An investigation into the applicability of rapid artificial intelligence-assisted compressed sensing in brain magnetic resonance imaging performed at 5 Tesla field strength

Abstract

Background

Brain magnetic resonance imaging (MRI) at 5 T offers unprecedented spatial resolution but is often limited by long scan times. Acceleration techniques, such as compressed sensing (CS) and artificial intelligence-assisted compressed sensing (ACS), have the potential to speed up the acquisition process while maintaining image quality. This study aims to evaluate and compare the performance of CS and ACS (with various acceleration factors) in brain MRI imaging at 5 T.

Methods

In this study, we enrolled 12 healthy volunteers and compared ACS-accelerated 5 T brain MRI with conventional methods of CS. The ACS acceleration factors for the brain protocol, consisting of 3D T1-weighted sequences and 2D T2-weighted sequences, were optimized in a pilot study on healthy volunteers (acceleration factor, 2.06–3.41× in T2-weighted imaging and 3.52–8.49× in T1-weighted imaging). We evaluated the images acquired from patients using various acceleration methods on the basis of acquisition times, the signal-to-noise ratio (SNR), the contrast-to-noise ratio, subjective image quality, and diagnostic agreement.

Results

Our findings revealed that ACS acceleration significantly reduced the acquisition times for T1- and T2-weighted sequences by up to 43% and 53%, respectively, compared with traditional CS at 5 T. Importantly, this acceleration was achieved while maintaining excellent image quality, demonstrated by higher or comparable SNR and contrast-to-noise ratio values.

Conclusions

The optimal ACS acceleration factors for 5 T brain MRI were determined to be 2.73× for 2D T2-weighted sequences and 6.5× for 3D T1-weighted sequences. ACS not only facilitates rapid imaging but also ensures comparable image quality and diagnostic performance, highlighting its potential to revolutionize high-field MRI scanning.