There is no doubt that computerized tomography (CT) is highly beneficial for patients when used appropriately for diagnostic purposes. However, worries have been raised concerning the possible risk of cancer induction from CT because of the dramatic increase of CT usage in medicine. It is crucial to keep the radiation dose as low as reasonably achievable to reduce this probable risk. This thesis is about to reduce X-ray radiation exposure to patients and/or CT operators via a new imaging modality that exploits the recent compressed sensing (CS) theory. Two efficient reconstruction algorithms based on total variation (TV) minimization of estimated images are proposed. Using fewer measurements than the traditional filtered back projection based algorithms or algebraic reconstruction techniques require, the proposed algorithms allow reducing the radiation dose without sacrificing the CT image quality even in the case of noisy measurements. Employing powerful methods to solve the TV minimization problem, both schemes have higher reconstruction speed than the recently introduced CS based algorithms.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614170/index.pdf |
| Date | 01 February 2012 |
| Creators | Bicer, Aydin |
| Contributors | Unver, Zafer |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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