Master s Thesis: Deep Learning for Metal Artifact Reduction in CT with Heterogeneous Knee Implants

• Interdisciplinary Environment: You will work in a highly educated team, fostering collaboration between computer science and clinical experts.
• Expert Feedback: Continuous supervision and feedback from medical professionals and machine learning experts will guide the research process., * Develop a Synthetic Implant Generation Pipeline: Create a pipeline to realistically introduce heterogeneous knee implants, such as knee prostheses, plates, and screws, into artifact-free CT volumes and generate corresponding CT images with simulated metal artifacts.
• Develop a Deep Learning-Based MAR Model: Use the generated paired synthetic data to train a model that reduces metal artifacts while preserving the underlying anatomical structures.
• Evaluate on Real Clinical Data: Assess how well the developed MAR approach generalizes to real CT scans containing different knee implant types, using quantitative image-quality metrics and qualitative evaluation of the reconstructed anatomy.

Application

Send an email with your CV and transcript of records to tim.mach@tum.de .

References

1. Lee, Jimin, et al. “Deep learning-based metal artifact reduction in CT for total knee arthroplasty.” Scientific Reports 15.1 (2025): 39587.
2. Zhang, Yanbo, and Hengyong Yu. “Convolutional neural network based metal artifact reduction in X-ray computed tomography.” IEEE Transactions on Medical Imaging 37.6 (2018): 1370-1381.
3. Lin, Wei-An, et al. “DuDoNet: Dual domain network for CT metal artifact reduction.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019.
4. Wellenberg, R. H. H., et al. “Metal artifact reduction techniques in musculoskeletal CT-imaging.” European Journal of Radiology 107 (2018): 60-69.

Kontakt: tim.mach@tum.de

• Strong Coding: Proficiency in Python and PyTorch, with the ability to develop and debug deep learning pipelines independently.
• Deep Learning Fundamentals: Solid understanding of convolutional neural networks and modern deep learning methods for image reconstruction, synthesis, or image-to-image translation.
• Independent Research: Ability to read, understand, and implement methods from current deep learning and medical imaging literature.
• 3D Image Processing: Interest in working with volumetric data, spatial transformations, image registration, and 3D image processing.
• Medical Imaging: Experience with CT data, DICOM/NIfTI, or libraries such as MONAI, SimpleITK, or 3D Slicer is beneficial but not required.

Technische Universität München

Academia

5,001-10,000 employees