X-ray Dissectography Improves Lung Nodule Detection
Principal Investigator
Name
Chuang Niu
Degrees
Ph.D.
Institution
Rensselaer Polytechnic Institute
Position Title
Postdoc
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-900
Initial CDAS Request Approval
Mar 30, 2022
Title
X-ray Dissectography Improves Lung Nodule Detection
Summary
Although chest radiographs are the most frequently performed imaging tests worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules inconspicuous and difficult to detect. In this study, we develop “X-ray dissectography” to dissect lungs digitally from a few radiographic projections, suppress the interference of irrelevant structures, and improve lung nodule detectability.
Aims
1. improve the image quality of X-ray image quality.
2. Improve the lung nodule detection performance.
Collaborators
Giridhar Dasegowda, Department of Radiology Massachusetts General Hospital, Harvard Medical School White 270-E, 55 Fruit St, Boston, MA 02114, USA
Pingkun Yan, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA
Mannudeep K. Kalra, Department of Radiology Massachusetts General Hospital, Harvard Medical School White 270-E, 55 Fruit St, Boston, MA 02114, USA
Ge Wang, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA