Task-Based Assessment and Optimization of Digital Breast Tomosynthesis

Persistent Link:
http://hdl.handle.net/10150/241931
Title:
Task-Based Assessment and Optimization of Digital Breast Tomosynthesis
Author:
Young, Stefano
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Digital breast tomosynthesis (DBT) is a new technology for breast cancer screening that promises to complement mammography or supersede it to become the standard for breast imaging. DBT involves taking multiple images in order to synthesize a new image that represents a slice through the breast volume -- hence the term tomosynthesis. The primary advantage of this paradigm is that it can reduce the amount of overlapping anatomy in the data, leading to improved visualization of potentially-cancerous findings. The difficulty in DBT is quantifying the advantages of the technology and determining the optimal conditions for its clinical use. This dissertation describes a virtual trial framework for assessing and optimizing DBT technology for the specific task of detecting small, low-contrast masses in the breast. It addresses each component of the imaging chain to some degree, from the patients/phantoms to the imaging hardware to the model observers used to measure signal detectability. The main focus, however, is on quantifying tradeoffs between three key parameters that affect image quality: (1) scan angle, (2) number of projections, and (3) exposure. We show that in low-density breast phantoms, detectability generally increases with both scan angle and number of projections in the anatomical-variability-limited (high-exposure) regime. We also investigate how breast density affects the optimal DBT scan parameters. We show task-specific results that support using an adaptive paradigm in DBT, where the imaging system reconfigures itself in response to information about the patient's breast density. The virtual framework described in this dissertation provides a platform for further investigations of image quality in 3D breast imaging.
Type:
text; Electronic Dissertation
Keywords:
image quality; model observers; tomosynthesis; x-ray imaging; Optical Sciences; 3D tomography; breast cancer
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Optical Sciences
Degree Grantor:
University of Arizona
Advisor:
Kupinski, Matthew A.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleTask-Based Assessment and Optimization of Digital Breast Tomosynthesisen_US
dc.creatorYoung, Stefanoen_US
dc.contributor.authorYoung, Stefanoen_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDigital breast tomosynthesis (DBT) is a new technology for breast cancer screening that promises to complement mammography or supersede it to become the standard for breast imaging. DBT involves taking multiple images in order to synthesize a new image that represents a slice through the breast volume -- hence the term tomosynthesis. The primary advantage of this paradigm is that it can reduce the amount of overlapping anatomy in the data, leading to improved visualization of potentially-cancerous findings. The difficulty in DBT is quantifying the advantages of the technology and determining the optimal conditions for its clinical use. This dissertation describes a virtual trial framework for assessing and optimizing DBT technology for the specific task of detecting small, low-contrast masses in the breast. It addresses each component of the imaging chain to some degree, from the patients/phantoms to the imaging hardware to the model observers used to measure signal detectability. The main focus, however, is on quantifying tradeoffs between three key parameters that affect image quality: (1) scan angle, (2) number of projections, and (3) exposure. We show that in low-density breast phantoms, detectability generally increases with both scan angle and number of projections in the anatomical-variability-limited (high-exposure) regime. We also investigate how breast density affects the optimal DBT scan parameters. We show task-specific results that support using an adaptive paradigm in DBT, where the imaging system reconfigures itself in response to information about the patient's breast density. The virtual framework described in this dissertation provides a platform for further investigations of image quality in 3D breast imaging.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectimage qualityen_US
dc.subjectmodel observersen_US
dc.subjecttomosynthesisen_US
dc.subjectx-ray imagingen_US
dc.subjectOptical Sciencesen_US
dc.subject3D tomographyen_US
dc.subjectbreast canceren_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorKupinski, Matthew A.en_US
dc.contributor.committeememberBarrett, Harrison H.en_US
dc.contributor.committeememberClarkson, Eric W.en_US
dc.contributor.committeememberPark, Suboken_US
dc.contributor.committeememberKupinski, Matthew A.en_US
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