• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of the Korean Vacuum Society
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• v.18 no.3
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• pp.236-243
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• 2009

Proton Engineering Frontier Project (PEFP) has supplied the metal ions to users by using an installed metal ion implanter of 120 keV. At present a feasibility study is being performed for a cobalt ion implantation. For a cobalt ion extraction we studied to sustain the high temperature($648^{\circ}C$) for metal ions vaporization from a cobalt chloride powder by using an alumina crucible in the ion source. The temperature condition of the crucible was satisfied with the plasma generation at the arc current of 120V and EHC power of 250W. The extracted beam current of $Co^+$ ions was dependent on the arc current in the plasma. The maximum beam current was $100{\mu}A$ at 0.18A of the arc current. The 3 peak currents of the extracted ions such as $Co^+$, $CoCl^+$ and $Cl^+$ were obtained by adjusting a mass analyzing magnet and the $Co^+$ ion beam peak current fraction as around 70% in the sum of the peak currents. The fluence of the implanted cobalt ions at the $10{\mu}A$ of the beam current and 90 minutes of the implantation time into an aluminum sample as measured around $1.74{\times}10^{17}#/cm^2$ by a quantitative analysis method of RBS (Rutherford Backscattering Spectrometry).

• Journal of the Korean Society of Radiology
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• v.9 no.5
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• pp.295-300
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• 2015

There are various type of particle accelerators such as Kyoungju 100-MeV proton beam accelerator in Korea. And Korea plans to build large particle accelerator such as heavy ion accelerator and 4th generation light source facility. The accelerated high energy particles of these facility produce 2nd neutron after nuclear reaction with target materials. And then these 2nd neutron activate structural materials and surrounding environment. Accordingly, it is important to consider the activation and shielding calculation on design of facility for safety operation. In this study, we tried to calculate and compare the neutron flux from the interaction $^{la}150$ beam with target material(Cu) according to thickness of iron and concrete shielding material by MCNPX 2.7 with nuclear library JENDL/HE 07and la150. To verify the properties of nuclear library, we compared computational results with experimental value. These results can be used for dose evaluation technology in planning of the shielding of large particle accelerator.