BISPL @ KAIST AI - BioImaging, Signal Processing, & machine Learning lab.
  • BISPL News
  • Professor
  • Members
  • Research
  • Publications
  • Github
  • Software
    • NIRS-SPM
    • k-t FOCUSS
    • Compressive MUSIC
    • Super-resolution Microscopy
    • Patch Low Rank MRI
    • Sparse SPM
    • ALOHA Inpainting
    • ALOHA for MR Recon
    • MR Artifact Removal using Robust ALOHA
    • MR Ghost Artifact correction using ALOHA
  • BISPL Hall of Fame
  • Recent Talks
  • COVID-19 data link
  • BISPL Snapshots
  • On the World Press
  • Intranet

MR Artifact Removal using Robust ALOHA

Picture
Purpose: MRI artifacts are originated from various sources including instability of an MR system, patient motion, inhomogeneities of gradient elds, etc. Such MRI artifacts are usually considered as irreversible, so additional artifact-free scan or navigator scan is necessary. To overcome these limitations, this paper proposes a novel compressed sensing-based approach for removal of various MRI artifacts.
Theory: Recently, the annihilating lter based low-rank Hankel matrix (ALOHA) approach was proposed. ALOHA exploits the fundamental duality between the low-rankness of weighted Hankel structured matrix and the sparsity of signal in a transform domain. Because MR artifacts usually appeared as sparse k-space components, the low-rank Hankel matrix from underlying artifact-free k-space data can be exploited to decompose the sparse outliers.
Methods: The sparse + low-rank decomposition framework using Hankel matrix was proposed for removal of MRI artifacts. Alternating direction method of multipliers (ADMM) algorithm was employed for the minimization of associated cost function with the initialized matrices from a factorization-based matrix completion.
Results: Experimental results demonstrated that the proposed algorithm can correct MR artifacts including herringbone (crisscross), motion, and zipper artifacts without image distortion.
Conclusion: The proposed method may be a robust correction solution for various MRI artifacts that can be represented as sparse outliers.
Download our MRM Paper

    Download - Robust ALOHA for Artifact Removal

Download
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


Copyright (c) 2025, BISPL
All Rights Reserved.

Picture