Electromagnetic Interference Rejection Strategy for 50-mT Portable Unshielded Whole-Body Magnetic Resonance Imaging With a Convolutional Neural Network Incorporating Attention Mechanism

Recently, there has been an impetus for developing low-field portable magnetic resonance imaging (MRI) technologies, driving a paradigm shift from conventional MRI scanners to point-of-care (POC) MRI scenarios. To facilitate the miniaturization of portable whole-body MRI, the weight of permanent magnet was reduced to 800 kg by shortening its axial depth, which allowed for the development of a 50-mT portable whole-body MRI prototype. The conventional MRI equipment usually uses Faraday shielded rooms to block electromagnetic interference (EMI) in complex environments, but it is difficult to apply in POC MRI scenarios. Furthermore, compared to the prevailing portable brain MRI scanner, the developed 50-mT short-axis portable whole-body MRI scanner is more susceptible to the EMI in open environments. To address this, a new active EMI rejection strategy featuring reduced hardware configuration and shorter model training time was proposed for unshielded portable whole-body MRI scanner. First, a multi-indicator evaluation method was designed to optimize the deployment of multiple EMI sensing coils. High-quality EMI sensing coils were selected, effectively reducing the hardware requirement by half. Then, the se-convolutional neural network (CNN) model with three-layer CNN and SENet attention mechanism was constructed to implement the EMI noise prediction and rejection. For model training, EMI noise data are acquired synchronously from multiple EMI sensing coils and the main MRI receive coil during the imaging data acquisition intervals, with a typical training time of about 2 min. The phantom and in vivo imaging experiments demonstrated the improved image quality and validated the outperformance of the proposed EMI rejection strategy. This EMI rejection method has the potential to turbocharge the deployment of portable whole-body MRI scanners in clinical POC diagnosis and treatment.