Popular methods in compressed sensing (CS) are dependent on deep learning
(DL), where large amounts of data are used to train non-linear reconstruction
models. However, ensuring generalisability over and access to multiple datasets
is challenging to realise for real-world applications. To address these
concerns, this paper proposes a single image, self-supervised (SS) CS-MRI
framework that enables a joint deep and sparse regularisation of CS artefacts.
The approach effectively dampens structured CS artefacts, which can be
difficult to remove assuming sparse reconstruction, or relying solely on the
inductive biases of CNN to produce noise-free images. Image quality is thereby
improved compared to either approach alone. Metrics are evaluated using
Cartesian 1D masks on a brain and knee dataset, with PSNR improving by 2-4dB on
average.

本文提出了一种单图像自监督压缩感知磁共振成像框架，能够实现压缩感知估计的深度与稀疏正则化，有效降低结构化压缩感知伪影，提高图像质量。通过对大脑和膝盖数据集使用 Cartesian 1D 掩码进行度量评估，平均峰值信噪比 (PSNR) 提高了 2-4dB。