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Magnetic Resonance Imaging

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

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Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling

Renzo Phellan, Thomas Lindner, Michael Helle

IEEE Transactions on Bio-Medical Engineering

|November 6, 2019

Summary

This study introduces advanced image processing for non-invasive cerebrovascular analysis using 4D ASL MRA. The new method accurately segments vessels and estimates blood flow, aiding in the study of cerebrovascular diseases.

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Area of Science:

Medical Imaging
Biomedical Engineering
Neuroscience

Background:

Cerebrovascular diseases are a leading cause of death and disability globally.
Digital subtraction angiography is the standard but invasive diagnostic tool.
Time-resolved 3D arterial spin labeling MRA (4D ASL MRA) offers a non-invasive alternative for cerebrovascular assessment.

Purpose of the Study:

To develop advanced medical image processing methods for extracting anatomical and hemodynamic information from 4D ASL MRA datasets.
To improve the accuracy of cerebrovascular segmentation and blood flow parameter estimation.

Main Methods:

Extended a prior segmentation method using blood flow data.
Estimated blood flow parameters by fitting a mathematical model to vascular signals.
Refined parameter estimations using regression techniques within cerebrovascular segmentation.
Evaluated the method on phantoms, healthy volunteers, and patient datasets.

Main Results:

Achieved high segmentation accuracy with Dice similarity coefficients of 0.957 (phantoms) and 0.938 (real datasets).
Refinement step improved similarity of estimated blood flow parameters to ground-truth values in phantoms.
Qualitative analysis indicated more realistic hemodynamic parameter estimations after refinement.

Conclusions:

The proposed method enables accurate segmentation and blood flow estimation in the cerebrovascular system using 4D ASL MRA.
This non-invasive approach can significantly aid clinicians and researchers in studying cerebrovascular diseases.