用神经隐式表示优化超声接收权重，三发即可实现高质量成像。
Learning Continuous Receive Apodization Weights via Implicit Neural Representation for Ultrafast ICE Ultrasound Imaging
- 用多层感知机将坐标和角度映射为连续复数权重。
- 仅需3次发射即达到26角度合成的图像质量。
- 适合追求高帧率与低硬件开销的超声成像研究者。
超快心内超声（ICE）采用非聚焦发射，可实现超过1 kHz的帧率以实时捕捉心脏动态。然而，衍射伪影导致图像质量下降，通常需多次发射才能获得满意分辨率和对比度。为此，我们提出一种基于隐式神经表示（INR）的框架，以连续方式编码复数接收加权系数，仅通过3次发散波（DW）发射即可实现高质量ICE重建。该方法利用多层感知机，将像素坐标与发射指向角映射为各接收通道的复数加权值。在大规模活体猪心脏ICE数据集上的实验表明，学习到的加权能有效抑制杂波并增强对比度，重建结果接近26角度复合的DW真实基准。研究表明，INR可为超声图像增强提供强大框架。
原文摘要 · Abstract (English)
Ultrafast intracardiac echocardiography (ICE) uses unfocused transmissions to capture cardiac motion at frame rates exceeding 1 kHz. While this enables real-time visualization of rapid dynamics, image quality is often degraded by diffraction artifacts, requiring many transmits to achieve satisfying resolution and contrast. To address this limitation, we propose an implicit neural representation (INR) framework to encode complex-valued receive apodization weights in a continuous manner, enabling high-quality ICE reconstructions from only three diverging wave (DW) transmits. Our method employs a multi-layer perceptron that maps pixel coordinates and transmit steering angles to complex-valued apodization weights for each receive channel. Experiments on a large in vivo porcine ICE imaging dataset show that the learned apodization suppresses clutter and enhances contrast, yielding reconstructions closely matching 26-angle compounded DW ground truths. Our study suggests that INRs could offer a powerful framework for ultrasound image enhancement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。