Liver Modeling via Multi-Modal Imaging and Generative Feedback
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Solution Overview
Problem
Current liver disease assessment methods rely heavily on limited medical imaging information, leading to incomplete treatment and recurrence in localized strategies for liver cancer, as they lack comprehensive and patient-specific data for therapy planning.
Innovation Solution
A method for liver modeling from medical scan data using multiple imaging modalities, generating anatomical, substrate, perfusion, and microvascular models to create a comprehensive, patient-specific computational model that predicts liver function changes due to therapy, enabling more informed treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple imaging modalities and generative modeling are used to create comprehensive liver models, then the completeness and accuracy of liver function assessment is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent combines multiple imaging modalities (CT, MRI, ultrasound) with different contrast agents and timing protocols to create a comprehensive anatomical model. This merging of diverse data sources provides complete liver function assessment by integrating structural, functional, and perfusion information that no single modality could provide alone.
Solution Approach 2:
The patent introduces a generative modeling system as an intermediary that processes and integrates data from multiple imaging modalities. This computational intermediary synthesizes the complex multi-modal data into unified anatomical models, managing the complexity while extracting comprehensive functional information.
2Adaptability or versatility
If patient-specific computational models are created from multiple imaging modalities, then the personalization and effectiveness of treatment planning is improved, but the time and computational resources required increase
Solution Approach 1:
The patent performs comprehensive multi-modal imaging and generative modeling during the pre-treatment planning phase. By completing the full anatomical model creation and functional assessment before treatment delivery, the system enables highly personalized treatment planning without delaying the actual therapeutic intervention.
Solution Approach 2:
The patent creates dynamic, patient-specific anatomical models that can be updated and adapted based on individual response to treatment. This dynamic modeling approach allows treatment plans to be personalized and adjusted in real-time based on observed changes in liver function and anatomy.
3Reliability
If comprehensive anatomical models integrating multiple imaging modalities are generated, then the completeness of treatment coverage is improved, but the difficulty of detecting and measuring liver function increases
Solution Approach 1:
The patent segments the liver into functional units and creates separate anatomical models for different liver segments based on multi-modal imaging data. This segmentation approach allows comprehensive treatment coverage planning by enabling precise localization and assessment of treatment effects in specific liver regions while simplifying the overall measurement complexity.
Solution Approach 2:
The patent uses different contrast agents that produce distinct signal characteristics (analogous to color changes) across multiple imaging modalities. These differential signal responses allow differentiation of various liver tissues, vessels, and pathologies, improving treatment outcome reliability while providing clear visual differentiation that simplifies functional assessment.
Data Source
AI summary
For liver modeling from medical scan data, multiple modalities of imaging are used. By using multiple modalities of imaging in combination with generative modeling, a more comprehensive and informed assessment may be performed. The generative modeling may allow feedback of effects of proposed therapy on function of the liver. This feedback is used to update the liver function information based on the imaging. Based on the computerized modeling with information from various imaging modes, an output based on more comprehensive information and patient personalized modeling and feedback may be provided to assist the physician.