Purpose

Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This work aimed to develop an automatic needle tracking algorithm for MRI-guided interventions based on the Mask Region Proposal-Based Convolutional Neural Network (R-CNN).

Methods

Mask R-CNN was adapted and trained to segment the needle feature using 250 intra-procedural images from 85 MRI-guided prostate biopsy cases and 180 real-time images from MRI-guided needle insertion in ex vivo tissue. The segmentation masks were passed into the needle feature localization algorithm to extract the needle feature tip location and axis orientation. The proposed algorithm was tested using 208 intra-procedural images from 40 MRI-guided prostate biopsy cases, and 3 real-time MRI datasets in ex vivo tissue. The algorithm results were compared with human-annotated references.

Results

In prostate datasets, the proposed algorithm achieved needle feature tip localization error with median Euclidean distance (dxy) of 0.71 mm and median difference in axis orientation angle (dθ) of 1.28°, respectively. In 3 real-time MRI datasets, the proposed algorithm achieved consistent dynamic needle feature tracking performance with processing time of 75 ms/image: (a) median dxy = 0.90 mm, median dθ = 1.53°; (b) median dxy = 1.31 mm, median dθ = 1.9°; (c) median dxy = 1.09 mm, median dθ = 0.91°.

Conclusions

The proposed algorithm using Mask R-CNN can accurately track the needle feature tip and axis on MR images from in vivo intra-procedural prostate biopsy cases and ex vivo real-time MRI experiments with a range of different conditions. The algorithm achieved pixel-level tracking accuracy in real time and has potential to assist MRI-guided percutaneous interventions.

中文翻译：

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使用Mask R-CNN进行MRI引导的经皮干预的自动针头跟踪。

目的

准确的针头跟踪为MRI引导的经皮介入治疗提供必要的信息。使用MR图像的被动式针头跟踪在不同情况下受到针头感应信号空隙特征变化的挑战。这项工作旨在为基于遮罩区域提案的卷积神经网络（R-CNN）开发用于MRI指导的干预的自动针头跟踪算法。

方法

使用来自85个MRI指导的前列腺活检病例的250道手术内图像和来自MRI指导的将针插入体内组织的180幅实时图像，对Mask R-CNN进行了调整和培训，以对针的特征进行分割。将分割蒙版传递到针特征定位算法中，以提取针特征尖端的位置和轴方向。使用来自40个MRI指导的前列腺活检病例的208个过程内图像以及离体组织中的3个实时MRI数据集对提出的算法进行了测试。将算法结果与人工注释参考进行比较。

结果

在前列腺数据集中，所提出的算法实现了针状特征尖端的定位误差，其中欧几里德距离的中值（d xy）为0.71 mm，轴取向角的中值差（dθ）为1.28°。在3个实时MRI数据集中，该算法以7​​5 ms /图像的处理时间实现了一致的动态针特征跟踪性能：（a）中值d xy  = 0.90 mm，中值dθ  = 1.53°；（b）中值d xy  = 1.31毫米，中值dθ  = 1.9°；（c）中值d xy  = 1.09 mm，中值dθ  = 0.91°。

结论

所提出的使用Mask R-CNN的算法可以在一系列不同条件下，在体内过程性前列腺活检病例和离体实时MRI实验中，准确跟踪MR图像上的针尖和轴。该算法可实时实现像素级跟踪精度，并有可能辅助MRI引导的经皮干预。