Cardiac Ablation Segment Selection Through Multimodal Image Overlap
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Solution Overview
Problem
Current invasive and non-invasive ablation techniques for cardiac arrhythmias suffer from user-dependent variability and lack of formal decision support, leading to inconsistent treatment outcomes due to manual conversion of imaging and clinical data into treatment targets.
Innovation Solution
A method and system for determining cardiac arrhythmia targets using multi-modal image mappings and risk profiles, combining electrical, anatomic, and functional mappings to identify abnormalities, and generating a decision support module for objective treatment planning, including non-invasive ablation options like SBRT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual conversion of imaging and clinical data into treatment targets is used, then flexibility in treatment planning is maintained, but user-dependent variability increases leading to inconsistent treatment outcomes
Solution Approach 1:
A computer-based decision support system acts as an intermediary between clinical data and treatment targets. The system processes multiple imaging modalities (CT, MRI, PET, SPECT) and clinical parameters through standardized algorithms to generate treatment targets, reducing manual variability while preserving clinical flexibility through adjustable parameters and multiple treatment options.
Solution Approach 2:
The system transforms qualitative clinical judgments into quantitative parameters by assigning weights to different imaging modalities and clinical factors. Treatment targets are defined by specific parameter thresholds (e.g., abnormality overlap percentage, risk score cutoffs), enabling consistent reproduction of treatment plans across different users while maintaining adaptability through parameter adjustment.
2Reliability
If invasive catheter ablation is used, then treatment effectiveness is improved, but patient risk increases due to invasive procedures
Solution Approach 1:
The system replaces mechanical invasive procedures with non-invasive imaging and computational analysis. Multiple imaging modalities substitute for invasive catheter-based mapping, and computer-based target definition replaces manual invasive procedures, achieving comparable treatment effectiveness with reduced patient risk.
Solution Approach 2:
The system performs comprehensive pre-treatment planning using non-invasive imaging to precisely define treatment targets before any intervention. Treatment targets are identified and validated through multiple imaging modalities and risk assessments in advance, allowing optimization of treatment approach (invasive vs. non-invasive) before patient exposure to procedural risks.
3Object-affected harmful factors
If non-invasive ablation using stereotactic body radiotherapy is used, then patient risk is reduced, but treatment precision may be compromised without formal decision support
Solution Approach 1:
A computer-based decision support system serves as an intermediary to ensure treatment precision in non-invasive ablation. The system integrates multiple imaging modalities (CT, MRI, PET, SPECT) and applies standardized algorithms to define treatment targets with high precision, enabling non-invasive SBRT to achieve accuracy comparable to invasive methods while maintaining reduced patient risk.
Solution Approach 2:
The system combines multiple imaging modalities (CT for anatomy, MRI for soft tissue, PET/SPECT for function) into a composite treatment plan. This multi-modal integration creates a comprehensive view of the treatment target, ensuring high precision for non-invasive ablation by compensating for the limitations of any single imaging modality.
4Adaptability or versatility
If ad hoc target selection is used, then treatment adaptability is maintained, but treatment consistency deteriorates due to lack of formal decision support
Solution Approach 1:
The system maintains treatment adaptability through adjustable parameters (imaging modality weights, abnormality thresholds, risk factor weights) while ensuring consistency through standardized algorithms. Clinicians can modify parameters to adapt to specific patient conditions, but the underlying decision framework remains consistent and reproducible across different treatment cases.
Solution Approach 2:
The decision support system provides dynamic treatment planning where parameters and weights can be adjusted based on specific patient conditions and clinical judgment. The system adapts to different scenarios (different imaging modalities, different arrhythmia types, different patient risk profiles) while maintaining a consistent decision-making framework through standardized algorithms and reproducible methodologies.
Data Source
AI summary
Disclosed herein are systems and methods for determining one or more cardiac arrhythmia targets for ablation. The method may include receiving one or more mappings, identifying an abnormality in the one or more mappings, combining the one or more mappings, and defining the one or more cardiac arrhythmia targets based on an overlap of the identified abnormality in the combined one or more mappings.