BISPL @ KAIST AI - BioImaging, Signal Processing, & machine Learning lab.
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NIRS-SPM

k-t FOCUSS

Compressive MUSIC

Super-resolution Microscopy

Patch Low Rank MRI

Sparse SPM

ALOHA Inpainting

ALOHA for MRI

Annihilating Filter-based Low-Rank Hankel Matrix (ALOHA) for MR reconstruction

Related publications

  1. Kyong Hwan Jin, Dongwook Lee, and Jong Chul Ye. "A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank hankel matrix,"  IEEE Trans. on Computational Imaging, vol 2, no. 4, pp. 480 - 495, Dec.  2016.
  2. Jong Chul Ye, Jong Min Kim, Kyong Hwan Jin and Kiryung Lee, "Compressive sampling using annihilating filter-based low-rank interpolation",  IEEE Trans. on Information Theory, 2016 (in press).
  3. Kyong Hwan Jin, Ji-Yong Um, Dongwook Lee, Juyoung Lee, Sung-Hong Park and Jong Chul Ye,  " MRI artifact correction using sparse + low-rank decomposition of annihilating lter-based Hankel matrix", Magnetic Resonance in Medicine (in press),  2016
  4. Juyoung Lee, Kyong Hwan Jin, and Jong Chul Ye, "Reference-free single-pass EPI Nyquist ghost correction using annihilating filter-based low rank Hankel  matrix (ALOHA)", Magnetic Resonance in Medicine, 10.1002/mrm.26077, Feb. 17, 2016.
  5. Dongwook Lee,, Kyong Hwan Jin, Eung Yeop Kim, Sung-Hong Park and Jong Chul Ye, "Acceleration of MR parameter mapping using annihilating filter-based low rank Hankel matrix (ALOHA)", Magnetic Resonance in Medicine, 10.1002/mrm.26081, Jan. 1, 2016.​
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   Abstract—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 framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on a novel observation that the transform domain sparsity in the primary space implies the low-rankness of weighted Hankel matrix in the reciprocal space. This converts pMRI and CS-MRI to a k-space interpolation problem using a structured matrix completion. Experimental results using in vivo data for single/multicoil imaging as well as dynamic imaging confirmed that the proposed method outperforms the state-of-the-art pMRI and CS-MRI.

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    Release note:  version 1.0, Feb. 20, 2017

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ABOUT US
Our research activities are primarily focused on the signal processing and machine learning  for high-resolution high-sensitivity image reconstruction from real world bomedical imaging systems. Such problems pose interesting challenges that often lead to investigations of fundamental problems in various branches of physics, mathematics, signal processing, biology, and medicine. While most of the biomedical imaging researchers are interested in addressing this problem using off-the-self tools from signal processing, machine learning, statistics, and optimization and combining their domain-specific knowledge, our approaches are unique in the sense that I believe that actual  imaging applications are a source of endless inspiration for new mathematical theories and we are eager to solve both specific applications and application-inspired fundamental theoretical problems. 
Our location
Graduate School of AI
KAIST
108  Taebong-ro, Seocho-gu,  Seoul,  06764
Republic of Korea


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