Health AI Central
Peer-Reviewed Publication
Radiol Adv2024;1(3):umae022.September 1, 2024Journal Article

An image-domain deep-learning denoising technique for accelerated parallel brain MRI: prospective clinical evaluation.

Laura Onac1, Lorand Dobai1, Andrei Mouraviev1, Mihai A Badila1, Denise Yap1, Samantha Kennedy1, Annie Nguyen1, Emi Gal1, Daniel K Sodickson2
1Ezra AI Inc, New York, NY 10016, United States.
2Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, United States.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts. PURPOSE: To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly accelerated MRI scans, improving image quality and reducing scan time. MATERIALS AND METHODS: Training was performed by retrospectively degrading sta…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.