Dark-Field X-Ray Model Respiration Compensation
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Solution Overview
Problem
Conventional X-ray imaging techniques provide poor attenuation contrast for soft tissues like lungs, and the inhalation state of patients affects dark-field X-ray signals, making accurate diagnosis of respiratory diseases challenging, especially for patients who cannot hold their breath during standard acquisition procedures.
Innovation Solution
A computer-implemented method generates a dark-field X-ray model that represents relationships between the X-ray signal, respiratory disease grade, and respiration state, allowing for the prediction and compensation of signal changes due to respiration state deviations, thereby improving diagnostic accuracy by standardizing signals to a reference respiratory state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DAX imaging is used to improve diagnostic accuracy for pulmonary disorders, then the ability to detect microstructural properties in lung parenchyma is improved, but the impact of respiration state variations on DAX signal strength makes accurate diagnosis difficult
Solution Approach 1:
The system uses a trained DAX model to predict respiration state from the DAX image signal, then feeds this prediction back to compensate for respiration-related variations. This closed-loop feedback mechanism allows the system to adapt to respiration state changes and maintain reliable diagnostic measurements despite the patient's inability to control breathing.
Solution Approach 2:
The patent changes the parameter being measured from raw DAX signal intensity to a compensated signal that accounts for respiration state. By modeling the relationship between respiration state and DAX signal, the system transforms the signal parameter to eliminate respiration-related variability, enabling consistent diagnostic accuracy across different breathing conditions.
2Manufacturing precision
If standard acquisition procedures requiring breath-holding are used, then image quality is improved, but patients with respiratory diseases cannot follow these procedures
Solution Approach 1:
The system performs self-correction by automatically detecting the patient's respiration state from the DAX image itself and compensating for its effects. This eliminates the need for external intervention (such as requiring the patient to hold their breath), allowing the imaging system to adapt to the patient's natural breathing pattern and produce diagnostic-quality images without procedural compliance issues.
3Measurement precision
If lower X-ray energies are used for DAX imaging, then sensitivity to microstructural properties is improved, but the DAX signal becomes more sensitive to respiration state changes
Solution Approach 1:
The patent converts the harmful effect of respiration sensitivity into a beneficial diagnostic tool. By training the DAX model on DAX images with known respiration states, the system learns to not only compensate for respiration-related signal variations but also to detect and quantify respiration state itself. This transforms a source of noise into a source of additional diagnostic information about lung function.
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
The model enhances the accuracy of respiratory disease diagnosis and grading by accounting for respiration state impacts on X-ray signals, enabling more reliable assessments even for patients unable to follow standard breathing protocols.
Implementation Method 1
DAX imaging is not only capable of capturing X-ray transmission but also small angle scatter, which occurs in healthy lung parenchyma but not in emphysematic lung areas
Data Source
AI summary
Concepts for generating a dark X-ray (DAX) model adapted to represent relations between a DAX signal, a respiratory state, and a respiratory disease grade, as well as concepts for using the DAX model are proposed. In particular, as relations between these variables are modelled, the difference between a DAX signal of a subject at a respiratory state, compared to a DAX signal of a subject at a reference respiratory state may be assessed. Thus, the diagnostic accuracy of DAX imaging may be improved.