Medical Image Template Selection for Automated Display
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Solution Overview
Problem
Medical image datasets, often 3D, cannot be directly displayed on devices and require calculation of a 2D or 3D display image, necessitating pre-defined settings that vary based on patient-specific information, such as disease type or treatment plans, which can be cumbersome to set manually.
Innovation Solution
A data processing method that acquires patient-specific information to select a template from a plurality of templates, using this information to calculate the display image by defining settings like sectional planes, windowing parameters, and zoom factors, thereby automating the process without user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual setting of display parameters is used, then flexibility and customization are improved, but operation time and complexity increase
Solution Approach 1:
The system pre-calculates and stores optimal display parameters for different disease types and treatment plans before actual use. When a patient's data is input, the system automatically retrieves and applies the pre-defined parameters, eliminating the need for manual setting while maintaining customization for different medical scenarios.
Solution Approach 2:
The system automatically selects and applies display parameters based on the patient-specific information provided, without requiring manual intervention. The automated selection process uses the patient's disease type and treatment plan to retrieve appropriate parameters, making the system self-sufficient in parameter configuration.
2Measurement precision
If multiple templates are used for different patient types, then measurement precision and diagnostic accuracy are improved, but device complexity increases
Solution Approach 1:
A single template management system handles multiple disease types and treatment plans by storing different parameter sets within the same system framework. The system universally processes various patient-specific information and automatically retrieves the appropriate template, eliminating the need for separate systems for each disease type while maintaining diagnostic accuracy.
Solution Approach 2:
Multiple templates with disease-specific parameters are pre-configured and stored in the system before actual use. Each template contains optimized display parameters for specific disease types and treatment plans. When needed, the system automatically selects the appropriate pre-configured template, providing high diagnostic accuracy without requiring complex real-time configuration.
3Manufacturing precision
If detailed patient-specific information is processed, then image accuracy and relevance are improved, but data processing time increases
Solution Approach 1:
The system pre-processes and categorizes patient-specific information such as disease type and treatment plan during data entry. This preliminary categorization enables rapid retrieval of corresponding display parameters without requiring complex real-time analysis, maintaining image accuracy while reducing processing time.
Solution Approach 2:
The system uses template copying to retrieve pre-defined display parameters based on matched patient-specific information. Instead of calculating optimal parameters from scratch for each patient, the system copies and applies proven parameter sets from templates that match the patient's disease type and treatment plan, ensuring accuracy while significantly reducing processing time.
Data Source
AI summary
A data processing method for calculating a medical display image to be displayed on a display device, comprising the steps of acquiring at least one image dataset representing a medical image of a patient, acquiring a patient-specific information dataset representing patient-specific information and calculating the display image from the at least one image dataset on the basis of a template, the template being selected from a plurality of templates in accordance with the acquired patient-specific information.