific to load-induced textural change, not to the resection itself.
If replicated, this supports using stress MRI plus T2 radiomics to screen for early, potentially reversible cartilage changes in patients with meniscal injury or after partial meniscectomy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: loading probably compresses cartilage and squeezes interstitial water, changing the spatial arrangement of T2 values; a natural test is serial imaging during recovery after unloading, where variance should drift back toward baseline as fluid returns.
Editorial inference: the texture features were computed on a single central coronal slice; full-volume 3D T2 acquisition would show whether the femoral variance gradient reflects the whole weight-bearing cartilage or only the central slice.
Editorial inference: if the same variance effect appears in vivo under body-weight or MRI-compatible loading, it would give clinicians a quantifiable early marker for load-related cartilage stress in patients after meniscectomy, not just in cadaveric specimens.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a genuine extension: human cadaver knees under controlled loading, with T2 radiomics rather than just mean T2, and a clean within-knee design. The descriptive pattern is coherent—variance and energy shift monotonically from unloaded to neutral to varus loading in the medial femur, while mean T2 does not. The loading device is validated by joint space narrowing, and the authors are honest about small sample size and single-slice limitations. That is real value, and the result is worth knowing about even if it is exploratory.

The soft spot is the central claim. The conclusion that radiomic features are "more sensitive" than mean T2 is built on the observation that variance reached significance in some pairwise comparisons while mean T2 did not. That is not a test of relative sensitivity. You need a formal interaction between parameter type and loading state, or at least a confidence interval for the difference in standardized effects. Without it, all you can say is that variance showed significant pairwise changes here and mean T2 did not—which is suggestive, but not the comparative claim in the title and abstract. The stress-test note has this right.

The multiplicity issue is also real, though secondary. The authors correct within each model (four comparisons), but they fit ten models (five parameters × two regions) and then pick out the significant ones. At N=5, that invites false positives. No effect sizes or confidence intervals are reported, which makes it hard to judge magnitude. The authors acknowledge N and single slice, but not the model-level multiplicity, and they do not discuss the missing interaction test. Inter-reader variability is also unassessed, which matters for radiomics even if segmentation was checked.

Where I would push back on the harshest reading: this is not a fitting-as-prediction problem or a circular analysis. The features come directly from the T2 maps, the direction of change matches the loading hypothesis, and the JSW data confirm the mechanical intervention worked. The main weakness is statistical interpretation, not data fabrication or incoherence.

My recommendation: send it to peer review. The referee should ask for a joint model comparing T2 vs. radiomic change, or a proper difference-of-effects test with confidence intervals, plus some accounting for the number of models. If the authors can supply that, the paper becomes a modest but solid contribution. If not, the conclusion should be toned down. Either way, the study is a good candidate for a reading group discussion about comparing p-values across separate models.

Referee Report

3 major / 6 minor

Summary. The paper reports an exploratory stress-MRI study in five human cadaveric knee joints, comparing mean T2 and four GLCM radiomic texture features (variance, homogeneity, contrast, energy) in medial femoral and tibial cartilage under unloaded (UL), neutrally loaded (LN), and 10° varus loaded (LV) conditions, before and after arthroscopic meniscectomy. Medial joint space width confirmed that varus loading and meniscectomy compress the medial compartment. Mean T2 showed no significant pairwise differences across pressure or meniscectomy states, whereas the radiomic feature 'variance' showed significant pressure-state differences in the medial femur (and partly in the medial tibia) after a four-fold Bonferroni correction within each model. The authors conclude that T2-based radiomic features are more sensitive than T2 mapping alone for assessing cartilage response to loading.

