Publications by Kyong Hwan Jin

Time-Dependent Deep Image Prior for Dynamic MRI

Michaël Unser, Harshit Gupta, Kyong Hwan Jin, Jaejun Yoo

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of t ...

Institute of Electrical and Electronics Engineers2021

CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction

Michaël Unser, Harshit Gupta, Quy Ha Nguyen, Michael Thompson McCann, Kyong Hwan Jin

Institute of Electrical and Electronics Engineers2018

Grid-Free Localization Algorithm Using Low-Rank Hankel Matrix for Super-Resolution Microscopy

Michaël Unser, Kyong Hwan Jin

2018

Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging

Kyong Hwan Jin

2018

Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal

Kyong Hwan Jin

2018

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Kyong Hwan Jin

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

3D Bpconvnet To Reconstruct Parallel Mri

Michaël Unser, Kyong Hwan Jin

In recent years, compressed sensing techniques have been applied to the reconstruction of parallel magnetic resonance (MR) images. Particularly for 3D MR signal, it is crucial to acquire fewer samples to reduce the distortions caused by long-time acquisiti ...

IEEE2018

Deep Convolutional Neural Network for Inverse Problems in Imaging

Michaël Unser, Emmanuel Augustin Julien Froustey, Michael Thompson McCann, Kyong Hwan Jin

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

2017

MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix

Kyong Hwan Jin

PurposeMagnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as ir ...

Wiley2017

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Kyong Hwan Jin

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

IEEE2017

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

Michaël Unser, Michael Thompson McCann, Kyong Hwan Jin

In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object clas ...

IEEE2017

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Kyong Hwan Jin

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general fr ...

IEEE2016

Fast and Robust Segmentation of the Striatum Using Deep Convolutional Neural Networks

Kyong Hwan Jin

Background: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 ma ...

Elsevier2016