Novel image reconstruction techniques with application to proton radiotherapy for optimisation of cancer treatment
Novel image reconstruction techniques with application to proton radiotherapy for optimisation of cancer treatment
批准号:
EP/R009988/1
负责人:
Nikolaos Dikaios
金额:
$12.88万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --
中文摘要
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英文摘要
One in two people will develop cancer, and cancer is the cause of approximately one third of all UK deaths. Radiotherapy accounts for >40% of curative treatments, owing its effectiveness in the ability to accurately target tumours. Proton Beam Therapy (PBT) is rapidly gaining momentum compared to x-ray/electron beams with more than 63 operating sites and 40 sites under construction worldwide. Protons have similar relative biological effectiveness (RBE) to photons, but an excellent depth-dose distribution profile that allows better conformation of dose distribution to target compared to x-rays or electrons, thereby reducing the integral dose to the body and avoiding to dose normal tissue structures near the tumour. This is crucial when treating growing children, to avoid side effects such as developmental delays, hormone deficiencies, effects on bone and muscle tissue, and hearing loss or damage to salivary glands. The two main methods to deliver proton beams is via passive spreading and spot scanning PBT. Spot scanning PBT is a new therapy that penetrates deeper and produces fewer neutrons than passive spreading, further decreasing the integral dose and the risk of secondary cancer, but is more sensitive to spatial errors increasing the risk of delivering the dose in the wrong place. Spot scanning PBT can deliver treatments in sub-mm accuracy, but because of imaging limitations prior to treatment, upon which the treatment is planned it currently cannot achieve more than 7 mm accuracy. To maximise the potential of PBT it is crucial to accurately know the dose distribution and be able to shape and control it, making imaging the number one challenge for accurate treatment planning. Based on the reconstructed images, proton stopping power maps are calculated, which inform us about the dose distribution. Due to the proton Bragg peak characteristic a miscalculation in the proton stopping power map (distance from the 90% to the 10% dose level is only a few mm) can result in the proton beam missing its target and damaging healthy tissue, while the tumour receives much lower dose. This work will initially quantify proton stopping power maps directly from proton CT (pCT), aiming to reduce range uncertainties and enhance PBT accuracy. pCT measures the energy loss for protons traveling along tracks allowing the estimation of the integrated relative electron density with respect to a reference medium along the proton path. Proton stopping power maps can be estimated directly by inverting the path integral. Current reconstruction algorithms, such as the filter back-projection approaches, falsely assume Gaussian energy straggling distribution, and do not account for multiple Coulomb scatter (MCS). Reconstructed pCT images become blurred by MCS, which results in a resolution of around 3-5 mm and the energy spread distributions in fact resemble asymmetric Gaussian functions due to electronic energy-loss straggling and MCS. The proposed pCT reconstruction will account for non-Gaussian energy loss distributions, and iteratively correct for MCS to improve the spatial resolution and accuracy of proton stopping power maps. With expected anatomical changes of both tumour and normal tissue during a typical 5-7 week course of radiation, relying solely on a pCT acquired before therapy will lead to under dosing of the tumour and/or unnecessary exposure of organs at risk to higher doses. This proposal addresses ways to fuse information from pCT (acquired before treatment) in the reconstruction of limited number of projections during spot scanning PBT, and update the proton stopping power map during the treatment. The proposed methods will make on-treatment imaging feasible, allowing for significant improvement in treatment planning.
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Proton Computed Tomography: A Case Study for Optimal Data Acquisition
质子计算机断层扫描:最佳数据采集案例研究
DOI:
10.1109/nss/mic42101.2019.9060034
发表时间:
2019
期刊:
影响因子:
--
作者:
[Panagiotidou M]
通讯作者:
Panagiotidou M
DOI:
10.1088/1361-6560/abee57
发表时间:
2021-05-10
期刊:
Physics in medicine and biology
影响因子:
3.5
作者:
[Collins-Fekete CA, Dikaios N, Bär E, Evans PM]
通讯作者:
Evans PM
Molière maximum likelihood proton path estimation approximated by cubic Bézier curve for scatter corrected proton CT reconstruction.
莫里哀最大似然质子路径估计通过三次贝塞尔曲线近似,用于散射校正质子 CT 重建。
DOI:
10.1088/1361-6560/ab9413
发表时间:
2020
期刊:
Physics in medicine and biology
影响因子:
3.5
作者:
[Lazos D]
通讯作者:
Lazos D
Integration of Proton Computed Tomography into the Open Source Software STIR
将质子计算机断层扫描集成到开源软件 STIR 中
DOI:
10.1109/nss/mic42101.2019.9059915
发表时间:
2019
期刊:
影响因子:
--
作者:
[Panagiotidou M]
通讯作者:
Panagiotidou M
Machine learning for proton path tracking in proton computed tomography.
质子计算机断层扫描中质子路径跟踪的机器学习。
DOI:
10.1088/1361-6560/abf1fd
发表时间:
2021
期刊:
Physics in medicine and biology
影响因子:
3.5
作者:
[Lazos D]
通讯作者:
Lazos D
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