Lung Ventilation Imaging via Integrated Jacobian Formulation
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Solution Overview
Problem
Current methods for computing CT-ventilation imaging face challenges such as numerical instability and poor reproducibility due to finite difference approximations in deformation field-based methods, leading to sensitivity to acquisition artifacts and uncertainty in volume change estimates.
Innovation Solution
The Integrated Jacobian Formulation (IJF) method is employed, which uses a sampling method to numerically integrate regional Jacobian formulations, providing robust estimates of volume changes with controllable uncertainty and generating consistent ventilation images across different acquisition types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If finite difference approximations are used in deformation field-based methods, then computational simplicity is improved, but numerical stability and reproducibility deteriorate
Solution Approach 1:
The patent changes the mathematical parameters from finite difference approximations to integrated Jacobian formulations with sampling methods. This transformation maintains computational feasibility while significantly improving numerical stability and reducing sensitivity to deformation field perturbations, thereby resolving the contradiction between computational simplicity and numerical reliability.
Solution Approach 2:
The patent substitutes the mechanical finite difference approximation approach with an integrated Jacobian formulation based on sampling methods. This replacement eliminates the numerical instability inherent in finite difference methods while preserving the essential function of computing volume changes, thus improving reliability without sacrificing computational tractability.
2Ease of manufacture
If finite difference approximations are used in deformation field-based methods, then computational simplicity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the computational parameters from finite difference approximations to integrated Jacobian formulations with systematic sampling. This change enables more accurate volume change estimates by reducing numerical errors and improving consistency across different acquisition types, thereby resolving the contradiction between computational simplicity and measurement precision.
3Measurement precision
If specialized MRI expertise and hyperpolarized noble gases are used, then ventilation imaging quality is improved, but device complexity and availability deteriorate
Solution Approach 1:
The patent creates a computational model that copies the functional capabilities of complex Hyp-MRI ventilation imaging using standard CT technology. By implementing integrated Jacobian formulations with sampling methods, the system reproduces high-quality ventilation images from conventional CT scanners, eliminating the need for specialized hyperpolarized gas equipment and MRI expertise while maintaining imaging quality.
Solution Approach 2:
The patent develops a universal computational framework that can process standard CT imaging data to produce ventilation images, making the technology accessible across multiple institutions without requiring specialized equipment. This multi-functional approach allows standard CT scanners to perform ventilation imaging, thereby reducing device complexity and increasing availability.
4Adaptability or versatility
If CT-ventilation methods are developed for radiation oncology applications, then clinical utility is improved, but verification and validation requirements increase device complexity
Solution Approach 1:
The patent implements integrated Jacobian formulations with sampling methods that provide quantifiable and controllable uncertainty estimates. This parameter transformation enables systematic verification and validation processes, making the CT-ventilation method suitable for radiation oncology applications while managing the complexity of clinical implementation through structured uncertainty quantification.
Data Source
AI summary
A method for processing images of lungs, the method comprising defining an inhale region of interest of the lungs at an inhale position and an exhale region of interest of the lungs at an exhale position, determining a spatial transformation of each voxel within the lungs between the lungs at the inhale position and the lungs at the exhale position to provide displacement vector estimates for each voxel within the lungs, and performing volume change inference operations to determine a volume change between the lungs at the inhale position and the lungs at the exhale position based on the inhale region of interest, the exhale region of interest, and the displacement vector estimates for each voxel within the lungs.