ORCID

http://orcid.org/0000-0002-7786-5059

Date of Award

Summer 8-15-2019

Author's School

School of Engineering & Applied Science

Author's Department

Electrical & Systems Engineering

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

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.

Language

English (en)

Chair

Joseph A. O'Sullivan

Committee Members

David G. Politte, Matthew Lew, Ulugbek Kamilov, Umberto Villa,

Comments

Permanent URL: https://doi.org/7936/e311-6802

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