Abstract: Segmentation of multi-energy CT data, including data in three or more energy bands. A user is enabled to input one or more region indicators in displayed CT data. At least some data is labelled based on the region indicators. Feature vectors are created for at least some data elements, which are then classified based on the labelled data elements and feature vectors. Feature vectors may be constructed using a Bag of Features or similar process. Classification may be performed using a Support Vector Machine classifier or other machine learning classifier.

Abstract: Segmentation of multi-energy CT data, including data in three or more energy bands. A user is enabled to input one or more region indicators in displayed CT data. Probability maps are generated and may be refined using distance metrics, which may include geodesic and Euclidean distance metrics. Segmentation may be based on the probability maps and/or refined probability maps. Segmentation of medical image data is also disclosed.

Abstract: Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.

Abstract: Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.

Abstract: Disclosed is a method for modelling a distorted energy spectrum in a multi-energy x-ray CT imaging system using probability distribution functions, the method including taking open beam measurements across a range of fluxes, estimating true interaction rates from said fluxes, calculating the probabilities of photons being counted, pileup order, and counts recorded to produce a model of the distorted energy spectrum. Also described are computer implemented methods and systems for modelling distortions and optimizing material images produced from multi-energy x-ray CT scanning systems using the modelling techniques.

Abstract: Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.

Abstract: Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.

Abstract: Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.