Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Abstract

Objective: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

Methods: First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution. Then, the speed of basic super-resolution imaging is further improved by reducing the number of excitations. Next, the feasibility of using cross-field super-resolution to map low-field low-resolution images to high-field ultra-high-resolution images is analyzed. Finally, by cascading basic and cross-field super-resolution, the quality of the low-field low-resolution image is improved to the level of the high-field ultra-high-resolution image.

Results: Under unshielded conditions, our 0.2 T scanner can achieve image quality comparable to that of a 1.5 T scanner (acquisition resolution of 512 × 512, spatial resolution of 0.45 mm²), and a single-orientation imaging time of less than 3.3 min.

Discussion: The proposed strategy overcomes the physical limitations of the hardware and rapidly acquires images close to the high-field level on a low-field unshielded MRI scanner. These findings have significant practical implications for the advances in MRI technology, supporting the shift from conventional scanners to point-of-care imaging systems.