Temporal mapping of thermal ablation areas
Various embodiments of the present disclosure include a thermal ablation probabilistic controller ( 30 ) employing an ablation probability model ( 32 ) trained to render a pixel ablation probability for each pixel of an ablation scan image illustrative of a static anatomical ablation. In operation, the thermal ablation probabilistic controller ( 30 ) spatially aligns a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation, and applies the ablation probability model ( 32 ) to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for each pixel of the ablation scan image illustrative of the static anatomical ablation.
1. A thermal ablation probabilistic controller, comprising:
a memory including an ablation probability model trained to render a pixel ablation probability for at least some pixels of an ablation scan image illustrative of a static anatomical ablation; and
at least one processor in communication with the memory, wherein the at least one processor is configured to:
spatially align a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation; and
apply the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.
2. The thermal ablation probabilistic controller of claim 1 , wherein the application of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets further includes the at least one processor configured to:
derive a temporal pixel intensity of a pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets;
derive a temporal pixel intensity of at least one neighboring pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets; and
apply an ablation probability rule to the temporal pixel intensities of the pixel and the at least one neighboring pixel to render the pixel ablation probability of the pixel.
3. The thermal ablation probabilistic controller of claim 1 , wherein the temporal pixel intensity valuation of at least some pixels of the temporal sequence of ablation scan datasets is one of:
an intensity average of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a median intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a maximum intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a minimum intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets; and
an intensity value derived from a standard deviation of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets.
4. The thermal ablation probabilistic controller of claim 1 , wherein the at least one processor is further configured to at least one of:
generate an ablation probability image derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation;
generate an ablation probability anatomical scan image derived from an anatomical image of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation; and
generate an ablation probability ablation scan image derived from the ablation scan image illustrative of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.
5. The thermal ablation probabilistic controller of claim 1 , wherein an intensity value of at least some pixels of the temporal sequence of ablation scan datasets is representative of one of an anatomical stiffness, an anatomical density or an anatomical temperature.
6. The thermal ablation probabilistic controller of claim 1 , wherein the application of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets includes the at least one processor configured to:
execute a temporal pixel intensity valuation, by the ablation probability model, of a temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets.
7. The thermal ablation probabilistic controller of claim 6 , wherein the application of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets further the at least one processor configured to:
execute a spatial pixel intensity assessment, by the ablation probability model, of the temporal pixel intensity valuation of one or each of at least one neighboring pixel of the spatially alignment of the temporal sequence of ablation scan datasets.
8. The thermal ablation probabilistic controller of claim 7 , wherein the application of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets further the at least one processor configured to:
apply, by the ablation probability model, at least one ablation probability rule to the temporal pixel intensity valuation and the spatial pixel intensity assessment the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation,
wherein the at least one ablation probability rules are based on a comparison of the temporal pixel intensity valuations of the pixel and the at least one neighboring pixel to an ablation threshold.
9. A non-transitory machine-readable storage medium encoded with instructions for execution by at least one processor of an ablation probability model trained to render a pixel ablation probability for at least some pixels of an ablation scan datasets representative of a static anatomical ablation, the non-transitory machine-readable storage medium comprising instructions to:
spatially align a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation; and
apply the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.
10. The non-transitory machine-readable storage medium of claim 9 , wherein the application of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets includes instructions to:
derive a temporal pixel intensity of a pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets;
derive a temporal pixel intensity of at least one neighboring pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets; and
apply an ablation probability rule to the temporal pixel intensity of the pixel and the temporal pixel intensity of at least one neighboring pixel to render the pixel ablation probability of the pixel.
11. The non-transitory machine-readable storage medium of claim 9 , further comprising instructions to at least one of:
generate an ablation probability image derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation;
generate an ablation probability anatomical scan image derived from an anatomical image of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation; and
generate an ablation probability ablation scan image derived from the ablation scan image illustrative of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.
