  Deep model-based architectures (DMBAs) integrating physical measurement
models and learned image regularizers are widely used in parallel magnetic
resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil
sensitivity maps (CSMs) as a component of the measurement model. However,
estimation of accurate CSMs is a challenging problem when measurements are
highly undersampled. Additionally, traditional training of DMBAs requires
high-quality groundtruth images, limiting their use in applications where
groundtruth is difficult to obtain. This paper addresses these issues by
presenting SPICE as a new method that integrates self-supervised learning and
automatic coil sensitivity estimation. Instead of using pre-estimated CSMs,
SPICE simultaneously reconstructs accurate MR images and estimates high-quality
CSMs. SPICE also enables learning from undersampled noisy measurements without
any groundtruth. We validate SPICE on experimentally collected data, showing
that it can achieve state-of-the-art performance in highly accelerated data
acquisition settings (up to 10x).
