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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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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

Aggregated motion estimation for real-time MRI reconstruction.

Housen Li¹, Markus Haltmeier, Shuo Zhang

¹Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany; Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.

Magnetic Resonance in Medicine

|November 19, 2013

Summary

This summary is machine-generated.

Real-time MRI reconstruction is improved with aggregated motion estimation (AME), enhancing temporal fidelity and reducing artifacts for dynamic imaging. This method integrates motion into the inverse problem, outperforming standard nonlinear inversion.

Keywords:

aggregated imaging inverse problems motion estimation nonlinear inversion parallel imaging real-time MRI

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging
Image Reconstruction

Background:

Real-time MRI reconstructs images from undersampled data using inverse problem solutions.
Current methods like nonlinear inversion (NLINV) may limit temporal fidelity due to assumptions on image continuity.

Purpose of the Study:

To develop and evaluate a novel real-time MRI reconstruction method incorporating motion estimation.
To improve temporal fidelity and reduce artifacts in dynamic MRI scans.

Main Methods:

Proposed a method integrating frame-to-frame deformations into the data consistency term of the inverse problem (Aggregated Motion Estimation - AME).
AME handles non-rigid motion, multi-channel data, and non-Cartesian sampling (radial) without additional measurements.
Simultaneously determined image and coil sensitivity profiles in a nonlinear formulation.

Main Results:

AME reconstructions showed improved temporal fidelity compared to NLINV.
Reduced residual undersampling artifacts were observed with AME.
Demonstrated effectiveness in motion phantoms and dynamic human physiological movements (heart, tongue).

Conclusions:

Nonlinear inverse reconstructions with AME enhance image quality and temporal acuity for real-time MRI.
This approach is effective for visualizing rapid dynamic processes.