Optimization in spectral X-ray/CT imaging measurements
Abstract/Contents
- Abstract
- Spectral imaging techniques can enhance clinical x-ray/CT applications by creating material discriminated images and facilitating quantitative analysis based on intrinsic tissue properties. This dissertation focused on improving the performance of spectral x-ray/CT imaging by optimizing system designs and scan protocols. I designed and demonstrated that a fixed K-edge x-ray filter can improve material decomposition precision of fast kVp-switching systems at modest flux penalty. Secondly, I optimized the protocols of dual source liver CT, where I showed that the dual energy blended images would not have as good contrast to noise ratio (CNR) as optimized single energy scans. Besides commercially available x-ray/CT systems, photon counting x-ray detectors (PCXD) are promising for spectral imaging applications, but for medical use they suffer from non-ideal energy response and slow counting speed. Edge-on PCXD with multi-layer segmentation was proposed to address the counting speed issue by splitting the counting burden into multiple layers. We hypothesized that segmenting detectors in the depth direction (a.k.a. "in-depth") and retaining the depth dependent signals can also significantly improve the spectral performance (e.g., noise of material discriminated images). Our research showed that the depth information is redundant when the pulse height information is perfect. With real-world, non-ideal energy response functions (ERF), however, the in-depth PCXD is more dose efficient than the edge-on PCXD. Firstly, when analytical ERF models were assumed, we found the benefit of the depth information varied across different detector materials but was relatively constant across various detector architectures, protocol settings, and the scanned object size. The dose efficiency improvement comes along with the fact that the depth information helps when photons that are recorded at wrong energies overlap with correctly measured ones. Next, we compared the in-depth and edge-on detectors using more realistic ERFs estimated from Monte Carlo simulation, which showed that the depth segmentation degrades the energy response by introducing inter-layer cross-talk. The results showed that severe data correlation between layers diminish the benefit from the depth segments, but with correction schemes applied, the dose improvement reappears. The improvements are ERF dependent, with high potential when ERF degradation is from K-escape photons and little impact for events in the Compton plateau.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Yao, Yuan |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Pelc, Norbert J |
| Thesis advisor | Pelc, Norbert J |
| Thesis advisor | Glover, Gary H |
| Thesis advisor | Nishimura, Dwight George |
| Advisor | Glover, Gary H |
| Advisor | Nishimura, Dwight George |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yuan Yao. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Yuan Yao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...