Tomography Motion Correction Using Redundant Data
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Solution Overview
Problem
Tomography machines face challenges in accurately detecting body movement between acquisition times, leading to blurred or impaired 3D images due to motion artifacts, especially when precise movement information is not available.
Innovation Solution
A method that compares pairs of acquisition data sets by calculating difference values for multiple virtual sectional planes, using a compression function to enhance robustness and convergence for motion correction, and forming an error matrix to select consistent data sets for sharp image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the body moves between acquisition times without precise tracking information, then motion artifacts occur causing blurred or impaired images, but adding tracking systems increases device complexity
Solution Approach 1:
The tomography machine uses its own existing acquisition data sets to detect body movement, rather than requiring external tracking systems. The intermediate function values are calculated from the projection data itself, allowing the system to self-diagnose motion artifacts using redundant information already present in the acquisition data
Solution Approach 2:
The patent transforms the problem from direct motion detection to detecting changes in intermediate function values derived from projection data. By calculating and comparing these intermediate values across different acquisition times, the system infers body movement without direct mechanical tracking
2Measurement precision
If sequential adjustment of geometry parameters is used to correct misalignment, then correction can be achieved, but multiple iterations are needed resulting in increased computational effort
Solution Approach 1:
The patent establishes a feedback loop where intermediate function values are calculated from acquisition data, compared against reference values, and used to iteratively adjust geometry parameters. The total error value provides continuous feedback on correction quality, enabling systematic optimization of alignment
Solution Approach 2:
The system performs preliminary calculation of intermediate function values for multiple virtual sectional planes before final reconstruction. This pre-processing allows identification of misalignment patterns early in the workflow, enabling corrective adjustments before committing to full 3D reconstruction
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
This approach allows for reliable detection of translational and rotational movements, enabling sharp 3D modeling of organs like the heart without relying on cardiac activity measurements, and corrects motion artifacts by iteratively adjusting geometry parameters, resulting in improved image clarity and reduced computational effort.
Implementation Method 1
each pixel value characterizes the effect of attenuation values of body elements of the body on a projection beam that has passed through the body elements successively
Data Source
AI summary
A method is provided for detecting a movement of a body between acquisition times of at least two acquisition data sets, wherein, for virtual sectional planes of the body, a first intermediate function value of attenuation values of all the body elements lying in the sectional plane is determined based on a first acquisition data set, and a second intermediate function value of the attenuation values is determined based on a second acquisition data set. For each sectional plane, a difference value is determined from the intermediate function values. A total error value for the two acquisition data sets is calculated by combining the difference values of all the sectional planes. The virtual sectional planes have a common line of intersection, and for the particular acquisition time, the difference between pairs of the sectional lines is at least one pixel.