Reducing Motion Artifacts in MRI of the Eye

Standard diagnostic techniques in ophthalmology cannot image through opaque tissues such as cataracts or tumours. Uveal melanoma (UM), the most common primary intraocular malignant tumour is diagnosed using ophthalmoscopy and ultrasound, which provide only a rough estimation of the tumour volume and its location. Proton beam therapy can potentially be used to treat the tumour. The lack of a high resolution image of the eye, however, means that therapy results in unnecessary damage to healthy tissue leading to severe side-effects such as irreversible destruction of the lens and glaucoma.

Magnetic resonance imaging (MRI) has great potential in ophthalmology. Ocular MRI has the potential to image the full tumour in 3D, allowing for a non-invasive screening and a more accurate radiation therapy. Eye movements, however, prevent effective clinical use of ocular MRI. Image artifacts are produced by these types of movement.

Assignment

The aim of this project is to develop data processing techniques such as compressive sensing and outlier detection/rejection to decrease the image artifacts and produce high resolution 3D images of the eye. More specifically, the fact that consecutive images of the eye should be very similar to each other can be exploited to compress the measurements and possibly detect and reject measurements that do not follow the considered model.

This project will be carried out in collaboration with the Leiden University Medical Center (Prof. Andrew G. Webb).

Requirements

For this project, we are looking for a master student in electrical engineering who is enthusiastic about working in a medical research setting and is committed to make this project a success. Programming skills in Matlab and/or C/C++ and basic knowledge of signal processing are highly appreciated. Good spoken and written English is a must. Expected project duration is about 9 months.

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Last modified: 2022-10-04