PhD student on Optimization and assessment of clinical x-ray breast imaging technologies

Job description

Every year, breast cancer causes the death of 500,000 women. Early detection and diagnosis of this disease, crucial for increased chances of survival, is usually performed with x-ray imaging. The field of breast cancer x-ray imaging is a very dynamic environment, with a flurry of new technologies and tools being continuously developed and introduced for clinical use aiming to improve breast cancer care. The latest advances in technology include digital breast tomosynthesis and contrast-enhanced spectral mammography, which are complementing or replacing digital mammography in clinical practice as first line work-up techniques for women with symptoms and women recalled from screening. Tomosynthesis is also undergoing extensive research for use in screening, as a replacement of digital mammography.

Similarly, the recent introduction of deep learning-based artificial intelligence has further expanded the possibilities for improving these imaging technologies. Deep learning algorithms are being developed not only for assisting the interpretation of breast images, but are also being investigated as methods to improve image quality and image reconstruction.

In this project you will develop and evaluate novel methods of image processing and of image interpretation, in order to optimize the quality of x-ray breast cancer images and the accuracy of radiologists’ interpreting them. This will involve the use of innovative physics and computer science approaches for image processing. The project will be performed at the Advanced X-ray Tomographic Imaging lab at Radboudumc, in close cooperation with Siemens Healthcare X-ray products in Erlangen, Germany.

Tasks consist of data collection, development and testing of image processing algorithms, design and performance of clinical reader studies to assess the value of proposed optimizations, and writing papers and giving presentations on the study results.

Your profile

  • You should be a creative and enthusiastic researcher with an MSc degree in medical physics, physics, biomedical engineering, technical medicine, or similar;
  • Experience or interest in radiology and/or image processing and affinity for biomedical research;
  • Excellent communication skills in English, both written and spoken.

Terms of employment

  • 36 hours a week
  • Temporary
  • 4 years
  • Date of publication: 27 July 2018
  • Deadline: 20 August 2018
  • Scale 10A: max € 40814 gross per year at full employment (incl. vacation bonus and end of year payments)
  • First interview scheduled: August 27, 2018


The Department of Radiology and Nuclear Medicine is one of the most active research departments of the Radboudumc. More than 100 researchers are continuously striving to optimize healthcare. The main research areas are prostate, breast and lung. Yearly about 200 breast cancer patients are treated here, and several thousand women undergo a diagnostic work-up. Close cooperation between clinicians and scientist is key to the success of breast research, and you will be working directly in this field.

The AXTI laboratory is focused on the development and optimization of new x-ray based imaging methods for breast cancer and chest imaging. The lab focuses on the use of medical physics approaches to improve image acquisition methods and processing algorithms, and to evaluate the clinical performance of new technology in new clinical applications. The group counts with wide-ranging expertise in image processing, reconstruction, and analysis, as well as radiation dosimetry, along with the design and performance of patient trials.

Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare.


For more information please contact Dr. Ioannis Sechopoulos by email.


Please apply by following Only if this does not work, you can send applications as a single pdf file to In this pdf file the following should be included: CV, list of followed courses and grades, letter of motivation, and preferably a reprint of your Master thesis or any publications in English you have written.

Please apply before August, 20.