Unsupervised deep learning for multi-channel MRI model estimation

摘要

An MRI apparatus performs multi-channel calibration acquisitions using a multi-channel receiver array and uses a convolutional neural network (CNN) to compute an estimated profile map that characterizes properties of the multi-channel receiver array. The profile map is composed of orthogonal vectors and transforms single-channel image space data to multi-channel image space data. The MRI apparatus performs a prospectively subsampled imaging acquisition and processes the resulting k-space data using the estimated profile map to reconstruct a final image. The CNN may be pretrained in an unsupervised manner using subsampled simulated multi-channel calibration acquisitions and using a regularization function included in a training loss function.