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Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

Anish Lahiri1, Jeffrey A Fessler1, Luis Hernandez-Garcia2

  • 1Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, Michigan, USA.

Magnetic Resonance in Medicine
|November 22, 2019
PubMed
Summary

Optimized Arterial Spin Labeling (ASL) using Magnetic Resonance Fingerprinting (MRF) improves perfusion imaging sensitivity and precision. This novel approach enhances the estimation of hemodynamic parameters, offering faster and more accurate brain imaging without contrast agents.

Keywords:
Cramer-Rao boundarterial spin labelingbrain hemodynamicsdeep learningestimationmagnetic resonance fingerprintingneural networksoptimizationprecisionregressionscan design

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Neuroimaging

Background:

  • Arterial Spin Labeling (ASL) provides non-invasive, quantitative perfusion imaging without contrast agents.
  • Magnetic Resonance Fingerprinting (MRF) enables simultaneous estimation of multiple physiological parameters.

Purpose of the Study:

  • To enhance the sensitivity of ASL signals within the MRF framework.
  • To develop a regression-based estimation framework for MRF-ASL.
  • To optimize ASL labeling durations for improved parameter estimation.

Main Methods:

  • ASL labeling durations were optimized using Cramer-Rao Lower Bound (CRLB) to boost MRF-ASL signal sensitivity.
  • A neural network regression framework was developed, trained on synthetic noisy ASL signals.
  • Methods were validated in silico and in vivo, compared against multi-post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL.

Main Results:

  • Optimized scan design enabled precise estimation of hemodynamic parameters and tissue properties in a single scan.
  • In silico validation showed an 86.7% correlation of perfusion estimates with ground truth.
  • In vivo studies demonstrated a 7-fold improvement in white matter perfusion Coefficient of Variation (CoV) and a 2-fold improvement in gray matter CoV compared to multi-PLD ASL.
  • Regression-based estimation achieved rapid map generation, with per-map estimation times around 1 second.

Conclusions:

  • Scan design optimization significantly enhances MRF-ASL precision.
  • Regression-based estimation provides rapid and accurate perfusion mapping.
  • The combined approach offers a powerful tool for advanced brain perfusion analysis.