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.

翻译：压缩感知领域的主流方法依赖于深度学习，通过大量数据训练非线性重建模型。然而，在实际应用中，保证模型泛化性及获取多数据集仍面临挑战。针对这些问题，本文提出一种单图像自监督压缩感知磁共振成像框架，该框架能够联合实现CS伪影的深度正则化与稀疏正则化。该方法可有效抑制结构化CS伪影——这类伪影若仅采用稀疏重建或单纯依赖CNN的归纳偏置生成无噪声图像，往往难以去除。相较于单独使用任意一种方法，本方法显著提升了图像质量。在脑部和膝盖数据集中采用笛卡尔一维掩模进行指标评估，PSNR平均提升2-4dB。