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Objective Characterization of Image ...

The University of Chicago., Medical Physics.

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Objective Characterization of Image Reconstruction Algorithms in Computed Tomography and Digital Breast Tomosynthesis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Objective Characterization of Image Reconstruction Algorithms in Computed Tomography and Digital Breast Tomosynthesis./
Author:	Rose, Sean Douglas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	171 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-01, Section: B.
Contained By:	Dissertations Abstracts International80-01B.
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790732
ISBN:	9780438083998

Objective Characterization of Image Reconstruction Algorithms in Computed Tomography and Digital Breast Tomosynthesis.
Rose, Sean Douglas.

Objective Characterization of Image Reconstruction Algorithms in Computed Tomography and Digital Breast Tomosynthesis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 171 p.

Source: Dissertations Abstracts International, Volume: 80-01, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2018.

This item must not be sold to any third party vendors.

Both X-ray computed tomography (CT) and digital breast tomosynthesis (DBT) have become important clinical tools for the non-invasive assessment and diagnosis of disease in the breast. The continuing development and refinement of image reconstruction methods has led to significant improvements in image quality in both of these modalities. In particular, sparsity exploiting iterative image reconstruction (IIR) methods have demonstrated potential for yielding images of improved quality from the high-noise and limited angular data encountered in breast CT and DBT, respectively; however, realization of these improvements typically requires manual tuning of numerous parameters, often in a case-by-case fashion. Even in the absence of complications introduced by the incorporation of sparsity-exploiting regularizers, it is often difficult to establish a direct link between the parameters involved in specifying an IIR algorithm and the quality of the resulting reconstruction. This thesis aims to address this issue in the context of breast CT and DBT. As a step towards this goal, we simplify the problem by considering IIR without sparsity exploiting regularizers. The primary focus of this thesis is the characterization of parameter trends for such IIR algorithms in breast CT and DBT.

ISBN: 9780438083998Subjects--Topical Terms:

3172799
Medical imaging.

Objective Characterization of Image Reconstruction Algorithms in Computed Tomography and Digital Breast Tomosynthesis.
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500 $a Source: Dissertations Abstracts International, Volume: 80-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Pan, Xiaochuan.
502 $a Thesis (Ph.D.)--The University of Chicago, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Both X-ray computed tomography (CT) and digital breast tomosynthesis (DBT) have become important clinical tools for the non-invasive assessment and diagnosis of disease in the breast. The continuing development and refinement of image reconstruction methods has led to significant improvements in image quality in both of these modalities. In particular, sparsity exploiting iterative image reconstruction (IIR) methods have demonstrated potential for yielding images of improved quality from the high-noise and limited angular data encountered in breast CT and DBT, respectively; however, realization of these improvements typically requires manual tuning of numerous parameters, often in a case-by-case fashion. Even in the absence of complications introduced by the incorporation of sparsity-exploiting regularizers, it is often difficult to establish a direct link between the parameters involved in specifying an IIR algorithm and the quality of the resulting reconstruction. This thesis aims to address this issue in the context of breast CT and DBT. As a step towards this goal, we simplify the problem by considering IIR without sparsity exploiting regularizers. The primary focus of this thesis is the characterization of parameter trends for such IIR algorithms in breast CT and DBT.
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