本文开发一种基于图形处理器(GPU) 加速的质子调强放疗鲁棒优化器，用于减小质子束射程不确定性和靶区定位偏差对质子放疗的影响。建立的鲁棒优化模型使用的目标函数包括9种边界剂量目标，分别是：无偏差情况、2种射程偏差(偏长与偏短)、6种摆位不确定性(前后、侧向、上下入射方向各2种正负偏差)。首先靶区和危及器官的剂量贡献矩阵使用笔形束算法计算得到，然后使用共轭梯度法优化目标函数让其满足约束条件，这两部分均采用GPU加速。头颈部、肺部和前列腺三个临床病例被用来检测本优化器的性能表现。与传统基于计划靶区(PTV) 的质子调强放疗计划相比，鲁棒优化器能够优化出对射程不确定性和摆位误差更加不敏感的治疗计划，让靶区实现了高剂量均匀性的同时危及器官OARs)也得到了更好的保护。经过100次迭代，三个病例的优化时间均在10s左右。该结果证明了基于GPU加速的质子调强放疗鲁棒优化器能够在短时间内设计出高鲁棒性的质子治疗计划，从而提高质子放射治疗的可靠性。
", "ds_source": "本文建立的质子强调放疗不确定性模型基于标准优化函数，函数中包含质子束射程不确定性和靶区定位误差，采用共轭梯度（CG）方法优化不确定性模型，利用GPU加速矩阵向量乘法运算。
", "ds_process_way": "鲁棒优化模型加工
", "ds_quality": "本文使用3个病例测试了鲁棒优化器的有效性，数据良好
", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "甘肃兰州", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 8074883, "ds_files_count": 2, "ds_format": "pdf", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e216cd2a-04a5-4c03-831e-edb4f7c9b689.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9971252d-7beb-4464-bc08-bdcc5a1d7dd1", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [], "quality_level": 3, "publish_time": "2023-11-27 10:23:23", "last_updated": "2023-11-27 10:23:23", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.IMP.DB4089.2023", "i18n": { "en": { "title": "Robust Optimizer for Intensity Modulated Proton Therapy based on GPU", "ds_format": "", "ds_source": "The uncertainty model is based on the standard optimization function, which includes the range uncertainty and target location error. Conjugate gradient (CG) method is used to optimize the uncertainty model, and GPU is used to accelerate the matrix vector multiplication.
", "ds_quality": "In this paper, three cases are used to test the effectiveness of the robust optimizer, and the data is good
", "ds_ref_way": "", "ds_abstract": "In this paper, a GPU accelerated robust optimizer for proton intensity modulated radiation therapy (IMRT) is developed to reduce the impact of range uncertainty and target localization bias. The objective function used in the robust optimization model includes 9 kinds of boundary dose objectives, which are: no deviation, 2 kinds of range deviation (long and short), 6 kinds of setup uncertainty (2 kinds of positive and negative deviation in front and back, side, up and down incident directions). Firstly, the dose contribution matrix of target area and organs at risk is calculated by pencil beam algorithm, and then the conjugate gradient method is used to optimize the objective function to meet the constraint conditions. Three clinical cases of head and neck, lung and prostate were used to test the performance of the optimizer. Compared with the traditional PTV based proton intensity modulated radiotherapy (IMRT) planning, the robust optimizer can optimize the treatment plan which is more insensitive to the range uncertainty and setup error, so that the target area can achieve high dose uniformity and the organs at risk (OARs) can be better protected. After 100 iterations, the optimization time of the three cases is about 10 seconds. The results show that the GPU accelerated robust optimizer can design a high robust proton therapy plan in a short time, so as to improve the reliability of proton therapy.
", "ds_time_res": "", "ds_acq_place": "Lanzhou, Gansu", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Robust optimization model processing
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "图形处理器", "射程不确定性", "百威不确定性", "质子调强放疗", "鲁棒性优化" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "安徽", "合肥" ], "ds_time_tags": [ 2018 ], "ds_contributors": [ { "true_name": "肖国青", "email": "xiaogq@impcas.ac.cn", "work_for": "中国科学院近代物理研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "肖国青", "email": "xiaogq@impcas.ac.cn", "work_for": "中国科学院近代物理研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "肖国青", "email": "xiaogq@impcas.ac.cn", "work_for": "中国科学院近代物理研究所", "country": "中国" } ], "category": "其他" }