Features Partner Sites Information LinkXpress hp
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
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. More...
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


40/80-Slice CT System
uCT 528
New
Mobile X-Ray System
K4W
Digital X-Ray Detector Panel
Acuity G4
New
MRI System
nanoScan MRI 3T/7T
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to MedImaging.net and get access to news and events that shape the world of Radiology.
  • Free digital version edition of Medical Imaging International sent by email on regular basis
  • Free print version of Medical Imaging International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of Medical Imaging International in digital format
  • Free Medical Imaging International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Nuclear Medicine

view channel
Image: Perovskite crystal boules are grown in carefully controlled conditions from the melt (Photo courtesy of Mercouri Kanatzidis/Northwestern University)

New Camera Sees Inside Human Body for Enhanced Scanning and Diagnosis

Nuclear medicine scans like single-photon emission computed tomography (SPECT) allow doctors to observe heart function, track blood flow, and detect hidden diseases. However, current detectors are either... Read more

General/Advanced Imaging

view channel
Image: The Angio-CT solution integrates the latest advances in interventional imaging (Photo courtesy of Canon Medical)

Cutting-Edge Angio-CT Solution Offers New Therapeutic Possibilities

Maintaining accuracy and safety in interventional radiology is a constant challenge, especially as complex procedures require both high precision and efficiency. Traditional setups often involve multiple... 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.