3D X-Ray Image Stitching With Parallax-Corrected Feature Detection
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Solution Overview
Problem
Existing imaging systems struggle to efficiently generate high-quality three-dimensional images of subjects, particularly during surgical procedures, due to limitations in capturing and stitching multiple projections with accurate parallax effects and minimizing parallax distortion.
Innovation Solution
An imaging system using a movable source and detector with a slotted filter that divides x-ray beams into multiple fans, allowing for precise acquisition of projections from different perspectives, which are then stitched together to form long views or 3D images, utilizing edge detection algorithms and machine learning for feature identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple projections are acquired and stitched together to form 3D images, then image quality and feature detection accuracy are improved, but parallax distortion increases and complicates the stitching process
Solution Approach 1:
The patent introduces a parallax correction module as an intermediary component between the projection acquisition and stitching processes. This module calculates and corrects parallax distortion in each projection before stitching, using reference features and transformation matrices to compensate for the distortion, thereby simplifying the overall stitching process while maintaining high feature detection accuracy
Solution Approach 2:
The system performs preliminary parallax correction on individual projections before the stitching operation. By pre-calculating correction parameters and applying transformations to each projection in advance, the system eliminates parallax distortion prior to stitching, making the stitching process simpler and more accurate
2Measurement precision
If a movable source and detector system is used to acquire projections from different perspectives, then 3D image quality is improved, but the system complexity and difficulty of operation increase
Solution Approach 1:
The imaging system incorporates automated features that allow it to self-calibrate and self-correct for parallax distortion. The system automatically identifies reference features, calculates transformation parameters, and applies corrections without requiring manual intervention, thereby maintaining high 3D image quality while simplifying operation
Solution Approach 2:
The system uses feedback from detected reference features to automatically adjust and correct parallax distortion. By continuously monitoring feature positions and using this information to refine transformation matrices, the system maintains high image quality while reducing operational complexity through automated closed-loop control
3Measurement precision
If edge detection algorithms and machine learning are used for feature identification, then feature detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary processing by detecting reference features and calculating parallax correction parameters before applying complex machine learning algorithms. This preliminary step reduces the complexity of subsequent feature identification by pre-establishing correction transformations, thereby reducing overall processing time while maintaining high accuracy
Solution Approach 2:
The feature identification process is segmented into multiple stages: preliminary reference feature detection, parallax correction parameter calculation, and final feature identification using machine learning. This segmentation allows computationally intensive operations to be performed on corrected, simplified data, reducing total processing time while maintaining high accuracy
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
Enables the generation of high-quality, parallax-free 3D images with improved feature detection and localization, facilitating precise surgical procedures such as spinal fusion and implant placement.
Implementation Method 1
An imaging system using a movable source and detector with a slotted filter that divides x-ray beams into multiple fans
Implementation Method 2
The different perspectives may be generated due to a parallax effect between different paths of x-rays from a single source to a detector through the subject
Implementation Method 3
The imaging system may acquire image data of an interior of the subject
Data Source
AI summary
A method and system is disclosed for analyzing image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. The image data may include selected overlap and be acquired with an imaging system that generates a plurality of perspectives for more than one location. An automatic system and method may then define or identify various features and/or allow for registration for alternative image data.