MRI Volume Data Alignment Using Single Control Point
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Solution Overview
Problem
Current methods for aligning 3D volume data from the same modality, such as MRI, are complex due to the need for multiple control points and handling both in-plane and out-of-plane translational offsets, especially in applications like diagnosing scoliosis where separate data sets of the upper and lower spine are required for a larger field-of-view.
Innovation Solution
A process and user interface that allows for 3D alignment of volume image data using a single control point per data set, minimizing error functions based on pixel intensity similarities in neighborhoods, and combining images based on in-plane and out-of-plane offsets to form a larger composite image with preserved resolution and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple control points are used to align 3D volume data from different modalities, then alignment accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts and eliminates the requirement for multiple control points by using a single control point approach. The system takes out the complex multi-point registration process and replaces it with a simplified single-point method that still achieves accurate alignment through automated error function minimization across the volume data.
Solution Approach 2:
The system performs self-alignment by automatically computing translational offsets through error function minimization without requiring multiple manually placed control points. The algorithm serves itself by using the single control point neighborhood information to automatically determine the optimal alignment between volume data sets.
2Manufacturing precision
If multiple control points are required for alignment, then alignment precision is improved, but the alignment process becomes more complex
Solution Approach 1:
The patent removes the burden of placing multiple control points by extracting only a single control point requirement. The complex multi-point process is replaced with a simple single-point interface that maintains alignment precision through automated computational methods.
Solution Approach 2:
The patent replaces the manual mechanical process of placing multiple control points with an automated computational system. The error function minimization algorithm substitutes for the manual multi-point registration process, making the system easier to operate while maintaining precision.
3Area of stationary object
If separate 3D volume data sets are acquired for upper and lower spine, then field-of-view is increased, but computational complexity increases
Solution Approach 1:
The patent merges separate 3D volume data sets into a single composite volume with a larger field-of-view. By using the single control point method with automated error minimization, the system combines the upper and lower spine data efficiently without requiring complex multi-point registration procedures.
Solution Approach 2:
The system performs self-alignment of the combined volume data by automatically computing the translational offsets through error function minimization. This eliminates the need for complex manual alignment procedures when merging multiple volume data sets, reducing computational complexity while maintaining accuracy.
Data Source
AI summary
An apparatus and method for joining two MRI image data sets to form a composite image. The images are joined together at one or more places along the common area by processing the first and second image data using the square of the normalized intensity difference between at least one group of pixels in the first image data and another group of pixels in the second image data.