Deep learning enabled fast 3D brain MRI at 0.055 tesla.
Christopher ManVick LauShi SuYujiao ZhaoLinfang XiaoYe DingGilberto K K LeungAlex T L LeongEd X WuPublished in: Science advances (2023)
In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- deep learning
- computed tomography
- magnetic resonance
- image quality
- resting state
- low cost
- white matter
- diffusion weighted imaging
- functional connectivity
- high resolution
- artificial intelligence
- air pollution
- convolutional neural network
- machine learning
- multiple sclerosis
- photodynamic therapy
- single molecule
- human health