AI boosts quality of brain MRI images

AI algorithms can improve the quality of brain MRI images, according to new findings published in Neurocomputing.

Applying post-processing techniques to improve image quality is helpful in computer vision, the study’s authors explained, as well as medical imaging.

The team implemented a hybrid deep learning technique that used both a convolutional neural network and a regular shifting mechanism to transform low-resolution images into high-resolution images. This process was applied to “a variety of images of different datasets” to evaluate its performance, and the results were promising in each instance.

“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,” lead author Karl Thurnhofer-Hemsi, University of Málaga in Spain, said in a prepared statement.

The authors did note that their method was “not the fastest one”—but there are ways to improve speed as their work continues.

“It should be considered that a single GPU was employed when using it,” they wrote in their analysis. “The use of more GPUs simultaneously may decrease almost linearly the total time.”

Michael Walter
Michael Walter, Managing Editor

Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.

Around the web

Compensation for heart specialists continues to climb. What does this say about cardiology as a whole? Could private equity's rising influence bring about change? We spoke to MedAxiom CEO Jerry Blackwell, MD, MBA, a veteran cardiologist himself, to learn more.

The American College of Cardiology has shared its perspective on new CMS payment policies, highlighting revenue concerns while providing key details for cardiologists and other cardiology professionals. 

As debate simmers over how best to regulate AI, experts continue to offer guidance on where to start, how to proceed and what to emphasize. A new resource models its recommendations on what its authors call the “SETO Loop.”