Significance. If the comparative claim were properly supported, the finding would be of practical interest: texture analysis of standard T2 maps might capture load-induced cartilage changes that mean T2 misses, potentially improving stress-MRI assessment of early degenerative changes. The study uses a carefully controlled loading device, validates load application via joint space width, and is candid about its main limitations (small sample, single-slice acquisition, no inter-reader variability). The exploratory nature is appropriate, and the data presentation is generally clear. However, the central 'more sensitive' conclusion is not directly tested by the statistical design, and the multiple-comparison burden across ten models further weakens the inference.

major comments (3)

[Abstract, Discussion, Conclusion; Table 4; Eq. 2] The headline claim that radiomic features are 'more sensitive' than mean T2 is not established by the analysis. Table 4 reports p-values from separate linear mixed-effects models per parameter and region (Eq. 2). Finding significant pairwise differences for variance and none for mean T2 does not imply that the variance effect is significantly larger than the T2 effect. A formal interaction test between parameter type and press_state in a joint model, or a comparison of standardized effect sizes with confidence intervals, is required to support the comparative claim. Without this, the conclusion should be limited to stating that variance showed significant changes while mean T2 did not, not that it was 'more sensitive.'
[Statistical analysis, Table 4] No correction is applied across the 10 fitted models (5 parameters × 2 regions). The four-fold Bonferroni correction only adjusts for the pairwise comparisons within each model. At α=0.05 over 10 models, the expected number of false-positive models is 0.5. For medial-femur variance, the UL–LN and LN–LV p-values (0.042 and 0.022) would not survive a 10-fold correction (0.42 and 0.22); only UL–LV (p<0.001) would. Report model-level adjusted p-values (e.g., Bonferroni or FDR across all tests) or explicitly justify the chosen error rate.
[Table 4, Results/Discussion] With N=5, the analysis reports only p-values and descriptive means/SD; no effect sizes or confidence intervals are given for the pairwise differences. The absence of significant mean-T2 changes may reflect low statistical power as much as a true lack of sensitivity. Provide standardized effect sizes (e.g., Cohen's d or partial R²) and confidence intervals for the key comparisons, especially for the medial-femur variance effects that drive the main conclusion.

minor comments (6)

[Tables 3 and 4] The row labels for 'Averaged effect' appear swapped: the men_state rows contain pressure-state comparisons (UL–LN, UL–LV, LN–LV), while the press_state row contains the meniscectomy comparison (INT–PM). In addition, the abbreviations list 'MF – medial tibia' in both tables; MF should be medial femur.
[Results, paragraph beginning 'Radiomic parameters...'] The sentence 'variance and energy ... showed a clear decrease and increase ... which proved to be significant for all pair-wise comparisons between pressure states (‘contrast’) and for all but UL vs. LN (‘energy’)' is garbled and inconsistent with Table 4. Please rephrase to state which parameters reached significance for which pairwise comparisons.
[Table 2] Minor typo: '2. 5 ± 0.3' should read '2.5 ± 0.3'.
[Methods, Radiomic features] The GLCM bin width (5 ms) is a free parameter that can substantially affect texture values. Please justify the choice or report a sensitivity analysis across reasonable bin widths.
[References] Reference [34] (Neumann et al.) is listed but does not appear to be cited in the text. Also, the text near Eq. 2 refers to the R package 'lmer4'; the correct name is 'lme4'.
[Methods, MRI measurements and Radiomic features] Minor language issues: 'i.e., i.e.,' is duplicated in the MRI measurements paragraph, and the sentence about energy ('where with values indicate that...') is incomplete.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: radiomic features are computed from measured T2 maps and compared across conditions without fitting the outcome or renaming inputs as predictions.

full rationale

The paper's quantitative workflow is: acquire multi-echo spin-echo data, fit Eq. 1 per voxel to obtain T2 maps, compute mean T2 and GLCM radiomic features (variance, homogeneity, contrast, energy) with PyRadiomics at a fixed bin width, then fit Eq. 2 linear mixed models separately per parameter and region, followed by Bonferroni-corrected pairwise contrasts. None of these steps fits a parameter to the outcome being compared, and no radiomic feature is defined in terms of mean T2 or vice versa. GLCM variance is a standard texture statistic computed from the same T2 maps; the paper does not fit radiomic features to pressure labels or claim a prediction from a fitted model. The self-citations ([11] for the loading device, [18] for feature choice) describe equipment and prior feature selection, not a uniqueness theorem or a fitted input, and they are not load-bearing for the observed p-values. The missing interaction test between parameter type and pressure state is a statistical-inference weakness regarding the comparative 'more sensitive' claim, but it is not circularity. Therefore the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the choice of radiomic features, preprocessing thresholds, and manual segmentations. No new physical entities are introduced.

