Persistent Link:
http://hdl.handle.net/10150/184538
Title:
Statistical decision making with a dual detector probe.
Author:
Hickernell, Thomas Slocum.
Issue Date:
1988
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:
Conventional imaging techniques for cancer detection have difficulty finding small, deep tumors. Single-detector radiation probes have been developed to search for deep lesions in a patient who has been given a tumor-seeking radiopharmaceutical. These probes perform poorly, however, when the background activity in the patient varies greatly from site to site. We have developed a surgical dual-detector probe that solves the problem of background activity variation, by simultaneously monitoring counts from a region of interest and counts from adjacent normal tissue. A comparison of counts from the detectors can reveal the class of tissue, tumor or normal, in the region of interest. In this dissertation we apply methods from statistical decision theory and derive a suitable comparison of counts to help us decide whether a tumor is present in the region of interest. We use the Hotelling trace criterion with a few assumptions to find a linear discriminant function, which can be reduced to a normalized subtraction of the counts for large background count-rate variations. If area under the ROC curve is our figure of merit, the likelihood ratio is the optimum discriminant. We model likelihood functions of the data given the "tumor" and "no-tumor" hypotheses, and calculate the likelihood ratio. Using a spatial response map of the dual probe, a computer torso phantom, and estimates of activity distribution, we simulate a surgical staging procedure to test the dual probe and the discriminant functions. Results of the simulations show that the dual probe effectively solves the problem of background activity variations when used with any of the discriminant functions derived in this dissertation.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Tumors -- Diagnosis.; Cancer -- Imaging.; Statistical decision.; Probes (Electronic instruments)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Optical Sciences; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Barrett, Harrison, H.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleStatistical decision making with a dual detector probe.en_US
dc.creatorHickernell, Thomas Slocum.en_US
dc.contributor.authorHickernell, Thomas Slocum.en_US
dc.date.issued1988en_US
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.abstractConventional imaging techniques for cancer detection have difficulty finding small, deep tumors. Single-detector radiation probes have been developed to search for deep lesions in a patient who has been given a tumor-seeking radiopharmaceutical. These probes perform poorly, however, when the background activity in the patient varies greatly from site to site. We have developed a surgical dual-detector probe that solves the problem of background activity variation, by simultaneously monitoring counts from a region of interest and counts from adjacent normal tissue. A comparison of counts from the detectors can reveal the class of tissue, tumor or normal, in the region of interest. In this dissertation we apply methods from statistical decision theory and derive a suitable comparison of counts to help us decide whether a tumor is present in the region of interest. We use the Hotelling trace criterion with a few assumptions to find a linear discriminant function, which can be reduced to a normalized subtraction of the counts for large background count-rate variations. If area under the ROC curve is our figure of merit, the likelihood ratio is the optimum discriminant. We model likelihood functions of the data given the "tumor" and "no-tumor" hypotheses, and calculate the likelihood ratio. Using a spatial response map of the dual probe, a computer torso phantom, and estimates of activity distribution, we simulate a surgical staging procedure to test the dual probe and the discriminant functions. Results of the simulations show that the dual probe effectively solves the problem of background activity variations when used with any of the discriminant functions derived in this dissertation.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectTumors -- Diagnosis.en_US
dc.subjectCancer -- Imaging.en_US
dc.subjectStatistical decision.en_US
dc.subjectProbes (Electronic instruments)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBarrett, Harrison, H.en_US
dc.contributor.committeememberBarber, H. Bradforden_US
dc.contributor.committeememberPatton, Dennis D.en_US
dc.identifier.proquest8905790en_US
dc.identifier.oclc701552779en_US
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