A PET/CT scanner for guiding treatment of head and neck cancer

用于指导头颈癌治疗的 PET/CT 扫描仪

基本信息

  • 批准号:
    10619509
  • 负责人:
  • 金额:
    $ 56.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-08 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

ABSTRACT/SUMMARY Head and neck carcinomas (HNC) are some of the most challenging cancers to effectively treat (five-year survival rates for some HNCs are as low as 25%); recurrence rates range from 8% to 43%. The number of new cases is rising due to the current, near epidemic of HPV-associated HNCs. Unfortunately, most treatments are associated with significant morbidity due to damage of sensitive structures in the region (spinal cord, salivary glands, parotid gland, esophagus, carotid arteries and thyroid gland). HNCs are treated with surgical excision, radiation therapy, or a combination of methods. Selection of therapy is based upon assessment of tumor location, size, proximity to bone, amount of infiltration into surrounding tissues and spread to regional lymph nodes performed with advanced imaging methods. Perhaps the most promising of these methods is metabolic-based imaging, specifically PET, often utilizing FDG. The non-optimal spatial resolution and fixed geometry of whole body scanners, however, limits PET in fulfilling its promise in this role by inhibiting the ability to accurately detect small tumor masses in lymph nodes, and in quantifying the size and nature of the primary tumor. Thus, there is an unmet need for improved PET/CT scanner technology to enhance treatment planning of HNCs. To address this opportunity, we propose the creation and testing of a lower-cost, flexible geometry, high-resolution PET/CT system (approaching the spatial resolution of pre-clinical PET scanners), called HNPET/CT, designed specifically for the imaging of the head and neck region. It will consist of a novel pair of large area, immersion-cooled, PET detectors and a cone beam CT (CBCT) scanner mounted on a rotating gantry whose geometry can be tailored to patient size and anatomy to be scanned. To capitalize on HNPET/CT’s high-resolution images, an image segmentation method that utilizes both CBCT and PET images will be developed and tested. We plan to explore the potential utility of HNPET/CT for enhancing therapy planning (surgical and radiation). This assessment will first be performed with anthropomorphic phantoms and then in a limited human trial. In addition to the novel detector design and adjustable geometry, HNPET/CT will introduce a new capability to the treatment planning of HNCs not yet broadly employed by end users in the clinical. It could enhance current planning techniques (reduce treatment margin size, for example), enable the effective application of advanced methods (dose painting, for example), and perhaps inspire development of new, more effective personalized treatment strategies that require high resolution, multi-modality imaging. The lower cost of the system promises the introduction of cutting-edge, image-guided treatments to under-served populations in areas treated by small or private clinics that often cannot afford such technology. This project will be performed by a multi-disciplinary academic industrial partnership joining the Departments of Radiology, Head and Neck Surgery and Radiation Oncology at West Virginia University, and Xoran Technologies, LLC.
摘要/总结 头颈癌(HNC)是一些最具挑战性的癌症,以有效地治疗(五年 一些HNC的存活率低至25%);复发率范围为8%至43%。的数量 由于目前HPV相关HNC的流行,新病例正在增加。不幸的是,大多数治疗 与由于该区域敏感结构(脊髓, 唾液腺、腮腺、食道、颈动脉和甲状腺)。HNC采用外科手术治疗 切除、放射治疗或方法的组合。治疗的选择基于对以下方面的评估: 肿瘤的位置、大小、与骨的接近程度、浸润到周围组织的数量和扩散到区域 用先进的成像方法检查淋巴结。也许这些方法中最有前途的是 基于代谢的成像,特别是PET,通常使用FDG。非最佳空间分辨率和固定 然而,全身扫描仪的几何结构限制了PET在履行其在这一角色中的承诺,因为它抑制了PET在人体内成像的能力。 准确检测淋巴结中的小肿瘤肿块,并定量原发性肿瘤的大小和性质, 肿瘤因此,存在对改进的PET/CT扫描器技术以增强治疗计划的未满足的需求 的HNC。为了抓住这个机会,我们建议创建和测试一个低成本,灵活的几何形状, 高分辨率PET/CT系统(接近临床前PET扫描仪的空间分辨率),称为 HNPET/CT,专为头颈部成像而设计。它将包括一对新颖的 大面积、浸没冷却式PET探测器和安装在旋转 扫描架的几何形状可以根据患者的体型和要扫描的解剖结构进行调整。为了利用 HNPET/CT的高分辨率图像,一种利用CBCT和PET图像的图像分割方法 将被开发和测试。我们计划探索HNPET/CT在增强治疗方面的潜在效用 计划(手术和放射)。该评估将首先使用拟人幻影进行, 然后在有限的人体试验中。除了新颖的探测器设计和可调几何结构外,HNPET/CT还将 为最终用户尚未广泛使用的HNC的治疗计划引入新的能力, 临床。它可以增强当前的计划技术(例如,减少治疗边缘大小), 先进方法的有效应用(例如,剂量绘画),也许还能激发 新的,更有效的个性化治疗策略,需要高分辨率,多模态成像。的 该系统成本较低,有望将尖端的图像引导治疗引入到服务不足的人群中。 小型或私人诊所治疗地区的人口,他们往往负担不起这种技术。这个项目 将由一个多学科的学术工业伙伴关系,加入放射科, 西弗吉尼亚大学头颈外科和放射肿瘤学,以及Xoran技术有限责任公司。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Deep residual-convolutional neural networks for event positioning in a monolithic annular PET scanner.
  • DOI:
    10.1088/1361-6560/ac0d0c
  • 发表时间:
    2021-07-12
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Jaliparthi G;Martone PF;Stolin AV;Raylman RR
  • 通讯作者:
    Raylman RR
Evaluation of advanced methods and materials for construction of scintillation detector light guides.
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

