Excessive imaging doses from repeated scans and poor image quality mainly due to scatter contamination are the two bottlenecks of cone-beam CT (CBCT) imaging. This study investigates a method that combines measurement-based scatter correction and a compressed sensing (CS)-based iterative reconstruction algorithm to generate scatter-free images from low-dose data. Scatter distribution is estimated by interpolating/extrapolating measured scatter samples inside blocked areas. CS-based iterative reconstruction is finally carried out on the under-sampled data to obtain scatter-free and low-dose CBCT images. In the tabletop phantom studies, with only 25% dose of a conventional CBCT scan, our method reduces the overall CT number error from over 220 HU to less than 25 HU, and increases the image contrast by a factor of 2.1 in the selected ROIs. Dual-energy CT (DECT) is another important application of CBCT. DECT shows promise in differentiating materials that are indistinguishable in single-energy CT and facilitates accurate diagnosis. A general problem of DECT is that decomposition is sensitive to noise in the two sets of projection data, resulting in severely degraded qualities of decomposed images. The first study of DECT is focused on the linear decomposition method. In this study, a combined method of iterative reconstruction and decomposition is proposed. The noise on the two initial CT images from separate scans becomes well correlated, which avoids noise accumulation during the decomposition process. To fully explore the benefits of DECT on beam-hardening correction and to reduce the computation cost, the second study is focused on an iterative decomposition method with a non-linear decomposition model for noise suppression in DECT. Phantom results show that our methods achieve superior performance on DECT imaging, with respect to noise reduction and spatial resolution.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/51903 |
| Date | 22 May 2014 |
| Creators | Dong, Xue |
| Contributors | Zhu, Lei |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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