Micro CT Denoising Using Low Parameter Models

Type: MA thesis

Status: finished

Date: March 15, 2021 - September 15, 2021

Supervisors: Fabian Wagner, Andreas Maier

Computed Tomography (CT) is widely used as a diagnostic tool due to its ability to acquire three-dimensional structures while preserving great bone-soft tissue contrast. Motivated by these contrast properties, it is instructive to use high-resolution CT imaging (Micro CT) in preclinical osteoporosis research to resolve bone structures in mice. Especially in vivo Micro CT imaging of mouse tibia bones is interesting for understanding osteoporosis and developing a medication [1]. However, radiation dose and image quality are strongly connected. A significant amount of radiation must be deposited in the imaged object to acquire a desired contrast. When scanning a living animal, the deposited energy will harm the tissue and increase the risk of cancer and other diseases. Therefore, minimizing the dose is crucial, which is usually connected to degraded image quality.
Using denoising algorithms can leverage image quality. Here, iterative reconstruction algorithms have been successfully applied in the past. While their algorithms are usually based on reasonable statistical assumptions, these methods are computationally costly and limited in their denoising performance. In recent years, deep learning approaches have shown promising results in terms of image quality.

The goal of this master thesis is to use the deep learning-based joint bilateral filtering (JBFnet) [2] to denoise Micro CT data of mouse tibia bones. The JBFnet is a promising approach for denoising Micro CT data as it requires only a few trainable parameters while achieving state-of-the-art denoising performance. Hence, the integrity of the denoised structures can be claimed which is crucial considering the tiny bone features that shall be restored. After achieving reasonable denoising results, multiple modifications of the JBFnet are planned to adapt the filtering better to the respective noise characteristics of the data. In the last part of the thesis, an extensive performance evaluation of the network and its modifications will be performed.


[1] A. Grüneboom, L. Kling, S. Christiansen, L. Mill, A. Maier, K. Engelke, H. H. Quick, G. Schett, and M. Gunzer, “Next-generation imaging of the skeletal system and its blood supply,” Nature Reviews Rheumatology, vol. 15, no. 9, pp. 533–549, 2019.

[2] M. Patwari, R. Gutjahr, R. Raupach, and A. Maier, “Jbfnet-low dose ct denoising by trainable joint bilateral filtering,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 506–515, Springer, 2020.