Tomography is a commonly used imaging method that utilizes x-rays or ultrasound to view a cross-section of the human body or other solid objects. It is often used in biology, radiology, archaeology, geophysics, materials science, atmospheric science, and many other scientific areas. The process involves a mathematical procedure known as tomographic reconstruction, which uses one of many reconstruction algorithms to produce a single computed tomogram out of multiple projection radiographs. Unfortunately, these reconstruction algorithms are inexact and reflect a compromise between the computational time required and image accuracy. What is needed is a tomographic approach that reconstructs quickly, preferably with enhanced focus and some depth of image.
Researchers at the University of New Mexico have developed a novel tomography imaging methodology that combines depth of field plenoptic imaging from just a few orientations, producing tomographic reconstruction that converges to an image with less data than traditional tomography. Currently, the researchers are experimenting with muon-based imaging in which the muon direction can be tracked, allowing them to track back to different distances from the detector to look for better focus/image convergence for depth of field. Using this methodology from a few orientations around the object allows tomography to be performed with a more condensed image spread from the detector at each angle than traditional back projection. This technique can be applied to non-penetrating radiation as well, such as combining plenoptic optical images for 3D field mapping. There may also be applications in medical imaging to speed image convergence if there is a scatter of x-rays in the body.
- Converges to an image better, quicker, and with less data than other tomographic approaches
- Allows for better focus/image convergence
- Provides more condensed image spread
- Materials Science
- Atmospheric Science
Name: Andrew Roerick