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Magnetic Resonance Imaging

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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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Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

Published on: May 31, 2024

Multi-stage automated local arterial input function selection in perfusion MRI.

Rami Tabbara¹, Alan Connelly^2,3, Fernando Calamante^1,4,5

¹The Florey Institute of Neuroscience and Mental Health, 245 Burgundy Street, Heidelberg, Melbourne, VIC, 3084, Australia.

Magma (New York, N.Y.)

|November 14, 2019

Summary

This summary is machine-generated.

This study introduces a new automated method for selecting arterial input functions in dynamic-susceptibility contrast MRI. The novel approach enhances cerebral blood flow quantification by reducing image artifacts in patients with cerebrovascular disease.

Keywords:

Arterial input function Bolus dispersion Cerebral blood flow Perfusion MRI Stroke

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Area of Science:

Neuroimaging
Medical Physics
Radiology

Background:

Dynamic-susceptibility contrast MRI enables cerebral blood flow (CBF) quantification using model-independent deconvolution.
Local arterial input function (AIF) deconvolution methods identify unique arterial regions and their corresponding AIFs for improved accuracy.
Current automated local AIF methods often rely on surrogate measures or custom scoring functions, limiting clinical application.

Purpose of the Study:

To introduce a novel, fully automated, multi-stage local AIF method for cerebral blood flow quantification.
To address the limitations of existing automated local AIF methods in clinical settings.
To improve the robustness and accuracy of perfusion quantification in patients with cerebrovascular disease.

Main Methods:

A fully automated, multi-stage local AIF method was developed, integrating signal-based cluster analysis and priority flooding.
The method defines arterial regions and their vascular supply origins efficiently.
The proposed method was validated on data from four patients with cerebrovascular disease exhibiting artifacts with a prevailing automated method.

Main Results:

The proposed local AIF method successfully eliminated image artifacts observed with a pre-existing automated method.
Absence of artifacts indicates improved AIF selection and more reliable perfusion quantification.
The method demonstrated superior performance in challenging cases with significant image artifacts.

Conclusions:

The developed multi-stage local AIF method offers a more robust approach to perfusion quantification.
This automated solution enhances the clinical applicability of local AIF deconvolution in MRI.
The findings suggest a significant advancement over current fully automated local AIF techniques for CBF estimation.