Přehled

Title: Quantitative and Automated Analysis of Medical Images from Modern CT Scanners

Study program: Biomedical Technology and Bioinformatics

Supervisor: Ing. Jiří Chmelík, PhD

Topic description:

Computed tomography (CT) is one of the most extensively used imaging modalities for the diagnosis of a wide range of diseases and pathological conditions. Recent advances in CT technology have led to the clinical deployment of modern scanners capable of multi-energy X-ray imaging through multilayer detector architectures and, in some cases, single-photon–level detection. These systems enable the generation of diverse parametric image types, including virtual monoenergetic images and material decomposition maps. Such capabilities substantially enhance the diagnostic value of CT imaging while enabling significant reductions in radiation dose, which is of major importance.

This PhD research will focus on the development of advanced image processing and image analysis methods for multiparametric CT data acquired using multilayer detector systems, with a particular emphasis on machine learning and deep learning techniques. The student will design, implement, and rigorously validate algorithms for key tasks such as image preprocessing, segmentation, detection, classification, and outcome prediction, while explicitly addressing the specific properties and challenges associated with multiparametric CT images.

The goal of the project is to develop a comprehensive computer-aided diagnosis framework that improves diagnostic accuracy, robustness, and reproducibility, accelerates image interpretation, and reduces inter- and intra-observer variability as well as routine clinical workload.

The research will be carried out at the Department of Biomedical Engineering, in close collaboration with external partners. These include national clinical institutions (FN Brno, VFN Prague, FNUSA/ICRC Brno) and international industrial and research organizations (Philips Healthcare, The Netherlands; DKFZ Heidelberg, Germany). These collaborations will support clinical validation of the developed methods and enable continuous interaction with medical experts.

Your task:

  • Get familiar with modern imaging techniques in medicine.

  • In cooperation with clinicians acquire the data and propose possible improvements in medical image processing.

  • Propose, implement and test novel image processing method to improve clinical outcomes.

Requirements:

  • Deep interest in scientific activities in the field of medical imaging, image processing, and machine learning.

  • Sound knowledge of programming languages (e.g., Python, MATLAB).

  • A relevant degree with appropriate engineering and/or IT knowledge, transferable to the scientific environment.

  • English communication skills.

We offer:

  • Our core objective is to provide doctoral students with a supportive and highly scientific work environment that fosters collaboration. 

  • The doctoral students complete 3-6 months of internships at partner universities abroad. 

  • The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project.

Relevant publications:

https://doi.org/10.1016/j.diii.2024.09.002

https://doi.org/10.1007/s13244-017-0571-4

https://doi.org/10.1002/acm2.13468

For more information about this topic please contact Jiří Chmelík – chmelikj@vut.cz

Application portal: https://www.vut.cz/eprihlaska/

Application deadline: April 30, 2026

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