Younes Hatem, Troccaz Jocelyne, Voros Sandrine
University of Grenoble Alpes, CNRS, TIMC-IMAG, F-38000, Grenoble, France.
Grenoble INP, INSERM, F-38000, Grenoble, France.
Med Phys. 2021 Mar;48(3):1144-1156. doi: 10.1002/mp.14628. Epub 2021 Jan 28.
New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real-time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three-dimensional (3D) US images.
The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar-agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds.
Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09 ± 0.61 mm on phantom image and 1.44 ± 0.45 mm on clinical images. On clinical images, the mean errors of individual seeds orientation was .
The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.
新的放射治疗方案，特别是适应性、聚焦或增强近距离放射治疗，需要从实时超声（US）图像中精确确定植入的放射性种子的位置和方向。这对于将它们与计划的位置和方向进行比较，并相应地自动调整剂量计划以进行下一次种子植入是必要的。图像模态、种子的小尺寸以及它们产生的伪影使得这成为一个非常具有挑战性的问题。本文工作的目的是建立并评估一种用于在三维（3D）超声图像中进行种子定位的稳健且自动的方法。
所提出的方法基于对用于将种子注射到前列腺中的针的预定位。这种预定位允许将搜索聚焦在针尖端周围的感兴趣区域（ROI）上。种子定位首先通过对ROI进行二值化处理，并分别使用贝叶斯分类器和支持向量机（SVM）去除误报。接下来是配准阶段，首先使用迭代最近点（ICP）来定位通过针插入的相连种子集（称为链），其次使用平方差和（SSD）作为相似性准则来细化每个种子的位置。ICP将链的几何模型与候选体素进行配准，而SSD将单个种子的外观模型与图像的一个子集进行比较。该方法在琼脂幻影的3D图像和临床3D图像数据集上进行了评估。它在成串和松散的种子上进行了测试。
将幻影和临床图像上的结果与手动定位进行了比较，在幻影图像上平均误差为1.09±0.61毫米，在临床图像上平均误差为1.44±0.45毫米。在临床图像上，单个种子方向的平均误差为 。
所提出的放射性种子定位算法稳健，在不同的超声图像上进行了测试，准确，平均误差值小，并返回五个圆柱形种子的自由度信息。
