상세정보
Export to Refworks부가기능
Multi-Dimensional Extension of the Alternating Minimization Algorithm in X-ray Computed Tomography
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Multi-Dimensional Extension of the Alternating Minimization Algorithm in X-ray Computed Tomography. |
| 개인저자 | Lu, Jingwei. |
| 단체저자명 | Washington University in St. Louis. Electrical Engineering. |
| 발행사항 | [S.l.]: Washington University in St. Louis., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 169 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781085651431 |
| 학위논문주기 | Thesis (Ph.D.)--Washington University in St. Louis, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: O'Sullivan, Joseph A. |
| 이용제한사항 | This item is not available from ProQuest Dissertations & Theses.This item must not be sold to any third party vendors. |
| 요약 | X-ray computed tomography (CT) is an important and effective tool in medical and industrial imaging applications. The state-of-the-art methods to reconstruct CT images have had great development but also face challenges. This dissertation derives novel algorithms to reduce bias and metal artifacts in a wide variety of imaging modalities and increase performance in low-dose scenarios.The most widely available CT systems still use the single-energy CT (SECT), which is good at showing the anatomic structure of the patient body. However, in SECT image reconstruction, energy-related information is lost. In applications like radiation treatment planning and dose prediction, accurate energy-related information is needed. Spectral CT has shown the potential to extract energy-related information. Dual-energy CT (DECT) is the first successful implementation of spectral CT. By using two different spectra, the energy-related information can be exported by reconstructing basis-material images. A sinogram-based decomposition method has shown good performance in clinical applications. However, when the x-ray dose level is low, the sinogram-based decomposition methods generate biased estimates. The bias increases rapidly when the dose level decreases. The bias comes from the ill-posed statistical model in the sinogram-decomposition method. To eliminate the bias in low-dose cases, a joint statistical image reconstruction (JSIR) method using the dual-energy alternating minimization (DEAM) algorithm is proposed. By correcting the ill-posed statistical model, a relative error as high as 15% in the sinogram-based decomposition method can be reduced to less than 1% with DEAM, which is an approximately unbiased estimation.Photon counting CT (PCCT) is an emerging CT technique that also can resolve the energy information. By using photon-counting detectors (PCD), PCCT keeps track of the energy of every photon received. Though PCDs have an entirely different physical performance from the energy-integrating detectors used in DECT, the problem of biased estimation with the sinogram-decomposition method remains. Based on DEAM, a multi-energy alternating minimization (MEAM) algorithm for PCCT is proposed. In the simulation experiments, MEAM can effectively reduce bias by more than 90%.Metal artifacts have been a concern since x-ray CT came into medical imaging. When there exist dense or metal materials in the scanned object, the image quality may suffer severe artifacts. The auxiliary sinogram alternating minimization (ASAM) algorithm is proposed to take advantages of two major categories of methods to deal with metal artifacts: the pre-processing method and statistical image reconstruction. With a phantom experiment, it has been shown that ASAM has better metal-artifact reduction performance compared with the current methods.A significant challenge in security imaging is that due to the large geometry and power consumption, low photon statistics are detected. The detected photons suffer high noise and heavy artifacts. Image-domain regularized iterative reconstruction algorithms can reduce the noise but also result in biased reconstruction. A wavelet-domain penalty is introduced which does not bring in bias and can effectively eliminate steaking artifacts. By combining the image-domain and wavelet-domain penalty, the image quality can be further improved.When the wavelet penalty is used, a concern is that no empirical way, like in the image-domain penalty, is available to determine the penalty weight. Laplace variational automatic relevance determination (Lap-VARD) method is proposed to reconstruct the image and optimal penalty weight choice at the same time. |
| 일반주제명 | Medical imaging. Physiology. Health sciences. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
