用深度学习提升超声造影成像分辨率，发现啁啾脉冲在低信噪比下表现最优。
Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging
- 用CNN直接从射频信号中去卷积定位微泡，突破脉冲长度对分辨率限制。
- 在无噪声条件下短脉冲性能最佳，但啁啾脉冲在低信噪比下更稳健且综合表现最好。
- 研究为临床实用化超分辨率超声提供关键脉冲设计依据，适合超声成像开发者参考。
解析动脉血流对理解心血管疾病、改善诊断和监测患者状况至关重要。超声造影成像利用微泡增强血池散射，实现血流的实时可视化。向量血流成像进一步拓展了超声的时空分辨能力。当前主要障碍是空间分辨率不足。通过在波束形成前对射频（RF）信号去卷积，可实现超分辨率成像，打破分辨率与脉冲持续时间的关联。卷积神经网络（CNN）可训练以局部估计去卷积核，从而直接在RF信号中超分辨定位微泡。然而，微泡对比度高度非线性，目前尚未充分挖掘CNN在微泡定位中的潜力。评估深度学习去卷积在复杂成像脉冲下的性能，对实际应用至关重要，尤其在信噪比受限且需符合安全指南的传输方案下。本研究训练CNN对谐波脉冲、啁啾脉冲及延迟编码脉冲序列的RF信号进行去卷积并定位微泡。此外，通过初步实验结果讨论体外与体内超分辨率的潜在挑战。结果显示，尽管所有脉冲下CNN均能准确定位微泡，短脉冲在无噪声条件下表现最佳；而啁啾脉冲在无噪声时性能相当，且抗噪声能力更强，在低信噪比条件下优于其他脉冲。
原文摘要 · Abstract (English)
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbubbles to enhance the scattering of the blood pool, allowing for real-time visualization of blood flow. Recent developments in vector flow imaging further expand the imaging capabilities of ultrasound by temporally resolving fast arterial flow. The next obstacle to overcome is the lack of spatial resolution. Super-resolved ultrasound images can be obtained by deconvolving radiofrequency (RF) signals before beamforming, breaking the link between resolution and pulse duration. Convolutional neural networks (CNNs) can be trained to locally estimate the deconvolution kernel and consequently super-localize the microbubbles directly within the RF signal. However, microbubble contrast is highly nonlinear, and the potential of CNNs in microbubble localization has not yet been fully exploited. Assessing deep learning-based deconvolution performance for non-trivial imaging pulses is therefore essential for successful translation to a practical setting, where the signal-to-noise ratio is limited, and transmission schemes should comply with safety guidelines. In this study, we train CNNs to deconvolve RF signals and localize the microbubbles driven by harmonic pulses, chirps, or delay-encoded pulse trains. Furthermore, we discuss potential hurdles for in-vitro and in-vivo super-resolution by presenting preliminary experimental results. We find that, whereas the CNNs can accurately localize microbubbles for all pulses, a short imaging pulse offers the best performance in noise-free conditions. However, chirps offer a comparable performance without noise, but are more robust to noise and outperform all other pulses in low-signal-to-noise ratio conditions.
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