Artificial Intelligence-Based Quality Assurance for Online Adaptive Radiotherapy

基于人工智能的在线自适应放射治疗质量保证

基本信息

  • 批准号:
    10589063
  • 负责人:
  • 金额:
    $ 53.91万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-03-09 至 2027-02-28
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Recently, the development of MR-LINACs has made high-quality online adaptative radiotherapy a clinical reality to account for the daily anatomical variations to preserve the treatment quality. MR-LINACs, combining modern radiotherapy linear accelerators (LINACs) with on-board magnetic resonance imaging (MRI), offer excellent soft-tissue contrast to allow accurate organ and tumor segmentation to precisely capture the daily anatomical changes of each patient. Coupled with advanced adaptive treatment planning systems, MR-LINAC is the ideal platform for online adaptive radiotherapy and will bring cancer radiotherapy to a new level of precision and personalization. However, this new format of radiotherapy also comes with new challenges for patient safety and plan quality checks that cannot be satisfactorily addressed with traditional quality assurance (QA) tools: 1) With the patient lying on the treatment couch waiting for the treatment to start, there is mounting pressure on the team to move through the workflow as fast as possible, which may increase the likelihood of making mistakes and thus an effective QA procedure is even more important. 2) Each adapted plan warrants a new QA process, adding substantial burdens to an already extremely time-constrained process. A QA process with high efficiency is needed. 3) Conventional QA procedures are quite complex, involving inputs from many stakeholders, and thus are human-power demanding and error-prone. An automatic QA procedure requiring minimal human interventions and communications is highly desired. 4) In addition to checking the quality of the adapted segmentation and treatment plan, it is also crucial for a QA procedure to ensure their consistency with the physician’s intentions/preferences in the original plan. 5) A QA tool that is able to predict the plan deliverability prior to treatments, without actually irradiating the patients, is needed for online adaptive radiotherapy. The overarching goal of this project is to develop an Artificial Intelligence (AI)-based QA system to address these urgent unmet clinical needs for MR-LINAC online adaptive radiotherapy, with four main components to: 1) intelligently assess the quality of the adapted target and organ-at-risk segmentations and their consistency with those in the original plan; 2) intelligently assess the quality of the adapted plan and its consistency with the original plan; 3) efficiently perform 2nd dose check with an AI-based near real-time independent dose engine; and 4) predict the measurement-based QA results of plan deliverability using prior knowledge and new adapted plan information. We have two Specific Aims: 1) System development, including data acquisition for AI model training, and development of four AI models; and 2) System translation and validation at multiple institutions, including developing transfer learning algorithm and package for automated model commissioning; and translation, fine-tuning and evaluation of the developed AI systems. The successful conduct of the proposed project will result in the first intelligent, efficient, reliable, and independent QA system to facilitate unleashing the full potential of MR-LINAC online adaptive radiotherapy to advance cancer care.
项目总结

项目成果

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Steve Bin Jiang其他文献

Steve Bin Jiang的其他文献

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{{ truncateString('Steve Bin Jiang', 18)}}的其他基金

Artificial Intelligence-Based Quality Assurance for Online Adaptive Radiotherapy
基于人工智能的在线自适应放射治疗质量保证
  • 批准号:
    10445135
  • 财政年份:
    2022
  • 资助金额:
    $ 53.91万
  • 项目类别:
A GPU-cloud based Monte Carlo simulation platform for National Particle Therapy Research Center
国家粒子治疗研究中心基于GPU云的蒙特卡罗模拟平台
  • 批准号:
    8811782
  • 财政年份:
    2015
  • 资助金额:
    $ 53.91万
  • 项目类别:
Determination of Research Needs and Specifications of The Research Beam Line and Related Infrastructure
确定研究需求和研究光束线及相关基础设施的规格
  • 批准号:
    8811781
  • 财政年份:
    2015
  • 资助金额:
    $ 53.91万
  • 项目类别:
Low dose cone beam CT for image guided adaptive radiotherapy
用于图像引导适应性放射治疗的低剂量锥形束 CT
  • 批准号:
    8619515
  • 财政年份:
    2011
  • 资助金额:
    $ 53.91万
  • 项目类别:
Low dose cone beam CT for image guided adaptive radiotherapy
用于图像引导适应性放射治疗的低剂量锥形束 CT
  • 批准号:
    8264781
  • 财政年份:
    2011
  • 资助金额:
    $ 53.91万
  • 项目类别:
Low dose cone beam CT for image guided adaptive radiotherapy
用于图像引导适应性放射治疗的低剂量锥形束 CT
  • 批准号:
    8026135
  • 财政年份:
    2011
  • 资助金额:
    $ 53.91万
  • 项目类别:
Low dose cone beam CT for image guided adaptive radiotherapy
用于图像引导适应性放射治疗的低剂量锥形束 CT
  • 批准号:
    8444698
  • 财政年份:
    2011
  • 资助金额:
    $ 53.91万
  • 项目类别:
A Tumor Tracking System for Image Guided Radiotherapy
用于图像引导放射治疗的肿瘤跟踪系统
  • 批准号:
    6985219
  • 财政年份:
    2005
  • 资助金额:
    $ 53.91万
  • 项目类别:
A Tumor Tracking System for Image Guided Radiotherapy
用于图像引导放射治疗的肿瘤跟踪系统
  • 批准号:
    7140120
  • 财政年份:
    2005
  • 资助金额:
    $ 53.91万
  • 项目类别:
A Tumor Tracking System for Image Guided Radiotherapy
用于图像引导放射治疗的肿瘤跟踪系统
  • 批准号:
    7555283
  • 财政年份:
    2005
  • 资助金额:
    $ 53.91万
  • 项目类别:
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