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Kafe 바로가기주관연구기관 | 서울대학교 Seoul National University |
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연구책임자 | 지의규 |
보고서유형 | 1단계보고서 |
발행국가 | 대한민국 |
언어 | 한국어 |
발행년월 | 2020-03 |
주관부처 | 과학기술정보통신부 Ministry of Science and ICT |
과제관리전문기관 | 한국연구재단 National Research Foundation of Korea |
등록번호 | TRKO202100020719 |
DB 구축일자 | 2022-04-02 |
키워드 | MR영상유도 방사선치료.합성CT.딥러닝.맞춤형 방사선치료.전향적 임상시험.MR imag guided radiotherapy.Synthetic CT.Deep learning.Patient-specific radiotherapy.Prospective clinical study. |
⚫ 환자 데이터베이스 구축
⚫ 방사선치료 특화 MR/CT 팬톰 개발
⚫ 딥러닝 기반 합성CT 생성 플랫폼 개발
⚫ 개발시스템 성능 정량화 모델 개발
(출처 : 보고서 요약서 3p)
□ Purpose
⚫ Institutional and international patient database construction
⚫ Development of deep learning-based universal synthetic CT generation system
⚫ Development of quantitative performance test model
⚫ System optimization with clinical feedback and the performance test results
⚫
□ Purpose
⚫ Institutional and international patient database construction
⚫ Development of deep learning-based universal synthetic CT generation system
⚫ Development of quantitative performance test model
⚫ System optimization with clinical feedback and the performance test results
⚫ Development of quality assurance (QA) protocol
⚫ Prospective clinical trial with the developed system
□ Contents
⚫ Patient database construction for training and validation of the deep learning algorithm
- Institutional database construction and international database construction through the international ViewRay user group
- Delineation of the tumor volume as well as organs at risk on the MR and CT images
⚫ Development of radiotherapy MR/CT phantom
⚫ Development of the deep learning-based synthetic CT generation system
⚫ Development of the deep learning-based universal synthetic CT generation system which is applicable to whole body regions
⚫ Development of the quantitative performance test model
- Evaluation on the agreement of the synthetic CT to the original CT
- Evaluation based on the radiation calculations and measurements
⚫ System optimization with clinical feedback and the performance test results
⚫ Development of QA protocol for maintenance of the system performance as well as safe clinical application
⚫ Prospective clinical trial with the developed system
□ Development results
⚫ Publication in high impact well circulated peer-reviewed journal
⚫ Patent application of the developed techniques
⚫ Knowledge/Know-how transfer of the derived system
⚫ Manpower cultivation by performing multidisciplinary studies
⚫ Global standard establishment
□ Expected Contribution
⚫ High-precision magnetic-resonance image-guided radiation therapy
⚫ Knowledge/Know-how transfer of the established system
⚫ Applicable to radiology field
⚫ Foundation technology for the deep learning application to the medical imaging
(출처 : SUMMARY 5p)
과제명(ProjectTitle) : | - |
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연구책임자(Manager) : | - |
과제기간(DetailSeriesProject) : | - |
총연구비 (DetailSeriesProject) : | - |
키워드(keyword) : | - |
과제수행기간(LeadAgency) : | - |
연구목표(Goal) : | - |
연구내용(Abstract) : | - |
기대효과(Effect) : | - |
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