Synthetic visualization and quantification of perfusion-related tissue viability
First Claim
1. A method for operating a computing system to generate a brain tissue viability map from an input image dataset acquired from a patient'"'"'s brain by a medical imaging system, comprising:
- computing, for each of one or more different perfusion parameters, an imaged perfusion map of perfusion values as a function of the input image dataset;
identifying a normal brain reference region as a function of at least one of the one or more imaged perfusion maps;
identifying, for each perfusion parameter, a normal perfusion value as a function of perfusion values in the normal brain reference region;
computing, for each perfusion parameter, a normalized perfusion map as a function of the imaged perfusion map and the normal perfusion value;
storing linguistic tissue classification rules characterizing each of a plurality of tissue viability classes as a function of one or more of the one or more perfusion parameters;
classifying elements of the input image dataset into one of the plurality of tissue viability classes as a function of the one or more normalized perfusion maps and the tissue classification rules; and
generating a brain tissue viability map as a function of the classified elements of the input image dataset.
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Abstract
A computing system and method for generating, displaying and manipulating synthetic 2D and 3D brain tissue viability images and associated metrics from multiple perfusion maps derived from CT or other imaging system input image datasets. Stored linguistic tissue classification rules characterizing infarct, ischemic and/or normal or other tissue classes as a function of one or more perfusion parameters are used. The perfusion maps are normalized to normal brain tissue regions. Elements of the input image dataset are classified into one of the tissue classes as a function of the normalized perfusion maps and the classification rules. The classification includes ranking each of the plurality of tissue classes for elements of the input image dataset, and assigning one of the classes to elements of the dataset as a function of the ranks.
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Citations
37 Claims
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1. A method for operating a computing system to generate a brain tissue viability map from an input image dataset acquired from a patient'"'"'s brain by a medical imaging system, comprising:
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computing, for each of one or more different perfusion parameters, an imaged perfusion map of perfusion values as a function of the input image dataset; identifying a normal brain reference region as a function of at least one of the one or more imaged perfusion maps; identifying, for each perfusion parameter, a normal perfusion value as a function of perfusion values in the normal brain reference region; computing, for each perfusion parameter, a normalized perfusion map as a function of the imaged perfusion map and the normal perfusion value; storing linguistic tissue classification rules characterizing each of a plurality of tissue viability classes as a function of one or more of the one or more perfusion parameters; classifying elements of the input image dataset into one of the plurality of tissue viability classes as a function of the one or more normalized perfusion maps and the tissue classification rules; and generating a brain tissue viability map as a function of the classified elements of the input image dataset. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. A method for operating a computing system to generate and display a brain tissue viability map from an input image dataset acquired from a patient'"'"'s brain by a medical imaging system, comprising:
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computing, for each of two or more different perfusion parameters, an imaged perfusion map of perfusion values as a function of the input image dataset; identifying a normal brain reference region as a function of at least one of the imaged perfusion maps; identifying, for each perfusion parameter, a normal perfusion value as a function of perfusion values in the normal brain reference region; computing, for each perfusion parameter, a normalized perfusion map as a function of the imaged perfusion map and the normal perfusion value; storing linguistic tissue classification rules characterizing each of a plurality of tissue viability classes as a function of one or more of the perfusion parameters; classifying elements of the input image dataset into one of the plurality of tissue viability classes as a function of the normalized perfusion maps and the tissue classification rules;
including;ranking each of the plurality of tissue classes for elements of the input image dataset; and assigning one of the plurality of tissue classes to elements of the input image dataset as a function of the ranks; generating a brain tissue viability map as a function of the classified elements of the input image dataset; and displaying a tissue viability image as a function of the brain tissue viability map. - View Dependent Claims (31, 32, 33)
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34. A computing system for generating and displaying a brain tissue viability map from an input image dataset acquired from a patient'"'"'s brain by a medical imaging system, comprising:
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a data store for storing information representative of; a plurality of perfusion parameter mapping algorithms; and linguistic tissue classification rules characterizing each of a plurality of tissue viability classes as a function of one or more perfusion parameters; a processor coupled to the data store, wherein the processor; computes, for each of two or more different perfusion parameters as a function of the perfusion parameter mapping algorithms, an imaged perfusion map of perfusion values as a function of the input image dataset; identifies a normal brain reference region as a function of at least one of the imaged perfusion maps; identifies, for each perfusion parameter, a normal perfusion value as a function of perfusion values in the normal brain reference region; computes, for each perfusion parameter, a normalized perfusion map as a function of the imaged perfusion map and the normal perfusion value; classifies elements of the input image dataset into one of the plurality of tissue viability classes as a function of the normalized perfusion maps and the tissue classification rules;
including;ranking each of the plurality of tissue classes for elements of the input image dataset; and assigning one of the plurality of tissue classes to elements of the input image dataset as a function of the ranks; and generates a brain tissue viability map as a function of the classified elements of the input image dataset; and a display to present a tissue viability image as a function of the brain tissue viability map. - View Dependent Claims (35, 36, 37)
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Specification