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Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data.

Mariya Doneva1, Thomas Amthor1, Peter Koken1

  • 1Philips Research Hamburg, Roentgenstrasse 24, Hamburg 22335, Germany.

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|February 23, 2017
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Summary
This summary is machine-generated.

This study introduces an iterative k-space reconstruction method for undersampled magnetic resonance fingerprinting (MRF) data. The novel approach improves parameter map quality by reducing computational complexity and eliminating Fourier transforms during reconstruction.

Keywords:
MR FingerprintingMatrix completion

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Undersampled magnetic resonance fingerprinting (MRF) data acquisition poses challenges for accurate parameter mapping.
  • Existing iterative reconstruction methods for undersampled MRF often require computationally intensive forward and inverse Fourier transforms in each iteration.
  • Low-rank matrix completion techniques have shown promise but can be complex to implement.

Purpose of the Study:

  • To develop a novel iterative reconstruction method for undersampled MRF data that operates entirely in k-space.
  • To reduce the computational complexity of MRF reconstruction.
  • To improve the quality of parameter maps derived from undersampled MRF data.

Main Methods:

  • An iterative reconstruction method was developed that operates entirely within k-space.
  • A low-dimensional data subspace is estimated from sparsely sampled k-space data.
  • Reconstruction involves projecting data onto this subspace via matrix multiplication, bypassing singular value decomposition.
  • The method reconstructs missing k-space samples before MRF dictionary matching.

Main Results:

  • The proposed k-space iterative reconstruction significantly improves the quality of MRF parameter maps compared to direct matching on undersampled data.
  • The method avoids the need for repeated forward and inverse Fourier transforms, reducing computational load.
  • Projection via matrix multiplication offers a computationally efficient alternative to singular value thresholding in low-rank matrix completion.

Conclusions:

  • The developed iterative k-space reconstruction method is an effective approach for undersampled MRF data.
  • This method enhances the accuracy and efficiency of MRF parameter mapping.
  • The technique holds potential for broader applications in accelerated MRI.