本文研究和开发了基于自适应遗传算法的质子调强放疗扫描路径优化方法，并在此基础上对质子调强放疗扫描时间进行初步临床评估。利用自适应遗传算法具有的较强容错性和全空间最优搜索能力开发点扫描质子调强路径优化模块，并将其集成到自主研发的放疗计划系统，选取AAPM TG-119 头颈部肿瘤和前列腺肿瘤模拟例题及两例临床病例进行测试，对比扫描路径优化前后质子放疗计划扫描路径长度。对于AAPMTG-119 头颈部肿瘤和前列腺肿瘤模拟例题，总扫描路径长度分别降低了27.17% 和18.72%，临床头颈部肿瘤和前列腺肿瘤病例总扫描路径长度分别降低了25.36% 和32.95%，优化前后路径长度降低比率与零权重扫描点及肿瘤解剖结构有关。基于自适应遗传算法的扫描路径优化方法可减少质子调强放疗计划扫描时间，从而缩短病人治疗时间，可广泛应用于质子重扫描临床技术。
", "ds_source": "本文利用自适应遗传算法具有的较强容错性和全空间最优搜索能力开发点扫描质子调强路径优化模块，并将其集成到自主研发的放疗计划系统，选取AAPM TG-119 头颈部肿瘤和前列腺肿瘤模拟例题及两例临床病例进行测试，对比扫描路径优化前后质子放疗计划扫描路径长度。
", "ds_process_way": "数据由临床病例测试得来
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", "ds_acq_start_time": "2017-07-01 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": 8023613, "ds_files_count": 2, "ds_format": "pdf", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "74ed9652-733b-4efc-9a4e-72de71c4f90e.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": "2023-12-27 11:29:32", "last_updated": "2023-12-27 11:29:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.IMP.DB4139.2023", "i18n": { "en": { "title": "IAGA Based Spot Scanning Path Optimization for Intensity Modulated Proton Therapy", "ds_format": "", "ds_source": "In this paper, the adaptive genetic algorithm is used to develop the point scan proton intensity modulated radiation path optimization module, which has strong fault tolerance and full space optimal search ability, and is integrated into the self-developed radiotherapy planning system. AAPM tg-119 head and neck tumor and prostate tumor simulation examples and two clinical cases are selected to test, The scanning path length of proton radiotherapy plan before and after scanning path optimization was compared.
", "ds_quality": "good
", "ds_ref_way": "", "ds_abstract": "In this paper, an adaptive genetic algorithm based scanning path optimization method for proton intensity modulated radiation therapy (IMRT) is studied and developed. The self-adaptive genetic algorithm (AGA) is used to develop the point scan proton intensity-modulated radiation path optimization module, which has strong fault tolerance and full space optimal search ability, and is integrated into the self-developed radiotherapy planning system. AAPM tg-119 head and neck tumor and prostate tumor simulation examples and two clinical cases are selected for testing, The scanning path length of proton radiotherapy plan before and after scanning path optimization was compared. For aapmtg-119 head and neck tumor and prostate tumor simulation cases, the total scanning path length was reduced by 27.17% and 18.72% respectively. The total scanning path length of clinical head and neck tumor and prostate tumor cases was reduced by 25.36% and 32.95% respectively. The reduction ratio of path length before and after optimization was related to zero weight scanning points and tumor anatomical structure. The scanning path optimization method based on adaptive genetic algorithm can reduce the scanning time of proton intensity modulated radiotherapy plan, thus shorten the treatment time of patients, which can be widely used in clinical technology of proton re scanning.
", "ds_time_res": "", "ds_acq_place": "Lanzhou, Gansu", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Data were obtained from clinical case tests
", "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": [ "点扫描", "质子调强放疗", "自适应遗传算法", "路径优化" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "兰州", "甘肃省" ], "ds_time_tags": [ 2017 ], "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" }