Purpose

Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion can lead to procedure failure and complications. Therefore, ultrasound guidance is widely used to improve needle placement accuracy. However, at steep and deep insertions, the visibility of the needle is lost. Computational methods for automatic needle tip localization could improve the clinical success rate in these scenarios.

Methods

We propose a novel algorithm for needle tip localization during challenging ultrasound-guided insertions when the shaft may be invisible, and the tip has a low intensity. There are two key steps in our approach. First, we enhance the needle tip features in consecutive ultrasound frames using a detection scheme which recognizes subtle intensity variations caused by needle tip movement. We then employ a hybrid deep neural network comprising a convolutional neural network and long short-term memory recurrent units. The input to the network is a consecutive plurality of fused enhanced frames and the corresponding original B-mode frames, and this spatiotemporal information is used to predict the needle tip location.

Results

We evaluate our approach on an ex vivo dataset collected with in-plane and out-of-plane insertion of 17G and 22G needles in bovine, porcine, and chicken tissue, acquired using two different ultrasound systems. We train the model with 5000 frames from 42 video sequences. Evaluation on 600 frames from 30 sequences yields a tip localization error of \(0.52\pm 0.06\) mm and an overall inference time of 0.064 s (15 fps). Comparison against prior art on challenging datasets reveals a 30% improvement in tip localization accuracy.

Conclusion

The proposed method automatically models temporal dynamics associated with needle tip motion and is more accurate than state-of-the-art methods. Therefore, it has the potential for improving needle tip localization in challenging ultrasound-guided interventions.

中文翻译：

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具有时间感知能力的深度神经网络，用于二维超声中的针尖定位

目的

在活检和区域麻醉等干预措施中，正确放置针头至关重要，在此期间，错误的针头插入可能会导致手术失败和并发症。因此，超声引导被广泛用于提高针的放置精度。但是，在陡峭和深处插入时，针头的可见度会丢失。在这些情况下，自动针尖定位的计算方法可以提高临床成功率。

方法

我们提出了一种新的算法，用于在挑战性的超声引导下插入时针尖定位，当轴可能不可见且针尖强度较低时。我们的方法有两个关键步骤。首先，我们使用一种检测方案来增强连续超声帧中的针尖特征，该检测方案可以识别由针尖移动引起的细微强度变化。然后，我们采用包含卷积神经网络和长短期记忆循环单元的混合深度神经网络。网络的输入是连续的多个融合增强帧和相应的原始B模式帧，并且该时空信息用于预测针尖位置。

结果

我们在离体数据集上评估了我们的方法，该数据集是使用两个不同的超声系统在牛，猪和鸡组织中在平面内和平面外插入17G和22G针插入的。我们从42个视频序列中以5000帧训练模型。对来自30个序列的600帧进行评估得出的尖端定位误差为\（0.52 \ pm 0.06 \） mm，总推断时间为0.064 s（15 fps）。与具有挑战性的数据集上的现有技术进行比较表明，尖端定位精度提高了30％。

结论

所提出的方法自动对与针尖运动相关的时间动力学进行建模，并且比最新技术更准确。因此，在具有挑战性的超声引导干预中，它具有改善针尖定位的潜力。