  Recovering high-quality images from undersampled measurements is critical for
accelerated MRI reconstruction. Recently, various supervised deep
learning-based MRI reconstruction methods have been developed. Despite the
achieved promising performances, these methods require fully sampled reference
data, the acquisition of which is resource-intensive and time-consuming.
Self-supervised learning has emerged as a promising solution to alleviate the
reliance on fully sampled datasets. However, existing self-supervised methods
suffer from reconstruction errors due to the insufficient constraint enforced
on the non-sampled data points and the error accumulation happened alongside
the iterative image reconstruction process for model-driven deep learning
reconstrutions. To address these challenges, we propose K2Calibrate, a K-space
adaptation strategy for self-supervised model-driven MR reconstruction
optimization. By iteratively calibrating the learned measurements, K2Calibrate
can reduce the network's reconstruction deterioration caused by statistically
dependent noise. Extensive experiments have been conducted on the open-source
dataset FastMRI, and K2Calibrate achieves better results than five
state-of-the-art methods. The proposed K2Calibrate is plug-and-play and can be
easily integrated with different model-driven deep learning reconstruction
methods.
