Mr. Hyeongmin Jin Profile

Hyeongmin Jin

Student at Seoul National Univ

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Publications (6)

Proceedings Article | 27 March 2019 Paper

How dependent are CT radiomic features on CT scan parameters?

Hyeongmin Jin, Jong Hyo Kim

Proceedings Volume 11050, 110500D (2019) https://doi.org/10.1117/12.2521488

KEYWORDS: Computed tomography, Feature extraction, CT reconstruction, Image segmentation, Lung cancer, Image quality

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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 | 27 February 2018 Paper

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Hyeongmin Jin, Changyong Heo, Jong Hyo Kim

Proceedings Volume 10575, 105753L (2018) https://doi.org/10.1117/12.2295010

KEYWORDS: Medical image processing, Machine learning, Computed tomography, Emphysema, Lung, CT reconstruction, Data conversion, Image segmentation, Modulation transfer functions, Databases, Chronic obstructive pulmonary disease

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Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 ± 7.28% for B50f data set, 10.82 ± 6.71% for the B30f data set, and 8.87 ± 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

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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Proceedings Article | 6 March 2013 Paper

Evaluation of nonlinear pre-sampled modulation transfer function in iterative reconstruction CT

Hyeong Min Jin, Jong Hyo Kim

Proceedings Volume 8668, 86684P (2013) https://doi.org/10.1117/12.2007823

KEYWORDS: Modulation transfer functions, Computed tomography, Infrared imaging, Image quality, Reconstruction algorithms, Spatial frequencies, Iodine, CT reconstruction, Imaging systems, Quantitative analysis

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