Research interests

  • Assessment of Crohn's disease severity

    Inflammatory bowel diseases such as Crohn's disease are a large healthcare problem in the Western World. Grading of Crohn’s disease severity is important to determine the proper treatment strategy and to quantify the response to the treatment.
    Currently, Magnetic Resonance Imaging is widely used for diagnosing and grading luminal Crohn’s disease (CD). Unfortunately, MRI has been shown to be accurate for severe disease cases (91% accuracy), but mediocre for mild disease activity or remission (62% accuracy).
    I aim to create a suite of fundamental tools for accurate assessment of CD severity. It will involve image analysis, classification and visualization algorithms to measure disease severity from MRI.
    I do this research in conjunction with leading clinical groups (University College London Hospital, AMC), technical institutes (ETH Zurich, Zuse Institute Berlin) and industrial partners (Biotronics3D Inc, Vodera Inc). In fact, there are 4 Phd Students, 2 postdocs, 2 research fellows, and a scientific programmer working on the project. It is funded from the European Community’s Seventh Framework Programme as the VIGOR++ Project. I am coordinating the project, which scored 15/15 and ended first of 640 research proposals!
  • Identifying biomarkers of neurological disorders using Diffusion Weighted MRI

    Diffusion Tensor MRI (DTI) provides information about changes in the brain's white matter, both physiologically (aging) and pathologically (e.g. Alzheimer's disease). DTI measures the ability of water molecules to move freely in the surrounding tissue. Healthy white matter tracts show high diffusion along and low diffusion across axons. Such anisotropy is measured by DTI. Although pathology is generally characterized by increased isotropy, it is not easily recognized especially due to the lack of a reference.
    My research focuses on identifying deviating structures in DTI data by developing methods that (i) improve tract characterization by explicitly modeling crossing tracts; (ii) establish both spatial and temporal registration; (iii) statistically model changes in white matter structure; (iv) identify spatiotemporal biomarkers..
  • Virtual colonoscopy: electronic cleansing and Computer Aided Detection  (CAD) of polyps

    Virtual colonoscopy (also called CT colonography) is a non-invasive method to screen for colonic polyps - the precursors of colon cancer .  
    Fecal remains may mimmick or obscure polyps due to its tissue equivalent intensity. An oral contrast agent ('fecal tagging') makes it distinguishable from the physiologic bowel wall. Over the past years, I have developed several methods for electronic cleansing. Electronic cleansing automatically segments the colon wall in the presence of fecal tagging.
    Also, to assist the radiologist in this time consuming screening task, I have worked on Computer Aided Detection systems for finding polyps.

    I now aim at making CAD work with patients taking a low fiber diet and scanning at a low radiation dose. This would make the whole procedure more patient friendly than it currently is. However, the tagged material may become inhomogeneously mixed with fecal matter and it will form complex structures such as thin layers. Also, there is much more noise in the images due to the low radiation dose. Current electronic cleansing and CAD systems are not designed to handle such data.



Medical Image Analysis

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