Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

Magnetic Resonance Imaging (MRI) is frequently used by physicians for diagnosing human tissue, which requires focusing on specific content regions. However, existing compressed-sensing MRI (CS-MRI) reconstruction methods do not optimize under-sampling adaptively based on content or utilize k-space sensing resources effectively. To address these issues, we propose CBRecNet, a model that combines under-sampling pattern optimization with content-based reconstruction network. We extract multi-scale features of the content and calibrate the features of the reconstruction network using a pixel attention mechanism, which can guide the optimizable pattern to get a more suitable sampling pattern. To validate the proposed CBRecNet, we conduct extensive experiments on the CHAOS dataset with a four-stage learning strategy and the result demonstrates a very favorable outcome in CS-MRI. Our model provides a new approach to calibrated feature schemes based on meaningful medical content and demonstrates promising applications in CS-MRI.

Variable block size motion estimation has contributed greatly to achieving an optimal interframe encoding, but involves high computational complexity and huge memory access, which is the most critical bottleneck in ultra-high-definition video encoding. This article presents a hardware-efficient block matching algorithm with an efficient hardware design that is able to reduce the computational complexity of motion estimation while providing a sustained and steady coding performance for high-quality video encoding. A three-level memory organization is proposed to reduce memory bandwidth requirement while supporting a predictive common search window. By applying multiple search strategies and early termination, the proposed design provides 1.8 to 3.7 times higher hardware efficiency than other works. Furthermore, on-chip memory has been reduced by 96.5% and off-chip bandwidth requirement has been reduced by 39.4% thanks to the proposed three-level memory organization. The corresponding power consumption is only 198mW at the highest working frequency of 500MHz. The proposed design is attractive for high-quality video encoding in real-time applications with low power consumption.