Engineers at Purdue University, W. Lafayette, IN, are using a system of models to extract specific information from huge collections of data and then reconstructing images in order to improve the performance of technologies ranging from medical CT scanners to digital cameras. Their new approach is called model-based iterative reconstruction (MBIR).
MBIR has been used in a new CT scanning technology that exposes patients to one-fourth the radiation of conventional CT scanners, due to increased efficiency achieved via the models and algorithms. MBIR reduces "noise" in the data, providing greater clarity that allows the radiologist or radiological technician to scan the patient at a lower dosage.
Traditionally, imaging sensors and software are designed to detect and measure a particular property. The new approach does the inverse, collecting huge quantities of data and later culling specific information from this pool of information using specialized models and algorithms. The models and algorithms in MBIR apply probability computations to extract the correct information, much as people use logical assumptions to solve problems.
MBIR also could bring more affordable CT scanners for airport screening. In conventional scanners, an X-ray source rotates at high speed around a chamber, capturing cross section images of luggage placed inside the chamber. However, MBIR could enable the machines to be simplified by eliminating the need for the rotating mechanism.
Purdue is part of a new Multi-University Research Initiative funded by the U.S. Air Force. Researchers will use the method to study the structure of materials, work that could lead to development of next-generation materials.
