Accelerating 7T Gradient-Echo Brain MRI With Generative AI Denoising.

Yixin Wang¹, Binxu Li², Yihao Liang³, Mackenzie Carlson³, Phillip DiGiacomo³, Hossein Moein Taghavi³, Julian Maclaren³, Meghan Bell³, Nancy Pham³, Johannes Hugo Decker³, Michael Lv³, Tie Liang³, Jason Wong³, Elizabeth Mormino⁴, Victor W Henderson⁴, Greg Zaharchuk³, Brian Rutt³, Wei Shao⁵, Marios Georgiadis³, Michael Zeineh⁶

¹Department of Bioengineering, Stanford University, Stanford, California.

²Department of Electrical Engineering, Stanford University, Stanford, California.

³Department of Radiology, Stanford School of Medicine, Stanford, California.

⁴Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California.

⁵Department of Electrical & Computer Engineering, University of Florida, Florida.

⁶Department of Radiology, Stanford School of Medicine, Stanford, California. Electronic address: mzeineh@stanford.edu.

PubMed DOI

Abstract

RATIONALE AND OBJECTIVES: This study aimed to efficiently denoise short 7 T MRI acquisitions to achieve the image quality of longer scans using a generative Artificial Intelligence (AI) model. MATERIALS AND METHODS: A 7T Conditional Diffusion Model (7TCDM) was trained on an in-house 7T dataset of 11 examinations consisting of multi-repetition 2D T2-weighted gradient-echo acquisitions. The model u…

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 Account Sign 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.