Cardiac SABR Workflow Using ML for ECG-CT Target Demarcation
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Solution Overview
Problem
Current cardiac stereotactic ablative radiotherapy (SABR) procedures for treating arrhythmias are time-consuming and lack accuracy in identifying and demarcating target regions due to manual processes and resolution discrepancies between different CT scanners, leading to potential inaccuracies in treatment planning.
Innovation Solution
An overall ablation workflow (OAW) system utilizing 3D machine learning models to generate patient-specific 3D meshes and demarcated meshes, integrating electrocardiogram data and CT scans to accurately identify target regions and avoidance structures, thereby enhancing the precision and efficiency of treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to identify and demarcate target regions, then medical providers can perform ablation procedures, but the process is time-consuming and lacks accuracy
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-based system that uses machine learning models to generate 3D meshes and identify target regions. The system automatically processes medical imaging data and electrocardiogram information to produce accurate treatment plans without requiring manual demarcation by medical providers, thereby improving both accuracy and reducing planning time.
2Reliability
If different CT scanners with different resolutions are used, then comprehensive patient imaging can be obtained, but resolution discrepancies lead to inaccuracies in treatment planning
Solution Approach 1:
The patent transforms imaging data from different CT scanners with varying resolutions into a unified 3D mesh representation. The system normalizes and integrates data from multiple sources, converting disparate resolution parameters into a consistent format that enables accurate target region identification and treatment planning, thereby resolving the precision issues caused by resolution discrepancies.
3Productivity
If automated systems are used to generate treatment plans, then planning time is reduced, but system complexity increases
Solution Approach 1:
The patent employs pre-trained machine learning models that have been prepared in advance to perform specific tasks such as generating 3D meshes from imaging data and identifying target regions. These pre-trained models enable the system to rapidly process patient-specific data without requiring complex real-time computations, thereby achieving high productivity while managing system complexity through the use of prepared, specialized algorithms.
Data Source
AI summary
A technology is provided for supporting cardiac stereotactic ablative radiotherapy (SABR) procedure. The technology collects an arrhythmia electrocardiogram (ECG) from the patient and a CT scan. The technology employs a mapping system to generate a demarcated generic three-dimensional (3D) mesh based on the ECG. The demarcated generic 3D mesh has a target for an ablation demarcated. The technology employs a 3D machine learning (ML) model to generate a patient-specific 3D mesh based on the CT scan. The technology employs a demarcation ML model to generate a demarcated patient-specific 3D mesh based on the patient-specific 3D mesh and the demarcated generic 3D mesh. The demarcated patient-specific 3D mesh has the target for the ablation demarcated to account for difference between cardiac geometry of the patient-specific 3D mesh and the demarcated generic 3D mesh.