Validation and translation of a non-invasive, MR-guided breast cancer therapy

非侵入性、MR 引导的乳腺癌疗法的验证和转化

基本信息

  • 批准号:
    10439144
  • 负责人:
  • 金额:
    $ 53.54万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-02-01 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

The treatment of early stage, localized breast cancer has evolved over several decades from highly invasive techniques to minimally invasive, breast-conserving therapies. Our breast-specific magnetic resonance-guided focused ultrasound system non-invasively delivers focused energy deep inside the body under high-resolution image guidance. The technical advances we have made poise MRgFUS as a nearly ready non-invasive ablation treatment for localized breast disease. We have developed a rapid, treatment planning package to predict ultrasound beam placement and assess potential beam aberration, a hybrid MR thermometry method to monitor temperature in both fat and aqueous based tissues and validated a novel MRI-registered whole mount histology technique that compares MRI metrics to histopathological analysis. This R37 2-year extension proposal will continue this work between experienced academic investigators at the University of Utah and industry investigators at Image Guided Therapy. This extension will finish and extend this important work, integrating each component into ThermoGuideTM, a clinic-ready focused ultrasound software control environment. Specifically, we will: 1) Use the rapid treatment planning technique to evaluate the necessity for phase aberration correction techniques in breast MRgFUS, 2) validate the accuracy and precision of our hybrid, breast-focused volumetric MR thermometry method, 3) apply our novel MRI-registered whole mount histology technique that compares MRI metrics to histopathological analysis to immunohistochemistry data and 4) evaluate the entire protocol in a feasibility treat-and-resect clinical trial targeting unifocal invasive breast cancer tumors. This R37 extension application will complete the validation and translation of the breast MRgFUS System in this proposal, providing an exciting new image-guided non-invasive treatment option for breast cancer patients.
早期局部乳腺癌的治疗已经从高度侵袭性发展了几十年, 微创保乳治疗技术。我们的乳腺特异性磁共振引导 聚焦超声系统在高分辨率下无创地将聚焦能量输送到体内深处 图像制导我们取得的技术进步使MRgFUS成为一种几乎现成的非侵入性 局部乳腺疾病的消融治疗。我们开发了一套快速的治疗计划, 预测超声束放置并评估潜在的束畸变,一种混合MR测温方法 监测脂肪和水基组织的温度,并验证了一种新的MRI注册的整体 将MRI指标与组织病理学分析进行比较的组织学技术。此R37 2年延期 一项提案将在犹他州大学经验丰富的学术研究人员之间继续这项工作, 影像引导治疗的行业调查员这一扩展将完成并扩展这一重要工作, 将每个组件集成到ThermoGuideTM中,这是一种临床就绪的聚焦超声软件控制 环境具体而言,我们将:1)使用快速治疗计划技术来评估 相位畸变校正技术在乳腺MRgFUS,2)验证了我们的准确性和精度 混合,乳房聚焦体积MR测温方法,3)应用我们新的MRI配准的整体支架 组织学技术,将MRI指标与组织病理学分析和免疫组织化学数据进行比较, 4)在针对单灶性浸润性乳腺癌的可行性治疗和切除临床试验中评价整个方案 癌症肿瘤此R37扩展应用程序将完成乳房的验证和翻译 MRgFUS系统,提供了一个令人兴奋的新的图像引导的非侵入性治疗选择, 乳腺癌患者。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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

{{ 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 }}

Allison Payne其他文献

Allison Payne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('Allison Payne', 18)}}的其他基金

Validation and translation of a non-invasive, MR-guided breast cancer therapy
非侵入性、MR 引导的乳腺癌疗法的验证和转化
  • 批准号:
    10328883
  • 财政年份:
    2018
  • 资助金额:
    $ 53.54万
  • 项目类别:

相似海外基金

Shared and Distributed Memory Parallel Pre-Conditioning and Acceleration Algorithms for "Spline- Enhanced" Spatial Discretisations
用于“样条增强”空间离散化的共享和分布式内存并行预处理和加速算法
  • 批准号:
    2907459
  • 财政年份:
    2023
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Studentship
Efficient algorithms and succinct data structures for acceleration of telescoping and related problems
用于加速伸缩及相关问题的高效算法和简洁数据结构
  • 批准号:
    RGPIN-2021-03147
  • 财政年份:
    2022
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Discovery Grants Program - Individual
Acceleration framework for training deep learning by cooperative with algorithms and computer architectures
通过与算法和计算机架构合作训练深度学习的加速框架
  • 批准号:
    21K17768
  • 财政年份:
    2021
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Efficient algorithms and succinct data structures for acceleration of telescoping and related problems
用于加速伸缩及相关问题的高效算法和简洁数据结构
  • 批准号:
    RGPIN-2021-03147
  • 财政年份:
    2021
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Discovery Grants Program - Individual
Material and Device Building Blocks for Hardware Acceleration of Machine Learning and Artificial Intelligence Algorithms
用于机器学习和人工智能算法硬件加速的材料和设备构建模块
  • 批准号:
    2004791
  • 财政年份:
    2020
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Continuing Grant
CIF: Small: Collaborative Research: Acceleration Algorithms for Large-scale Nonconvex Optimization
CIF:小型:协作研究:大规模非凸优化的加速算法
  • 批准号:
    1909291
  • 财政年份:
    2019
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Standard Grant
Acceleration of trigger algorithms with FPGAs at the LHC implemented using higher-level programming languages
使用高级编程语言在 LHC 上使用 FPGA 加速触发算法
  • 批准号:
    ST/S005560/1
  • 财政年份:
    2019
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Training Grant
CIF: Small: Collaborative Research: Acceleration Algorithms for Large-scale Nonconvex Optimization
CIF:小型:协作研究:大规模非凸优化的加速算法
  • 批准号:
    1909298
  • 财政年份:
    2019
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Standard Grant
Acceleration of trigger algorithms with FPGAs at the LHC implemented using higher-level programming languages
使用高级编程语言在 LHC 上使用 FPGA 加速触发算法
  • 批准号:
    2348748
  • 财政年份:
    2019
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Studentship
OAC Core: Small: Enabling High-fidelity Turbulent Reacting-Flow Simulations through Advanced Algorithms, Code Acceleration, and High-order Methods for Extreme-scale Computing
OAC 核心:小型:通过高级算法、代码加速和超大规模计算的高阶方法实现高保真湍流反应流模拟
  • 批准号:
    1909379
  • 财政年份:
    2019
  • 资助金额:
    $ 53.54万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了