K-space synthesis for MR imaging in the presence of gradient...

Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation

Reexamination Certificate

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Reexamination Certificate

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06636756

ABSTRACT:

BACKGROUND OF THE INVENTION
This invention relates to magnetic resonance imaging (MRI) systems and more particularly to such systems used for reconstructing corrected MR images.
Conventional MR scanners rely on gradient field linearity to achieve linear mapping of a spin's spatial location to its resonance frequency. Given that such a linear relationship between position and frequency is strictly maintained inside the imaged region, the MR signal measurements can be interpreted as samples of the imaged region's spatial spectrum. This powerful interpretation constitutes the foundation of Fourier transform (FT) based MR imaging techniques. Fourier transform based MR imaging techniques rely on establishing a liner gradient magnetic field across the imaged region and modeling MR data as k-space samples. In the presence of gradient field nonlinearity however, this interpretation becomes inaccurate and direct FT reconstruction with the MR data generally results in images with geometrical and intensity distortions. Existing methods correct the distortions based on quantifying positioning errors and intensity alterations in the reconstructed images. While such image-space compensation methods have proven to be effective coping with minor nonlinearities, their accuracy fall short in cases where other design factors, including speed, openness, driver power and etc., mandate significant compromise in gradient field linearity.
What is needed is an effective method for MR imaging that is able to correct for gradient field non-linearities.
SUMMARY OF THE INVENTION
A method for reconstructing a corrected Magnetic Resonance (MR) image from acquired image data having distortions due to gradient field non-linearity comprises correcting the distortions in the image data during acquisition of the image data in k-space and reconstructing the image data into the corrected MR image.


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