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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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Related Experiment Video

Updated: May 22, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Myocardial blood flow quantification from MRI by deconvolution using an exponential approximation basis.

Gilion Hautvast¹, Amedeo Chiribiri, Niloufar Zarinabad

¹Imaging Systems—MR, Philips Healthcare, Best 5684 PC, Netherlands. gilion.hautvast@philips.com

IEEE Transactions on Bio-Medical Engineering

|May 12, 2012

Summary

This summary is machine-generated.

Exponential deconvolution accurately quantifies myocardial blood flow using cardiovascular magnetic resonance imaging. This method, combined with motion correction, offers efficient and reproducible results for clinical use.

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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

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Area of Science:

Cardiovascular imaging
Biomedical engineering
Medical physics

Background:

Myocardial blood flow (MBF) quantification is crucial for diagnosing and managing cardiovascular diseases.
Perfusion cardiovascular magnetic resonance (CMR) is a key imaging modality for assessing MBF.
Accurate and efficient MBF quantification methods are needed for clinical routine.

Purpose of the Study:

To evaluate the efficacy of exponential deconvolution for MBF quantification from perfusion CMR.
To assess the accuracy and reproducibility of this deconvolution technique.

Main Methods:

Utilized simulated signal intensity curves, phantom acquisitions, and clinical CMR data.
Employed exponential approximation basis for deconvolution.
Integrated automated respiratory motion correction and myocardial contour delineation.

Main Results:

Demonstrated accurate quantification of myocardial blood flow using exponential deconvolution.
Validated the method across simulated, phantom, and clinical datasets.
Showcased the efficiency and reproducibility of the approach.

Conclusions:

Exponential deconvolution is a reliable method for accurate MBF quantification in perfusion CMR.
The integration with automated motion correction enhances clinical applicability.
This technique facilitates efficient and reproducible MBF assessment in routine clinical practice.