Abstract

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Measurement(s)

breast vasculature surface model • compressed breast model

Technology Type(s)

image segmentation • finite element-based method

Factor Type(s)

patient

Sample Characteristic - Organism

Homo sapiens

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16477779

Details

Title
Optical breast atlas as a testbed for image reconstruction in optical mammography
Author
Xing Yidan 1   VIAFID ORCID Logo  ; Duan Yubo 2 ; P Indurkar Padmeya 3 ; Qiu Anqi 1 ; Chen, Nanguang 1   VIAFID ORCID Logo 

 National University of Singapore, Biomedical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
 Hangzhou One-North Medical Technologies, Hangzhou, China (GRID:grid.4280.e) 
 National University of Singapore, Mechanical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2577913040
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.