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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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Paired Cisterna Magna Nanoinjection and Laser Speckle Contrast Imaging Assay to Study Cerebral Blood Flow Regulation In Vivo
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Cerebral blood flow estimation in vivo using local tissue reference functions.

Jayme Cameron Kosior1, Michael R Smith, Robert Karl Kosior

  • 1Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada.

Journal of Magnetic Resonance Imaging : JMRI
|December 20, 2008
PubMed
Summary

Local reference functions (LRFs) provide accurate cerebral blood flow (CBF) estimates comparable to arterial input functions (AIFs) in stroke patients. This simplifies perfusion imaging, offering reliable data until in vivo AIF quantification is feasible.

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Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry
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Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry

Published on: January 19, 2020

Area of Science:

  • Neuroimaging
  • Medical Physics
  • Radiology

Background:

  • Cerebral blood flow (CBF) quantification is crucial for diagnosing and managing cerebrovascular diseases.
  • Traditional methods rely on arterial input functions (AIFs), which are challenging to obtain in vivo.
  • Local reference functions (LRFs) derived from tissue bolus signals offer a potential alternative.

Purpose of the Study:

  • To evaluate the efficacy of using tissue-derived local reference functions (LRFs) instead of arterial input functions (AIFs) for deconvolution-based cerebral blood flow (CBF) quantification.
  • To compare cross-calibrated CBF (CBF(CC)) estimates derived using both AIFs and LRFs in ischemic stroke patients.

Main Methods:

  • CBF(CC) maps were generated using singular value decomposition (SVD) deconvolution with both AIFs and white matter (WM) LRFs in 28 ischemic stroke patients.
  • Cross-calibration was performed using a normal WM value of 23.7 mL/minute/100 g.
  • Median CBF(CC) estimates were extracted from normal gray matter (GM) and ischemic tissue.

Main Results:

  • Median CBF(CC) estimates derived from AIFs and LRFs showed strong agreement in both GM and ischemic tissue across all patients.
  • Average paired differences were minimal (0.4 ± 1.7 mL/minute/100 g in GM, -0.4 ± 1.4 mL/minute/100 g in ischemic tissue).
  • Statistical analysis (Wilcoxon signed-rank test) revealed no significant differences between AIF- and LRF-derived CBF(CC) measurements (P > 0.05).

Conclusions:

  • LRF-based CBF(CC) estimates are as accurate as AIF-based estimates when patient-specific cross-calibration is applied.
  • LRF-based methods offer a simplified and potentially more reliable approach for perfusion imaging, especially when in vivo AIF quantification is not feasible.
  • This approach could enhance the clinical utility of perfusion imaging in stroke assessment.