Jiyoung Choi

Bio: Jiyoung Choi is an academic researcher from Samsung. The author has contributed to research in topics: Image processing & Iterative reconstruction. The author has an hindex of 7, co-authored 30 publications receiving 160 citations. Previous affiliations of Jiyoung Choi include KAIST & Hitachi.

Sparsity driven metal part reconstruction for artifact removal in dental CT

Abstract: Metal artifact removal (MAR) is one of the most important issues in x-ray CT reconstruction. Various methods have been suggested for metal artifact removal, among which projection modification and iterative methods are most popular. While those methods mainly focus on removing background artifacts, for some applications such as dental CT the correct reconstruction of metallic inserts is also important. For this application, we formulate the MAR problem as a sparse recovery problem since metallic inserts usually occupy very little volume within a field of view. One of the main advantages of this approach is to overcome the inconsistency of sinograms from metal artifacts by imposing a geometric constraint, "sparsity". As a side product of this formulation, a significant reduction of the sample views is feasible for metal part reconstruction without sacrificing quality, thanks to the compressed sensing theory, which minimizes the additional computational overhead. Numerical results confirm that metallic inserts can be accurately reconstructed with a significant reduction of computation time.

X-ray imaging apparatus and method for controlling the same

Abstract: An X-ray imaging apparatus is provided The X-ray imaging apparatus includes an X-ray source configured for irradiating X-rays to a subject; a filtering unit configured for controlling a dose of X-rays irradiated to the subject; and a processor configured for distinguishing and setting up an uninterested region in an X-ray image obtained based in the irradiated X-rays, and for controlling the filtering unit to set a dose of X-rays irradiated into the uninterested region

Data consistency-driven scatter kernel optimization for x-ray cone-beam CT

Abstract: Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation.

Compressed sensing metal artifact removal in dental CT

Abstract: Metal artifact removal (MAR) has been an important issue in dental X-ray CT due to the presence of metal implant and fillings. The practical use of most existing MAR methods have limitations due to their inherent drawbacks. In this research, we propose a novel MAR algorithm in dental CT. Based on the sparse volume occupation of the metallic inserts, we can formulate the MAR problem as a sparse recovery problem within the compressed sensing framework. One of the main advantages of employing compressed sensing theory in MAR problem is that the sparseness of the metallic objects allows us to reduce the view samples significantly without loss of image quality, accelerating the proposed MAR algorithm drastically. Experimental results using real dental CT scanner measurements show that our algorithm can perform accurate metallic artifact removal very quickly.

X-Ray Diffraction

Abstract: Interpretation of X-ray Powder Diffraction Patterns . By H. Lipson and H. Steeple. Pp. viii + 335 + 3 plates. (Mac-millan: London; St Martins Press: New York, May 1970.) £4.

Rapidly Desorbing Fractions of PAHs in Contaminated Sediments as a Predictor of the Extent of Bioremediation

Abstract: In the present study, the desorption kinetics of 15 PAHs (two to six rings) from sediments were determined before and after bioremediation in a bioreactor or landfarm. Desorption kinetics were measured with a method in which the water phase was kept PAH-free by Tenax TA beads. For almost all degraded PAHs, rapidly desorbing fractions (desorption rate constants > 0.1 h-1) were much smaller after bioremediation than before treatment whereas the slowly desorbing amounts remained unchanged. Thus, mainly the rapidly desorbing PAHs are degraded during bioremediation. The extent of possible PAH degradation could be roughly predicted from the initial rapidly desorbing fraction. For nondegraded PAHs, the rapidly desorbing fractions were substantial (up to 55%) and remained unchanged by remediation. The magnitude of the rapidly desorbing fractions of the nondegraded PAHs suggests that their persistence is due to microbial factors, not bioavailability.

Handbook of mathematical methods in imaging

Abstract: Linear Inverse Problems.- Large-Scale Inverse Problems in Imaging.- Regularization Methods for Ill-Posed Problems.- Distance Measures and Applications to Multi-Modal Variational Imaging.- Energy Minimization Methods.- Compressive Sensing.- Duality and Convex Programming.- EM Algorithms.- Iterative Solution Methods.- Level Set Methods for Structural Inversion and Image Reconstructions.- Expansion Methods.- Sampling Methods.- Inverse Scattering.- Electrical Impedance Tomography.- Synthetic Aperture Radar Imaging.- Tomography.- Optical Imaging.- Photoacoustic and Thermoacoustic Tomography: Image Formation Principles.- Mathematics of Photoacoustic and Thermoacoustic Tomography.- Wave Phenomena.- Statistical Methods in Imaging.- Supervised Learning by Support Vector Machines.- Total Variation in Imaging.- Numerical Methods and Applications in Total Variation Image Restoration.- Mumford and Shah Model and its Applications in Total Variation Image Restoration.- Local Smoothing Neighbourhood Filters.- Neighbourhood Filters and the Recovery of 3D Information.- Splines and Multiresolution Analysis.- Gabor Analysis for Imaging.- Shaper Spaces.- Variational Methods in Shape Analysis.- Manifold Intrinsic Similarity.- Image Segmentation with Shape Priors: Explicit Versus Implicit Representations.- Starlet Transform in Astronomical Data Processing.- Differential Methods for Multi-Dimensional Visual Data Analysis.- Wave fronts in Imaging, Quinto.- Ultrasound Tomography, Natterer.- Optical Flow, Schnoerr.- Morphology, Petros.- Maragos.- PDEs, Weickert. - Registration, Modersitzki. - Discrete Geometry in Imaging, Bobenko, Pottmann.-Visualization, Hege.- Fast Marching and Level Sets, Osher.- Couple Physics Imaging, Arridge.- Imaging in Random Media, Borcea.- Conformal Methods, Gu.- Texture, Peyre.- Graph Cuts, Darbon.- Imaging in Physics with Fourier Transform (i.e. Phase Retrieval e.g Dark field imaging), J. R. Fienup.- Electron Microscopy, Oktem Ozan.- Mathematical Imaging OCT (this is also FFT based), Mark E. Brezinski.- Spect, PET, Faukas, Louis.

Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty

Abstract: Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.