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Temporal registration of mammograms by finite element simulation of MR breast volume deformation

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Material Information

Title:
Temporal registration of mammograms by finite element simulation of MR breast volume deformation
Physical Description:
Book
Language:
English
Creator:
Qiu, Yan
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla.
Publication Date:

Subjects

Subjects / Keywords:
Breast cancer
Non-rigid registration
X-ray
Computer aided analysis
Compression simulation
Dissertations, Academic -- Computer Science and Engineering -- Doctoral -- USF   ( lcsh )
Genre:
bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: Clinically it is important to combine information provided by mammographic images from multiple views or at different times. Taking regular mammographic screening and comparing corresponding mammograms are necessary for early detection of breast cancer, which is the key to successful treatment. However, mammograms taken at different times are often obtained under different compressions, orientations or body positions. A temporal pair of mammograms may vary quite significantly due to the spatial disparities caused by the variety in acquisition environments, including the 3D position of the breast, the amount of the pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We have implemented and utilized finite element models for temporal registration of digital mammography. In our work, we applied the patient specific breast model, where patients have both mammograms and MRIs available, and generic model, where only patient mammograms are available. After we applied the temporal registration algorithm, the average error among the 14 patient datasets was 3.4 plus/minus 0.86 mm for Euclidean distance and 4.3 plus/minus 0.52 mm for predicted 2D lesion position. With generic model, the average error among the 14 patient datasets using the measure of Euclidean distance between the predicted lesion position in T1 and T2 was 5.0 plus/minus 0.74 mm for Euclidean distance and 5.7 plus/minus 0.83 mm for predicted 2D lesion position. Compared with the average lesion size (10mm~40mm), this error is acceptable. With lesion correspondence, our finite element method can be used to suppress technical variations (e.g., mammogram positioning or compression) and to emphasize genuine alterations in the breast.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2009.
Bibliography:
Includes bibliographical references.
System Details:
Mode of access: World Wide Web.
System Details:
System requirements: World Wide Web browser and PDF reader.
Statement of Responsibility:
by Yan Qiu.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 82 pages.
General Note:
Includes vita.

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
aleph - 002317650
oclc - 660868962
usfldc doi - E14-SFE0003132
usfldc handle - e14.3132
System ID:
SFS0027448:00001


This item is only available as the following downloads:


Full Text
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Temporal registration of mammograms by finite element simulation of MR breast volume deformation
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ABSTRACT: Clinically it is important to combine information provided by mammographic images from multiple views or at different times. Taking regular mammographic screening and comparing corresponding mammograms are necessary for early detection of breast cancer, which is the key to successful treatment. However, mammograms taken at different times are often obtained under different compressions, orientations or body positions. A temporal pair of mammograms may vary quite significantly due to the spatial disparities caused by the variety in acquisition environments, including the 3D position of the breast, the amount of the pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We have implemented and utilized finite element models for temporal registration of digital mammography. In our work, we applied the patient specific breast model, where patients have both mammograms and MRIs available, and generic model, where only patient mammograms are available. After we applied the temporal registration algorithm, the average error among the 14 patient datasets was 3.4 plus/minus 0.86 mm for Euclidean distance and 4.3 plus/minus 0.52 mm for predicted 2D lesion position. With generic model, the average error among the 14 patient datasets using the measure of Euclidean distance between the predicted lesion position in T1 and T2 was 5.0 plus/minus 0.74 mm for Euclidean distance and 5.7 plus/minus 0.83 mm for predicted 2D lesion position. Compared with the average lesion size (10mm~40mm), this error is acceptable. With lesion correspondence, our finite element method can be used to suppress technical variations (e.g., mammogram positioning or compression) and to emphasize genuine alterations in the breast.
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