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Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and

Zhilang Qiu1, Siyuan Hu1, Walter Zhao1

  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Magnetic Resonance in Medicine
|December 16, 2023
PubMed
Summary

This study introduces a new method for multidimensional MR fingerprinting (mdMRF) to eliminate motion-induced artifacts, enabling faster and more accurate brain imaging without gating. The technique reconstructs high-quality images and quantitative maps efficiently.

Keywords:
MR fingerprintingdiffusion MRIlow-rank matrix completionlow-rank subspace reconstructiontime-resolved image reconstruction

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Area of Science:

  • Magnetic Resonance Imaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Physiological motion during multidimensional MR fingerprinting (mdMRF) can induce measurement errors, leading to shading artifacts.
  • Existing methods often require prospective gating or navigation, increasing scan time and complexity.
  • There is a need for robust reconstruction techniques that can handle motion-induced errors without additional hardware or prolonged scans.

Purpose of the Study:

  • To propose and validate a novel reconstruction method for mdMRF that addresses shading artifacts caused by physiological motion.
  • To achieve artifact-free, high-resolution, and time-resolved image reconstruction without the need for navigating or gating.
  • To enable simultaneous and efficient quantification of key MRI parameters.

Main Methods:

  • A two-procedure approach involving self-calibration (temporally local matrix completion) and subspace reconstruction (temporally global).
  • Self-calibration reconstructs low-resolution images from undersampled k-space data.
  • Subspace reconstruction utilizes these low-resolution images to generate aliasing-free, high-resolution, time-resolved images, followed by outlier detection to remove corrupted data.

Main Results:

  • Successfully reconstructed aliasing-free, high-resolution, time-resolved images with accurate representation of measurement errors.
  • Demonstrated robust automatic detection and removal of corrupted images.
  • Generated artifact-free T1, T2, and ADC maps simultaneously with high scan efficiency (<20s/slice).
  • Showcased robustness across different scanners, parameters, and subjects.

Conclusions:

  • The proposed mdMRF reconstruction method effectively alleviates shading artifacts from motion-induced errors.
  • Enables simultaneous, artifact-free quantification of T1, T2, and ADC without prospective gating.
  • Offers robustness and high scan efficiency for clinical applicability.