News | Magnetic Resonance Imaging (MRI) | September 15, 2022

New research closes the gap between traditional and deep learning methods 

University of Minnesota Twin Cities researchers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs without relying on the use of newer deep learning methods. Credit: Intelligent Medical Imaging and Image Processing Lab, University of Minnesota

University of Minnesota Twin Cities researchers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs without relying on the use of newer deep learning methods. Credit: Intelligent Medical Imaging and Image Processing Lab, University of Minnesota 


September 15, 2022 — University of Minnesota Twin Cities scientists and engineers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs to improve healthcare. 

The paper is published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS), a peer-reviewed, multidisciplinary, high-impact scientific journal. 

“MRIs take a long time because you’re acquiring the data in a sequential manner. You have to fill up the frequency space of your image in a successive manner,” explained Mehmet Akcakaya, the Jim and Sara Anderson Associate Professor in the University of Minnesota Department of Electrical and Computer Engineering and senior author of the paper. “We want to make MRIs faster so that patients are there for shorter times and so that we can increase the efficiency in the healthcare system. This paper explores a way of doing this while making sure that we maintain a good performance.” 

For the last decade or so, scientists have been making MRIs faster using a technique called compressed sensing, which uses the idea that images can be compressed into smaller sizes, akin to zipping a .jpeg on a computer.  

More recently, researchers have been looking into using deep learning, a type of machine learning, to speed up MRI image reconstruction. Instead of capturing every frequency during the MRI procedure, this process skips over frequencies and uses a trained machine learning algorithm to predict the results and fill in those gaps.  

Many studies have shown deep learning to be better than traditional compressed sensing by a large margin. However, there are some concerns with using deep learning—for example, having insufficient training data could create a bias in the algorithm that might cause it to misinterpret the MRI results. 

Using a combination of modern data science tools and machine learning ideas, the University of Minnesota Twin Cities researchers have found a way to fine-tune the traditional compressing method to make it nearly as high-quality as deep learning. 

Akcakaya said this finding provides a new research direction for the field of MRI reconstruction. 

“What we’re saying is that there’s a lot of hype surrounding deep learning in MRIs, but maybe that gap between new and traditional methods isn’t as big as previously reported,” Akcakaya said. “We found that if you tune the classical methods, they can perform very well. So, maybe we should go back and look at the classical methods and see if we can get better results. There is a lot of great research surrounding deep learning as well, but we’re trying to look at both sides of the picture to see where we can find the best performance, theoretical guarantees, and stability.” 

For more information: https://cse.umn.edu/


Related Content

News | Prostate Cancer

July 11, 2024 — GE HealthCare’s MIM Software, a global provider of medical imaging analysis and artificial intelligence ...

Time July 11, 2024
arrow
News | Prostate Cancer

July 2, 2024 — A new editorial paper was published in Oncoscience (Volume 11) on May 20, 2024, entitled, “Deep learning ...

Time July 02, 2024
arrow
News | Clinical Trials

June 27, 2024 — Prenuvo, which makes whole-body MRI screening for early cancer detection and other diseases, has ...

Time June 27, 2024
arrow
News | Pediatric Imaging

June 25, 2024 — Rady Children’s Hospital-San Diego, one of the nation’s top pediatric health care systems, today ...

Time June 25, 2024
arrow
News | MRI Breast

June 12, 2024 — Royal Philips recently announced the 1,111th installation of its revolutionary BlueSeal 1.5T magnet ...

Time June 12, 2024
arrow
News | Artificial Intelligence

June 11, 2024 — A new study led by researchers at Emory AI.Health, published in the Journal of Computers in Medicine and ...

Time June 11, 2024
arrow
News | Breast Imaging

June 7, 2024 — Scholars and studies funded by Susan G. Komen(R), the world’s leading breast cancer organization ...

Time June 07, 2024
arrow
News | Radiopharmaceuticals and Tracers

June 7, 2024 — Shine Technologies, LLC, a pioneer in next-generation fusion-based technology, today announced a new ...

Time June 07, 2024
arrow
News | Oncology Information Management Systems (OIMS)

May 30, 2024 — RaySearch Laboratories AB announced the release of the latest version of RayCare, the next generation ...

Time May 30, 2024
arrow
News | Radiology Business

May 29, 2024 — Strategic Radiology added a third California member to the nation’s leading coalition of independent ...

Time May 29, 2024
arrow
Subscribe Now