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中文摘要
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描述(申请人提供):肺癌和上腹部癌症采用放射治疗。有明确的证据表明,局部控制率和存活率与高辐射剂量水平有关。正常的组织保留对于预防并发症也是至关重要的,特别是在化疗和放射治疗同时进行的情况下。因此,肿瘤靶向具有重要意义,对于那些受呼吸器运动影响的部位来说,这尤其具有挑战性。多年来,人们一直致力于开发适应运动的新的模拟、规划和交付技术。然而,由于计划和治疗过程之间的运动差异很大,这些新颖的努力因缺少高质量的4D图像引导模式而受到影响。特别是在日益广泛使用的超分割或立体定向全身放射手术中,高分次剂量使其对靶向错误的容忍度较低。此外,适应性放射治疗(ART)具有补偿剂量传递错误的潜力。由于呼吸运动和肿瘤缩小,这对肺和上腹部放射治疗特别重要。为了准确评估治疗适应所需的剂量,需要高质量的锥束CT(4DCBCT)。最近,4DCBCT被开发为提供呼吸相分辨率图像以指导治疗。由于以下原因,其临床应用受到限制。(1)电流 重建算法需要长扫描协议。然而,即使在4分钟的扫描下,图像质量仍然低于标准的1分钟静态物体3D CBCT,更不用说CT了。(2)T易受散布污染。截断问题进一步降低了图像质量,并导致视野外的解剖遗漏。这些降低了剂量计算的准确性。放射治疗的一个独特特征是在治疗模拟中可获得特定于患者的先验信息。我们相信,通过结合先验信息以及采用新的重建方法,上述问题可以得到解决。这项提议的总体目标是开发和验证4DCBCT重建系统,该系统在标准的1分钟3D CBCT扫描协议下检索具有组合几何和强度精度的高质量4DCBCT。我们的方法是通过运动矢量域重建4DCBCT,这与传统的基于图像强度域的重建方法有根本的不同。我们的初步研究证明了这一项目的可行性。我们的目标将通过追求以下具体目标(SA)来实现。SA1.我们将开发一个完整的4DCBCT重建系统。SA2.我们将验证我们的系统,并展示其临床优势。完成后,一个新的4DCBCT重建系统将被开发和全面测试。它的临床优势将得到证明。这种系统的临床引入将为接受IGRT和未来ART的肺癌和上腹部癌症患者带来巨大的好处。
英文摘要
DESCRIPTION (provided by applicant): Radiotherapy is employed for lung and upper abdominal cancer treatments. There are clear evidences that local control and survival rates are correlated with high radiation dose level. Normal tissue sparing is also critical to prevent complications, especially in concurrent chemo- and radio- therapies. Tumor targeting is hence of significant importance, which is particularly challenging for those sites affected by respirator motions. Over the years, extensive efforts have been devoted to developing novel simulation, planning, and delivery techniques to accommodate motion. Yet, due to large motion variations between planning and treatment sessions, those novel efforts are compromised by the missing of high-quality 4D image-guidance modality. Especially in the increasingly used hyper-fractionation or stereotactic body radio-surgery, the high fractional dose makes them less forgiving to targeting error. Moreover, adaptive radiation therapy (ART) holds the potential to compensate errors in dose delivery. This is particularly important for lung and upper abdominal radiotherapy because of respiratory motion and tumor shrinkage. To accurately evaluate delivered dose for treatment adaptation calls for high-quality 4D cone beam CT (4DCBCT). Recently, 4DCBCT has been developed to provide respiratory-phase resolved images for treatment guidance. Its clinical applications are limited due to the following reasons. (1) Current reconstruction algorithms require long scan protocol. Yet, even under a 4-min scans, the image quality is still inferior to that of a standard 1-min 3D CBCT of static objects, let alone CT. (2) t is susceptible to scatter contamination. Truncation problem further degrades image quality and causes missing anatomy outside the field of view. These deteriorate dose calculations accuracy. One unique feature of radiotherapy is the availability of patient-specific prior information at treatment simulation. We believe that the aforementioned problems can be solved by incorporating the prior information, as well as employing novel reconstruction methods. The overall goal of this proposal is to develop and validate a 4DCBCT reconstruction system that retrieves high-quality 4DCBCT with combined geometry and intensity accuracies under a standard 1-min 3D CBCT scan protocol. Our method reconstructs 4DCBCT via motion vector domain, which is fundamentally different from conventional approaches via image intensity domain. The feasibility of this project has been demonstrated by our preliminary studies. Our goal will be accomplished by pursuing the following specific aims (SAs). SA1. We will develop a complete 4DCBCT reconstruction system. SA2. We will validate our system and demonstrate its clinical advantages. Upon completion, a novel 4DCBCT reconstruction system will have been developed and comprehensively tested. Its clinical advantages will have been demonstrated. Clinical introduction of such a system will lead to immense benefits to lung and upper abdominal cancer patients under IGRT and future ART.
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Next generation small animal radiation research platform
  • 批准号:
    10680056
  • 项目类别:
  • 资助金额:
    $15.88万
  • 财政年份:
    2022
  • 负责人:
    Xun Jia
  • 依托单位:
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
Human-like automated radiotherapy treatment planning via imitation learning
  • 批准号:
    10610971
  • 项目类别:
  • 资助金额:
    $60.6万
  • 财政年份:
    2021
  • 负责人:
    Xun Jia
  • 依托单位:
海外基金