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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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Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

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Automatic selection of arterial input function on dynamic contrast-enhanced MR images.

Denis Peruzzo¹, Alessandra Bertoldo, Francesca Zanderigo

¹University of Padova, Department of Information Engineering, Via Gradenigo 6/B, 35131 Padova, Italy.

Computer Methods and Programs in Biomedicine

|April 5, 2011

Summary

This study introduces an automated method for deriving the arterial input function (AIF) from dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data. This approach improves the accuracy of cerebral blood flow (CBF) and mean transit time (MTT) quantification.

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Area of Science:

Neuroimaging
Medical Physics
Radiology

Background:

Accurate quantification of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) using dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) relies on precise determination of the arterial input function (AIF).
Current methods for AIF extraction include manual delineation and automated algorithms, each with inherent limitations.

Purpose of the Study:

To develop and validate a novel automated algorithm for deriving the AIF from the middle cerebral artery (MCA) in DSC-MRI data.
To compare the performance of the proposed automated AIF method against existing state-of-the-art algorithms using simulated data and against manual AIF extraction using clinical data.

Main Methods:

The algorithm identifies the MCA region of interest (ROI) and employs recursive cluster analysis to isolate arterial voxels for AIF estimation.
Performance was evaluated using in silico (simulated) datasets and clinical DSC-MRI data.

Main Results:

On simulated data, the proposed method accurately reconstructed the true AIF and demonstrated reduced susceptibility to partial volume effect bias compared to other automated algorithms.
Clinical data analysis showed that the automated AIF resulted in cerebral blood flow (CBF) and mean transit time (MTT) maps with enhanced contrast compared to those derived from manual AIF extraction.

Conclusions:

The developed automated AIF method provides a reliable and accurate approach for DSC-MRI data analysis.
This method enhances the reliability of physiological parameter estimates and offers a more quantitatively robust physiological picture of cerebral hemodynamics.