Earticle

초록

영어

The exciting advancement related to the “modeling of digital human” in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation- transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

목차

ABSTRACT
Introduction
Observations and Discussion
1. Phantoms
2. Autosegmentation to create person-specific phantoms using AI
3. Real-Time Monte Carlo to Calculate Organ Doses
4. Examples of person-specific phantoms and GPU-based Monte Carlo methods
5. Limitations
Conclusion
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
AUTHOR CONTRIBUTION
REFERENCES

키워드

저자

• Zhao Peng [ School of Nuclear Science and Technology, University of Science and Technology of China, Hefei; 2Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei ]
• Ning Gao [ School of Nuclear Science and Technology, University of Science and Technology of China, Hefei; 2Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei ]
• Bingzhi Wu [ School of Nuclear Science and Technology, University of Science and Technology of China, Hefei; 2Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei ]
• Zhi Chen [ School of Nuclear Science and Technology, University of Science and Technology of China, Hefei; 2Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei ]
• X. George Xu [ School of Nuclear Science and Technology, University of Science and Technology of China, Hefei; 2Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei; Department of Radiotherapy, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  방사선방어학회지 [Journal of Radiation Protection and Research]

• 간기

  계간

• pISSN

  2508-1888

• 수록기간

  1976~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 559 DDC 629