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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

Shota Ishida1, Yasuhiro Fujiwara2, Yuki Matta3

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Deep neural networks (DNNs) improve multiparametric arterial spin labeling (MP-ASL) accuracy and speed for quantifying cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). Supervised DNNs (DNNSup) demonstrated superior performance over unsupervised DNNs (DNNUns) and lookup table methods (LUT).

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arterial cerebral blood flowarterial spin labelingdeep neural networkphysics‐informed unsupervised learningsupervised learning

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Multiparametric arterial spin labeling (MP-ASL) is crucial for quantifying cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa).
  • Low signal-to-noise ratio (SNR) in MP-ASL limits accuracy and necessitates complex, time-consuming parameter estimation.
  • Deep neural networks (DNNs) present a promising solution to overcome these MP-ASL limitations.

Purpose of the Study:

  • To develop and evaluate simulation-based DNNs for MP-ASL parameter estimation.
  • To compare the performance of a supervised DNN (DNNSup), a physics-informed unsupervised DNN (DNNUns), and the conventional lookup table (LUT) method.
  • To assess DNN performance using both simulated and in vivo data.

Main Methods:

  • MP-ASL data acquired during resting state and a breath-holding task.
  • Evaluation of accuracy and noise immunity using resting-state data.
  • Statistical comparison of CBF and CBVa between resting and task states; assessment of reproducibility.

Main Results:

  • DNNSup exhibited higher accuracy, noise immunity, and a six-fold faster computation time compared to LUT.
  • All methods detected task-induced increases in CBF and CBVa, with larger effect sizes observed for DNNSup and DNNUns.
  • Reproducibility was comparable and satisfactory across all tested methods (DNNSup, DNNUns, and LUT).

Conclusions:

  • Supervised DNNs (DNNSup) significantly outperform unsupervised DNNs (DNNUns) and lookup table methods (LUT) in MP-ASL parameter estimation.
  • DNNSup offers superior accuracy, noise immunity, and computational efficiency for MP-ASL.
  • DNNs represent a powerful tool for advancing quantitative neuroimaging with MP-ASL.