Temporal mapping of thermal ablation areas

Patent metadata

Field	Value
Publication number	US-12193871-B2
Application number	US-201917295462-A
Country	US
Kind code	B2
Filing date	Nov 15, 2019
Priority date	Nov 20, 2018
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

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.

First claim

The invention claimed is:

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 abla

Assignees

• Koninklijke Philips Nv

Inventors

• Kruecker Jochen
• Sethuraman Shriram
• Meral Faik Can
• Shi William Tao
• Leyvi Evgeniy

Classifications

• G06V10/26

  Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion · CPC title

• A61B2018/00577

  Ablation · CPC title

• A61B18/00

  Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body (eye surgery A61F9/007; ear surgery A61F11/00) · CPC title

• A61B8/485

  involving measuring strain or elastic properties · CPC title

• A61B8/085

  for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules · CPC title