Advanced applications of dual-energy computed tomography in radiation oncology

双能计算机断层扫描在放射肿瘤学中的高级应用

• 批准号: RGPIN-2016-06363
• 负责人: Carrier, JeanFrançois
• 金额: $ 2.4万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710264
• 关键词: Advanced; applications; dual; energy; computed

In radiation oncology, personalized 3D treatment planning is made possible by the scanning of every patient using medical imaging. The current clinical paradigm is based on computed tomography (CT), complemented by other imaging technologies when available and relevant. For treatment planning purposes, the images offer quantitative characterization of the patient's anatomy needed for radiation dose calculation and for organ delineation. Treatment planning is based on single energy CT (SECT): an x-ray source with a poly-energetic photon beam that crosses the patient and a detection system that measures the transmitted intensity. Recently, a new CT technology is commercially available; the dual-energy CT (DECT). This technology offers the possibility to simultaneously scan the patient using two different x-ray energies (two distinct sources and two distinct detection systems). Two different images are thus simultaneously obtained. At the CHUM, we installed one of the first DECT devices dedicated to radiation oncology treatment planning in 2013. Our research hypothesis is that this DECT can help improving treatment planning in radiation oncology on various levels. A research program was developed around this device involving medical physicists, radiation oncologists, medical residents and graduate students. The current grant proposal covers part of the global departmental research program. DECT for brachytherapy: The usage of DECT for prostate brachytherapy dosimetry calculation has been suggested by the AAPM TG186 report. We will build a Monte Carlo re-calculation platform to study improvements in dose calculation accuracy for low-dose rate brachytherapy. Reduction in artifact will be attempted based on standard decomposition methods proposed by Siemens and by in-house methods. The integration of this DECT data into the clinical system will be performed based on collaboration with Varian combining DECT data and Acuros. DECT for protontherapy: We will develop a Monte Carlo simulation environment to study the improvement in the accuracy of dose calculation due to DECT imaging. We will also study the integration of DECT image data into commercial protontherapy treatment planning systems. We have a research licenses from elekta and Varian on this specific subject. DECT for contrast-enhanced images: DECT imaging can provide a so-called “virtual non-contrast” (VNC) image. Based on tissue-decomposition methods, the VNC technique can transform an image initially filled with injected contrast agent into a virtual image without the contrast agent. Removing contrast agent from an image will also modify other high-Z regions of a given image. We will evaluate the value of this technique for specific anatomies. We will study the integration of such images into treatment planning systems. Improved VNC techniques will be developed in-house based on improved decomposition techniques.

在放射肿瘤学中，个性化的3D治疗计划通过使用医学成像扫描每个患者而成为可能。目前的临床模式是基于计算机断层扫描（CT），辅以其他成像技术，如果可用和相关。出于治疗计划的目的，图像提供辐射剂量计算和器官描绘所需的患者解剖结构的定量表征。 治疗计划基于单能CT（SECT）：一种X射线源，具有穿过患者的多能光子束和测量透射强度的检测系统。最近，一种新的CT技术已商用;双能CT（DECT）。该技术提供了使用两种不同的X射线能量（两个不同的源和两个不同的检测系统）同时扫描患者的可能性。因此，同时获得两个不同的图像。 在CHUM，我们于2013年安装了第一台专用于放射肿瘤治疗计划的DECT设备。我们的研究假设是，这种DECT可以帮助改善放射肿瘤学在各个层面上的治疗计划。围绕该设备开发了一项研究计划，涉及医学物理学家、放射肿瘤学家、住院医师和研究生。目前的拨款提案涵盖了全球部门研究计划的一部分。 近距离放射治疗的DECT：AAPM TG 186报告建议使用DECT进行前列腺近距离放射治疗剂量学计算。我们将建立一个蒙特卡罗重新计算平台，研究如何提高低剂量率近距离放射治疗的剂量计算精度。将根据西门子提出的标准分解方法和内部方法尝试减少伪影。将在与瓦里安合作的基础上，结合DECT数据和Acuros，将该DECT数据整合到临床系统中。 用于质子治疗的DECT：我们将开发一个蒙特卡罗模拟环境，研究由于DECT成像剂量计算的准确性的提高。我们还将研究将DECT图像数据整合到商业质子治疗计划系统中。我们拥有elekta和Varian关于该特定主题的研究许可证。 用于对比度增强图像的DECT：DECT成像可以提供所谓的“虚拟非对比度”（VNC）图像。基于组织分解方法，VNC技术可以将最初填充有注射的造影剂的图像转换为没有造影剂的虚拟图像。从图像中去除造影剂也将修改给定图像的其他高Z区域。我们将评估该技术在特定解剖结构中的价值。我们将研究将这些图像整合到治疗计划系统中。将根据改进的分解技术在内部开发改进的VNC技术。