Medical Image Processing Apparatus Parameter Database
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Solution Overview
Problem
Existing medical image processing techniques, such as those described in Patent Literature 1, fail to effectively adjust image quality to meet user-defined standards, as they rely on pre-trained models and existing image data, which may not align with desired quality and are device-specific, leading to inefficiencies in achieving desired image quality across different apparatuses.
Innovation Solution
A medical image processing apparatus that uses a database to associate image feature values with adjustment parameters, allowing users to input desired image quality data and calculate suitable adjustment parameters for imaging and reconstruction conditions, thereby facilitating the presentation of parameters to achieve the desired image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-trained models and existing image data are used for image quality adjustment, then the processing speed is improved, but the image quality may not align with user-defined standards and device-specific variations persist
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing the relationship between image feature values and adjustment parameters in a database during system initialization or offline processing. When a user requests image quality adjustment, the system quickly retrieves pre-computed parameters based on the input image's feature values, avoiding real-time complex calculations and enabling fast adjustment while maintaining consistency across different devices.
2Ease of operation
If trial and error method is used to adjust image quality parameters, then the image quality can be customized to user preference, but the time and effort required increases significantly
Solution Approach 1:
The system implements feedback by automatically analyzing the input medical image to extract feature values, comparing these features against the database of pre-stored relationships, and presenting optimized adjustment parameters to the user. This automated feedback loop eliminates the need for manual trial and error, allowing users to achieve desired image quality customization quickly by simply reviewing and confirming the system's recommendations.
Solution Approach 2:
The system performs self-service by autonomously extracting image features, querying the database for matching adjustment parameters, and presenting the results without requiring user intervention in the iterative adjustment process. The user only needs to provide the input image and optionally review the suggested parameters, significantly reducing the time and effort compared to manual trial and error methods.
3Measurement precision
If device-specific trained models are used, then the model accuracy for that specific device is improved, but the adaptability to different device models decreases
Solution Approach 1:
The system achieves universality by creating a standardized database structure that stores the relationship between image feature values and adjustment parameters in a device-agnostic manner. The database is designed to accommodate multiple device models, with each entry containing device-specific information alongside universal image feature relationships. This allows the same system to serve multiple device models while maintaining measurement precision for each specific device through proper data organization and retrieval.
Data Source
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AI summary
Provided is a means of easily presenting conditions for obtaining an image quality desired by a user, and this achieves savings in time and effort required for repetitive adjustment of the image quality. A medical image processing apparatus comprises a reception unit configured to receive image quality data reflecting a user's desire for an image quality of a medical image, a feature value calculation unit configured to calculate an image feature value with respect to the image quality data, and a parameter presentation unit configured to use a database associating adjustment parameters related to the image quality with the image feature value, to determine and present the adjustment parameters suitable for the image quality data received by the reception unit.