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




Artificial Intelligence Improves Brain MRI Resolution

By MedImaging International staff writers
Posted on 27 Jan 2020
Print article
Image: Dr. Karl Thurnhofer compared native and enhanced MRIs (Photo courtesy of UMA)
Image: Dr. Karl Thurnhofer compared native and enhanced MRIs (Photo courtesy of UMA)
Super-resolution (SR) techniques can be applied to magnetic resonance imaging (MRI) scans by training a convolutional neuronal network (CNN), claims a new study.

Researchers at the University of Málaga (UMA; Spain) developed a CNN to which a regularly spaced shifting mechanism over the input image was applied to increase resolution, with the aim of substantially improving the quality of the resulting image; this enables specialists to identify brain-related pathologies such physical injuries, cancer, or language disorders with increased accuracy and definition. The deep learning (DL) process can be formed autonomously, without any supervision, allowing an identification effort that the human eye would not be capable of doing.

According to the researchers, the results obtained from applying the CNN on different MRI images show a considerable improvement both in the restored image and in the residual image, without an excessive increase in computing time. In addition, the images provide increased resolution without distorting the patients' brain structures, and favorably compared to other SR techniques, such as the peak signal-to-noise (SNR) ratio, the structural similarity index, and Bhattacharyya coefficient metrics. The study was published on October 22, 2019, in Neurocomputing.

“Deep learning is based on very large neural networks, and so is its capacity to learn, reaching the complexity and abstraction of a brain,” said lead author Karl Thurnhofer, PhD, of the UMA department of computer languages and computer science. “So far, the acquisition of quality brain images has depended on the time the patient remained immobilized in the scanner; with our method, image processing is carried out later on the computer.”

Deep learning is part of a broader family of AI machine learning methods that is based on learning data representations, as opposed to task specific algorithms. It involves CNN algorithms that use a cascade of many layers of nonlinear processing units for feature extraction, conversion, and transformation, with each successive layer using the output from the previous layer as input to form a hierarchical representation.

Related Links:
University of Málaga

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Breast Imaging Workstation
SecurView
Laptop Ultrasound Scanner
PL-3018
New
DR Flat Panel Detector
1500L

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

Industry News

view channel
Image: Samsung Medison CEO Mr. Yongkwan Kim and Bracco Imaging CEO Dr. Fulvio Renoldi Bracco endorsed a MoU agreement (Photo courtesy of Bracco Group)

Samsung and Bracco Enter Into New Diagnostic Ultrasound Technology Agreement

Samsung Medison (Seoul, South Korea) and Bracco Imaging (Milan, Italy) have entered into a Memorandum of Understanding (MoU) agreement to pioneer a new area for diagnostic ultrasound devices and contrast agents.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.