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Updated: Jun 9, 2026

Harmonic Nanoparticles for Regenerative Research

Harmonic Nanoparticles for Regenerative Research

Published on: May 1, 2014

Coherence regularization for SENSE reconstruction with a nonlocal operator (CORNOL).

Sheng Fang¹, Kui Ying, Li Zhao

¹Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.

Magnetic Resonance in Medicine

|September 1, 2010

Summary

This summary is machine-generated.

Coherence regularization enhances parallel imaging reconstruction by preserving image structures. This novel method reduces noise in sensitivity encoding (SENSE) without significant image degradation, outperforming traditional techniques.

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Last Updated: Jun 9, 2026

Harmonic Nanoparticles for Regenerative Research

Harmonic Nanoparticles for Regenerative Research

Published on: May 1, 2014

Area of Science:

Medical Imaging
Image Reconstruction
Regularization Techniques

Background:

Sensitivity encoding (SENSE) reconstruction in parallel imaging is prone to noise amplification at high reduction factors.
Existing regularization methods often introduce interstructure smoothness, leading to loss of image details and structure degradation.

Purpose of the Study:

To introduce coherence regularization for SENSE reconstruction.
To enhance image structure preservation during noise suppression in parallel imaging.

Main Methods:

Developed an energy functional for coherence regularization based on adaptive image filtering and diffusion equations.
Utilized a nonlocal operator from nonlocal mean filtering for structure detection.
Applied the method to penalize only intrastructure intensity changes, preserving interstructure changes.

Main Results:

Coherence regularization effectively suppresses noise in SENSE reconstruction, particularly at high reduction factors.
Demonstrated significantly less image degradation compared to Tikhonov and total variation methods.
Phantom and in vivo experiments validated the method's performance.

Conclusions:

Coherence regularization is a promising technique for improving the quality of parallel imaging reconstruction.
It offers superior noise reduction while preserving critical image structures.
This method advances the field of medical image reconstruction by minimizing artifacts.