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Your search keyword '"Chung, Hyunkoo"' showing total 7 results
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7 results on '"Chung, Hyunkoo"'

2. Multi-spin-echo spatial encoding provides three-fold improvement of temperature precision during intermolecular zero quantum thermometry

Author

Davis, Ryan M, Zhou, Zijian, Chung, Hyunkoo, and Warren, Warren S.

Subjects
Models, Statistical, Echo-Planar Imaging, Phantoms, Imaging, Temperature, Contrast Media, Reproducibility of Results, Hyperthermia, Induced, Signal-To-Noise Ratio, Image Enhancement, Magnetic Resonance Imaging, Article, Bone Marrow, Image Interpretation, Computer-Assisted, Anisotropy, Humans, Quantum Theory, Computer Simulation, Algorithms
Abstract

Intermolecular multiple quantum coherences (iMQCs) are a source of MR contrast with applications including temperature imaging, anisotropy mapping, and brown fat imaging. Because all applications are limited by signal-to-noise ratio (SNR), we developed a pulse sequence that detects intermolecular zero quantum coherences with improved SNR.A previously developed pulse sequence that detects iMQCs, HOMOGENIZED with off resonance transfer (HOT), was modified with a multi-spin echo spatial encoding scheme (MSE-HOT). MSE-HOT uses a series of refocusing pulses to generate a stack of images that are averaged in postprocessing for higher SNR. MSE-HOT performance was quantified by measuring its temperature accuracy and precision during hyperthermia of ex vivo red bone marrow samples.MSE-HOT yielded a three-fold improvement in temperature precision relative to previous pulse sequences. Sources of improved precision were 1) echo averaging and 2) suppression of J-coupling in the methylene protons of fat. MSE-HOT measured temperature change with an accuracy of 0.6°C.MSE-HOT improved the temperature accuracy and precision of HOT to a level that is sufficient for hyperthermia of bone marrow.

Published
2015

7. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging.

Author

Chung H, Lu G, Tian Z, Wang D, Chen ZG, and Fei B

Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Published
2016
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