Thermal Ablation Probabilistic Controller
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Solution Overview
Problem
Current ultrasound-based methods for monitoring percutaneous thermal ablation face challenges in accurately visualizing the ablation boundary due to artifacts and low signal-to-noise ratio, leading to incomplete ablations or damage to healthy tissues.
Innovation Solution
A probabilistic approach using a thermal ablation probabilistic controller that employs a trained ablation probability model to render pixel probabilities for each pixel in ablation scan images, aligning temporal sequences of scan datasets to identify static and dynamic anatomical ablations, and applying rules to determine the likelihood of ablation areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasound-based methods are used to monitor ablation, then real-time imaging is available, but artifacts and low signal-to-noise ratio make ablation boundary identification difficult
Solution Approach 1:
The patent combines multiple ultrasound scan images taken at different time points during ablation into a single composite image. By merging temporal information from multiple scans, the system accumulates signal data while averaging out random noise and artifacts, thereby improving both the reliability of monitoring and the precision of ablation boundary detection.
Solution Approach 2:
The patent transitions from analyzing single static images to utilizing temporal sequences of images. By adding the time dimension and processing scans across multiple time points, the system extracts more reliable information about ablation boundaries that cannot be obtained from individual snapshots alone.
2Productivity
If individual ablation monitoring images are analyzed at discrete time points, then real-time feedback is provided, but the contours of ablation areas become difficult to identify due to artifacts
Solution Approach 1:
The system performs preliminary processing of multiple scan images before final interpretation. By pre-computing composite images that integrate information from multiple time points, the system prepares enhanced boundary information in advance, making subsequent ablation monitoring more efficient and reducing information loss about ablation contours.
3Reliability
If thermal ablation is performed to destroy tumor tissue, then cancer treatment is achieved, but healthy critical structures near the ablation area may be damaged
Solution Approach 1:
The patent implements continuous feedback monitoring by analyzing composite images from multiple time points during ablation. This enhanced monitoring provides real-time information about the expanding ablation zone, allowing operators to adjust treatment parameters to ensure complete tumor destruction while preventing damage to adjacent healthy structures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides accurate and reliable monitoring of ablation areas, improving the identification of ablation boundaries and reducing the risk of incomplete treatments or damage to healthy structures by generating probabilistic images that clearly delineate the ablation zones.
Implementation Method 1
spatially aligning a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation
Implementation Method 2
applying the ablation probability model to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for each pixel
Data Source
AI summary
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.