Deep learning meets need for speed in spine MRI

By Erik L. Ridley, staff writer

November 15, 2021 --

Thursday, December 2 | 9:30 a.m.-10:30 a.m. | SSNR14-5 | Room TBA
In this session, researchers will show how a deep learning-based image reconstruction method can deliver up to 72% faster spine MRI scan times along with perceived improvements in image quality.

Researchers led by presenter Dr. Lawrence Tanenbaum of imaging services provider RadNet prospectively evaluated a commercially available k-space-based reconstruction application (Air Recon DL, GE Healthcare) in a study involving 26 consecutive patients and two neuroradiologists. All patients received standard-of-care, as well as accelerated spine MRI exams -- 72% faster for cervical scans and 64% faster for lumbar scans.

Next, the two readers evaluated the different image series paired in side-by-side datasets, which were blinded and randomized in sequence and also left-to-right display order. They ranked the image features on a 5-point Liker scale.

The AI-reconstructed images not only were assessed to be qualitatively better but also yielded high interrater agreement. In addition, no aberrations were detected on these images.

"[Deep learning] enables 64% to 72% spine MRI scan time reduction as well as what radiologists perceive as enhanced image quality with benefits in [signal-to-noise ratio], image sharpness and artifact reduction over [standard-of-care] and [accelerated] images without [deep-learning] processing, providing gains in efficiency and portending practice utility for routine use," the authors wrote.

Accelerate your AI education by attending this Thursday talk.

This paper received a Roadie 2021 award for the most popular abstract by page views in this Road to RSNA section.


To read this and get access to all of the exclusive content on create a free account or sign-in now.

Member Sign In:
MemberID or Email Address:  
Do you have a password?
No, I want a free membership.
Yes, I have a password:  
Forgot your password?
Sign in using your social networking account:
Sign in using your social networking