Calibrationless reconstruction of uniformly‐undersampled multi‐channel MR data with deep learning estimated ESPIRiT maps

Abstract

Purpose To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (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 the coil and protocol‐specific data.

Parallel imaging reconstruction using spatial nulling maps

“…Such image-domain coil maps offer more choices in using prior information related to coil sensitivity. For example, in recent studies, 49,50 the coil sensitivity information can be directly estimated by a deep learning neural network from the undersampled imaging data, where prior knowledge such as subject-coil geometry information is used. They offer parallel imaging approaches that require a few number of or no ACS lines.…”