Medical Image Registration via Automatic Cross-Section Selection
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Solution Overview
Problem
Conventional image registration methods are time-consuming and dependent on user selection of specific points, planes, or volumes, leading to low-quality registration results and increased processing time due to the need for repeated attempts to achieve high-quality alignment between different medical image data sets.
Innovation Solution
A user-friendly image registration method and apparatus that maps anatomical structures of a target object, automatically selects a cross-section image as a basis for registration, and acquires second volume data based on a predetermined correlation with the first cross-section image, using a combination of CT and ultrasound image acquisition modalities to facilitate fast and accurate image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user directly selects specific points, planes, or volumes for image registration, then the registration can be performed, but the process takes a relatively long time and requires repeated attempts to achieve high-quality alignment
Solution Approach 1:
The system automatically performs preliminary actions by selecting candidate points, planes, and volumes for registration without requiring user intervention. The control unit automatically selects at least one of a specific point, a specific plane, and a specific volume from the plurality of points, planes, and volumes, thereby preparing the registration data in advance and eliminating the time-consuming manual selection process while maintaining high registration quality
Solution Approach 2:
The image registration system performs self-service by autonomously selecting registration candidates and executing the registration process without user intervention. The control unit automatically identifies suitable points, planes, or volumes from the acquired image data and completes the registration, allowing the system to serve itself and eliminate repeated manual attempts, thus reducing processing time while ensuring high-quality alignment
2Ease of operation
If manual selection of registration points, planes, or volumes is performed, then user control is maintained, but the operation becomes cumbersome and requires repeated attempts
Solution Approach 1:
The system enables self-service operation by automatically selecting registration candidates and performing registration without requiring user interaction. The control unit autonomously processes the image data, selects appropriate features, and completes alignment, making the operation extremely simple for users while dramatically reducing the time required compared to manual repeated attempts
Solution Approach 2:
The system performs preliminary automatic selection of registration candidates before user interaction is needed. The control unit pre-identifies suitable points, planes, or volumes from the image data and prepares them for registration, thereby simplifying the user operation to minimal or no input while reducing processing time through advance preparation
3Measurement precision
If multiple image acquisition modalities are combined for registration, then comprehensive alignment is achieved, but the system complexity increases
Solution Approach 1:
The image registration system achieves multi-functionality by supporting registration between images from different acquisition modalities (ultrasound, CT, MRI, endoscope, photographing devices). The control unit is designed to handle various image types and formats uniformly, allowing comprehensive alignment across multiple modalities while maintaining a unified system architecture that does not significantly increase complexity
Solution Approach 2:
The control unit serves as an intermediary that standardizes the registration process across different image modalities. It provides a common framework for acquiring, processing, and aligning images from various sources, thereby achieving comprehensive multi-modality registration accuracy while avoiding the complexity of separate processing systems for each modality
Data Source
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AI summary
An image registering method includes extracting a first cross-section image from first volume data about a target object, which is acquired via a first image acquisition modality; displaying the first cross-section image; acquiring second volume data about the target object, the second volume data including a second cross-section image corresponding to the first cross-section image and position information of a probe, via a second image acquisition modality using the probe; and registering the first volume data with the second volume data, based on the first cross-section image, the second cross-section image, and the position information of the probe.