976076 Alberta Inc.

Calgary Scientific Inc.

Hongmei Zhu

J Ross Mitchell

T Chen Fong

Bradley G Goodyear

Robert Brown

Ross Mitchell

M Louis Lauzon

Gregory S Mayer

Yunyan Zhang

Alan Law

The present invention relates to a method for filtering time-varying MR signal data prior to image reconstruction. A one-dimensional FT is applied to the time-varying MR signal data along each frequency-encode line of k space. The phase p of each complex pair (R,I) of the FT transformed data is calculated to create a phase profile for each frequency-encode line. This process is repeated for all time points of the time-varying MR signal data. The time course of each point within the phase profile is then transformed into Stockwell domain producing ST spectra. Frequency component magnitudes indicative of an artifact are determined and replaced with a predetermined frequency component magnitude. Each of the ST spectra is then collapsed into a one-dimensional function. New real and imaginary values (R',I&#x2032;) of the complex Fourier data are calculated based on the collapsed ST spectra which are transformed using one-dimensional inverse Fourier transformation for producing filtered time-varying MR signal data. The method for filtering time-varying MR signal data is highly advantageous by easily identifying high-frequency artifacts within the ST spectrum and filtering only frequency components near the artifacts. Therefore, high-frequency artifacts are substantially removed while the frequency content of the remaining signal is preserved, enabling for example detection of subtle frequency changes occurring over time.

Filtering artifact from fMRI data using the stockwell transform

The present invention relates to a method for visualizing ST data based on principal component analysis. ST data indicative of a plurality of local S spectra, each local S spectrum corresponding to an image point of an image of an object are received. In a first step principal component axes of each local S spectrum are determined. This step is followed by the determination of a collapsed local S spectrum by projecting a magnitude of the local S spectrum onto at least one of its principal component axes, thus reducing the dimensionality of the S spectrum. After determining a weight function capable of distinguishing frequency components within a frequency band a texture map for display is generated by calculating a scalar value from each principal component of the collapsed S spectrum using the weight function and assigning the scalar value to a corresponding position with respect to the image. The visualization method according to the invention is a highly beneficial tool for image analysis substantially retaining local frequency information but not requiring prior knowledge of frequency content of an image. Employment of the visualization method according to the invention is highly beneficial, for example, for motion artifact suppression in MRI image data, texture analysis and disease specific tissue segmentation.

Visualization of S transform data using principal-component analysis

The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.

Image texture segmentation using polar S-transform and principal component analysis

The present invention relates to a method and system for distributed computing an S transform dataset of multidimensional image data of an object. The multidimensional image data are fast Fourier transformed into Fourier domain producing a Fourier spectrum. The respective Fourier frequencies are then partitioned into a plurality of portions of frequencies for simultaneously processing. Processing of each of the plurality of portions of the Fourier frequencies is assigned to a respective processor of a plurality of processors. The Fourier spectrum of multidimensional image data and each of the plurality of portions of the Fourier frequencies is transmitted to the respective processor. The portions of the Fourier frequencies are then simultaneously processed in order to produce the S transform dataset. The S transform data are then collected and stored. The method and system for computing the S transform according to the invention provides a substantially increased computation speed enabling use of the S transform for practical applications in a clinical setting.

Distributed vector processing of the S transform for medical applications

The present invention relates to a method for filtering time-varying MR signal data prior to image reconstruction. A one-dimensional FT is applied to the time-varying MR signal data along each frequency-encode line of k space. The phase p of each complex pair (R, I) of the FT transformed data is calculated to create a phase profile for each frequency-encode line. This process is repeated for all time points of the time-varying MR signal data. The time course of each point within the phase profile is then transformed into Stockwell domain producing ST spectra. Frequency component magnitudes indicative of an artifact are determined and replaced with a predetermined frequency component magnitude. Each of the ST spectra is then collapsed into a one-dimensional function. New real and imaginary values (R', I&#x2032;) of the complex Fourier data are calculated based on the collapsed ST spectra which are transformed using one-dimensional inverse Fourier transformation for producing filtered time-varying MR signal data. The method for filtering time-varying MR signal data is highly advantageous by easily identifying high-frequency artifacts within the ST spectrum and filtering only frequency components near the artifacts. Therefore, high-frequency artifacts are substantially removed while the frequency content of the remaining signal is preserved, enabling for example detection of subtle frequency changes occurring over time.

The present invention relates to a method and system for enhancing resolution of a magnetic resonance image of an object. The method combines information from a plurality of low-resolution images with a Field Of View shifted by a distance less than a pixel width to create a synthesized image having substantially improved image quality. Information from the low-resolution images is merged and application of an aperture function enhances the SNR of the synthesized image resulting in synthesized images having a substantially higher spatial resolution as well as a substantially increased SNR. The method and system for enhancing resolution of a magnetic resonance image is highly beneficial for a MRI practitioner by substantially improving image quality, thus facilitating diagnostic methods such as texture analysis and disease specific tissue segmentation.

Synthetic aperture MRI

The present invention relates to a method for processing magnetic resonance signal data. magnetic resonance signal data in dependence upon a magnetic resonance signal time series are received. The magnetic resonance signal data are then transformed into a time-frequency Stockwell domain using a localizing time window having a frequency dependent window width in order to provide multi-resolution in the time-frequency domain. The Stockwell transformed magnetic resonance signal data are then processed in the Stockwell domain, for example, filtered based on time-frequency information of the Stockwell transformed magnetic resonance signal data. The processed Stockwell transformed magnetic resonance signal data are then transformed into Fourier domain by summing the Stockwell transformed magnetic resonance signal data over time indices of the Stockwell domain. In a further step the Fourier transformed magnetic resonance signal data are then transformed into time domain using inverse Fourier transformation. In another embodiment the method for processing magnetic resonance signals is extended for processing two-dimensional magnetic resonance signal image data in a space-frequency Stockwell domain. The method for processing magnetic resonance signals according to the invention using the Stockwell transform overcomes many limitations of the Fourier framework of existing magnetic resonance signal processing tools. It is highly advantageous by providing frequency and time/space information while keeping a close connection with the Fourier formalism, which allows implementation of the method according to the present invention into existing Fourier-based signal processing tools.

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Calgary, Canada

Hongmei Zhu