Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CT

使用元素分辨运动补偿锥形束CT精确引导肝癌立体定向放射治疗

基本信息

  • 批准号:
    10112840
  • 负责人:
  • 金额:
    $ 39.27万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-03-15 至 2023-02-28
  • 项目状态:
    已结题

项目摘要

Project Summary Liver cancers, both primary and metastatic, are increasing in incidence and are associated with significant mortality. Stereotactic Body Radiotherapy (SBRT) has been established as an effective, safe, and feasible first- line option in the local control of unresectable hepatic malignancies. Nonetheless, a large margin (typically ~1cm) has to be used in current liver SBRT to accommodate tumor positioning uncertainty under cone-beam CT (CBCT) image guidance. Because of respiratory motion and vanishing tumor contrast, the tumor target cannot be visualized in CBCT. Inferior tumor positioning approach based on anatomical or implanted surrogates are clinical standard, leading to substantial tumor position uncertainty and a typical margin size of ~1cm. Consequently, high dose to a large volume of normal tissue is delivered, causing a toxicity concern, especially in patients with liver dysfunction caused by cancer and/or treatments is more substantial. In addition, normal tissue toxicity limits further dose escalation to improve clinical benefits. This issue is expected to become more severe, when extending SBRT to a wider patient population, e.g. those with a large tumor size. Several emerging imaging approaches have showed potential to improve image guidance accuracy but also encountered challenges. To date, there is no approach that can provide accurate, reliable, and clinically translatable image guidance for liver SBRT. Recently, our group has made a breakthrough towards reconstructing elemental composition image using a standard CBCT platform. Employing a kVp-switching technique, a novel image reconstruction method with spatial and spectral image regularization, as well as a sparse-dictionary based element decomposition method, we achieved ~3% accuracy in elemental composition as tested in phantom studies. We have also accumulated extensive experience in reconstructing high-quality CBCT images under respiratory motion. Armed with these successes, the overall goal of this study is to develop a novel element-resolved and motion-compensated (ERMC-) CBCT to image iodine contrast agent using only 20% contrast injection in a standard treatment planning CT scan for precise (uncertainty <2mm) image guidance in liver SBRT. We will pursue three specific aims (SAs): SA1. Develop the overall ERMC- CBCT system. SA2. Optimize scan parameters via phantom studies. SA3. Perform studies in 10 patient cases to test safety, feasibility, and tumor positioning accuracy of ERMC-CBCT based image guidance. The innovation of this project is a novel ERMC-CBCT system and its application for a clinically significant problem of tumor localization in liver SBRT. Besides the significance of substantially improved localization accuracy and therefore clinical potential of normal tissue sparing and dose escalation, our project also holds the significance of utilizing CBCT to its maximal potential for many other advanced image guidance tasks and quantitative applications. The ERMC-CBCT system is developed on a conventional CBCT platform, the most widely available image-guidance platform in radiotherapy, ensuring its translatability.
项目摘要 原发性和转移性肝癌的发病率正在增加,并且与显著的 mortality.立体定向体部放射治疗(SBRT)已被确立为一种有效、安全和可行的首选治疗方法。 线选择在局部控制不可切除的肝脏恶性肿瘤。尽管如此,大幅度(通常 ~1cm),以适应锥束下肿瘤定位的不确定性 CT(CBCT)图像引导。由于呼吸运动和消失的肿瘤对比度, 在CBCT中无法显示。基于解剖或植入的下方肿瘤定位入路 替代物是临床标准,导致实质性的肿瘤位置不确定性和典型的边缘大小 ~1cm。因此,高剂量被输送到大体积的正常组织,引起毒性问题, 特别是在患有由癌症和/或治疗引起的肝功能障碍的患者中更显著。此外,本发明还提供了一种方法, 正常的组织毒性限制了进一步的剂量递增以改善临床益处。预计这一问题将 当将SBRT扩展到更广泛的患者人群(例如肿瘤尺寸较大的患者)时,会变得更加严重。 几种新兴的成像方法已经显示出提高图像引导精度的潜力,但也 遇到了挑战。到目前为止,还没有一种方法可以提供准确,可靠,临床上, 用于肝脏SBRT的可平移图像引导。最近,我们的小组取得了突破, 使用标准CBCT平台重建元素组成图像。采用kVp开关 技术,一种新的图像重建方法与空间和光谱图像正则化,以及 基于稀疏字典的元素分解方法,我们实现了~3%的元素组成的准确性 如在体模研究中测试的。我们也积累了丰富的经验,重建高品质的 呼吸运动下的CBCT图像。有了这些成功,本研究的总体目标是 开发一种新型元素分辨和运动补偿(ERMC-)CBCT,用于碘造影剂成像 在标准治疗计划CT扫描中仅使用20%造影剂注射,以实现精确(不确定性<2 mm) 肝脏SBRT中影像引导。我们将追求三个具体目标(SA):SA 1。制定总体ERMC- CBCT系统。SA 2.通过体模研究优化扫描参数。SA 3.在10例患者病例中进行研究 测试基于ERMC-CBCT的图像引导的安全性、可行性和肿瘤定位准确性。的 本项目的创新是一种新型ERMC-CBCT系统及其在临床重要问题中的应用 肿瘤在肝脏SBRT中的定位。除了显著提高定位精度和 因此,正常组织保留和剂量递增的临床潜力,我们的项目也具有重要意义 将CBCT最大限度地用于许多其他高级图像引导任务和定量 应用. ERMC-CBCT系统是在传统CBCT平台上开发的, 在放射治疗中提供图像引导平台,确保其可平移性。

