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 hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Software Developed to Help Reduce the Risks of Radiation Exposure from CT Scans

By MedImaging International staff writers
Posted on 12 Jun 2011
Print article
A new US$1.2 million study is looking to develop software for calculating and tracking a patient's radiation exposure from diagnostic X-ray computed tomography (CT) scans.

Funded by the US Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering (NIBIB; Bethesda, MD, USA), the software aims to arm radiologists, medical physicists, and patients with more accurate data for making informed decisions about the potential risks and benefits of CT scan procedures.

This plays into a larger goal of governmental agencies and hospitals of reducing the number of unnecessary CT scans performed in the United States and around the world, said project leader Prof. X. George Xu. "Radiation exposure from imaging procedures such as CT scans has elevated to an alarming level in the United States and elsewhere in recent years," said Prof. Xu, a nuclear engineering professor in the department of mechanical, aerospace, and nuclear engineering at Rensselaer Polytechnic Institute (Troy, NY, USA). "The radiation exposure from a single CT scan is still relatively small when compared with the clinical benefit of the procedure, but patients often receive multiple scans during the course of their diagnostic or therapeutic procedure. Our new software should help to record the exposures more accurately and more consistently."

A recent report by the US National Council on Radiation Protection and Measurements (NCRP; Bethesda, MD, USA), of which Prof. Xu is a member, details how the US population is now exposed to seven times more radiation every year from medical imaging exams than it was in 1980. Whereas CT scans only account for 10% of diagnostic radiologic exams, the procedure contributes disproportionately about 67% to the US national collective medical radiation exposure.

To help mitigate this risk, several national and international bodies have called for the establishment of a centralized, patient-specific "dose registry" system. Such a system would monitor over time the amount of CT scans a patient undergoes, and the radiation exposure resulting from those procedures. However, current software packages for tracking CT scan radiation exposure have basic imitations and are insufficient for such a critical task, according to Prof. Xu.

The new software Prof. Xu and his team are developing, VirtualDose, takes into consideration a patient's individual characteristics, including age, sex, pregnancy, height, and weight. By entering these data into the software, the program creates a virtual three-dimensional (3D) phantom closely matching with the patient. These anatomically realistic phantoms accurately model the patient's internal organs, and define how radiation interacts with each organ. The phantom, in turn, allows physicians and researchers to compare the levels of radiation exposure a patient gets from different CT scanning protocols or different scanner designs.

Current software for CT radiation dose reporting uses outdated models of patients, and frequently lacks necessary software features, Prof. Xu noted. This makes it nearly impossible to effectively track and record radiation exposure to organs from X-rays.

Dr. Xu reported that personalized virtual phantoms are particularly important for predicting radiation exposure from CT scans for the groups most sensitive to radiation children and pregnant women. These groups are ignored by nearly all dose measurement software, he noted.

This project builds from Prof. Xu's research on virtual phantoms for computer simulation using Monte Carlo methods.

Related Links:

Rensselaer Polytechnic Institute
National Council on Radiation Protection and Measurements


New
Gold Member
X-Ray QA Meter
T3 AD Pro
New
Ultrasound Imaging System
P12 Elite
New
40/80-Slice CT System
uCT 528
New
3T MRI Scanner
MAGNETOM Cima.X

Print article
Radcal

Channels

Radiography

view channel
Image: The new X-ray detector produces a high-quality radiograph (Photo courtesy of ACS Central Science 2024, DOI: https://doi.org/10.1021/acscentsci.4c01296)

Highly Sensitive, Foldable Detector to Make X-Rays Safer

X-rays are widely used in diagnostic testing and industrial monitoring, from dental checkups to airport luggage scans. However, these high-energy rays emit ionizing radiation, which can pose risks after... Read more

MRI

view channel
Image: Artificial intelligence models can be trained to distinguish brain tumors from healthy tissue (Photo courtesy of 123RF)

AI Can Distinguish Brain Tumors from Healthy Tissue

Researchers have made significant advancements in artificial intelligence (AI) for medical applications. AI holds particular promise in radiology, where delays in processing medical images can often postpone... Read more

Nuclear Medicine

view channel
Image: Example of AI analysis of PET/CT images (Photo courtesy of Academic Radiology; DOI: 10.1016/j.acra.2024.08.043)

AI Analysis of PET/CT Images Predicts Side Effects of Immunotherapy in Lung Cancer

Immunotherapy has significantly advanced the treatment of primary lung cancer, but it can sometimes lead to a severe side effect known as interstitial lung disease. This condition is characterized by lung... Read more

General/Advanced Imaging

view channel
Image: Cleerly offers an AI-enabled CCTA solution for personalized, precise and measurable assessment of plaque, stenosis and ischemia (Photo courtesy of Cleerly)

AI-Enabled Plaque Assessments Help Cardiologists Identify High-Risk CAD Patients

Groundbreaking research has shown that a non-invasive, artificial intelligence (AI)-based analysis of cardiac computed tomography (CT) can predict severe heart-related events in patients exhibiting symptoms... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.