Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning

Alicia Durrer; Carol C. Hasler; Edwin Li; Gyozo Lehoczky; Marcus Mumme; Michael Wehrli; Paul Friedrich; Philippe C. Cattin; Volodimir Buchakchiyskiy

REVIEW 4 major objections 6 minor 26 references

Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Pseudo-healthy knee MRI generation normalizes trochlear dysplasia measures in 49 scans.

desk verdict The application is promising and the code is out, but the headline clinical claim rests on 16 unblinded single-rater measurements—send it to review, not to press. read the letter →

arxiv 2412.09962 v1 pith:GDTVYUJV submitted 2024-12-13 eess.IV

Michael Wehrli , Alicia Durrer , Paul Friedrich , Volodimir Buchakchiyskiy , Marcus Mumme , Edwin Li , Gyozo Lehoczky , Carol C. Hasler

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Philippe C. Cattin

This is my paper · ORCID

classification eess.IV

keywords Pseudo-HealthyTrochlearDysplasiaDiffusionModelsInpaintingWaveletKneeMRISurgicalPlanningImageGeneration

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 paper tries to establish that a wavelet diffusion model trained on healthy knee MRI scans can generate a patient-specific pseudo-healthy image of the trochlear groove for adolescents with trochlear dysplasia, simply by masking the pathological region and inpainting. An orthopedic surgeon's measurements on 49 dysplastic knee scans show that after inpainting the sulcus angle and trochlear groove depth move toward normal values and the Dejour severity stage drops, with many severe cases becoming measurable for the first time. If the claim holds, surgeons would gain a concrete, per-patient visual target for reshaping the femur during trochleoplasty, a procedure that currently has no standardized preoperative plan.

What carries the argument

The machinery is the Wavelet Diffusion Model (WDM), a denoising diffusion probabilistic model whose forward and reverse processes operate on the Haar wavelet decomposition of the volume instead of the raw image. At every reverse step, the network receives the noisy wavelet coefficients concatenated with the wavelet transform of the masked pathological scan and the mask itself, and it is trained to predict the denoised volume with an MSE loss plus an $\ell^1$ penalty on high-frequency components to suppress edge noise. This conditioning on the masked scan is what ties the generated healthy anatomy to the individual patient's patella.

What would settle it

Measure the congruence between the generated trochlear groove and the patient's patella in a cohort that later undergoes trochleoplasty, and compare the generated target with the groove shape that actually stabilizes the patella; if the two disagree, the pseudo-healthy image is not a reliable surgical target.

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Extended reading notes

Core claim

The central claim is that a diffusion model operating in the wavelet domain can restore a plausible healthy trochlear shape while remaining conditioned on the patient's own patella. The evidence: in 41 of 49 pathological scans the Dejour classification decreased by one or more stages, and in the 16 scans measurable both before and after, the sulcus angle fell from a mean of $154^\circ$ to $145^\circ$ ($p = 0.001$) and groove depth increased from 3.6 mm to 5.2 mm ($p = 0.0002$). Additionally, 33 of 49 severe cases had unmeasurable sulcus angle and groove depth before inpainting, but 46 of 49 were measurable afterward. The paper concludes that pseudo-healthy MR generation is feasible and can serve as a patient-specific planning aid for trochleoplasty.

Load-bearing premise

The model is trained only on healthy adult knee MRI scans and is applied to adolescent dysplasia patients without adapting for growth plates, swelling, or scanner differences, so the generated 'healthy' groove may not actually match that patient's patella.

Editorial extensions

If this is right

Surgeons can preview a patient-specific healthy trochlear shape from standard preoperative MRI, without needing an additional CT scan and its radiation.
Severe dysplasia cases where sulcus angle and groove depth cannot be measured become quantifiable after inpainting, providing numerical targets for the surgical correction.
The same masked-inpainting recipe could transfer to other joints or bone deformities that have a recognizable healthy shape but no standardized operative plan.
With a broader pediatric training set, the method might handle the currently excluded cases of dislocated patellae, swelling, and growth-plate edema.

Reading between the lines

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

A direct test of the planning value would be to register the generated groove to the patient's patella and measure patellotrochlear congruence, separating 'looks healthy' from 'fits this patella'.
The pseudo-healthy image could be converted into a quantitative resection guide, e.g., the volume to remove to reach the generated groove surface.
If combined with virtual patella repositioning, the same model might extend to dislocated-patella cases the authors had to exclude.

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Signed reviews

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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

Best read as an early clinical feasibility report, not as evidence that the method normalizes anatomy. The new thing here is not the diffusion model—that is the Basel group's WDM—but the transfer to trochlear dysplasia: a patella-conditioned bowl mask that targets the pathological trochlear region, and a clinical evaluation with a surgeon measuring sulcus angle, trochlear groove depth, and Dejour stage. Using standard pre-op MR instead of CT is a genuine practical advantage for this mostly adolescent population, and the authors make code available and state limitations. That part is solid.

The weak joint is the quantitative claim. The SA/TGD "significant improvement" uses 16 paired scans where both pre and post measurements were possible; 33 of 49 were too severe to measure before inpainting. That is a selected subsample, and regression to the mean is not addressed. The measurements were taken by a single deputy attending surgeon, unblinded, with no intra- or inter-rater reliability. The inpainted region is visually obvious and the comparison is side by side, so expectation bias is a real threat. The Dejour improvement in 41/49 has the same problem. I cannot distinguish these p-values from rater bias or measurement noise.

The domain shift from adult fastMRI knees to adolescent TD patients is acknowledged but not tested, and the claim that the inpainted anatomy aligns with the patient's patella is asserted rather than verified geometrically. There is also no baseline comparison—no simple anatomical prior, no statistical shape model—so I cannot tell how much of the apparent normalization is due to the diffusion model specifically. The missing L1 weight for the regularization term is minor.

All that said, this is a coherent, honest application paper with a useful clinical target. It deserves referee time, but the authors should be pushed to add blinded multi-rater measurements, report all cases including the severe ones where feasible, and compare against a non-generative baseline. I would not cite the quantitative result as it stands, but I would mention the approach in related work.

Referee Report

4 major / 6 minor

Summary. The paper proposes a 3D wavelet diffusion model trained on healthy fastMRI knee volumes to inpaint the trochlear region in patients with trochlear dysplasia, generating patient-specific pseudo-healthy MR images intended to support trochleoplasty planning. The method masks the patellar region with a 30 mm offset, conditions the model on the masked image and mask in the wavelet domain, and evaluates the result by having one orthopedic surgeon measure sulcus angle, trochlear groove depth, and Dejour classification on 49 pathological scans before and after inpainting. The authors report significant SA/TGD changes on 16 paired cases, Dejour improvement in 41/49 cases, and MSE/PSNR/SSIM scores on a fastMRI test set. The code is publicly released.

Significance. If the quantitative clinical claims hold, this is a useful proof-of-concept: it offers a patient-specific target shape for trochleoplasty from standard MR images, without additional radiation, and it is conditioned on the patient's own patella. Concrete strengths are the use of a public training dataset, released code, a clear clinical motivation, and an honest statement of applicability limitations. The central quantitative claim, however, is currently carried by a small, non-blinded, single-rater measurement study; the reported p-values should not be read as established evidence until measurement reliability, blinding, and selection effects are addressed.

major comments (4)

