METHODS TO FACILITATE AND GUIDE DATA ANALYSIS USING MRµTEXTURE AND METHOD OF APPLICATION OF MRµTEXTURE TO DIAGNOSIS OF COVID-19 AND OTHER MULTI-ORGAN DISEASES
A method for calibration of the MRμTexture method is presented wherein a plurality of model datasets representing a continuum of structures with a continuum of biomarker values is generated by morphing data of a 2D structure or 3D structure of a first known disease state to a 2D structure or a 3D structure of a second known disease state. MRμTexture is applied in silico to extract a simulation data set of texture prevalence for a selected one of a plurality of intermediate morphed conditions corresponding to the plurality of model datasets.
1 . A method for calibration of the MRμTexture method comprising:
generating a plurality of model datasets representing a continuum of structures with a continuum of biomarker values by morphing data of a 2D structure or 3D structure of a first known disease state to a 2D structure or a 3D structure of a second known disease state; and
applying MRμTexture in silico to extract a simulation data set of texture prevalence for a selected one of a plurality of intermediate morphed conditions corresponding to the plurality of model datasets.
2 . The method as defined in claim 1 wherein the step of generating a continuum of structures comprises modeling the structure in silico and morphing the in silico model.
3 . The method as defined in claim 1 wherein the step of generating a continuum of structures comprises employing histology as a ground truth for the first known disease state and the second known disease state.
4 . The method as defined in claim 1 wherein the 2D structure or 3D structure is a representation of a selected one of bone, liver, prostate, brain, pancreas, or organs in general.
5 . The method as defined in claim 1 wherein the step of applying MRμTexture in silico to extract a data set of texture prevalence includes varying the contrast.
6 . The method as defined in claim 1 wherein the step of applying MRμTexture in silico to extract a dataset of texture prevalence comprises:
setting a first receiver bandwidth to delineate a length of a VOI;
varying the bandwidth and measuring a mean and range in the datasets to quantify the texture in a segment of a feature size spectrum for a select set of k-values.
7 . The method as defined in claim 1 wherein the step of applying MRμTexture in silico to extract a data set of texture prevalence includes preforming a Fourier analysis of the 2D or 3D in silico model and selecting the Fourier coefficients along the axis corresponding to the desired analysis direction in the VOI to provide a Fourier series of k-encoded simulated MRμTexture method signals.
8 . The method as defined in claim 7 wherein the step of applying MRμTexture in silico to extract a data set of texture prevalence further includes simulating the signal for a single k-encode by first summing the signal values for all points on the one or two axes (for 2D and 3D models respectively) orthogonal to the analysis direction in the VOI for each point along the analysis direction to generates a 1D signal intensity vs. position array along the analysis direction of the VOI; multiplying the array by a complex sinusoid with a wavelength corresponding to the desired k-encode wherein the complex sum of the points in this product array provides a simulated MRμTexture method signal.
9 . The method as defined in claim 1 further comprising:
correlating variation in MRμTexture signal across a structure with variation in genetic heterogeneity within the structure; and,
informing a library of MRμTexture signature vs. genomic signature.