Improved MRI Guided Interventions at 0.55T: Real-time Interactive Imaging with Deep Learning Reconstruction

Motivation: Contemporary low-field MRI systems hold great promise for guiding interventions. However, due to inherently reduced polarization at lower field, it is more challenging to achieve high-spatiotemporal-resolution with sufficient SNR in interactive real-time imaging. Goal(s): To improve real-time interactive imaging for MRI guided interventions at 0.55T by leveraging deep learning image reconstruction. Approach: We implemented deep learning image reconstruction for interactive real-time imaging, and compared its performance with conventional parallel imaging reconstruction and compressed-sensing on a biopsy phantom and a healthy volunteer. Results: Deep learning image reconstruction allows for accelerated interactive real-time imaging, achieving image quality that compared favorably with conventional reconstructions and compressed-sensing. Impact: The proposed method has the potential to further empower 0.55T MRI as a viable interventional guidance platform by leveraging deep learning image reconstruction in accelerated interactive real-time imaging.