Machine learning spectral FFR-CT
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.
1. 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.
2. The system of claim 1 , wherein the processor circuitry is further configured to extract a feature from the spectral CT volumetric image data, wherein the extracted feature includes at least one of a spectrally enhanced anatomical feature, a spectrally enhanced plaque morphological and functional feature, and a spectrally enhanced physiological feature.
3. The system of claim 2 , wherein the spectrally enhanced anatomical feature includes a geometrical and topological feature.
4. The system of claim 3 , wherein the processor circuitry is further configured to enhance an anatomical feature using different spectral images to determine a presence of different anatomical tissue of a coronary tree anatomy.
5. The system of claim 3 , wherein the processor circuitry is further configured to enhance an anatomical feature using different mono-energetic images to find a boundary between different anatomical tissue of a coronary tree anatomy.
6. The system of claim 3 , wherein the processor circuitry is further configured to enhance an anatomical feature using spectrally enabled regularization.
7. The system of claim 2 , wherein the processor circuitry is further configured to extract a spectral morphological feature by detecting, segmenting and classifying using the spectral volumetric image data.
8. The system of claim 2 , wherein the spectrally enhanced physiological feature includes a quantity related to coronary blood flow.
9. The system of claim 8 , wherein the spectrally enhanced physiological feature includes one or more of a spectral myocardium deficit or a spectral collateral flow.
10. The system of claim 1 , wherein the machine learning engine estimates the FFR value at a predetermined location of a coronary tree by applying a function on a feature describing the predetermined location.
11. The system of claim 10 , wherein the function describes a statistical relationship between the feature and the FFR value.
12. 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.
13. The non-transitory computer readable medium of claim 12 , wherein the machine learning engine estimates the FFR value at a predetermined location of a coronary tree by applying a function on a feature describing the predetermined location.
14. The non-transitory computer readable medium of claim 13 , wherein the function describes a statistical relationship between the feature and the FFR value.
15. 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.