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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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Image reconstruction in k-space from MR data encoded with ambiguous gradient fields.

Gerrit Schultz¹, Daniel Gallichan, Hans Weber

¹Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany.

Magnetic Resonance in Medicine

|April 30, 2014

Summary

This summary is machine-generated.

Reconstruction in k-space has limits with non-bijective gradient fields, unlike image-space reconstruction. Image-space methods better resolve aliasing artifacts caused by these non-bijective fields, offering practical advantages.

Keywords:

PatLoc gradient k-space magnetic resonance imaging nonlinear reconstruction

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Area of Science:

Magnetic Resonance Imaging (MRI)
Image Reconstruction
Spatial Encoding

Background:

The analogy between parallel imaging and non-bijective encoding fields breaks down in k-space.
Ambiguities in k-space reconstruction are only partially resolvable, unlike in image space.

Purpose of the Study:

To explore the limitations of k-space image reconstruction using non-bijective gradient fields.
To compare k-space and image-space reconstruction performance with non-bijective encoding.

Main Methods:

Development and testing of image-space and k-space reconstruction algorithms.
Utilizing multi-channel radiofrequency data acquisitions.
Validation through numerical simulations and in vivo measurements.

Main Results:

Non-bijective gradient fields reduce the information gained from autocalibration data in k-space reconstruction.
K-space reconstruction can only unfold undersampling artifacts.
Image-space reconstruction can resolve aliasing caused by non-bijective gradient fields.

Conclusions:

Reconstruction in image space and k-space are similar for standard imaging.
A fundamental difference exists for non-bijective encoding fields, impacting algorithm selection.
Image-space reconstruction offers superior aliasing resolution with non-bijective fields.