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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Deep learning-based MR fingerprinting ASL ReconStruction (DeepMARS).

Qiang Zhang1, Pan Su2, Zhensen Chen3

  • 1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

Magnetic Resonance in Medicine
|February 5, 2020
PubMed
Summary

DeepMARS, a deep learning method, reconstructs MR fingerprinting arterial spin labeling (MRF-ASL) perfusion maps faster and more reproducibly than conventional dictionary matching. This advance improves the analysis of cerebral blood flow (CBF) and bolus arrival time (BAT).

Keywords:
DeepMARSMRF-ASLdeep learningreconstructionreproducibility

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neuroimaging

Background:

  • Magnetic Resonance Fingerprinting (MRF) combined with Arterial Spin Labeling (ASL) offers advanced perfusion mapping.
  • Traditional reconstruction methods like dictionary matching (DM) are computationally intensive and can lack reproducibility.
  • Accurate and efficient perfusion mapping is crucial for diagnosing cerebrovascular diseases like Moyamoya.

Purpose of the Study:

  • To develop and validate a novel deep learning-based method, DeepMARS, for rapid and reproducible reconstruction of MRF-ASL perfusion maps.
  • To compare the performance of DeepMARS against conventional dictionary matching (DM) in terms of speed, accuracy, and reproducibility.
  • To assess the clinical utility of DeepMARS for evaluating perfusion deficits in Moyamoya disease.

Main Methods:

  • A fully connected neural network (DeepMARS) was trained using simulated MRF-ASL data from single- and two-compartment models.
  • DeepMARS was evaluated on MRF-ASL data from healthy subjects and Moyamoya patients, comparing computation time, R², and ICC with DM.
  • Linear mixed models assessed the correlation between DeepMARS and Look-Locker ASL for key perfusion parameters like bolus arrival time (BAT) and cerebral blood flow (CBF).

Main Results:

  • DeepMARS demonstrated significantly faster reconstruction times (<0.5 ms/voxel) compared to DM (>4 s/voxel).
  • DeepMARS achieved higher R² and improved ICC for BAT and CBF compared to DM, indicating enhanced accuracy and reproducibility.
  • In Moyamoya patients, DeepMARS accurately depicted hypoperfused areas and delayed BAT, consistent with angiographic findings.

Conclusions:

  • DeepMARS offers a substantial improvement in speed and reproducibility for MRF-ASL perfusion map reconstruction.
  • The deep learning approach provides accurate and reliable estimates of perfusion parameters, outperforming conventional methods.
  • DeepMARS holds promise for efficient and accurate clinical assessment of cerebrovascular conditions.