free parameters (3)

GLCM bin width = 5 ms
Chosen for discretizing T2 maps; affects radiomic feature values. No justification or sensitivity analysis provided.
T2 outlier threshold = 100 ms
Voxels with T2 > 100 ms are excluded; chosen by hand, affects mean T2 and radiomic features.
Fit quality threshold r^2 = 0.8
Voxels with fit r^2 < 0.8 are excluded; chosen by hand, affects calculated T2 and features.

assumptions (4)

domain assumption T2 relaxation time reflects cartilage composition
Basis for using T2 mapping as a proxy for cartilage health (Introduction).
domain assumption Radiomic texture features on T2 maps capture meaningful tissue properties
Assumed in interpreting variance changes as tissue-level textural changes rather than noise (Discussion).
domain assumption Manual segmentation of cartilage is accurate and consistent across states
Segmentations by one observer, verified by another; no inter-reader variability assessment (Methods, Segmentation).
standard math Linear mixed model assumptions hold for the small sample
Normality and random effects assumptions for lmer with N=5 (Statistical analysis).

how reviews work

0 comments

Cite this review

Pith. "Pith review of T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study." pith.science (2026). https://pith.science/paper/BZ5PKRMC

@misc{pith2026250815309,
  author       = {Pith},
  title        = {Pith review of: T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BZ5PKRMC}},
  note         = {Machine review of arXiv:2508.15309}
}

abstract

Objective: To assess response-to-loading in a human cadaveric knee joint model under different loading conditions before and after meniscectomy Design: In this prospective study, stress magnetic resonance imaging was performed using an MR-compatible loading device and quantitative T2 mapping in unloaded (UL), 0${\deg}$ neutrally loaded (LN) and 10${\deg}$ varus loaded (LV) condition before and after meniscectomy. Mean T2 values and four radiomic texture parameters were assessed within the cartilage of medial femur (MF) and medial tibia (MT) for all conditions. Results: Medial joint space width decreased from UL to LN to LV and after meniscectomy (all p<0.05). T2 values did not show any significant dependency on pressure or meniscectomy (all p>0.05). The radiomic parameter variance could assess loading induced textural T2 changes in the MF (UL vs. LN: p=0.042; UL vs. LV: p<0.001; LN vs. LV: p=0.022), and, in part, in the MT (LN vs. LV: p<0.013). Meniscectomy did not significantly alter the T2 mean values or radiomic parameters, respectively. Conclusions: T2-based radiomic features were more sensitive to assess cartilage response to loading than T2-mapping alone.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

[1]

Prevalence Trends of Site- Speciﬁc Osteoarthritis From 1990 to 2019: Findings From the Global Burden of Disease Study 2019,

H. Long, Q. Liu, Y. Yin, K. Wang, N. Diao, Y. Zhang, J. Lin and A. Guo, "Prevalence Trends of Site- Speciﬁc Osteoarthritis From 1990 to 2019: Findings From the Global Burden of Disease Study 2019," Arthritis & Rheumatology, vol. 74, no. 7, p. 1172–1183, 2022

work page 1990
[2]

Recent Advances in MRI of Articular Cartilage,

G. E. Gold, C. A. Chen, S. Koo, B. A. Hargreaves and N. K. Bangerter, "Recent Advances in MRI of Articular Cartilage," American Journal of Roentgenology, vol. 193, no. 3, p. 628–638, 2009

work page 2009
[3]

State of the Art: Imaging of Osteoarthritis—Revisited 2020,

F. W. Roemer, S. Demehri, P. Omoumi, T. M. Link, R. Kijowski, S. Saarakkala, M. D. Crema and A. Guermazi, "State of the Art: Imaging of Osteoarthritis—Revisited 2020," Radiology, vol. 296, pp. 5-21, 2020

work page 2020
[4]

