Hy-PCF：進行性クロスドメイン融合のための新しいハイブリッドCNN-トランスフォーマーによる平面波画像再構成

Plane-wave (PW) imaging, which provides high temporal resolution, has gained a significant attention. However, it is constrained by inherent lack of focus, requiring support from beamforming methods, such as Coherent Plane-Wave Compounding (CPWC), which can diminish the temporal advantages. Therefore, it is crucial to develop an advanced approach capable of addressing the trade-off between quality and frame rate. This paper proposes, for the first time, a hybrid progressive cross-domain fusion CNN-Transformer network (Hy-PCF), which is designed to simultaneously utilize both channel data and single-angle PW image for image reconstruction. Further, we design a novel Sparse Cross-Attention Guided Information Perception (SCAIP) module and use the single-angle PW image as guidance to extract information from the channel data. The qualitative and quantitative experimental findings demonstrate that Hy-PCF not only outperforms DAS significantly but also achieves performance comparable to the target CPWC. Hy-PCF represents a promising hybrid-CNN-Transformer network for PW imaging, offering a novel solution to enhance image quality, a critical advancement for clinical practice.