IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Feb;69(2):543-552. doi: 10.1109/TUFFC.2021.3126530. Epub 2022 Jan 27.
Instrumented ultrasonic tracking is used to improve needle localization during ultrasound guidance of minimally invasive percutaneous procedures. Here, it is implemented with transmitted ultrasound pulses from a clinical ultrasound imaging probe, which is detected by a fiber-optic hydrophone integrated into a needle. The detected transmissions are then reconstructed to form the tracking image. Two challenges are considered with the current implementation of ultrasonic tracking. First, tracking transmissions are interleaved with the acquisition of B-mode images, and thus, the effective B-mode frame rate is reduced. Second, it is challenging to achieve an accurate localization of the needle tip when the signal-to-noise ratio is low. To address these challenges, we present a framework based on a convolutional neural network (CNN) to maintain spatial resolution with fewer tracking transmissions and enhance signal quality. A major component of the framework included the generation of realistic synthetic training data. The trained network was applied to unseen synthetic data and experimental in vivo tracking data. The performance of needle localization was investigated when reconstruction was performed with fewer (up to eightfold) tracking transmissions. CNN-based processing of conventional reconstructions showed that the axial and lateral spatial resolutions could be improved even with an eightfold reduction in tracking transmissions. The framework presented in this study will significantly improve the performance of ultrasonic tracking, leading to faster image acquisition rates and increased localization accuracy.
仪器化超声跟踪用于改善微创经皮介入程序中超声引导下的针定位。在这里，它是通过临床超声成像探头的发射超声脉冲来实现的，这些脉冲由集成在针中的光纤水听器检测。然后，对检测到的发射信号进行重建，形成跟踪图像。当前超声跟踪的实现存在两个挑战。首先，跟踪发射与 B 模式图像的采集交错进行，因此有效 B 模式帧率降低。其次，当信噪比低时，实现针尖的精确定位具有挑战性。为了解决这些挑战，我们提出了一个基于卷积神经网络（CNN）的框架，该框架通过较少的跟踪传输来保持空间分辨率，并增强信号质量。该框架的一个主要组成部分包括生成逼真的合成训练数据。训练好的网络应用于未见的合成数据和实验体内跟踪数据。当使用较少（最多八倍）的跟踪传输进行重建时，研究了针定位的性能。基于 CNN 的常规重建处理表明，即使跟踪传输减少了八倍，轴向和侧向空间分辨率也可以得到提高。本研究提出的框架将显著提高超声跟踪的性能，从而提高图像采集速度和定位精度。