12. The non-transitory machine-readable storage medium of claim 9 , wherein the instructions to apply the ablation probability model the spatial alignment of the temporal sequence of ablation scan datasets includes instructions to:
execute, by the ablation probability model, a temporal pixel intensity valuation of a temporal sequence of at least some pixels of the spatially alignment of the temporal sequence of ablation scan datasets.
13. The non-transitory machine-readable storage medium of claim 12 , wherein the temporal pixel intensity valuation of at least some pixels of the temporal sequence of ablation scan datasets is one of:
an intensity average of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a median intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a maximum intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets;
a minimum intensity value of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets; and
an intensity value derived from a standard deviation of the temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets.
14. The non-transitory machine-readable storage medium of claim 12 , wherein the instructions to apply the ablation probability model the spatial alignment of the temporal sequence of ablation scan datasets further includes instructions to:
compute, by the ablation probability model, a spatial pixel intensity assessment of the temporal pixel intensity valuation of one or each of at least one neighboring pixel of the spatial alignment of the temporal sequence of ablation scan datasets.
15. The non-transitory machine-readable storage medium of claim 14 , wherein the instructions to apply the ablation probability model the spatial alignment of the temporal sequence of ablation scan datasets further includes instructions to:
apply, by the ablation probability model, at least one ablation probability rule to the temporal pixel intensity valuation and the spatial pixel intensity assessment the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation,
wherein the at least one ablation probability rules are based on a comparison of the temporal pixel intensity valuations of the pixel and the at least one neighboring pixel to an ablation threshold.
16. A thermal ablation probabilistic method executable by a thermal ablation probabilistic controller including an ablation probability model trained to render a pixel ablation probability for at least some pixels of an ablation scan datasets representative of a static anatomical ablation,
the thermal ablation probabilistic method comprising:
spatially aligning, by the thermal ablation probabilistic controller, a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation; and
applying, by the thermal ablation probabilistic controller, the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.
17. The thermal ablation probabilistic method of claim 16 , wherein the applying, by the thermal ablation probabilistic controller, of the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets includes:
deriving a temporal pixel intensity of a pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets;
deriving a temporal pixel intensity of at least one neighboring pixel of the ablation scan image illustrative of the static anatomical ablation from the spatial alignment of the temporal sequence of ablation scan datasets; and
applying an ablation probability rule to the temporal pixel intensity of the pixel and the temporal pixel intensity of at least one neighboring pixel to render the pixel ablation probability of the pixel.
18. The thermal ablation probabilistic method of claim 16 , wherein the applying, by the thermal ablation probabilistic controller, of the ablation probability model the spatial alignment of the temporal sequence of ablation scan datasets includes:
executing, by the ablation probability model, a temporal pixel intensity valuation of a temporal sequence of at least some pixels of the spatial alignment of the temporal sequence of ablation scan datasets; and
executing, by the ablation probability model, a spatial pixel intensity of the temporal pixel intensity valuation of one or each of at least one neighboring pixel of at least some pixels the spatial alignment of the temporal sequence of ablation scan datasets.
19. The thermal ablation probabilistic method of claim 16 , wherein the applying, by the thermal ablation probabilistic controller, of the ablation probability model the spatial alignment of the temporal sequence of ablation scan datasets further includes:
applying, by the ablation probability model, at least one probability rule to the temporal pixel intensity valuation and the spatial pixel intensity assessment to render the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation,
wherein the at least one ablation probability rules are based on a comparison of the temporal pixel intensity valuations of the pixel and the at least one neighboring pixel to an ablation threshold.
20. The thermal ablation probabilistic method of claim 16 , further comprising at least one of:
generating an ablation probability image derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation;
generating an ablation probability anatomical scan image derived from an anatomical image of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation; and
generating an ablation probability ablation scan image derived from the ablation scan image illustrative of the static anatomical ablation and further derived from the pixel ablation probability for at least some pixels of the ablation scan image illustrative of the static anatomical ablation.