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Related Experiment Video

Updated: Jun 2, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Sensitivity encoding reconstruction with nonlocal total variation regularization.

Dong Liang1, Haifeng Wang, Yuchou Chang

  • 1Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA.

Magnetic Resonance in Medicine
|April 19, 2011
PubMed
Summary
This summary is machine-generated.

Nonlocal total variation (TV) regularization improves sensitivity encoding (SENC) MRI reconstruction by reducing noise and artifacts. This advanced method preserves image details better than conventional TV regularization, avoiding blocky effects.

Related Experiment Videos

Last Updated: Jun 2, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Signal Processing

Background:

  • Sensitivity encoding (SENC) MRI reconstruction faces poor signal-to-noise ratio with high acceleration factors due to ill-conditioning.
  • Total variation (TV) regularization improves edge preservation over Tikhonov but can introduce blocky artifacts.

Purpose of the Study:

  • To investigate nonlocal TV regularization for enhanced noise suppression in SENC MRI reconstruction.
  • To overcome the blocky effect associated with conventional TV regularization.

Main Methods:

  • Developed a nonlocal TV regularization method using a weighted nonlocal gradient function.
  • Incorporated image structure prior information into weighting factors for generalized neighbors.
  • Applied the method to in vivo MRI data for noise reduction and detail preservation.

Main Results:

  • Nonlocal TV regularization effectively suppresses noise in SENC reconstruction.
  • The method preserves fine image details superiorly compared to existing techniques.
  • Blocky artifacts were overcome, unlike with standard TV regularization.

Conclusions:

  • Nonlocal TV regularization offers significant advantages for SENC MRI reconstruction.
  • It provides superior noise reduction and detail preservation while avoiding common artifacts.
  • This technique enhances overall image quality in accelerated MRI scans.