With deep learning algorithms, standard CT technology produces spectral
images

Date:
October 19, 2020
Source:
Rensselaer Polytechnic Institute
Summary:
Engineers have demonstrated how a deep learning algorithm can
be applied to a conventional computerized tomography (CT) scan
in order to produce images that would typically require a higher
level of imaging technology known as dual-energy CT.



FULL STORY ========================================================================== Bioimaging technologies are the eyes that allow doctors to see inside
the body in order to diagnose, treat, and monitor disease. Ge Wang, an
endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research
to coupling those imaging technologies with artificial intelligence in
order to improve physicians' "vision."

==========================================================================
In research published today in Patterns, a team of engineers led by
Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images
that would typically require a higher level of imaging technology known
as dual-energy CT.

Wenxiang Cong, a research scientist at Rensselaer, is first author on
this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.

"We hope that this technique will help extract more information from a
regular single-spectrum X-ray CT scan, make it more quantitative, and
improve diagnosis," said Wang, who is also the director of the Biomedical Imaging Center within the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Conventional CT scans produce images that show the shape of tissues within
the body, but they don't give doctors sufficient information about the composition of those tissues. Even with iodine and other contrast agents,
which are used to help doctors differentiate between soft tissue and vasculature, it's hard to distinguish between subtle structures.

A higher-level technology called dual-energy CT gathers two datasets in
order to produce images that reveal both tissue shape and information
about tissue composition. However, this imaging approach often requires
a higher dose of radiation and is more expensive due to needed additional hardware.

"With traditional CT, you take a grayscale image, but with dual-energy
CT you take an image with two colors," Wang said. "With deep learning, we
try to use the standard machine to do the job of dual-energy CT imaging."
In this research, Wang and his team demonstrated how their neural network
was able to produce those more complex images using single-spectrum
CT data. The researchers used images produced by dual-energy CT to
train their model and found that it was able to produce high-quality approximations with a relative error of less than 2%.

"Professor Wang and his team's expertise in bioimaging is giving
physicians and surgeons 'new eyes' in diagnosing and treating disease,"
said Deepak Vashishth, director of CBIS. "This research effort is a prime example of the partnership needed to personalize and solve persistent
human health challenges."

========================================================================== Story Source: Materials provided by
Rensselaer_Polytechnic_Institute. Original written by Torie Wells. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Wenxiang Cong, Yan Xi, Paul Fitzgerald, Bruno De Man, Ge
Wang. Virtual
Monoenergetic CT Imaging via Deep Learning. Patterns, 2020; 100128
DOI: 10.1016/j.patter.2020.100128 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201019133700.htm

--- up 8 weeks, 6 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)