Dr. Miao and colleagues at UCLA have developed a novel algorithm that quickly processes high quality image reconstruction of data acquired through Equally-Sloped Tomography.
Tomography is an imaging technique that uses cross-sectional images to depict an object. The utility of this technique ranges across medical imaging of the body to geophysical imaging of the Earth’s surface. There is continuous work to advance tomographic analysis to improve image quality. One such method that was developed is Equally-Sloped Tomography (EST), which is capable of reconstructing images from under-sampled and noisy data sets. EST has been shown to produce higher quality images than other conventional tomographic techniques, but it is limited by the high computation requirements, which slow down its processing speed. Further improvements on EST are necessary to make it usable for time-sensitive applications.
UCLA researchers have developed a novel algorithm that improves upon the established EST techniques by significantly reducing the computational time required to reconstruct images. This algorithm not only overcomes the high computation requirements of EST, but also results in higher quality images. The quality of these images is comparable to images reconstructed by other conventional methods, such as filtered back projection (FBP), but requires 80-90% less radiation. As EST has been adapted to be used for a variety of tomographic modalities, this algorithm can be applied to a variety of fields.
|United States Of America||Issued Patent||8,611,626||12/17/2013||2009-529|
tomography, computed tomography (CT), equally-sloped tomography (EST), image reconstruction, image processing, algorithm, reconstruction algorithm