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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Related Experiment Video

Updated: Jun 12, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: self-feeding sparse SENSE.

Feng Huang1, Yunmei Chen, Wotao Yin

  • 1Advanced Concept Development, Invivo Corporation, Gainesville, Florida, USA. fhuang@invivocorp.com

Magnetic Resonance in Medicine
|June 22, 2010
PubMed
Summary
This summary is machine-generated.

A new algorithm enhances sparse Magnetic Resonance Imaging (MRI) reconstruction for faster, more robust partially parallel imaging (PPI). This method achieves high acceleration factors with reduced noise and artifacts, improving clinical MRI applications.

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

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Last Updated: Jun 12, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Area of Science:

  • Medical Imaging
  • Biophysics
  • Computer-Aided Diagnosis

Background:

  • Sparsity enforcement in Magnetic Resonance Imaging (MRI) reconstruction aids partially parallel imaging (PPI) by reducing noise and artifacts.
  • Existing PPI techniques face challenges with speed and robustness in sparsity-constrained reconstruction.

Purpose of the Study:

  • To develop a fast and robust numerical algorithm for sparsity-constrained PPI.
  • To improve the clinical applicability of SENSE (Sparse Sensitivity Encoding) at high acceleration factors.

Main Methods:

  • Introduced an auxiliary variable to decompose the original minimization problem into two simpler subproblems.
  • Implemented a specific version for Cartesian trajectory data, termed self-feeding Sparse Sensitivity Encoding (SENSE).
  • The computational cost involves two conventional SENSE reconstructions and one spatially adaptive image denoising step.

Main Results:

  • Achieved significantly lower root mean square error (RMSE) at high acceleration factors, despite a doubled reconstruction time.
  • Demonstrated a net acceleration factor of 5 along one dimension with low RMSE using an eight-channel head coil.
  • The algorithm exhibited insensitivity to parameter choices, enhancing its practical usability.

Conclusions:

  • The developed algorithm offers a fast and robust solution for sparsity-constrained PPI.
  • The self-feeding SENSE method enhances MRI reconstruction quality and clinical applicability, particularly at high acceleration factors.