Evaluation of the Articular Cartilage of the Knee Joint: Value of Adding a T2 Mapping Sequence to a Routine MR Imaging Protocol,

R. Kijowski, D. G. Blankenbaker, d. M. A. Rio, G. S. Baer and B. K. Graf, "Evaluation of the Articular Cartilage of the Knee Joint: Value of Adding a T2 Mapping Sequence to a Routine MR Imaging Protocol," Radiology, no. 267(2), pp. 503-513, 2013

work page 2013
[5]

Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook,

S. Gao, C. Peng, G. Wang, C. Deng, Z. Zhang and Y. Liu, "Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook," European Journal of Radiology, vol. 181, p. 111826, 2024

work page 2024
[6]

Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability,

E.-M. Winkelmeyer, J. Schock, L. M. Wollschläger, P. Schad, M. S. Huppertz, N. Kotowski, A. Prescher, C. Kuhl, D. Truhn and S. Nebelung, "Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability," Diagnostics, vol. 4, no. 11, p. 1035, 2021

work page 2021
[7]

The MRI posterior drawer test to assess posterior cruciate ligament functionality and knee joint laxity,

L. M. Wollschläger, K. L. Radke, J. Schock, N. Kotowski, D. Latz, D. Kanschik, T. J. Filler, S. Caspers, G. Antoch, J. Windolf, D. B. Abrar and S. Nebelung, "The MRI posterior drawer test to assess posterior cruciate ligament functionality and knee joint laxity," Scientific Reports, vol. 11, no. 1, p. 19687, 2021

work page 2021
[8]

Micro- and Macroscale Assessment of Posterior Cruciate Ligament Functionality Based on Advanced MRI Techniques,

L. M. Wilms, K. L. Radke, D. B. Abrar, D. Latz, J. Schock, M. Frenken, J. Windolf, G. Antoch, T. J. Filler and S. Nebelung, "Micro- and Macroscale Assessment of Posterior Cruciate Ligament Functionality Based on Advanced MRI Techniques," Diagnostics, vol. 10, p. 1790, 2021

work page 2021

Show all 34 references

[9]

Comprehensive Assessment of Medial Knee Joint Instability by Valgus Stress MRI,

M. Ciba, E.-M. Winkelmeyer, J. Schock, P. Schad, N. Kotowski, T. Nolte, L. Wollschläger, M. Knobe, A. Prescher, C. Kuhl, D. Truhn and S. Nebelung, "Comprehensive Assessment of Medial Knee Joint Instability by Valgus Stress MRI," Diagnostics, no. 11, p. 1433, 2021

2021
[10]

No pressure, no diamonds? - Static vs. dynamic compressive in-situ loading to evaluate human articular cartilage functionality by functional MRI,

D. Truhn, K. T. Zwingenberger, J. Schock, D. B. Abrar, K. L. Radke, M. Post, K. Linka, M. Knobe, C. Kuhl and S. Nebelung, "No pressure, no diamonds? - Static vs. dynamic compressive in-situ loading to evaluate human articular cartilage functionality by functional MRI," Journal...

2021
[11]

Magnetic resonance imaging of human knee joint functionality under variable compressive in-situ loading and axis alignment,

P. Schad, M. Wollenweber, J. Thüring, J. Schock, J. Eschweiler, G. Palm, K. Radermacher, F. Eckstein, A. Prescher, C. Kuhl, D. Truhn and S. Nebelung, "Magnetic resonance imaging of human knee joint functionality under variable compressive in-situ loading and axis alignment," J...

2020
[12]

Loaded versus unloaded magnetic resonance imaging (MRI) of the knee: Effect on meniscus extrusion in healthy volunteers and patients with osteoarthritis,

R. Patel, M. Eltgroth, R. B. Souza, C. A. Zhang, S. Majumdar, T. M. Link and D. Motamedi, "Loaded versus unloaded magnetic resonance imaging (MRI) of the knee: Effect on meniscus extrusion in healthy volunteers and patients with osteoarthritis," Eur J Radiol Open, vol. 3, pp. ...

