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




Deep Learning Enables Accurate, Automated Quality Control Image Assessment for Liver MR Elastography

By MedImaging International staff writers
Posted on 21 Aug 2024
Image: The study demonstrated an automated DL-based approach for classifying liver 2D MRE diagnostic quality (Photo courtesy of Georgia Tech)
Image: The study demonstrated an automated DL-based approach for classifying liver 2D MRE diagnostic quality (Photo courtesy of Georgia Tech)

Hepatic disease impacts millions globally, with many more individuals having undetected stages of fatty liver disease. If left undiagnosed and untreated, these conditions can progress to cirrhosis, which involves irreversible liver scarring. Typically, a biopsy follows an abnormal blood test result to diagnose and monitor liver tissue, but this procedure carries risks and consumes time. To circumvent these issues, non-invasive techniques like magnetic resonance elastography (MRE) have been developed. MRE, which merges ultrasound and MRI technology, visualizes liver stiffness levels to indicate scarring and has become a favored method for diagnosing liver issues. Nonetheless, MRE scans can fail due to several factors such as patient movement, specific physiological traits, or technical issues like incorrect wave generation. The growing demand for diagnostic services combined with workforce shortages underscores the need for a reliable, automated method to classify MRE image quality to enhance efficiency and minimize repeat procedures.

Now, researchers at the George W. Woodruff School of Mechanical Engineering (Atlanta, GA, USA) have successfully utilized deep learning to significantly improve the accuracy of MRE image quality assessments. By using five deep-learning training models, they achieved an accuracy of 92% on retrospective patient images, which varied in liver stiffness. This technology also achieved a return of the analyzed data within seconds, enabling technicians to make necessary adjustments on the spot to avoid the need for additional patient visits due to initial low-quality scans.

The findings, detailed in the Journal of Magnetic Resonance Imaging, further advance efforts for automating MRE quality reviews using deep learning—a relatively untapped area in medical imaging technology. This research not only sets a benchmark for future studies on other organs like the spleen or kidneys but may also extend to automating image quality control in conditions like breast cancer or muscular dystrophy, where tissue stiffness is a critical marker of health and disease progression. The team plans to further test their models on Siemens Healthineers magnetic resonance scanners in the upcoming year, potentially transforming diagnostic processes across various medical fields.

Related Links:
George W. Woodruff School of Mechanical Engineering

Computed Tomography System
Aquilion ONE / INSIGHT Edition
Multi-Use Ultrasound Table
Clinton
New
Ultrasound Needle Guidance System
SonoSite L25
Wall Fixtures
MRI SERIES

Channels

Ultrasound

view channel
Image: The new implantable device for chronic pain management is small and flexible (Photo courtesy of The Zhou Lab at USC)

Wireless Chronic Pain Management Device to Reduce Need for Painkillers and Surgery

Chronic pain affects millions of people globally, often leading to long-term disability and dependence on opioid medications, which carry significant risks of side effects and addiction.... Read more

Nuclear Medicine

view channel
Image: The diagnostic tool could improve diagnosis and treatment decisions for patients with chronic lung infections (Photo courtesy of SNMMI)

Novel Bacteria-Specific PET Imaging Approach Detects Hard-To-Diagnose Lung Infections

Mycobacteroides abscessus is a rapidly growing mycobacteria that primarily affects immunocompromised patients and those with underlying lung diseases, such as cystic fibrosis or chronic obstructive pulmonary... 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 © 2000-2025 Globetech Media. All rights reserved.