Ultrasonic Diagnosis System Scan Plane Alignment via Tissue Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Precise alignment of a scan plane with an observation cross section in ultrasonic diagnosis systems is challenging, requiring proficiency and is not easily reproducible, with existing technologies lacking effective support for probe operation, particularly in using tissue models for navigation.
Innovation Solution
An ultrasonic diagnosis system that includes a tomographic image forming unit, a search unit, and a support information generating unit, which uses a tissue model to identify the current scan plane and generate probe operation support information to align the scan plane with the observation cross section, reducing the complexity by determining the spatial relationship and narrowing down the number of provisional cross sections based on camera images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a tissue model is used to identify the current scan plane and generate probe operation support information, then the alignment accuracy between scan plane and observation cross section is improved, but the calculation complexity increases due to comparing tomographic images with multiple provisional model cross sections
Solution Approach 1:
The patent pre-processes the tissue model to extract multiple provisional model cross sections before the actual diagnosis process. By preparing these reference cross sections in advance, the system can quickly compare the current scan plane with pre-defined anatomical structures, reducing real-time calculation complexity while maintaining alignment accuracy.
Solution Approach 2:
The patent divides the three-dimensional tissue model into multiple two-dimensional provisional cross sections at different depths and orientations. This segmentation allows the system to compare the current scan plane with specific reference sections rather than processing the entire 3D model, significantly reducing calculation complexity while preserving measurement precision.
2Reliability
If multiple provisional model cross sections are set with respect to the tissue model to improve identification accuracy, then the reliability of scan plane identification is improved, but the time required for processing increases
Solution Approach 1:
The system pre-generates and stores multiple provisional model cross sections representing different anatomical levels and orientations before the actual examination. During diagnosis, the current scan plane is quickly matched against these pre-prepared references, maintaining high identification reliability while minimizing real-time processing time.
3Measurement precision
If a complex positioning system is used to track probe position and orientation, then the navigation accuracy is improved, but the device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the patient's anatomy through a tissue model derived from medical images. This virtual model serves as a navigation map that can be processed computationally without requiring complex physical positioning systems. The scan plane position is tracked by comparing with the virtual model, achieving navigation accuracy while avoiding complex hardware.
Solution Approach 2:
The patent replaces complex mechanical positioning systems with computational image matching methods. Instead of using sophisticated sensors and mechanical trackers to determine probe position, the system uses software-based comparison between the current scan plane and the virtual tissue model, achieving similar navigation accuracy with simpler equipment.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A current cross section searching unit (38) identifies a current cross section corresponding to a scan plane by setting a group of provisional cross sections with respect to a tissue model corresponding to a target tissue, and calculating a degree of similarity between data of each provisional cross section and tomographic image data. An operation support information generating unit (44) generates operation support information from difference information indicating a spatial relationship between the current cross section and a target cross section (corresponding to an observation cross section). The group of provisional cross sections set with respect to the tissue model is narrowed down based on a camera image.