Mathew, C. (2011) Coronary Artery Segmentation from Non-Contrast Cardiac CT for the purpose of Calcium Scoring. Master's Thesis / Essay, Computing Science.
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Abstract
The primary objective of this project is to propose and implement a mechanism to segment coronary arteries in non-contrast CT datasets for the purpose of calcium scoring. Since the procedure of using CT to obtain non-contrast data is minimally invasive and relatively inexpensive, the ability to identify and measure calcified plaque in such datasets is of immense importance to both the patient being treated as well as the radiologist / surgeon making the diagnosis. The core idea is a generic solution pipeline which is implemented by selecting appropriate methods already existing in the literature and adapting them to solve specific sub-problems. The solution pipeline attempts to reduce the 3D cardiac CT dataset to a set of points which (potentially) represent the center points of the coronary arteries. This is done both for pre-processed model datasets and patient datasets, following which a similarity test is performed to compare the given patient dataset with the model dataset(s). Due to the inherent noise and lack of detail in the patient non-contrast dataset, the resulting point set of the patient dataset includes false positives, i.e. points which do not belong to the arteries. A successful similarity test determines the points in the patient dataset which should represent the arteries and these points are used as center points to reconstruct the arteries. All plaque within these regions can then be identified and used to compute the calcium score. Since the proposed solution is still in the experimental phase, a number of assumptions have been made in implementing each method and verification of results have been mostly visual in nature. Even though the methods are experimental in nature, they have provided enough impetus for further investigation.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:45 |
| Last Modified: | 15 Feb 2018 07:45 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/9529 |
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