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

项目总结/摘要 在这个项目周期中，我们将扩展OpenIGTLink软件，以实现个性化的精确制导， 在规划和控制过程中考虑到患者特定特征的情况下， 引导机器人干预（IGRI），以实现更好的临床结果。特别是，我们将重点提高 前列腺IGRI的针放置准确性，作为一个简单的，但代表性的应用， 个性化精确制导经皮穿刺针放置在这两个方面都起着重要作用。 前列腺癌的诊断和治疗。在美国，每年进行近100万例前列腺活检。 在美国，用活检针从前列腺中取样组织进行病理检查。 经证实的病灶可以用近距离放射治疗或使用施源器进行热消融进行局部治疗 针取决于临床适应症和患者的偏好。对于这些程序，准确的针头 放置对于避免假阴性或确保最佳剂量分布至关重要。需求 近年来，由于临床医生现在能够精确定位亚区域，准确的针头放置越来越多 由于成像技术的最新进展，特别是多参数成像， 磁共振成像（MRI）。然而，以前的研究所达到的体内准确度从未 由于物理相互作用导致针偏离，匹配肿瘤亚区靶向水平 在针和异质组织之间。为了解决这个问题，我们将针偏差 使用OpenIGTLink软件在IGRI系统中建立模型。我们的假设是，模型适应于 个体患者将能够避免或补偿针偏离，导致改善的体内生物相容性。 针放置准确性。该项目包括以下四个具体目标：（目标1）制定一个 生物力学模型与基于图像的半自动模型生成;（目的2）开发数据驱动的 （3）扩展IGRI平台，使其更好地应用于生物力学模型， 使用OpenIGTLink进行基于闭环控制;（目的4）评估生物力学、数据驱动和混合 模拟体内针放置。

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.