WARF: P130056US01

Statistical Noise Map for Reducing X-Ray Exposure


Guang-Hong Chen, Jie Tang

The Wisconsin Alumni Research Foundation (WARF) is seeking commercial partners interested in developing a method to account for noisy data during X-ray imaging.
OVERVIEWIterative image reconstruction techniques can be used to produce high quality tomographic images from noise-contaminated data. It is known that a statistical weight is critical for great image quality. However, getting a good estimate of statistical noise means repeating the data acquisition process many times. These multiple scans inflict higher X-ray exposure on the imaged patient.
THE INVENTIONUW–Madison researchers have developed a system and method for estimating a statistical noise map from a single X-ray exposure. This map accounts for noise acquired with X-ray imaging systems, including computed tomography (CT), tomosynthesis and C-arm systems.

The method reconstructs an image from acquired data using any standard filtered back projection (FBP) algorithm. This image is used as a baseline to estimate a noise standard distribution map. The raw projection data represents a typical measurement among many repeated measurements under the same experimental conditions. Therefore, this data can be used to generate several (e.g., 20 or more) noisy data sets.

These data sets are used to reconstruct noisy images that can be subtracted from the original image, resulting in a statistical noise map. This map accounts for a physical model of noise.
  • Diagnostic X-ray imaging
  • Capable of producing a statistical noise map from a single acquisition
  • Cuts X-ray exposure to patient
  • Can significantly boost reconstruction speed
Contact Information
For current licensing status, please contact Jeanine Burmania at or 608-960-9846.
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