Abstract
Based on the breast imaging and reporting data system (BI-RADS) for mammography (MMG) and types of cancer cells detected, a patient is listed into various categories which determine whether they should undergo biopsy or not. Generally, patients under the BI-RADS category 4 or 5 have to go through surgery. During the surgery, a pathological examination is performed with the help of a microscope and additional X-ray images of the removed tissue or breast specimen are taken to determine the positive and negative surgical margins. Although the pathological examination is the best way to determine carcinoma at the inked margin, it consumes a significant amount of time and makes the duration of the surgery longer. In this study, we propose the open-type carbon nanotube (CNT)-based X-ray system, which can be helpful to determine the carcinoma on breast specimen during breast surgery. The technique proposed in this study successfully obtained X-ray images of a breast specimen with visibly clear cancer masses. These results could pave the way for efficient determination of surgical margins by eliminating the time-consuming histological procedures.
Original language | English |
---|---|
Title of host publication | Medical Imaging 2020 |
Subtitle of host publication | Physics of Medical Imaging |
Editors | Guang-Hong Chen, Hilde Bosmans |
Publisher | SPIE |
ISBN (Electronic) | 9781510633919 |
DOIs | |
Publication status | Published - 2020 |
Event | Medical Imaging 2020: Physics of Medical Imaging - Houston, United States Duration: 16 Feb 2020 → 19 Feb 2020 |
Publication series
Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
---|---|
Volume | 11312 |
ISSN (Print) | 1605-7422 |
Conference
Conference | Medical Imaging 2020: Physics of Medical Imaging |
---|---|
Country/Territory | United States |
City | Houston |
Period | 16/02/20 → 19/02/20 |
Bibliographical note
Publisher Copyright:© 2020 SPIE
Keywords
- Breast specimen imaging
- CNT
- Cancer cells
- Field emission X-ray
- Open-type x-ray system