2016
[13]

Zonal differences in meniscus MR relaxation times in response to in vivo static loading in knee osteoarthritis,

N. E. Calixto, D. Kumar, K. Subburaj, J. Singh, J. Schooler, L. Nardo, X. Li, R. B. Souza, T. M. Link and S. Majumdar, "Zonal differences in meniscus MR relaxation times in response to in vivo static loading in knee osteoarthritis," J Orthop Res, vol. 34, no. 2, pp. 249-61, 2016

2016
[14]

Total resection of any segment of the lateral meniscus may cause early cartilage degeneration: Evaluation by magnetic resonance imaging using T2 mapping,

K. Murakami, Y. Arai, K. Ikoma, K. Kato, H. Inoue, S. Nakagawa, Y. Fujii, K. Ueshima, H. Fujiwara and T. Kubo, "Total resection of any segment of the lateral meniscus may cause early cartilage degeneration: Evaluation by magnetic resonance imaging using T2 mapping," Medicine, ...

2018
[15]

T2 Mapping Values in Postmeniscectomy Knee Articular Cartilage after Running: Early Signs of Osteoarthritis?,

D. Lindner, Y. Chechik, Y. Beer, S. Tal, O. Lysyy, T. Blumenfeld-Katzir, N. Ben-Eliezer and G. Agar, "T2 Mapping Values in Postmeniscectomy Knee Articular Cartilage after Running: Early Signs of Osteoarthritis?," J Knee Surg, 2020

2020
[16]

Influence of medial meniscectomy on stress distribution of the femoral cartilage in porcine knees: a 3D reconstructed T2 mapping study,

T. Shiomi, T. Nishii, S. Tamura, H. Tanaka, K. Murase, H. Yoshikawa and N. Sugano, "Influence of medial meniscectomy on stress distribution of the femoral cartilage in porcine knees: a 3D reconstructed T2 mapping study," Osteoarthritis and Cartilage, vol. 20, no. 11, pp. 1383-...

2012
[17]

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee and G. Gerig, "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability," Neuroimage, vol. 31, no. 3, pp. 1116-28, 2006

2006
[18]

Longitudinal T2 Mapping and Texture Feature Analysis in the Detection and Monitoring of Experimental Post-Traumatic Cartilage Degeneration,

M. S. Huppertz, J. Schock, K. L. Radke, D. B. Abrar, M. Post, C. Kuhl, D. Truhn and S. Nebelung, "Longitudinal T2 Mapping and Texture Feature Analysis in the Detection and Monitoring of Experimental Post-Traumatic Cartilage Degeneration," Life, vol. 11, no. 3, p. 201
[19]

[Online]

Pyradiomics. [Online]. Available: https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.glcm . [Accessed 31 01 2025]

2025
[20]

R (4.3.1) : A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.,

R. C. Team, "R (4.3.1) : A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.," [Online]. Available: https://www.R-project.org. [Accessed 26 November 2024]

2024
[21]

The effects of acute loading on T1rho and T2 relaxation times of tibiofemoral articular cartilage,

R. B. Souza, C. Stehling, B. T. Wyman, M.-P. H. L. Graverand, X. Li, T. M. Link and S. Majumdar, "The effects of acute loading on T1rho and T2 relaxation times of tibiofemoral articular cartilage," Osteoarthritis and Cartilage, vol. 18, no. 12, pp. 1557-1563, 2010

2010
[22]

Change in knee cartilage T2 in response to mechanical loading,

T. Nishii, K. Kuroda, Y. Matsuoka, T. Sahara and H. Yoshikawa, "Change in knee cartilage T2 in response to mechanical loading," Journal of Magnetic Resonance Imaging, vol. 28, no. 1, pp. 175-180, 2008

2008
[23]

Functional MR Imaging Mapping of Human Articular Cartilage Response to Loading,

S. Nebelung, B. Sondern, S. Oehrl, M. Tingart, B. Rath, T. Pufe, S. Raith, H. Fischer, C. Kuhl, H. Jahr and D. Truhn, "Functional MR Imaging Mapping of Human Articular Cartilage Response to Loading," Radiology, vol. 282, no. 2, pp. 464-474, 2017

