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ML2C - Machine Learning for robust, real-time dosimetry and MultiLeaf Collimator verification

ML2C - 用于稳健、实时剂量测定和 MultiLeaf Collimator 验证的机器学习

基本信息

批准号：
ST/T002646/1
负责人：
Johannes Velthuis
金额：
$ 6.27万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FT002646%2F1
关键词：
ML2C Machine Learning robust real

项目摘要

STFC is heading a collaboration with ICEC and CERN to develop low cost radiotherapy for challenging environments like low income countries. When delivering radiotherapy, acceptance testing and routine (e.g. monthly) quality assurance of LINACs is a time-consuming task involving ancillary equipment such as water-equivalent dosimeters and phantoms, and requiring the presence of a specialist expert (in the UK the Medical Physics Expert). A shortfall in local expertise is a common factor associated with significant downtime of LINAC systems and of erroneous treatments. This is a particularly difficult problem in low income countries where there is a significant shortage of well-trained experts.We are developing a real-time treatment verification system for radiotherapy that can autonomously perform the Quality Assurance measurements and verify the treatments in real time. This means that the system can successfully be used to deliver treatment without the need for a locally present highly skilled expert. Our current system works well, but our algorithms are not robust for problems that may occur in the device. We have started to use machine learning techniques to develop more robust algorithms. The first results are very promising. We now want to continue the development and furthermore produce algorithms to monitor the health of our detector system and the linac.

STFC正在与ICEC和CERN合作，为低收入国家等具有挑战性的环境开发低成本放射治疗。在进行放射治疗时，LINAC的验收测试和常规（例如每月）质量保证是一项耗时的任务，涉及辅助设备，如水当量剂量计和体模，并需要专业专家（在英国为医学物理专家）在场。当地专业知识的不足是与LINAC系统的重大停机时间和错误治疗相关的常见因素。在低收入国家，这是一个特别困难的问题，因为那里严重缺乏训练有素的专家。我们正在开发一个实时治疗验证系统，用于放射治疗，可以自主执行质量保证测量，并在真实的时间验证治疗。这意味着该系统可以成功地用于提供治疗，而不需要当地的高技能专家。我们目前的系统运行良好，但我们的算法对于设备中可能发生的问题并不鲁棒。我们已经开始使用机器学习技术来开发更强大的算法。第一批结果非常有希望。我们现在想继续开发，并进一步产生算法来监控我们的探测器系统和直线加速器的健康状况。