Literature DB >> 35992632

Artificial Intelligence in Radiation Therapy.

Yabo Fu1, Hao Zhang2, Eric D Morris3, Carri K Glide-Hurst4, Suraj Pai5, Alberto Traverso5, Leonard Wee5, Ibrahim Hadzic5, Per-Ivar Lønne6, Chenyang Shen7, Tian Liu1, Xiaofeng Yang1.   

Abstract

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Entities:  

Keywords:  Artificial Intelligence; Image Reconstruction; Image Registration; Image Segmentation; Image Synthesis; Radiotherapy; Treatment Planning

Year:  2021        PMID: 35992632      PMCID: PMC9385128          DOI: 10.1109/TRPMS.2021.3107454

Source DB:  PubMed          Journal:  IEEE Trans Radiat Plasma Med Sci        ISSN: 2469-7303


  244 in total

1.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets.

Authors:  Richard Castillo; Edward Castillo; Rudy Guerra; Valen E Johnson; Travis McPhail; Amit K Garg; Thomas Guerrero
Journal:  Phys Med Biol       Date:  2009-03-05       Impact factor: 3.609

2.  elastix: a toolbox for intensity-based medical image registration.

Authors:  Stefan Klein; Marius Staring; Keelin Murphy; Max A Viergever; Josien P W Pluim
Journal:  IEEE Trans Med Imaging       Date:  2009-11-17       Impact factor: 10.048

3.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Authors:  M R Avendi; Arash Kheradvar; Hamid Jafarkhani
Journal:  Med Image Anal       Date:  2016-02-06       Impact factor: 8.545

4.  Automatic configuration of the reference point method for fully automated multi-objective treatment planning applied to oropharyngeal cancer.

Authors:  Rens van Haveren; Ben J M Heijmen; Sebastiaan Breedveld
Journal:  Med Phys       Date:  2020-03-05       Impact factor: 4.071

5.  Reduced lung-cancer mortality with low-dose computed tomographic screening.

Authors:  Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks
Journal:  N Engl J Med       Date:  2011-06-29       Impact factor: 91.245

6.  4D-CT deformable image registration using multiscale unsupervised deep learning.

Authors:  Yang Lei; Yabo Fu; Tonghe Wang; Yingzi Liu; Pretesh Patel; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2020-04-20       Impact factor: 3.609

7.  Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy.

Authors:  Qiulan Zeng; Yabo Fu; Zhen Tian; Yang Lei; Yupei Zhang; Tonghe Wang; Hui Mao; Tian Liu; Walter J Curran; Ashesh B Jani; Pretesh Patel; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2020-06-26       Impact factor: 3.609

8.  Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy.

Authors:  Yanhua Mai; Fantu Kong; Yiwei Yang; Linghong Zhou; Yongbao Li; Ting Song
Journal:  Radiat Oncol       Date:  2018-12-05       Impact factor: 3.481

Review 9.  Recent advances of PET imaging in clinical radiation oncology.

Authors:  M Unterrainer; C Eze; H Ilhan; S Marschner; O Roengvoraphoj; N S Schmidt-Hegemann; F Walter; W G Kunz; P Munck Af Rosenschöld; R Jeraj; N L Albert; A L Grosu; M Niyazi; P Bartenstein; C Belka
Journal:  Radiat Oncol       Date:  2020-04-21       Impact factor: 3.481

10.  A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

Authors:  Dan Nguyen; Troy Long; Xun Jia; Weiguo Lu; Xuejun Gu; Zohaib Iqbal; Steve Jiang
Journal:  Sci Rep       Date:  2019-01-31       Impact factor: 4.379

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