A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2024-12-24 DOI:10.1002/mp.17601
Hui Khee Looe, Philipp Reinert, Julius Carta, Björn Poppe
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引用次数: 0

Abstract

Background

Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming. Independent calculation-based methods leveraging advanced dose algorithms provide a reduced workload but cannot account for machine performance during delivery.

Purpose

This study introduces a novel unified deep-learning (DL) framework to enhance PSQA. The framework can combine the strengths of measurement- and calculation-based approaches.

Methods

A comprehensive artificial training dataset, comprising 400,000 samples, was generated based on a rigorous mathematical model that describes the physical processes of radiation transport and interaction within both the medium and detector. This artificial data was used to pretrain the DL models, which were subsequently fine-tuned with a measured dataset of 400 IMRT segments to capture the machine-specific characteristics. Additional measurements of five IMRT plans were used as the unseen test dataset. Within the unified framework, a forward prediction model uses plan parameters to predict the measured dose distributions, while the backward prediction model reconstructs these parameters from actual measurements. The former enables a detailed control point (CP)-wise analysis. At the same time, the latter facilitates the reconstruction of treatment plans from the measurements and, subsequently, dose recalculation in the treatment planning system (TPS), as well as an independent second check software (VERIQA). This method has been tested with an OD 1600 SRS and an OD 1500 detector array with distinct spatial resolution and detector arrangement in combination with a dedicated upsampling model for the latter.

Results

The final models could deliver highly accurate predictions of the measurements in the forward direction and the actual delivered plan parameters in the backward direction. In the forward direction, the test plans reached median gamma passing rates better than 94% for the OD 1600 SRS measurements. The upsampled OD 1500 measurements show similar performance with similar median gamma passing rates but a slightly higher variability. The 3D gamma passing rates from the comparisons between the original and reconstructed dose distributions in patients lie between 95.4% and 98.2% for the OD 1600 SRS and 94.7% and 98.5% for the interpolated OD 1500 measurements. The dose volume histograms (DVH) of the original and the reconstructed plans, recalculated in both the TPS and VERIQA, were evaluated for the organs at risk and targets based on clinical protocols and showed no clinically relevant deviations.

Conclusions

The flexibility of the implemented model architecture allows its adaptability to other delivery techniques and measurement modalities. Its utilization also reduces the requirements of the measurement devices. The proposed unified framework could play a decisive role in automating QA workflow, especially in the context of real-time adaptive radiation therapy (ART).

Abstract Image

一个统一的深度学习框架,用于增强调强放疗计划的患者特异性质量保证。
背景:现代放射治疗技术,如调强放射治疗(IMRT)和体积调制电弧治疗(VMAT),使用复杂的通量调节策略来实现最佳的患者剂量分布。确保其准确性需要严格的患者特异性质量保证(PSQA),传统上通过检测器阵列的预处理测量来完成。这些方法虽然有效,但既费力又费时。利用先进剂量算法的独立计算方法减少了工作量,但无法解释机器在交付过程中的性能。目的:本研究引入一种新的统一深度学习(DL)框架来增强PSQA。该框架可以结合基于测量和基于计算的方法的优势。方法:基于一个严格的数学模型,生成了一个包含400,000个样本的综合人工训练数据集,该模型描述了介质和探测器内辐射传输和相互作用的物理过程。这些人工数据用于预训练深度学习模型,随后使用400个IMRT片段的测量数据集对模型进行微调,以捕获机器特定的特征。五个IMRT计划的附加测量值被用作未见的测试数据集。在统一的框架内,正向预测模型使用计划参数预测测量剂量分布,后向预测模型根据实际测量重建这些参数。前者支持详细的控制点(CP)分析。同时,后者有助于从测量中重建治疗计划,并随后在治疗计划系统(TPS)中重新计算剂量,以及独立的二次检查软件(VERIQA)。该方法已在OD 1600 SRS和OD 1500探测器阵列上进行了测试,它们具有不同的空间分辨率和探测器排列方式,并结合了用于后者的专用上采样模型。结果:最终模型能够对正向测量和反向实际交付的计划参数进行高精度的预测。在正向测试中,OD 1600 SRS测量的中位伽马通过率高于94%。上采样OD 1500测量显示出类似的性能,具有相似的中位伽马通过率,但变异性略高。OD 1600 SRS的患者原始剂量分布和重建剂量分布的3D伽马及格率在95.4%和98.2%之间,插值OD 1500测量的及格率在94.7%和98.5%之间。在TPS和VERIQA中重新计算原计划和重建计划的剂量体积直方图(DVH),根据临床方案评估危险器官和靶标,未显示临床相关偏差。结论:实现的模型架构的灵活性允许它适应其他交付技术和测量模式。它的使用也降低了对测量设备的要求。提出的统一框架可以在自动化QA工作流程中发挥决定性作用,特别是在实时适应性放射治疗(ART)的背景下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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