Mr. Chul Kyun Ahn Profile

Chul Kyun Ahn

PhD Candidate at Seoul National Univ

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Publications (6)

Proceedings Article | 3 April 2023 Poster + Paper

How to overcome the data limited segmentation in abdominal CT: multi-planar UNet coupled with augmented contrast-boosting

Sihwan Kim, Chulkyun Ahn, Jong Hyo Kim

Proceedings Volume 12464, 124642S (2023) https://doi.org/10.1117/12.2654171

KEYWORDS: Image segmentation, Computed tomography, Data modeling, Education and training, Artificial intelligence, Spleen, Evolutionary algorithms, Performance modeling, Image restoration, Contrast agents

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Proceedings Article | 16 March 2020 Paper

Synthesis of ghost-free panoramic radiographs from dental CBCT images

Do Il Lee, Sihwan Kim, Chulkyun Ahn, Changyong Heo, Jong Hyo Kim

Proceedings Volume 11312, 113122U (2020) https://doi.org/10.1117/12.2549929

KEYWORDS: Panoramic photography, Radiography, Teeth, Spine, 3D image processing, Skull, Computed tomography, 3D acquisition, Image resolution, X-rays

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Proceedings Article | 27 March 2019 Paper

Combined low-dose simulation and deep learning for CT denoising: application in ultra-low-dose chest CT

Chulkyun Ahn, Changyong Heo, Jong Hyo Kim

Proceedings Volume 11050, 110500E (2019) https://doi.org/10.1117/12.2521539

KEYWORDS: Computed tomography, Chest, Denoising, Image quality, Reconstruction algorithms, Lung, Convolutional neural networks, Tissues, Visualization, Infrared imaging

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Proceedings Article | 1 March 2019 Paper

Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA

Chul Kyun Ahn, Hyeongmin Jin, Changyong Heo, Jong Hyo Kim

Proceedings Volume 10948, 1094846 (2019) https://doi.org/10.1117/12.2513144

KEYWORDS: Denoising, Image quality, Computed tomography, Angiography, Convolutional neural networks, Aorta, Electrocardiography, Radiation oncology, Medicine, Radiology

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Proceedings Article | 9 March 2018 Paper

A deep learning-enabled iterative reconstruction of ultra-low-dose CT: use of synthetic sinogram-based noise simulation technique

Chul Kyun Ahn, Zepa Yang, Changyong Heo, Hyeongmin Jin, Byungjun Park, Jong Hyo Kim

Proceedings Volume 10573, 1057335 (2018) https://doi.org/10.1117/12.2294013

KEYWORDS: Computed tomography, Denoising, Image quality, Wavelets, CT reconstruction, Image denoising, Convolutional neural networks, Image analysis

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Effective elimination of unique CT noise pattern while preserving adequate image quality is crucial in reducing radiation dose to ultra-low-dose level in CT imaging practice. In this study, we present a novel Deep Learning-enable Iterative Reconstruction (Deep IR) approach for CT denoising which incorporate a synthetic sinogram-based noise simulation technique for training of Convolutional Neural Network (CNN). Regular dose CT images from 25 patients were used from Seoul National University Hospital. The CT scans were performed at 140 kVp, 100 mAs, and reconstructed with standard FBP technique using B60f kernel. Among them, 20 patients were randomly selected as a training set and the rest 5 patients were used for a test set. We applied a re-projection technique to create a synthetic sinogram from the DICOM CT image, and then a simulated noise sinogram was generated to match the noise level of 10mAs according to Poisson statistic and the system noise model of the given scanner (Somatom Sensation 16, Siemens). We added the simulated noise sinogram to the re-projected synthetic sinogram to generate a simulated sinogram of ultra-low dose scan. We also created the simulated ultra-low-dose CT image by applying FBP reconstruction of the simulated noise sinogram with B60f kernel. A CNN model was created using a TensorFlow framework to have 10 consecutive convolution layer and activation layer. The CNN was trained to learn the noise in sinogram domain: the simulated noisy sinogram of ultra-low dose scan was fed into its input nodes with the output node being fed by the simulated noise sinogram. At test phase, the noise sinogram from the CNN output was reconstructed with using B60f kernel to create a noise CT image, which in turn was subtracted from the simulated ultra-low-dose CT image to produce a Deep IR CT image. The performance was evaluated quantitatively in terms of structural similarity (SSIM) index, peak signal-to-noise ratio (PSNR) and noise level measurement and qualitatively in CT image by comparing the noise pattern and image quality. Compared to low-dose image, denoising image of the SSIM and the PSNR were improved from 0.75 to 0.80, 28.61db to 32.16 respectively. The noise level of denoising image was reduced to an average of 56 % of that of low-dose image. The noise pattern in reconstructed noise CT was indistinguishable from that of real CT images, and the image quality of Deep IR CT image was overall much higher than that of simulated ultra-low-dose CT.

Proceedings Article | 3 March 2017 Paper

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

Chul Kyun Ahn, Changyong Heo, Heongmin Jin, Jong Hyo Kim

Proceedings Volume 10134, 101342O (2017) https://doi.org/10.1117/12.2254264

KEYWORDS: Convolutional neural networks, Breast, Mammography, Neural networks, Digital mammography, Feature extraction, X-ray imaging, X-rays, Radiology, Image segmentation

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