项目成果

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Xun Jia其他文献

Xun Jia的其他文献

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{{ truncateString('Xun Jia', 18)}}的其他基金

Next generation small animal radiation research platform
下一代小动物辐射研究平台
  • 批准号:
    10680056
  • 财政年份:
    2022
  • 资助金额:
    $ 39.27万
  • 项目类别:
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
基于对抗性的虚拟 CT 工作流程,用于评估医学影像中的人工智能
  • 批准号:
    10592427
  • 财政年份:
    2022
  • 资助金额:
    $ 39.27万
  • 项目类别:
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
基于对抗性的虚拟 CT 工作流程,用于评估医学影像中的人工智能
  • 批准号:
    10391652
  • 财政年份:
    2022
  • 资助金额:
    $ 39.27万
  • 项目类别:
Human-like automated radiotherapy treatment planning via imitation learning
通过模仿学习制定类似人类的自动放射治疗计划
  • 批准号:
    10610971
  • 财政年份:
    2021
  • 资助金额:
    $ 39.27万
  • 项目类别:
Human-like automated radiotherapy treatment planning via imitation learning
通过模仿学习制定类似人类的自动放射治疗计划
  • 批准号:
    10406863
  • 财政年份:
    2021
  • 资助金额:
    $ 39.27万
  • 项目类别:
Intelligent treatment planning for cancer radiotherapy
癌症放疗智能治疗计划
  • 批准号:
    10363727
  • 财政年份:
    2019
  • 资助金额:
    $ 39.27万
  • 项目类别:
Intelligent treatment planning for cancer radiotherapy
癌症放疗智能治疗计划
  • 批准号:
    10190850
  • 财政年份:
    2019
  • 资助金额:
    $ 39.27万
  • 项目类别:
Intelligent treatment planning for cancer radiotherapy
癌症放疗智能治疗计划
  • 批准号:
    10593946
  • 财政年份:
    2019
  • 资助金额:
    $ 39.27万
  • 项目类别:
Next generation small animal radiation research platform
下一代小动物辐射研究平台
  • 批准号:
    10895120
  • 财政年份:
    2018
  • 资助金额:
    $ 39.27万
  • 项目类别:
Next generation small animal radiation research platform
下一代小动物辐射研究平台
  • 批准号:
    10331746
  • 财政年份:
    2018
  • 资助金额:
    $ 39.27万
  • 项目类别:

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