Postdoc in Deep Learning for X-ray Computed Tomographic Reconstruction (f/m)

Position nr.: 
Dept. of Physics (Vision Lab)
Date posted: 
Wednesday, 1 February, 2023
Position status: 
Full Time Salaried
Project title: 

Postdoc in AI-accelerated X-ray Computed Tomographic Reconstruction


Computed tomography (CT) is a well-known technique in radiology, in which X-rays are emitted from different directions around a patient. These X-rays are attenuated by different tissue types on their path, and are finally measured by a detector. These projections are used in a reconstruction algorithm to calculate cross-section images, representing the local X-ray absorption in the patient. False assumptions on the physical process are necessary to solve this reconstruction problem but often result in artifacts which have a negative impact on the reconstructed image quality. These artifacts worsen with lower dose or in cheaper modalities such as cone beam CT and tomosynthesis systems. Standard approaches have been used for many years to (partly) tackle these artefacts.
With this project proposal we want to introduce convolutional neural networks in the reconstruction to reduce these artifacts. It will result in better image quality, especially at low dose and for cheaper modalities. Convolutional neural networks recently gathered vast interest. This uptake motivates us to apply it within our imaging solutions.
The general target of this project is to improve current tomographic reconstruction algorithms by introducing a machine learning approach, in particular convolutional neural networks. Very recent academic research has shown the potential of such an approach. You will implement, improve and investigate the usability in medical CT images, and develop novel neural network approaches. In this way, you will build a competitive CT reconstruction as a basic technology for future 3D modalities.


The candidate will primarily work with developing computed tomography reconstruction and classification methods with industrial application. A core part of the project is the development of novel neural network approaches, and their implementation, improvement and investigation of the usability in medical CT images.
Furthermore, is it required that the candidate participate with the rest of the team to achieve the overall goals of the projects to ensure a successful implementation of the obtained solutions with the end goal of integration in a commercial X-ray scanner.


• Candidates should have a PhD degree or equivalent in computer science, physics, mathematics or engineering
• Experience with image analysis preferably advanced feature based recognition, classification methods and machine learning for image analysis
• Experience with tomographic reconstruction techniques is an advantage
• Enthusiasm for working with industry related problems
• Good programming skills and experience with GPU implementation is an advantage
• Skills for working in a team

Labs involved: 

The Vision Lab, an imec ( research group at the University of Antwerp ( ), has an open position for a Postdoc to work on developing advanced image reconstruction methods for X-ray phase contrast tomography. Tomography is an image reconstruction technique that leans strongly on large-scale numerical mathematics, particularly linear algebra. It has a wide range of applications in medicine (CT-scans), industry (nondestructive testing, inspection and quality control) and science (3D characterization and material analysis).
This project is in collaboration with Agfa Healthcare.

Our offer: 

• An exciting 1-year research project in a dynamic and international context (with possibility for extension)
• Multidisciplinary research: cooperation with strong academic research groups (collaboration with Agfa Healthcare)
• a world-class research environment with state-of-the-art instrumentation

Starting date: 
to be discussed

Interested? Send Jan Sijbers ( and Jan De Beenhouwer ( a motivation letter, a detailed CV (including courses, honours, grades, previous work, publications,…) and contact info of two references.