Mr. Chang-Won Kim Profile

Chang-Won Kim

at Seoul National Univ College of Medicine

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Publications (3)

Proceedings Article | 22 March 2012 Paper

An image-based approach to low-dose CT simulation

Chang Won Kim, Jong Hyo Kim

Proceedings Volume 8313, 83134D (2012) https://doi.org/10.1117/12.912393

KEYWORDS: X-ray computed tomography, Computed tomography, Modulation transfer functions, Signal attenuation, Image filtering, Apodization, Optical filters, Sensors, Chest, X-rays

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Proceedings Article | 17 March 2011 Paper

Contrast adaptive total p-norm variation minimization approach to CT reconstruction for artifact reduction in reduced-view brain perfusion CT

Chang-Won Kim, Jong-Hyo Kim

Proceedings Volume 7961, 79612W (2011) https://doi.org/10.1117/12.877614

KEYWORDS: Brain, X-ray computed tomography, Reconstruction algorithms, CT reconstruction, X-rays, Image segmentation, Tissues, Head, Neuroimaging, Image filtering

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Perfusion CT (PCT) examinations are getting more frequently used for diagnosis of acute brain diseases such as hemorrhage and infarction, because the functional map images it produces such as regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), and mean transit time (MTT) may provide critical information in the emergency work-up of patient care. However, a typical PCT scans the same slices several tens of times after injection of contrast agent, which leads to much increased radiation dose and is inevitability of growing concern for radiation-induced cancer risk. Reducing the number of views in projection in combination of TV minimization reconstruction technique is being regarded as an option for radiation reduction. However, reconstruction artifacts due to insufficient number of X-ray projections become problematic especially when high contrast enhancement signals are present or patient's motion occurred. In this study, we present a novel reconstruction technique using contrast-adaptive TpV minimization that can reduce reconstruction artifacts effectively by using different p-norms in high contrast and low contrast objects. In the proposed method, high contrast components are first reconstructed using thresholded projection data and low p-norm total variation to reflect sparseness in both projection and reconstruction spaces. Next, projection data are modified to contain only low contrast objects by creating projection data of reconstructed high contrast components and subtracting them from original projection data. Then, the low contrast projection data are reconstructed by using relatively high p-norm TV minimization technique, and are combined with the reconstructed high contrast component images to produce final reconstructed images. The proposed algorithm was applied to numerical phantom and a clinical data set of brain PCT exam, and the resultant images were compared with those using filtered back projection (FBP) and conventional TV reconstruction algorithm. Our results show the potential of the proposed algorithm for image quality improvement, which in turn may lead to dose reduction.

Proceedings Article | 9 March 2010 Paper

Automated estimation of breast density on mammogram using combined information of histogram statistics and boundary gradients

Youngwoo Kim, Changwon Kim, Jong-Hyo Kim

Proceedings Volume 7624, 76242F (2010) https://doi.org/10.1117/12.844083

KEYWORDS: Breast, Mammography, Tissues, Breast cancer, Statistical analysis, Image segmentation, Digital filtering, Feature extraction, Medicine, Digital mammography

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