深度学习指导下的仅医生可用的碳离子放射治疗计划系统。

DR-only Carbon-ion radiotherapy treatment planning via deep learning.

机构信息

Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Phys Med. 2022 Aug;100:120-128. doi: 10.1016/j.ejmp.2022.06.016. Epub 2022 Jul 4.

DOI:10.1016/j.ejmp.2022.06.016

PMID:35797919

Abstract

PURPOSE

To evaluate the feasibility of patient-specific digital radiography (DR)-only treatment planning for carbon ion radiotherapy in anthropomorphic thorax-and-abdomen phantom and head-and-neck patients.

METHODS

The study was conducted on the anthropomorphic phantom and head-and-neck patients. We collected computed tomography (CT) and DR images of the phantom and cone beam CT (CBCT) and DR images of the patients, respectively. Two different deep neural networks were established to correlate the relationships between DR and digitally reconstructed radiograph (DRR) images, as well as DRR and CT images. The similarity between CT and predicted CT images was evaluated by computing the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Dose calculations on the predicted CT images were compared against the true CT-based dose distributions for carbon-ion radiotherapy treatment planning with intensity-modulated pencil-beam spot scanning. Relative dose differences in the target volumes and organ-at-risks were computed and three-dimensional gamma analyses (3 mm, 3%) were performed.

RESULTS

The average MAE, RMSE, PSNR and SSIM of the framework were 0.007, 0.144, 37.496 and 0.973, respectively. The average relative dose differences between the predicted CT- and CT-based dose distributions at the same carbon-ion irradiation settings for the phantom and the patients were <2% and ≤4%, respectively. The average gamma pass-rates were >98% for the predicted CT-based versus CT-based carbon ion plans of the phantom and the patients.

CONCLUSION

We have demonstrated the feasibility of a patient-specific DR-only treatment planning workflow for heavy ion radiotherapy by using deep learning approach.

摘要

目的

评估在人体胸腹部体模和头颈部患者中，使用特定于患者的数字射线照相术（DR）进行碳离子放射治疗计划的可行性。

方法

本研究在人体体模和头颈部患者上进行。我们分别采集体模的 CT 和 DR 图像、患者的锥形束 CT（CBCT）和 DR 图像。建立了两个不同的深度神经网络，以关联 DR 和数字重建射线照相（DRR）图像之间以及 DRR 和 CT 图像之间的关系。通过计算平均绝对误差（MAE）、均方根误差（RMSE）、峰值信噪比（PSNR）和结构相似性（SSIM），分别评估 CT 和预测 CT 图像之间的相似性。在强度调制铅笔束点扫描碳离子放射治疗计划中，对预测 CT 图像进行剂量计算，并与真实 CT 基剂量分布进行比较。计算靶区和危及器官的相对剂量差异，并进行三维伽马分析（3mm，3%）。

结果

该框架的平均 MAE、RMSE、PSNR 和 SSIM 分别为 0.007、0.144、37.496 和 0.973。在相同的碳离子照射条件下，体模和患者的预测 CT-和 CT 基剂量分布之间的平均相对剂量差异均<2%和≤4%。预测 CT 基与 CT 基碳离子计划的平均伽马通过率>98%，适用于体模和患者。