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Medical applications of example-based super-resolution

Principal Investigator
Name
Ramin Zabih
Degrees
Ph.D.
Institution
Joan & Sanford I. Weill Medical College of Cornell University
Position Title
Associate Professor of Computer Science in Radiology
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-279
Initial CDAS Request Approval
Jan 31, 2017
Title
Medical applications of example-based super-resolution
Summary
Example-based super-resolution (EBSR) reconstructs a high-resolution image from a low-resolution image, given a training set of high-resolution images. In this project I plan to implement some applications of EBSR to medical imaging. A particular interesting application, which I call "x-ray voxelization", approximates the result of a CT scan from an x-ray image. This is useful for reducing radiation dose received by patients and can dramatically improve dose-controlled precision.
Aims

In the CT arm of NLST, we plan to treat the localizer scout image (taken by the CT technologist prior to the main scan) like a chest radiograph. We will then use the axial images from the helical CT scan to train the x-ray voxelization algorithm, and compare with the results of existing x-ray voxelization algorithms.

Collaborators

Akshay Bhat, Cornell University
Chen Wang, Cornell University
Charles Herrmann, Cornell University