[§4.1, Fig. 7] The headline SA/TGD improvement rests on only 16 paired scans measured by a single deputy attending surgeon, with no blinding, no repeatability check, and no inter-rater reliability analysis. Because the before/after images are visually distinguishable and are compared side by side, expectation bias can systematically shift the measurements toward the healthy range. Please provide blinded repeated measurements with intra-class correlation coefficients and Bland-Altman limits, preferably with a second rater, and report a sensitivity analysis that includes all 49 scans rather than only the 16 in which SA/TGD were measurable before inpainting. Without this, the reported p=0.001 and p=0.0002 values cannot be separated from rater bias or measurement noise.
[§3.4, §4.1] The Dejour improvement in 41 of 49 scans is also based on the same unblinded single-rater evaluation. Please provide a blinded or independently adjudicated Dejour classification, report the full contingency table including the cases in which SA/TGD could not be measured before inpainting, and clarify how the pre-inpainting Dejour stage was assigned in those 33 cases. If the pre-inpainting classification was necessarily subjective for severe cases, the 41/49 improvement rate should be interpreted with that caveat stated explicitly.
[§2.4, Fig. 3] The training procedure does not specify how the masked image m1 and mask m2 are generated during training. This matters because inference uses a 30 mm bowl-shaped mask around the patella, and a systematic mismatch between training and inference masks changes the conditional distribution the model has learned. Please state the training mask sampling protocol (random, anatomical, or derived from SegmentAnyBone) and, ideally, include an ablation on mask placement and mask size.
[§5, §3.1] The model is trained on adult fastMRI knees and applied to adolescent TD patients, a domain shift the authors acknowledge in Section 5. Because the intended use is surgical planning, the fastMRI reconstruction metrics (MSE/PSNR/SSIM) and the observed reduction in dysplasia measurements are not sufficient to establish anatomical correctness for a given patient. Please add a concrete validation that the generated trochlea is congruent with the patient's own patella, for example by measuring patellotrochlear contact or alignment, or by comparing against an adolescent healthy control group. Absent such evidence, the claim that the output 'should theoretically align with the respective patient's patella' remains an unverified assumption.

minor comments (6)

[§4.1] The phrase 'quantitativ analysis' should be corrected to 'quantitative analysis'.
[Abstract] The abstract states 'significantly improves the SA, TGD and D\'ejour classification in a study with 49 knee MR scans,' but the SA/TGD statistics are computed on only 16 scans; the abstract should qualify this to avoid overstating the sample size.
[§2.4, Eq. (3)] Please specify whether the L1 regularization on high-frequency wavelet coefficients is applied to the predicted coefficients or to the ground-truth coefficients; the current notation leaves this ambiguous.
[§2.1] The 30 mm mask offset is a free parameter; please state how this value was chosen and whether the results are sensitive to it.
[§3.5] The description of computing SSIM from a 'full SSIM image' from torchmetrics is unclear; standard SSIM is a scalar, so please clarify the exact procedure used.
[References] Reference [3] appears incomplete in the reference list; please provide the full bibliographic information.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the clinical outcome metrics (SA, TGD, Dejour) are external to the training loss, and the self-cited method papers are adopted as tools rather than as load-bearing evidence for the clinical claim.

full rationale

The derivation chain is not circular. The model is trained on healthy fastMRI knee scans with a pixel-wise MSE loss plus an L1 sparsity term on high-frequency wavelet components (Section 2.4, Eq. 3); no clinical parameter such as sulcus angle, trochlear groove depth, or Dejour classification enters the loss. At inference, pathological regions are masked and the diffusion model inpaints a pseudo-healthy volume (Sections 2.5 and 3.1). The reported improvements in SA, TGD, and Dejour classification (Section 4.1) are measured by an orthopedic surgeon on the before/after images, which are external benchmarks not constructed to optimize those metrics. The paper uses two prior self-authored methods (refs [19] and [20]) for the inpainting and wavelet diffusion framework, but these are standard method adoptions, not uniqueness theorems or fitted parameters that force the clinical result. The model weights are fitted to healthy-image statistics, which is ordinary generative-model training, not a fitted input renamed as a prediction. The limitations acknowledged in Section 5 (e.g., domain shift, growth plates, swelling) further indicate that the authors do not claim a mathematically forced outcome. Concerns about single-rater, non-blinded measurements and the selection of 16 measurable scans are correctness/validity issues, not circularity. Therefore the central claim retains independent empirical content; the only mild concern is reliance on self-authored prior method papers, which does not rise to circularity.

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

The central claim rests on three classes of assumptions: (1) that healthy adult fastMRI knee appearance transfers to adolescent TD patients (training data in Section 3.1, inference on in-house data), (2) that the fixed 30mm mask captures the pathological region while preserving the conditioning signal of the patella (Section 2.1), and (3) that a single surgeon's non-blinded measurements of SA/TGD/Dejour are a valid ground truth (Section 3.4). The model weights are the main fitted component. No new entities are introduced.

free parameters (3)