2017
[24]

Comparative T2 and T1ρ mapping of patellofemoral cartilage under in situ mechanical loading with prospective motion correction,

T. Lange, B. R. Knowles, M. Herbst, K. Izadpanah and M. Zaitsev, "Comparative T2 and T1ρ mapping of patellofemoral cartilage under in situ mechanical loading with prospective motion correction," Journal of Magnetic Resonance Imaging, vol. 46, no. 2, pp. 452-460, 2017

2017
[25]

Immediate and Delayed Effects of Joint Loading Activities on Knee and Hip Cartilage: A Systematic Review and Meta-analysis,

S. L. Coburn, K. M. Crossley, J. L. Kemp, S. J. Warden, T. J. West, A. M. Bruder, B. F. Mentiplay and A. G. Culvenor, "Immediate and Delayed Effects of Joint Loading Activities on Knee and Hip Cartilage: A Systematic Review and Meta-analysis," Sports Medicine Open, vol. 9, p. 56, 2023

2023
[26]

Higher Body Mass Index Is Associated With Biochemical Changes in Knee Articular Cartilage After Marathon Running: A Quantitative T2-Relaxation MRI Study,

Z. Wang, S. Ai, F. Tian, M. H. L. Liow, S. Wang, J. Zhao and T.-Y. Tsai, "Higher Body Mass Index Is Associated With Biochemical Changes in Knee Articular Cartilage After Marathon Running: A Quantitative T2-Relaxation MRI Study," Orthopaedic Journal of Sports Medicine, vol. 8, ...

2020
[27]

Optimization of knee cartilage texture analysis of quantitative MRI T2 maps,

V. Janáčová, V. Juras, P. Szomolanyi and S. Trattnig, "Optimization of knee cartilage texture analysis of quantitative MRI T2 maps," Osteoarthritis and Cartilage, vol. 29, pp. S329 - S330
[28]

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study,

A. Peuna, J. Thevenot, S. Saarakkala, M. Nieminen and E. Lammentausta, "Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study," Osteoarthritis and Cartilage, vol. 29, no. 6, pp. 859-869, 2021

2021
[29]

T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative,

K. L. Urish, M. G. Keffalas, J. R. Durkin, D. J. Miller, C. R. Chu and T. J. Mosher, "T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative," Osteoarthritis Cartilage, vol. 21, no. 10, pp. 1550-7, 2013

2013
[30]

Y. Xie, Y. Dan, H. Tao, C. Wang, C. Zhang, Y. Wang, J. Yang, G. Yang and S. Chen, "Radiomics Feature Analysis of Cartilage and Subchondral Bone in Differentiating Knees Predisposed to Posttraumatic Osteoarthritis after Anterior Cruciate Ligament Reconstruction from Healthy Kne...

2021
[31]

Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement,

A. S. Chaudhari, M. J. Grissom, Z. Fang, B. Sveinsson, J. H. Lee, G. E. Gold, B. A. Hargreaves and K. J. Stevens, "Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement," American Journal of Roen...

2021
[32]

Sakai, M

T. Sakai, M. Yoneyama, S. Zhang, K. Kitsukawa, H. Yokota, R. Ichikawa, Y. Aoki, A. Watanabe, Y. Sato, N. Yanagawa, D. Murayama, H. Ito, S. Ochi and T. Miyati, "Clinical evaluation of 3D high- resolution isotropic knee MRI using Multi-Interleaved X-prepared TSE with inTUitive R...

2024
[33]

Two for One—Combined Morphologic and Quantitative Knee Joint MRI Using a Versatile Turbo Spin-Echo Platform,

T. Lemainque, N. Pridöhl, M. Huppertz, M. Post, C. Yüksel, R. Siepmann, K. L. Radke, S. Zhang, M. Yoneyama, A. Prescher, C. Kuhl, D. Truhn and S. Nebelung, "Two for One—Combined Morphologic and Quantitative Knee Joint MRI Using a Versatile Turbo Spin-Echo Platform," Diagnostic...

