Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, W1W 7TY, UK.
Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
Int J Comput Assist Radiol Surg. 2023 Feb;18(2):395-399. doi: 10.1007/s11548-022-02728-7. Epub 2022 Sep 3.
Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.
The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.
Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.
The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.
仪器化超声跟踪在超声引导下微创经皮手术中提供了针的定位。在这里，提出了一种基于卷积神经网络（CNN）的后处理框架，以提高超声跟踪图像的空间分辨率。
定制的超声跟踪系统包括一个带有集成光纤超声（US）发射器的针和一个用于接收这些传输和获取 B 型 US 图像的临床 US 探头。为了对从接收的光纤 US 传输中重建的跟踪图像进行后处理，使用了基于 ResNet 架构的最近开发的框架，该框架使用纯合成数据集进行了训练。使用在体内的胎儿羊心脏中进行的针插入获得的数据对该框架进行了初步评估。跟踪图像的轴向和侧向空间分辨率被用作训练网络的性能指标。
CNN 的应用提高了跟踪图像的空间分辨率。在三次针插入中，针尖深度范围为 23.9 至 38.4mm，侧向分辨率从 2.11 毫米提高到 1.58 毫米，轴向分辨率从 1.29 毫米提高到 0.46 毫米。
结果强烈表明 CNN 有可能提高超声跟踪图像的空间分辨率，从而提高针尖定位的准确性。这些改进可能具有广泛的适用性和影响，可提高程序效率并降低并发症风险。
