# Towards MR-Based Trochleoplasty Planning

arXiv: https://arxiv.org/abs/2508.06076 · HTML: https://mlsanity.com/paper/2508.06076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [WIRE: Wavelet Implicit Neural Representations](https://mlsanity.com/paper/2301.05187/) (2023)
- [TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI](https://mlsanity.com/paper/2405.19492/) (2024)
- [WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis](https://mlsanity.com/paper/2402.19043/) (2024)
- [MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images](https://mlsanity.com/paper/2203.12362/) (2022)
- [Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations](https://mlsanity.com/paper/2303.15065/) (2023)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis](https://mlsanity.com/paper/2411.17203/) (2024)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning](https://mlsanity.com/paper/2412.09962/) (2024)

## Built on by (most cited first, 1 of 1)

- [SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI](https://mlsanity.com/paper/2609.10125/) (2026)

## Abstract (arXiv)

To treat Trochlear Dysplasia (TD), current approaches rely mainly on low-resolution clinical Magnetic Resonance (MR) scans and surgical intuition. The surgeries are planned based on surgeons experience, have limited adoption of minimally invasive techniques, and lead to inconsistent outcomes. We propose a pipeline that generates super-resolved, patient-specific 3D pseudo-healthy target morphologies from conventional clinical MR scans. First, we compute an isotropic super-resolved MR volume using an Implicit Neural Representation (INR). Next, we segment femur, tibia, patella, and fibula with a multi-label custom-trained network. Finally, we train a Wavelet Diffusion Model (WDM) to generate pseudo-healthy target morphologies of the trochlear region. In contrast to prior work producing pseudo-healthy low-resolution 3D MR images, our approach enables the generation of sub-millimeter resolved 3D shapes compatible for pre- and intraoperative use. These can serve as preoperative blueprints for reshaping the femoral groove while preserving the native patella articulation. Furthermore, and in contrast to other work, we do not require a CT for our pipeline - reducing the amount of radiation. We evaluated our approach on 25 TD patients and could show that our target morphologies significantly improve the sulcus angle (SA) and trochlear groove depth (TGD). The code and interactive visualization are available at https://wehrlimi.github.io/sr-3d-planning/.