Machine learning spectral FFR-CT
AssigneeKoninklijke Philips N.V.
InventorsMordechay Pinchas Freiman, Liran Goshen
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Abstract
A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.
Claims15
A system, comprising:
- a memory that stores a plurality of instructions;
- processor circuitry configured to carry out the plurality of instructions to execute a machine learning engine configured to map spectrally enhanced features extracted from spectral computed tomography (CT) volumetric image data onto fractional flow reserve (FFR) values to determine the FFR value with spectral volumetric image data, wherein the spectral CT volumetric image data include data for at least two different energies and/or energy ranges;
- and
- a display configured to visually present the determined FFR value.
A non-transitory computer readable medium having one or more executable instructions stored thereon, which when executed by processor circuitry, cause the processor circuitry to:
- receive spectral computed tomography (CT) volumetric image data, wherein the spectral CT volumetric image data include data for at least two different energies and/or energy ranges;
- process the spectral CT volumetric image data with a machine learning engine configured to map spectrally enhanced features extracted from the spectral CT volumetric image data onto fractional flow reserve (FFR) values to determine a FFR value;
- and
- visually present the FFR value.
A computer-implemented method, comprising:
- receiving spectral computed tomography (CT) volumetric image data, wherein the spectral CT volumetric image data include data for at least two different energies and/or energy ranges;
- processing the spectral CT volumetric image data with a machine learning engine configured to map spectrally enhanced features extracted from the spectral CT volumetric image data onto fractional flow reserve (FFR) values;
- estimate an FFR value at a predetermined location of a coronary tree by applying a function on a feature describing the predetermined location, wherein the function describes a statistical relationship between the feature and the FFR value;
- and
- visually presenting the FFR value.
Dependencies are inferred from the claim text.