News | Computed Tomography (CT) | October 20, 2020

Standard CT Technology Produces Spectral Images with Deep Learning Algorithms

Rensselaer, First-Imaging, and GE Global researchers develop a deep neural network to perform nearly as well as more complex dual-energy CT imaging technology

Rensselaer, First-Imaging, and GE Global researchers develop a deep neural network to perform nearly as well as more complex dual-energy CT imaging technology

October 20, 2020 — 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 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."

For more information: www.rpi.edu

Related Content

Shimadzu Medical Systems USA, a leading manufacturer of advanced medical X-ray imaging systems, has announced that the Trinias unity edition product line has been awarded a contract from Vizient, Inc., a healthcare performance improvement company, effective Sept. 1, 2021.
News | X-Ray | September 17, 2021
September 17, 2021 — Shimadzu Medical Systems USA, a leading manufacturer of advanced medical...
Avoiding contrast dyes for imaging tests not necessary if concerned about iodine allergy, peer-reviewed study concludes #MRI

Getty Images

News | Contrast Media Injectors | September 16, 2021
September 16, 2021 — FDB (First Databank), a leading provider of drug and medical device knowledge that helps healthc
This is an example of 3-D ultrasound imaging on a breast, designed to help increase efficiency and diagnostic accuracy in any practice. Image courtesy of Hologic.

This is an example of TriVu ultrasound imaging on a breast, designed to help increase efficiency and diagnostic accuracy in any practice. Image courtesy of Hologic.

Feature | Breast Imaging | September 15, 2021 | By Jennifer Meade
The...
While the Mammography Quality Standards Act (MQSA) and the introduction of EQUIP (Enhancing Quality Using the Inspection Program) have been successful in standardizing and enhancing mammographic imaging quality, inadequate breast positioning can dramatically impact the ability of radiologists and technicians to quickly and accurately detect breast cancer and potentially malignant lesions in their patients

Getty Images

Feature | Mammography | September 15, 2021 | By Christopher Austin, M.D. and Randy D. Hicks, M.D., MBA
Revised guidelines for lung cancer screening eligibility are perpetuating disparities for racial/ethnic minorities, according to a new study in Radiology.

Getty Images

News | Lung Imaging | September 15, 2021
September 15, 2021 — Revised guidelines for...
To get more flexibility and cost savings from storage, healthcare organizations are increasing their investments in the cloud
Feature | Information Technology | September 15, 2021 | By Kumar Goswami
Healthcare organizations today are storing petabytes of medical imaging data — lab slides,...
Revenues for teleradiology reading service providers are forecast to follow a similar profile over this period.

Outlook for 2021 and Beyond. As displayed in the figure below, these six market drivers are projected to result in teleradiology reading service volumes increasing by 21% in 2021 and nearly doubling by 2025. Revenues for teleradiology reading service providers are forecast to follow a similar profile over this period.

Feature | Teleradiology | September 15, 2021 | By Arun Gill
The closely tied relationship between...