Dr. Min Kook Cho Profile

Dr. Min Kook Cho

Head of R&D Center / Vice President at Osstem Implant Co., Ltd.

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Publications (9)

Proceedings Article | 2 April 2024 Poster + Paper

Deep-learning-based metal extraction with no reference in dental CBCT for metal artifact reduction

Hyunwoo Lee, Sunjung Kim, Min Kook Cho

Proceedings Volume 12926, 1292625 (2024) https://doi.org/10.1117/12.3006139

KEYWORDS: Cone beam computed tomography, Image segmentation, Machine learning, Image processing, Image processing algorithms and systems, Reconstruction algorithms

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Proceedings Article | 1 April 2024 Poster + Paper

Deep learning-based dose estimation in cone-beam computed tomography

Jinwoo Kim, Ho Kyung Kim, Minkook Cho

Proceedings Volume 12925, 129253G (2024) https://doi.org/10.1117/12.3006162

KEYWORDS: Cone beam computed tomography, Computed tomography, Simulations, X-ray computed tomography, 3D image processing, X-rays

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Proceedings Article | 1 April 2024 Poster + Paper

Deep learning-based enhancement of ultra-low-dose cone-beam CT image using simulated data

Sunjung Kim, Hyunwoo Lee, Won-Jin Yi, Min Kook Cho

Proceedings Volume 12925, 129253M (2024) https://doi.org/10.1117/12.3006589

KEYWORDS: Cone beam computed tomography, Image quality, Image enhancement, Deep learning, Computed tomography, X-ray computed tomography, Denoising

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The main challenge for low-dose computed tomography (CT) imaging is the poor noise characteristics of resultant images. Photon starvation leads to high noise in projection data and consequently the reconstructed images suffer from noise and streaks. In order to achieve a lower dose during imaging, it is necessary not only to minimize the mA but also to increase the scan speed, thereby reducing the time of radiation exposure to the patient. The cone-beam CT (CBCT) images obtained by applying all the methods of reducing mA and capturing images quickly, resulting in a radiation dose lower than the existing low-dose technique, are defined as ultra-low dose (ULD) images. In order to improve the quality of ULD images, an approach using deep learning methods was attempted. For this, a pair of ULD images and the corresponding target normal dose (ND) image to be improved should be available. However, due to the difficulty of obtaining ULD images, we propose a method of converting existing normal dose CBCT images into ULD images and using them for deep learning training. The ULD images simulated using the proposed method showed minimal differences compared to the images acquired under actual ULD imaging conditions. For the simulated ULD, there was a 2.02% difference in CT numbers compared to the actual ULD, and a 1.69% difference in noise levels. Training deep learning with simulated ULD data, which closely resembles the actual data with minimal differences, using the same parameters as training with real data, yielded performance similar to training with actual data. Through visual examination and quantitative analysis, it was verified that training deep learning on an extensive dataset of simulated ULD data results in improved performance, contrasting with the challenges associated with obtaining actual data. Utilizing the proposed approach is anticipated to enable the effective application of deep learning in demanding medical domains where obtaining real data presents challenges, ultimately leading to the attainment of desired results.

Proceedings Article | 1 April 2024 Poster + Paper

Motion correction algorithm for cone-beam computed tomography

Jiyoung Lee, Jinwoo Kim, Min Kook Cho

Proceedings Volume 12925, 129253J (2024) https://doi.org/10.1117/12.3006336

KEYWORDS: Cone beam computed tomography, Image restoration, Calibration, Image registration, Computed tomography, CT reconstruction

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Proceedings Article | 31 March 2016 Paper

A framework for analytical estimation of patient-specific CT dose

Hanbean Youn, Jin Woo Kim, Hosang Jeon, Jiho Nam, Seungman Yun, Min Kook Cho, Ho Kyung Kim

Proceedings Volume 9783, 97833J (2016) https://doi.org/10.1117/12.2216726

KEYWORDS: Computed tomography, Monte Carlo methods, Scanners, Statistical analysis, Ray tracing, Photons, Image analysis, Algorithm development, Computer simulations, Calibration, Chest, Head, X-ray computed tomography, X-rays, Signal attenuation

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Proceedings Article | 23 March 2010 Paper

Deblurring in digital tomosynthesis by iterative self-layer subtraction

Hanbean Youn, Jee Young Kim, SunYoung Jang, Min Kook Cho, Seungryong Cho, Ho Kyung Kim

Proceedings Volume 7622, 76225J (2010) https://doi.org/10.1117/12.844137

KEYWORDS: Sensors, Algorithm development, Reconstruction algorithms, Chest, Image quality, Computer simulations, Image restoration, Numerical simulations, X-rays, X-ray sources

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Proceedings Article | 19 March 2010 Paper

Feasibility of proton tomosynthesis system in proton therapy

Min Kook Cho, Jungwook Shin, Ho Kyung Kim, Myonggeun Yoon, Dongho Shin, Se Byeong Lee, Sung Yong Park

Proceedings Volume 7622, 762204 (2010) https://doi.org/10.1117/12.844539

KEYWORDS: Sensors, X-rays, Cancer, X-ray computed tomography, Calibration, Computed tomography, Radiotherapy, X-ray imaging, Optical filters, Tomography

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Proceedings Article | 13 March 2009 Paper

CBCT/CBDT equipped with the x-ray projection system for image-guided proton therapy

Min Kook Cho, Jin Sung Kim, Young-Bin Cho, Hanbean Youn, Sung Yong Park, Seungryong Cho, Ho Kyung Kim

Proceedings Volume 7258, 72582V (2009) https://doi.org/10.1117/12.811525

KEYWORDS: Sensors, X-rays, X-ray computed tomography, Modulation transfer functions, Calibration, Image quality, X-ray imaging, Projection systems, Imaging systems, Signal to noise ratio

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Proceedings Article | 13 March 2009 Paper

Clinical micro-CT for dental imaging

Hanbean Youn, Min Kook Cho, Cheol-Soon Shon, Bong Hae Cho, Chang Hyuk Kim, Ho Kyung Kim

Proceedings Volume 7258, 72582W (2009) https://doi.org/10.1117/12.811533

KEYWORDS: Sensors, Modulation transfer functions, Imaging systems, Tomography, Signal to noise ratio, Skull, X-ray computed tomography, Spatial frequencies, Quantum efficiency, Sensor performance

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Showing 5 of 9 publications

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