OpenIGTLink: a network communication interface for closed-loop image-guided interventions

项目摘要/摘要 在此项目周期中，我们将扩展OpenIGTLink软件以实现个性化精确制导， 如果在计划和控制图像的过程中考虑了患者的特定特征- 引导式机器人干预(IGRI)以实现更好的临床结果。特别是，我们将着力提高 前列腺IGRI的置针准确性，作为一种简单而又具有代表性的应用 个性化精确制导。经皮穿刺针放置在两种治疗方法中都发挥着重要作用。 前列腺癌的诊断和治疗。在美国，每年有近100万例前列腺活检 在美国，用活检针从前列腺组织中提取组织进行病理检查。 确诊的病变可以用近距离放射治疗或使用敷贴器的热消融进行局部治疗。 根据临床适应症和患者的喜好选择针头。对于这些程序，精确的针头 放置位置对于避免假阴性或确保最佳剂量分布至关重要。对 近年来，随着临床医生能够精确定位次区域，精确的针头放置正在增长 多亏了成像技术的最新进步，特别是多参数成像技术 磁共振成像(MRI)。然而，以前的研究达到的活体准确性从来没有 由于物理相互作用导致针头偏差，匹配肿瘤分区靶向的水平 在针头和异质组织之间。为了解决这个问题，我们将考虑针头偏差 使用OpenIGTLink软件的IGRI系统中的模型。我们的假设是，这些模型适用于 个别患者将能够避免或补偿针的偏差，导致体内情况的改善 针的放置精度。该项目包括以下四个具体目标：(目标1)制定 基于图像的生物力学模型半自动生成；(目标2)开发数据驱动的 预测模型并与生物力学模型相结合；(目标3)扩展IGRI平台用于模型- 基于使用OpenIGTLink的闭环控制；(目标4)评估生物力学、数据驱动和混合 活体模型置针。

Project Summary/Abstract In this cycle of the project, we will extend OpenIGTLink software to enable individualized precision guidance, where patient-specific characteristics are taken into account in the planning and control process for image- guided robotic interventions (IGRI) to achieve a better clinical outcome. In particular, we will focus on improving needle placement accuracy for IGRI of the prostate, as a simple, and yet a representative application of individualized precision guidance. Percutaneous needle placement plays a fundamental role in both the diagnosis and treatment of prostate cancer. Nearly one million prostate biopsies are performed annually in the United States, where tissues are sampled from the prostate with a biopsy needle for pathological examination. The confirmed lesions may then be focally treated with brachytherapy or thermal ablation using applicator needles depending on clinical indications and patient preference. For those procedures, accurate needle placement is essential to avoid false-negatives or ensure an optimal dose distribution. The demand for accurate needle placement is growing in recent years, as clinicians are now capable of pinpointing subregions in a heterogeneous tumor thanks to the recent progress in imaging technologies, particularly multiparametric magnetic resonance imaging (MRI). However, the in vivo accuracies achieved by previous studies have never matched the level of tumor subregion targeting due to needle deviation as a result of physical interaction between the needle and the heterogeneous tissue. To address this issue, we will incorporate needle deviation models in an IGRI system using OpenIGTLink software. Our hypothesis is that the models adapted to individual patients will enable avoidance or compensation of needle deviation, leading to an improved in vivo needle placement accuracy. The project comprises the following four specific aims: (Aim 1) Develop a biomechanical model with image-based semi-automatic model generation; (Aim 2) Develop a data-driven prediction model and hybridize with the biomechanical model; (Aim 3) Extend the IGRI platform for model- based closed-loop control using OpenIGTLink; (Aim 4) Evaluate biomechanical, data-driven, and hybrid models in vivo needle placement.