Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data.

Robust Electromagnetic Interference (EMI) Elimination via Simultaneous Sensing and Deep Learning Prediction for RF Shielding-free MRI

At present, MRI scans are performed inside a fully-enclosed RF shielding room, posing stringent installation requirement and unnecessary patient discomfort. We aim to develop an electromagnetic interference (EMI) cancellation strategy for MRI with no or incomplete RF shielding. In this study, a simultaneous sensing and deep learning driven EMI cancellation strategy is presented to model, predict and remove EMI signals from acquired MRI signals. Specifically, during each MRI scan, separate EMI sensing coils placed in various spatial locations are utilized to simultaneously sample environmental and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). Then a CNN model is trained using the EMI characterization data to relate EMI signals detected by EMI sensing coils to EMI signals in MRI receive coil. This model is utilized to retrospectively predict and remove EMI signals components detected by MRI receive coil during the MRI signal acquisition window. We implemented and demonstrated this strategy for various EMI sources on a mobile ultra-low-field 0.055 T permanent magnet MRI scanner and a 1.5 T superconducting magnet MRI scanner with no or incomplete RF shielding. Our experimental results demonstrate that the method is highly effective and robust in predicting and removing various EMI sources from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (64 MHz), producing final image signal-to-noise ratios that are comparable to those obtained using a fully enclosed RF shielding. Our proposed strategy enables MRI operation with no or incomplete RF shielding, alleviating MRI installation and operational requirements. It is also potentially applicable to other scenarios of accurate RF signal detection or discrimination in presence of external and internal EMI or RF sources.