Imaging Apparatus Automatic Distance Setting for 3D Data
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Solution Overview
Problem
Conventional imaging apparatuses require manual processes to set distances from reference planes for generating 3D data, which is time-consuming and prone to human error, especially when dealing with complex anatomical structures like the corpus callosum.
Innovation Solution
An imaging apparatus with an image processing unit that automatically sets distances from reference planes using stored reference data, similarity calculations, and Doppler information to generate 3D data from cross-sectional images, reducing the need for manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to set distances from reference planes for generating 3D data, then the user has control over the process, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system automatically sets the rendering area distance by extracting reference planes from cross-sectional images and calculating distances based on stored reference data, enabling the system to serve itself without manual intervention. This eliminates time-consuming manual operations while maintaining accurate distance setting through automated reference plane extraction and similarity-based selection.
Solution Approach 2:
The system stores reference data including distances to reference planes in advance. When generating 3D data, the system retrieves and uses this pre-stored reference data to automatically determine rendering area distances, eliminating the need for real-time manual measurement and calculation while ensuring consistent accuracy.
2Reliability
If manual processes are used to set distances from reference planes for generating 3D data, then the user can adjust parameters, but the process becomes complex and error-prone
Solution Approach 1:
The system automatically extracts reference planes from cross-sectional images using image processing algorithms and calculates optimal rendering area distances based on stored reference data. This self-service approach eliminates manual operation complexity while ensuring consistent and reliable distance setting through automated, repeatable processes.
Solution Approach 2:
The system replaces manual mechanical measurement and adjustment processes with automated image processing and data retrieval operations. The image processing unit automatically identifies reference planes and calculates distances, substituting human manual operations with computational processes that are both simpler to execute and more reliable.
3Productivity
If automated methods are used to set distances from reference planes, then the process becomes faster and more accurate, but the system complexity increases
Solution Approach 1:
The system stores reference data including distances to reference planes in advance during system setup or from previous measurements. When generating 3D data, the system quickly retrieves this pre-prepared reference data and applies it automatically, achieving high productivity without requiring complex real-time calculations or measurements.
Solution Approach 2:
The system creates a simplified representation by storing reference plane distances as copyable reference data. Instead of performing complex geometric calculations each time, the system copies and applies pre-determined distance values from the stored reference data, significantly speeding up the 3D data generation process while managing system complexity.
Data Source
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AI summary
Disclosed herein is an imaging apparatus and a controlling method thereof, the imaging apparatus includes an image processing unit generating volume images of an object including a region of interest and extracting a reference plane of the volume images and an area setting unit automatically setting a distance from the reference plane, wherein the image processing unit may generate a 3D data of a region of interest based on a cross sectional data of the reference plane and a cross sectional data contained in a plurality of cross sectional images of the volume images existing in the distance.