Purdue Research Foundation

Ge Precision Healthcare LLC

University of Notre Dame Du Lac

Obaidullah Rahman

Charles Addison Bouman

Brian Edward Nett

Ken David Sauer

Jie Tang

Roman Melnyk

Madhuri Mahendra Nagare

Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Noise preserving models and methods for resolution recovery of x-ray computed tomography images

Growing community of inventors

South Bend, IN, United States of America

Obaidullah Rahman