用隐式神经表示重建超声图像，提升快速成像的清晰度。
Modulated INR with Prior Embeddings for Ultrasound Imaging Reconstruction
- 基于时延I/Q数据提取嵌入，调制隐式神经网络进行重建。
- 在活体心脏超声数据上优于现有最佳方法，细节更清晰。
- 适合做超声图像增强或医学影像重建的研究者参考。
超快超声成像通过极高的帧率实现对快速生理动态的可视化，但高速采集常导致空间分辨率下降和图像质量受损，源于波束未聚焦及伴随的伪影。本文提出一种新型调制隐式神经表示（INR）框架，利用基于坐标的神经网络，结合从时延I/Q通道数据中提取的潜在嵌入，实现高质量超声图像重建。方法融合复杂Gabor小波激活函数与调节器网络，有效捕捉I/Q信号的振荡性和相位敏感特性。在活体心内超声（ICE）数据集上的评估表明，该框架优于现有最先进方法。研究结果不仅凸显了基于INR建模在超声图像重建中的优势，也为该类框架在其他医学成像模态中的应用提供了新思路。
原文摘要 · Abstract (English)
Ultrafast ultrasound imaging enables visualization of rapid physiological dynamics by acquiring data at exceptionally high frame rates. However, this speed often comes at the cost of spatial resolution and image quality due to unfocused wave transmissions and associated artifacts. In this work, we propose a novel modulated Implicit Neural Representation (INR) framework that leverages a coordinate-based neural network conditioned on latent embeddings extracted from time-delayed I/Q channel data for high-quality ultrasound image reconstruction. Our method integrates complex Gabor wavelet activation and a conditioner network to capture the oscillatory and phase-sensitive nature of I/Q ultrasound signals. We evaluate the framework on an in vivo intracardiac echocardiography (ICE) dataset and demonstrate that it outperforms the compared state-of-the-art methods. We believe these findings not only highlight the advantages of INR-based modeling for ultrasound image reconstruction, but also point to broader opportunities for applying INR frameworks across other medical imaging modalities.
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