Systems and methods for analyzing pathologies utilizing quantitative imaging

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the tissue characteristics image model provides a partitioning scheme for multi-phase level sets where analyte image regions are defined by characteristic functions χ as a function of level set functions ϕ for each tissue characteristic type;
• and
• wherein iteratively adjusting the tissue characteristics image model includes:
• computing the characteristic functions from the level set functions for the current iteration;
• computing blurred characteristic functions based on a IIR Gaussian blurring given a point spread function (PSF) for the imaging device;
• computing image intensity constants for the blurred characteristic functions;
• and
• computing level set updates based on a gradient descent approach to minimize the energy function.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the tissue characteristics image model provides a partitioning scheme for multi-phase level sets where analyte image regions are defined by characteristic functions χ as a function of level set functions ϕ for each tissue characteristic type;
• wherein the image is of a vascular or pulmonary vessel, wherein the level set functions include a first level set for the vessel lumen and each contiguous region representing an analyte is initialized as its own level set;
• and
• wherein at least one of (i) the analyte regions represent calcification regions or (ii) the analyte regions further include lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH).

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the tissue characteristics image model provides a partitioning scheme for multi-phase level sets where analyte image regions are defined by characteristic functions χ as a function of level set functions ϕ for each tissue characteristic type;
• wherein the image is of a vascular or pulmonary vessel, wherein the level set functions include a first level set for the vessel lumen and each contiguous region representing an analyte is initialized as its own level set;
• wherein candidate analyte regions are evaluated using a morphological watershed applied to an inverted image;
• and
• wherein an upper range (percentile) value within each region in the watershed image is computed and a per-region threshold is determined as between the upper range percentile value and a value for the image background.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein deblurring and segmentation is performed for analytes representing high-contrast bright regions, the method further comprising sub-pixel thresholding on the deblurred image and a second pass deblurring and segmentation and segmentation of low-contrast regions.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the energy function includes a prior probability component;
• wherein the prior probability component weights the model toward the most likely configurations from prior distribution and away from physically and biologically unrealistic solutions;
• and
• wherein prior probability is modeled with respect to tissue characteristic region boundaries, true deblurred tissue characteristic image intensities, and scanner blur.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the energy function includes a prior probability component;
• and
• wherein the prior probability component models a priori probability of each pixel belonging to a given tissue characteristic class and a priori smoothness of the boundary, represented with curvature.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the energy function includes a prior probability component;
• and
• wherein the prior probability component models (i) tissue characteristic prior probability, (ii) curvature prior probability, and (iii) the tissue characteristic image intensity prior probability.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image;
• wherein the energy function includes a biological component;
• and
• wherein the biological portion of the model incorporates histology-driven knowledge of the structure and growth patterns of analytes.

A computer-implemented method for deblurring an image including one or more analyte image regions, the method comprising:

• using an imaging device to acquire a measured image g;
• initializing a tissue characteristics image model representing a true underlying image f, wherein the tissue characteristics image model provides a partitioning scheme for multi-phase level sets where analyte image regions are defined by characteristic functions χ as a function of level set functions ϕ for each tissue characteristic type;
• computing volume fraction from characteristic functions;
• iteratively adjusting the tissue characteristics image model in order to minimize an energy function which models imaging physics relating to the appearance of different analytes in a Bayesian framework;
• and
• outputting a deblurred image.