Search Results - "Chang, Yuchou"
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Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning
Published in Healthcare technology letters (01-08-2019)“…Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting…”
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Architecture design for performing grasp‐and‐lift tasks in brain–machine‐interface‐based human‐in‐the‐loop robotic system
Published in IET cyber-physical systems (01-09-2019)“…Human‐in‐the‐loop robotic system is an emerging technique in recent years. Human intelligence as well as machine intelligence are incorporated to accomplish…”
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Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction
Published in Magnetic resonance in medicine (01-09-2012)“…GRAPPA linearly combines the undersampled k‐space signals to estimate the missing k‐space signals where the coefficients are obtained by fitting to some…”
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Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
Published in Pattern recognition (01-09-2008)“…This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based…”
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5
Deep learning-based rigid motion correction for magnetic resonance imaging: A survey
Published in Meta-radiology (01-06-2023)“…Physiological and physical motions of the subjects, e.g., patients, are the primary sources of image artifacts in magnetic resonance imaging (MRI), causing…”
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Adaptive Volterra Filter for Parallel MRI Reconstruction
Published in EURASIP journal on advances in signal processing (26-07-2019)“…Parallel magnetic resonance imaging (MRI) technique is able to accelerate MRI speed for reducing costs and enhancing patient’s comfortability. Parallel MRI can…”
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Improving GRAPPA using cross-sampled autocalibration data
Published in Magnetic resonance in medicine (01-04-2012)“…In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration signal (ACS) lines are acquired with a frequency‐encoding…”
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Group feature selection for enhancing information gain in MRI reconstruction
Published in Physics in medicine & biology (21-02-2022)“…Magnetic resonance imaging (MRI) has revolutionized radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside the body…”
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Efficient GRAPPA reconstruction using random projection
Published in 2013 IEEE 10th International Symposium on Biomedical Imaging (01-04-2013)“…As a data-driven technique, GRAPPA has been widely used for parallel MRI reconstruction. In GRAPPA, a large amount of calibration data is desirable for…”
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Conference Proceeding -
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A kernel approach to parallel MRI reconstruction
Published in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (01-03-2011)“…GRAPPA has been widely used as a k-space-based parallel MRI reconstruction technique. It linearly combines the acquired k-space signals to estimate the missing…”
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Conference Proceeding -
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A hybrid total-variation minimization approach to compressed sensing
Published in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) (01-05-2012)“…Compressed sensing (CS) has been successfully applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. Total variation (TV)…”
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Conference Proceeding -
12
Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor
Published in EURASIP Journal on Image and Video Processing (01-01-2008)“…Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale,…”
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k-t CSPI: A dynamic MRI reconstruction framework for combining compressed sensing and parallel imaging
Published in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) (01-05-2012)“…In this paper, we propose a new dynamic MR image reconstruction technique that combines the compressed sensing-based dynamic methods with parallel imaging…”
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Conference Proceeding -
14
Improving deep PROPELLER MRI via synthetic blade augmentation and enhanced generalization
Published in Magnetic resonance imaging (01-05-2024)“…In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction…”
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A dual-interpolator method for improving parallel MRI reconstruction
Published in Magnetic resonance imaging (01-10-2022)Get full text
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A kernel approach to compressed sensing parallel MRI
Published in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) (01-05-2012)“…Compressed sensing (CS) and parallel imaging (PI) have been widely studied for accelerating MRI reconstruction. Furthermore, the serial combination methods of…”
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Conference Proceeding -
17
Image reconstruction from phased-array MRI data based on multichannel blind deconvolution
Published in 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (01-04-2010)“…In this paper we consider image reconstruction from multichannel phased array MRI data without prior knowledge of the coil sensitivity functions. A new…”
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Conference Proceeding -
18
An ensemble approach for accelerated and noise-resilient parallel MRI reconstruction utilizing CycleGANs
Published in Machine vision and applications (01-11-2024)“…Magnetic Resonance Imaging (MRI) is often constrained by long acquisition times. Accelerated acquisition techniques can reduce scan time but may introduce…”
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Kernel Principal Component Analysis of Coil Compression in Parallel Imaging
Published in Computational and mathematical methods in medicine (01-01-2018)“…A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage…”
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Sensitivity encoding reconstruction with nonlocal total variation regularization
Published in Magnetic resonance in medicine (01-05-2011)“…In sensitivity encoding reconstruction, the issue of ill conditioning becomes serious and thus the signal‐to‐noise ratio becomes poor when a large acceleration…”
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