Model weights (encoder/decoder of WDM) = 57,942,472 parameters
Learned on 1,216 healthy fastMRI volumes (Section 3.6, Table 1). The entire inpainting output is a function of these fitted weights, so the central claim is contingent on this fit.
Mask offset around patella = 30 mm
Chosen by hand in Section 2.1 to form the bowl-shaped mask; the size of the inpainted region is a free choice that directly influences which anatomy is regenerated.
L1 regularization weight on high-frequency wavelet coefficients = not reported
Equation 3 adds an L1 penalty to high-frequency components, but the paper never states the weighting factor, leaving a reproducibility gap and an untracked tuning parameter.

assumptions (4)

domain assumption Healthy fastMRI knee MRIs are representative of the target TD patient anatomy
The model is trained exclusively on fastMRI healthy knees (Section 3.1) and applied to adolescent patients with dysplasia; no pediatric or pathology-matched training data are used.
domain assumption The 30mm bowl mask captures all pathologically relevant trochlear tissue while preserving the patella
Section 2.1: the mask is formed by a fixed 30mm offset around the patella; if dysplastic bone extends beyond this or if the patella is not found, the inpainting may fail or be mis-conditioned.
domain assumption Surgeon measurements of SA, TGD, and Dejour classification are reliable and unbiased
Section 3.4: a single deputy attending orthopedic surgeon performed all measurements; no inter- or intra-observer variability is reported, and blinding is not described.
domain assumption Appearance-based inpainting conditioned on surrounding structures is sufficient to produce patient-specific anatomy
The method (Sections 2.4-2.5) uses only local image context; no registration to a healthy atlas or explicit bone-shape constraint is used, yet the paper claims the result aligns with the patient's patella.

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Cite this review

Pith. "Pith review of Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning." pith.science (2026). https://pith.science/paper/GDTVYUJV

@misc{pith2026241209962,
  author       = {Pith},
  title        = {Pith review of: Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GDTVYUJV}},
  note         = {Machine review of arXiv:2412.09962}
}

read the original abstract

Purpose: Trochlear Dysplasia (TD) is a common malformation in adolescents, leading to anterior knee pain and instability. Surgical interventions such as trochleoplasty require precise planning to correct the trochlear groove. However, no standardized preoperative plan exists to guide surgeons in reshaping the femur. This study aims to generate patient-specific, pseudo-healthy MR images of the trochlear region that should theoretically align with the respective patient's patella, potentially supporting the pre-operative planning of trochleoplasty. Methods: We employ a Wavelet Diffusion Model (WDM) to generate personalized pseudo-healthy, anatomically plausible MR scans of the trochlear region. We train our model using knee MR scans of healthy subjects. During inference, we mask out pathological regions around the patella in scans of patients affected by TD, and replace them with their pseudo-healthy counterpart. An orthopedic surgeon measured the sulcus angle (SA), trochlear groove depth (TGD) and D\'ejour classification in MR scans before and after inpainting. The code is available at https://github.com/wehrlimi/Generate-Pseudo-Healthy-Knee-MRI . Results: The inpainting by our model significantly improves the SA, TGD and D\'ejour classification in a study with 49 knee MR scans. Conclusion: This study demonstrates the potential of WDMs in providing surgeons with patient-specific guidance. By offering anatomically plausible MR scans, the method could potentially enhance the precision and preoperative planning of trochleoplasty, and pave the way to more minimally invasive surgeries.

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Reference graph

Works this paper leans on

26 extracted references · 24 canonical work pages

[1]

EFORT open reviews 3(5), 240–247 (2018)

Batailler, C., Neyret, P.: Trochlear dysplasia: imaging and treatment options. EFORT open reviews 3(5), 240–247 (2018)

work page 2018
[2]

JAAOS-Journal of the American Academy of Orthopaedic Surgeons 19(1), 8–16 (2011)

Bollier, M., Fulkerson, J.P.: The role of trochlear dysplasia in patellofemoral insta- bility. JAAOS-Journal of the American Academy of Orthopaedic Surgeons 19(1), 8–16 (2011)

work page 2011
[3]

Arthroskopie 36(6), 371–372

Liebensteiner, M., Dirisamer, F., et al.: Diagnostik des patellofemoralgelenks. Arthroskopie 36(6), 371–372. Accessed 2024-03-06

work page 2024
[4]

