We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




AI Approach Lowers Radiation Exposure from CT Imaging

By MedImaging International staff writers
Posted on 06 Jul 2019
Print article
Image: Research shows machine learning has the potential to advance medical imaging, particularly CT scanning, by reducing radiation exposure and improving image quality (Photo courtesy of Axis Imaging News).
Image: Research shows machine learning has the potential to advance medical imaging, particularly CT scanning, by reducing radiation exposure and improving image quality (Photo courtesy of Axis Imaging News).
Engineers at the Rensselaer Polytechnic Institute (Troy, NY, USA) worked along with radiologists at Massachusetts General Hospital (Boston, MA, USA) and Harvard Medical School (Boston, MA, USA) to demonstrate that machine learning has the potential to vastly advance medical imaging, particularly computerized tomography (CT) scanning, by reducing radiation exposure and improving image quality. The team believes that their new research findings make a strong case for harnessing the power of artificial intelligence (AI) to improve low-dose CT scans.

Over the past several years, there has been significant focus on low-dose CT imaging techniques to alleviate concerns over patient exposure to X-ray radiation associated with widely used CT scans. However, reducing radiation can affect image quality. Engineers across the world have attempted to solve this problem by designing iterative reconstruction techniques to help sift through and remove interferences from CT images. However, the drawback is that these algorithms sometimes remove useful information or falsely alter the image.

In the latest research, the team attempted to address this persistent challenge by using a machine-learning framework. The developed a dedicated deep neural network and compared their best results to the best of what three major commercial CT scanners could produce with iterative reconstruction techniques. The researchers were looking to determine how the performance of their deep learning approach compared to the selected representative iterative algorithms currently being used clinically. They found that the deep learning algorithms developed by the Rensselaer team performed as well as, or better than, those current iterative techniques in an overwhelming majority of cases.

The researchers also found that their deep learning method was much quicker and allowed the radiologists to fine-tune the images according to clinical requirements. The positive results were realized without access to the original, or raw, data from all the CT scanners, and a more specialized deep learning algorithm is likely to perform even better if original CT data is made available, according to the researchers. They believe that these results confirm that deep learning could help produce safer, more accurate CT images while also running more rapidly than iterative algorithms.

“Radiation dose has been a significant issue for patients undergoing CT scans. Our machine learning technique is superior, or, at the very least, comparable, to the iterative techniques used in this study for enabling low-radiation dose CT,” said Ge Wang, the Clark & Crossan Endowed Chair Professor of biomedical engineering at Rensselaer, and a corresponding author on this paper. “It’s a high-level conclusion that carries a powerful message. It’s time for machine learning to rapidly take off and, hopefully, take over.”

Related Links:
Rensselaer Polytechnic Institute
Massachusetts General Hospital
Harvard Medical School

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Dose Calibration Electrometer
PC Electrometer
Digital Radiography Acquisition Software
VXvue with PureImpact
New
Ultrasound Table
Ergonomic Advantage (EA) Line

Print article

Channels

Radiography

view channel
:	Image: The AI model could be a valuable adjunct to human radiologists in breast cancer diagnoses and risk prediction (Photo courtesy of 123RF)

AI Model Predicts 5-Year Breast Cancer Risk from Mammograms

Approximately 13% of U.S. women, or one in every eight, are predicted to develop invasive breast cancer over their lifetime, with 1 in 39 women (3%) succumbing to the illness, according to the American... Read more

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The CIARTIC Move self-driving mobile C-arm has received FDA clearance (Photo courtesy of Siemens)

Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery

Intraoperative imaging faces significant challenges due to staff shortages and the high demands placed on surgical teams in the operating room (OR). A common challenge during many OR procedures is the... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.