Preclinical MR fingerprinting (MRF) at 7 T: effective quantitative imaging for rodent disease models

Ying Gao1, Yong Chen, Dan Ma

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

NMR in Biomedicine
|February 3, 2015
PubMed

Insights

We developed a novel preclinical Magnetic Resonance Fingerprinting (MRF) method for high-field MRI. This technique reduces motion artifacts, enabling accurate quantitative T1, T2, and proton density mapping in preclinical research.

Area of Science:

  • Preclinical MRI
  • Quantitative Imaging
  • Biomedical Engineering

Background:

  • High-field preclinical MRI is crucial for disease assessment and therapy evaluation in rodent models.
  • Conventional MRI methods suffer from motion artifacts, compromising data accuracy.
  • Magnetic Resonance Fingerprinting (MRF) offers a novel solution for quantitative MRI.

Purpose of the Study:

  • To implement and evaluate an initial preclinical 7.0-T MRF technique for quantitative MRI.
  • To assess the performance of MRF in mitigating motion artifacts and quantifying relaxation times.
  • To explore the utility of MRF in preclinical disease models.

Main Methods:

  • Developed a preclinical 7.0-T MRF implementation using a fast imaging with steady-state free precession (FISP) sequence.
  • Acquired 600 MRF images with evolving T1 and T2 weighting in approximately 30 minutes.
  • Utilized dictionary-based matching to generate quantitative T1, T2, and proton density maps.

Main Results:

  • Demonstrated reproducible and differentiated quantitative estimates in vitro using phantoms.
  • Showcased inherent resistance to respiratory motion artifacts in vivo in mouse kidneys.
  • Observed sensitivity to known pathology in mouse brain tumor models.

Conclusions:

  • Preclinical MRF provides accurate quantitative T1, T2, and proton density mapping.
  • MRF exhibits robustness against motion artifacts in high-field preclinical MRI.
  • MRF holds significant potential for diverse preclinical imaging applications and quantitative biomarker discovery.

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