Arthroskopie 36(6), 387–401 (2023)

Dirisamer, F., Attal, R., et al.: Klinische untersuchung des patellofemoralgelenks. Arthroskopie 36(6), 387–401 (2023)

work page 2023
[5]

: Therapiealgorithmus der patellainstabilit¨ at

Wierer, G., Pfeiffer, T., et al. : Therapiealgorithmus der patellainstabilit¨ at. Arthroskopie 36(6), 415–418 (2023)

work page 2023
[6]

EFORT open reviews 1(5), 160–166 (2016)

Hasler, C.C., Studer, D.: Patella instability in children and adolescents. EFORT open reviews 1(5), 160–166 (2016)

work page 2016
[7]

The American journal of sports medicine 32(5), 1114–1121 (2004)

Fithian, D.C., Paxton, E.W., et al.: Epidemiology and natural history of acute patellar dislocation. The American journal of sports medicine 32(5), 1114–1121 (2004)

work page 2004
[8]

Orthopaedic Journal of Sports Medicine 11(5), 23259671231171378 (2023)

Blønd, L., Barfod, K.W.: Trochlear shape and patient-reported outcomes after arthroscopic deepening trochleoplasty and medial patellofemoral ligament recon- struction: a retrospective cohort study including mri assessments of the trochlear groove. Orthopaedic Journal of Sports Medicine 11(5), 23259671231171378 (2023)

work page 2023

Show all 26 references

[9]

Operative Techniques in Sports Medicine 23(2), 136–142 (2015)

Blønd, L.: Arthroscopic deepening trochleoplasty: the technique. Operative Techniques in Sports Medicine 23(2), 136–142 (2015)

2015
[10]

Knee Surgery, Sports Traumatology, Arthroscopy 2, 19–26 (1994)

Dejour, H., Walch, G., et al.: Factors of patellar instability: an anatomic radiographic study. Knee Surgery, Sports Traumatology, Arthroscopy 2, 19–26 (1994)

1994
[11]

Knee Surgery, Sports Traumatology, Arthroscopy 14, 707–712 (2006)

Biedert, R.M., Albrecht, S.: The patellotrochlear index: a new index for assessing patellar height. Knee Surgery, Sports Traumatology, Arthroscopy 14, 707–712 (2006)

2006
[12]

Operative Techniques in Sports Medicine 23(2), 114–122 (2015)

Hinckel, B.B., Arendt, E.A., et al.: Trochleoplasty: historical overview and dejour technique. Operative Techniques in Sports Medicine 23(2), 114–122 (2015)

2015
[13]

Current Reviews in Musculoskeletal Medicine 11, 231–240 (2018) 11

Nolan, J.E., Schottel, P.C., et al.: Trochleoplasty: indications and technique. Current Reviews in Musculoskeletal Medicine 11, 231–240 (2018) 11

2018
[14]

The Journal of Bone & Joint Surgery British Volume 88(10), 1331–1335 (2006)

Von Knoch, F., B¨ ohm, T., et al.: Trochleaplasty for recurrent patellar disloca- tion in association with trochlear dysplasia: a 4-to 14-year follow-up study. The Journal of Bone & Joint Surgery British Volume 88(10), 1331–1335 (2006)

2006
[15]

European Radiology, 1–12 (2024)

Barbosa, R.M., Serrador, L., et al.: Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height. European Radiology, 1–12 (2024)

2024
[16]

International Journal of Computer Assisted Radiology and Surgery, 1–9 (2024)

Fang, X., Deng, H.H., et al.: Patient-specific reference model estimation for orthognathic surgical planning. International Journal of Computer Assisted Radiology and Surgery, 1–9 (2024)

2024
[17]

The Knee 21(2), 518–523 (2014)

Van Haver, A., Mahieu, P., et al.: A statistical shape model of trochlear dysplasia of the knee. The Knee 21(2), 518–523 (2014)

2014
[18]

Sports Medicine, Arthroscopy, Rehabilitation, Therapy & Technology 4, 1–8 (2012)

Beaufils, P., Thaunat, M., et al.: Trochleoplasty in major trochlear dyspla- sia: current concepts. Sports Medicine, Arthroscopy, Rehabilitation, Therapy & Technology 4, 1–8 (2012)