RAYMOND ROBERT RAYLMAN其他文献

RAYMOND ROBERT RAYLMAN的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('RAYMOND ROBERT RAYLMAN', 18)}}的其他基金

A PET/CT scanner for guiding treatment of head and neck cancer
用于指导头颈癌治疗的 PET/CT 扫描仪
  • 批准号:
    10161756
  • 财政年份:
    2020
  • 资助金额:
    $ 56.68万
  • 项目类别:
A PET/CT scanner for guiding treatment of head and neck cancer
用于指导头颈癌治疗的 PET/CT 扫描仪
  • 批准号:
    10390411
  • 财政年份:
    2020
  • 资助金额:
    $ 56.68万
  • 项目类别:
PET-EPRI
PET-EPRI
  • 批准号:
    9759919
  • 财政年份:
    2018
  • 资助金额:
    $ 56.68万
  • 项目类别:
PET-EPRI
PET-EPRI
  • 批准号:
    9916750
  • 财政年份:
    2018
  • 资助金额:
    $ 56.68万
  • 项目类别:
ADVANCED IMAGING CORE
先进的成像核心
  • 批准号:
    8364958
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Development of a Combined MRI-PET System for Contemporaneous Functional Imaging
开发用于同步功能成像的 MRI-PET 组合系统
  • 批准号:
    7730090
  • 财政年份:
    2009
  • 资助金额:
    $ 56.68万
  • 项目类别:
Development of a Combined MRI-PET System for Contemporaneous Functional Imaging
开发用于同步功能成像的 MRI-PET 组合系统
  • 批准号:
    7915244
  • 财政年份:
    2009
  • 资助金额:
    $ 56.68万
  • 项目类别:
Development of a Combined MRI-PET System for Contemporaneous Functional Imaging
开发用于同步功能成像的 MRI-PET 组合系统
  • 批准号:
    8096561
  • 财政年份:
    2009
  • 资助金额:
    $ 56.68万
  • 项目类别:
Development of a Combined MRI-PET System for Contemporaneous Functional Imaging
开发用于同步功能成像的 MRI-PET 组合系统
  • 批准号:
    8286966
  • 财政年份:
    2009
  • 资助金额:
    $ 56.68万
  • 项目类别:
Development of a PEM-PET-CT Breast Imaging and Biopsy Device
PEM-PET-CT 乳腺成像和活检设备的开发
  • 批准号:
    7590070
  • 财政年份:
    2003
  • 资助金额:
    $ 56.68万
  • 项目类别:

相似海外基金

REU Site: Algorithm Design --- Theory and Engineering
REU网站:算法设计---理论与工程
  • 批准号:
    2349179
  • 财政年份:
    2024
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Standard Grant
REU Site: Quantum Machine Learning Algorithm Design and Implementation
REU 站点:量子机器学习算法设计与实现
  • 批准号:
    2349567
  • 财政年份:
    2024
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Standard Grant
Product structures theorems and unified methods of algorithm design for geometrically constructed graphs
几何构造图的乘积结构定理和算法设计统一方法
  • 批准号:
    23K10982
  • 财政年份:
    2023
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Algorithm Design in Strategic and Uncertain Environments
战略和不确定环境中的算法设计
  • 批准号:
    RGPIN-2016-03885
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Discovery Grants Program - Individual
Human-Centered Algorithm Design for High Stakes Decision-Making in Public Services
以人为本的公共服务高风险决策算法设计
  • 批准号:
    DGECR-2022-00401
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Discovery Launch Supplement
Human-Centered Algorithm Design for High Stakes Decision-Making in Public Services
以人为本的公共服务高风险决策算法设计
  • 批准号:
    RGPIN-2022-04570
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Discovery Grants Program - Individual
Algorithm Design
算法设计
  • 批准号:
    CRC-2015-00122
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Canada Research Chairs
Control Theory and Algorithm Design for Nonlinear Systems Based on Finite Dimensionality of Holonomic Functions
基于完整函数有限维的非线性系统控制理论与算法设计
  • 批准号:
    22K17855
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Scalable Algorithm Design for Unbiased Estimation via Couplings of Markov Chain Monte Carlo Methods
通过马尔可夫链蒙特卡罗方法耦合进行无偏估计的可扩展算法设计
  • 批准号:
    2210849
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Continuing Grant
Spectral Techniques in Algorithm Design and Analysis
算法设计和分析中的谱技术
  • 批准号:
    RGPIN-2020-04385
  • 财政年份:
    2022
  • 资助金额:
    $ 56.68万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了