Please use this identifier to cite or link to this item:
Title: Development of a patient-specific deformable image registration model for breasts using positron emission tomography combined with magnetic resonance imaging by biomechanical strategy
Authors: Xue, Cheng
Advisors: Tang, Fuk Hay (HTI)
Lai, Christopher (HTI)
Keywords: Breast -- Imaging.
Image registration.
Breast -- Imaging.
Imaging systems in medicine.
Issue Date: 2017
Publisher: The Hong Kong Polytechnic University
Abstract: The simulation of large deformations of the breast has great potential for applications in the medical field, such as breast cancer diagnosis, image guided surgery, surgery planning and breast image registration. However, the positioning of the patient body will differ during each screening modality. Large-scale deformations of the breast during movement mean that modeling of the breast is a difficult task. It is therefore necessary to formulate a mechanical model of the breast that can predict the deformations of the breast during scanning. In this thesis, I propose an individualized biomechanical model to predict large-scale deformations of the breast in the supine to prone positions. The model combines finite element analysis with affine transformation. The mechanical properties of the breast tissues are individually assigned by using an optimization process, which allows the model to be patient-specific. Image registration with the use of positron emission tomography (PET) and magnetic resonance imaging (MRI) has been extensively studied in the literature. The biomechanical model of the breast is thus evaluated by using MRI and PET/computed tomography images from Hong Kong and American samples. The differences in the breast volume and density are determined by the biomechanical model in this study. Deformations in the breast images of both the Asian and American samples due to the effect of gravity are successfully modeled by using the finite element method. The accuracy of the developed model is determined by using the target registration error (TRE) of the lesion. The TRE for the Hong Kong and American samples is 4.77±2.20 mm and 8.40±7.15 mm, respectively. The results show that this model is able to accurately predict deformations of the breast in the supine to prone positions for images from both populations. In addition, the TRE has been found to be correlated with the image density, which indicates that this model can more accurately predict deformations of breasts with less density. A decision tree has also been generated through data mining to predict the registration accuracy.
Description: PolyU Library Call No.: [THS] LG51 .H577P HTI 2017 Xue
xviii, 156 pages :color illustrations
Rights: All rights reserved.
Appears in Collections:Thesis

Files in This Item:
File Description SizeFormat 
b29527880_link.htmFor PolyU Users208 BHTMLView/Open
b29527880_ira.pdfFor All Users (Non-printable)2.1 MBAdobe PDFView/Open
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

Last Week
Last month
Citations as of Feb 12, 2019


Citations as of Feb 12, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.