2012
[19]

In: MICCAI Workshop on Deep Generative Models, pp

Durrer, A., et al.: Denoising diffusion models for 3d healthy brain tissue inpaint- ing. In: MICCAI Workshop on Deep Generative Models, pp. 87–97 (2024). Springer

2024
[20]

In: MICCAI Workshop on Deep Generative Models, pp

Friedrich, P., et al.: Wdm: 3d wavelet diffusion models for high-resolution medical image synthesis. In: MICCAI Workshop on Deep Generative Models, pp. 11–21 (2024). Springer

2024
[21]

arXiv preprint arXiv:2401.12974 (2024)

Gu, H., Colglazier, R., et al.: Segmentanybone: A universal model that segments any bone at any location on mri. arXiv preprint arXiv:2401.12974 (2024)

2024 arXiv
[22]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Li, Q., Shen, L., et al.: Wavelet integrated cnns for noise-robust image classifi- cation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7245–7254 (2020)

2020
[23]

Radiology: Artificial Intelligence 2(1), 190007 (2020)

Knoll, F., Zbontar, J., et al.: fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning. Radiology: Artificial Intelligence 2(1), 190007 (2020)

2020
[24]

arXiv preprint arXiv:1811.08839 (2018)

Zbontar, J., Knoll, F., et al.: fastmri: An open dataset and benchmarks for accelerated mri. arXiv preprint arXiv:1811.08839 (2018)

2018 arXiv
[25]

In: Intraoperative Imaging and Image-guided Therapy, pp

Kikinis, R., Pieper, S.D., et al.: 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative Imaging and Image-guided Therapy, pp. 277–289. Springer, New York, NY, USA (2013)

2013
[26]

https://slicer.org/ Accessed 2024-09-11 12

3D Slicer Image Computing Platform. https://slicer.org/ Accessed 2024-09-11 12

2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

EFORT open reviews 3(5), 240–247 (2018)

Batailler, C., Neyret, P.: Trochlear dysplasia: imaging and treatment options. EFORT open reviews 3(5), 240–247 (2018)

work page 2018

[2] [2]

JAAOS-Journal of the American Academy of Orthopaedic Surgeons 19(1), 8–16 (2011)

Bollier, M., Fulkerson, J.P.: The role of trochlear dysplasia in patellofemoral insta- bility. JAAOS-Journal of the American Academy of Orthopaedic Surgeons 19(1), 8–16 (2011)

work page 2011

[3] [3]

Arthroskopie 36(6), 371–372

Liebensteiner, M., Dirisamer, F., et al.: Diagnostik des patellofemoralgelenks. Arthroskopie 36(6), 371–372. Accessed 2024-03-06

work page 2024

[4] [4]

Arthroskopie 36(6), 387–401 (2023)

Dirisamer, F., Attal, R., et al.: Klinische untersuchung des patellofemoralgelenks. Arthroskopie 36(6), 387–401 (2023)

work page 2023

[5] [5]

: Therapiealgorithmus der patellainstabilit¨ at

Wierer, G., Pfeiffer, T., et al. : Therapiealgorithmus der patellainstabilit¨ at. Arthroskopie 36(6), 415–418 (2023)

work page 2023

[6] [6]

EFORT open reviews 1(5), 160–166 (2016)

Hasler, C.C., Studer, D.: Patella instability in children and adolescents. EFORT open reviews 1(5), 160–166 (2016)

work page 2016

[7] [7]

The American journal of sports medicine 32(5), 1114–1121 (2004)

Fithian, D.C., Paxton, E.W., et al.: Epidemiology and natural history of acute patellar dislocation. The American journal of sports medicine 32(5), 1114–1121 (2004)

work page 2004

[8] [8]

Orthopaedic Journal of Sports Medicine 11(5), 23259671231171378 (2023)

Blønd, L., Barfod, K.W.: Trochlear shape and patient-reported outcomes after arthroscopic deepening trochleoplasty and medial patellofemoral ligament recon- struction: a retrospective cohort study including mri assessments of the trochlear groove. Orthopaedic Journal of Sports Medicine 11(5), 23259671231171378 (2023)

work page 2023