2024
[34]

Cartilage degeneration post-meniscectomy performed for degenerative disease versus trauma: data from the Osteoarthritis Initiative,

J. Neumann, K. Kern, D. Sun, S. C. Foreman, G. B. Joseph, A. S. Gersing, M. C. Nevitt, C. E. McCulloch, A. Quitzke and T. M. Link, "Cartilage degeneration post-meniscectomy performed for degenerative disease versus trauma: data from the Osteoarthritis Initiative," Skeletal Rad...

2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Prevalence Trends of Site- Speciﬁc Osteoarthritis From 1990 to 2019: Findings From the Global Burden of Disease Study 2019,

H. Long, Q. Liu, Y. Yin, K. Wang, N. Diao, Y. Zhang, J. Lin and A. Guo, "Prevalence Trends of Site- Speciﬁc Osteoarthritis From 1990 to 2019: Findings From the Global Burden of Disease Study 2019," Arthritis & Rheumatology, vol. 74, no. 7, p. 1172–1183, 2022

work page 1990

[2] [2]

Recent Advances in MRI of Articular Cartilage,

G. E. Gold, C. A. Chen, S. Koo, B. A. Hargreaves and N. K. Bangerter, "Recent Advances in MRI of Articular Cartilage," American Journal of Roentgenology, vol. 193, no. 3, p. 628–638, 2009

work page 2009

[3] [3]

State of the Art: Imaging of Osteoarthritis—Revisited 2020,

F. W. Roemer, S. Demehri, P. Omoumi, T. M. Link, R. Kijowski, S. Saarakkala, M. D. Crema and A. Guermazi, "State of the Art: Imaging of Osteoarthritis—Revisited 2020," Radiology, vol. 296, pp. 5-21, 2020

work page 2020

[4] [4]

Evaluation of the Articular Cartilage of the Knee Joint: Value of Adding a T2 Mapping Sequence to a Routine MR Imaging Protocol,

R. Kijowski, D. G. Blankenbaker, d. M. A. Rio, G. S. Baer and B. K. Graf, "Evaluation of the Articular Cartilage of the Knee Joint: Value of Adding a T2 Mapping Sequence to a Routine MR Imaging Protocol," Radiology, no. 267(2), pp. 503-513, 2013

work page 2013

[5] [5]

Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook,

S. Gao, C. Peng, G. Wang, C. Deng, Z. Zhang and Y. Liu, "Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook," European Journal of Radiology, vol. 181, p. 111826, 2024

work page 2024

[6] [6]

Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability,

E.-M. Winkelmeyer, J. Schock, L. M. Wollschläger, P. Schad, M. S. Huppertz, N. Kotowski, A. Prescher, C. Kuhl, D. Truhn and S. Nebelung, "Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability," Diagnostics, vol. 4, no. 11, p. 1035, 2021

work page 2021

[7] [7]

The MRI posterior drawer test to assess posterior cruciate ligament functionality and knee joint laxity,

L. M. Wollschläger, K. L. Radke, J. Schock, N. Kotowski, D. Latz, D. Kanschik, T. J. Filler, S. Caspers, G. Antoch, J. Windolf, D. B. Abrar and S. Nebelung, "The MRI posterior drawer test to assess posterior cruciate ligament functionality and knee joint laxity," Scientific Reports, vol. 11, no. 1, p. 19687, 2021

work page 2021

[8] [8]

Micro- and Macroscale Assessment of Posterior Cruciate Ligament Functionality Based on Advanced MRI Techniques,

L. M. Wilms, K. L. Radke, D. B. Abrar, D. Latz, J. Schock, M. Frenken, J. Windolf, G. Antoch, T. J. Filler and S. Nebelung, "Micro- and Macroscale Assessment of Posterior Cruciate Ligament Functionality Based on Advanced MRI Techniques," Diagnostics, vol. 10, p. 1790, 2021

work page 2021