Company Filing History:
Years Active: 2005-2009
Title: T Chen Fong: Innovator in Medical Imaging Technology
Introduction
T Chen Fong is a prominent inventor based in Calgary, Canada. He has made significant contributions to the field of medical imaging, particularly in the area of magnetic resonance imaging (MRI). With a total of 5 patents, his work focuses on enhancing the quality and accuracy of MRI data analysis.
Latest Patents
One of his latest patents is titled "Filtering artifact from fMRI data using the Stockwell transform." This invention presents a method for filtering time-varying MR signal data prior to image reconstruction. A one-dimensional Fourier Transform (FT) is applied to the time-varying MR signal data along each frequency-encode line of k space. The phase of each complex pair 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 of the complex Fourier data are calculated based on the collapsed ST spectra, which are transformed using one-dimensional inverse Fourier transformation to produce filtered time-varying MR signal data. This method is highly advantageous as it easily identifies high-frequency artifacts within the ST spectrum and filters only frequency components near the artifacts. Consequently, high-frequency artifacts are substantially removed while preserving the frequency content of the remaining signal, enabling the detection of subtle frequency changes occurring over time.
Another notable patent is "Visualization of S transform data using principal-component analysis." This 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 corresponding to an image point of an object, are received. The principal component axes of each local S spectrum are determined, 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. This process reduces 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