Abstract: Embodiments determine the density of materials and objects imaged using computed tomography (CT). An embodiment co-images a material of unknown density with a calibrant of known density. The digitally reconstructed CT images are segmented into the unknown material phase and the known material phases of the calibrant. Image intensity histograms of the unknown material are obtained. Using the known calibrant densities, an intensity-density mapping is calculated to determine the unknown material density based on its measured CT intensity. The method described herein utilizes a novel deconvolution of the intensity histograms to correct for CT imaging artifacts that impact the material phase intensities. The density of micronized materials and features such as particles, microspheres, porosity, and multiphase composites which are difficult to measure with conventional techniques can be measured using the embodiments.

Abstract: Embodiments are directed to methods and systems that generate images with structural features, including microstructures, from exemplar images. Such embodiments use an image generator steered by input attributes to generate structural features of synthetic images with the specified input attributes. The generated attributes can either accurately match the exemplar attributes or vary according to user control, without being limited to attribute combinations that are represented in the exemplar data. This enables extrapolation to novel structures that can be generated, analyzed, and optimized in silico without incurring additional data acquisition costs, e.g., costs for manufacturing, sample preparation, imaging, and/or segmentation. Embodiments enable accurate synthesis of images with target attributes of structure features, including on larger spatial domains, higher dimensions, and attributes not directly controlled or supervised during model training.

Abstract: Systems, methods, and computer-readable media to reconstruct thin wall features of domain features in images, which are marginally resolved by a multi-dimensional image, using geometrical continuity. The technique permits reconstruction of features such as structure walls that have a variable thickness, where a thin portion of the wall feature is marginally resolved by an image due to resolution limitations, and the marginally resolved portion of the feature is incompletely segmented. Using the systems, methods, and computer-readable media, such a wall feature can be recognized as a broken wall that misrepresents the completeness of the feature, and can be reconstructed as a completed wall feature.

Abstract: A method using high resolution imaging data, artificial intelligence-based quantitative image analytics, and image-based release prediction is disclosed to facilitate the determination of microstructure equivalence between two representative samples, such as pharmaceutical and material products. A computer-implemented method of evaluating microstructural equivalence of samples includes quantitatively comparing corresponding parameters of microstructure feature matrices, such as parameters for particle size distribution, porosity, uniformity of spatial distribution, and release rate of a material phase, to permit evaluating whether the samples meet a microstructural equivalence standard within an error tolerance.

Abstract: A method for computing the release rate of a controlled release drug and medical device using a combination of imaging data and computational physics is described. The method employs a three-dimensional digital representation of a drug sample, derived from two-dimensional or three-dimensional imaging, which captures the drug active pharmaceutical ingredient (API), excipients, and porosity with distinctive contrasts. Direct numerical simulations are conducted on the three-dimensional digital representation to derive effective transport properties of the API going through a porous matrix or membrane. Drug release rate can be predicted more efficiently than laboratory-based methods. When there is strong heterogeneity presented in the drug, a further method is described that engages imaging and release simulations at multiple scales. Computerized systems and programs for performing the methods are also described.

Abstract: A method for computing physical properties of materials, such as two-phase and three-phase relative permeabilities through a porous material, is described. The method employs single or multi-scale digital images of a representative sample which capture one or multiple fractionations of a micro-structure size cascade at the respective, required imaging resolutions. At a high resolution, the method computes basic physical properties, such as absolute permeabilities with a numerical method such as computational fluid dynamics solving the Navier-Stokes equation, and capillary pressure with simulations solving Young-Laplace equation. Saturation states of multiple fluids are combined to derive capillary pressure relationships at low resolutions when necessary.