Literature DB >> 14596304

Fuzzy logic guided inverse treatment planning.

Hui Yan1, Fang-Fang Yin, Huaiqun Guan, Jae Ho Kim.   

Abstract

A fuzzy logic technique was applied to optimize the weighting factors in the objective function of an inverse treatment planning system for intensity-modulated radiation therapy (IMRT). Based on this technique, the optimization of weighting factors is guided by the fuzzy rules while the intensity spectrum is optimized by a fast-monotonic-descent method. The resultant fuzzy logic guided inverse planning system is capable of finding the optimal combination of weighting factors for different anatomical structures involved in treatment planning. This system was tested using one simulated (but clinically relevant) case and one clinical case. The results indicate that the optimal balance between the target dose and the critical organ dose is achieved by a refined combination of weighting factors. With the help of fuzzy inference, the efficiency and effectiveness of inverse planning for IMRT are substantially improved.

Mesh:

Year:  2003        PMID: 14596304     DOI: 10.1118/1.1600739

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  8 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Authors:  Chenyang Shen; Yesenia Gonzalez; Peter Klages; Nan Qin; Hyunuk Jung; Liyuan Chen; Dan Nguyen; Steve B Jiang; Xun Jia
Journal:  Phys Med Biol       Date:  2019-05-29       Impact factor: 3.609

3.  Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.

Authors:  Chenyang Shen; Dan Nguyen; Liyuan Chen; Yesenia Gonzalez; Rafe McBeth; Nan Qin; Steve B Jiang; Xun Jia
Journal:  Med Phys       Date:  2020-03-28       Impact factor: 4.071

4.  Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy.

Authors:  Chenyang Shen; Liyuan Chen; Yesenia Gonzalez; Xun Jia
Journal:  Med Phys       Date:  2021-02-16       Impact factor: 4.071

5.  Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.

Authors:  Florian Stieler; Hui Yan; Frank Lohr; Frederik Wenz; Fang-Fang Yin
Journal:  Radiat Oncol       Date:  2009-09-25       Impact factor: 3.481

6.  A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

Authors:  Chenyang Shen; Liyuan Chen; Xun Jia
Journal:  Phys Med Biol       Date:  2021-06-23       Impact factor: 3.609

7.  The application of distance transformation on parameter optimization of inverse planning in intensity-modulated radiation therapy.

Authors:  Hui Yan; Fang-Fang Yin
Journal:  J Appl Clin Med Phys       Date:  2008-04-16       Impact factor: 2.102

8.  A plan template-based automation solution using a commercial treatment planning system.

Authors:  Xiaotian Huang; Hong Quan; Bo Zhao; Wing Zhou; Charles Chen; Yan Chen
Journal:  J Appl Clin Med Phys       Date:  2020-03-16       Impact factor: 2.102

  8 in total

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