CT Image Analysis for Selective Diagnostic Image Generation
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Solution Overview
Problem
The generation of unnecessary images in CT systems strains storage capacity and network traffic, and the on-demand image generation process is inefficient for timely diagnosis.
Innovation Solution
An information processing apparatus using a learning device to analyze CT images and control the generation of specific images based on the analysis results, reducing unnecessary image creation and enhancing time efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic generation of multiple image types is implemented, then diagnosis time is reduced, but storage capacity is strained and network traffic increases
Solution Approach 1:
The system performs preliminary analysis of the patient's clinical information and preliminary scan data to predict which image types will be needed for diagnosis. This preliminary action enables the system to proactively generate only the necessary image types in advance, avoiding unnecessary image generation while ensuring timely availability of required images for diagnosis.
Solution Approach 2:
The system extracts and selects only the essential image types from the potentially multiple image types that could be generated. By analyzing the clinical indications and patient characteristics, the system extracts and generates only the specific image types that are actually needed for diagnosis, thereby reducing the total volume of image data while maintaining diagnostic effectiveness.
2Adaptability or versatility
If automatic generation of multiple image types is implemented, then comprehensive image availability is improved, but network traffic is increased
Solution Approach 1:
The system performs preliminary analysis of clinical information to predict which images will be needed, enabling selective generation and transmission of only necessary images to the PACS, thereby reducing unnecessary network traffic while maintaining adaptability to diagnostic needs.
Solution Approach 2:
The system incorporates feedback mechanisms where the radiologist's actual image selection and usage patterns are analyzed to refine the prediction model. This feedback loop improves the accuracy of predicting which images are actually needed, thereby reducing network traffic by preventing transmission of images that will not be used.
3Quantity of substance
If on-demand image generation is used, then storage capacity is reduced, but diagnosis time efficiency is decreased
Solution Approach 1:
The system performs preliminary analysis of clinical information and preliminary scan data to predict needed image types before the radiologist requests them. This enables the system to proactively generate and prepare only the necessary images in advance, reducing both the quantity of images stored and the time required for image generation when needed.
Solution Approach 2:
The system provides self-service by automatically analyzing clinical information and generating appropriate images without requiring manual intervention or explicit requests from the radiologist. This automated self-service approach reduces both storage requirements by generating only necessary images and time efficiency by eliminating delays associated with manual image generation requests.
Data Source
AI summary
Provided are an information processing apparatus, an information processing method, and a program that can suppress the generation of unnecessary images in a CT apparatus and provide images useful for diagnosis in a time-efficient manner. An information processing apparatus includes a processor, in which the processor is configured to: acquire a first image captured by a CT apparatus; execute an analysis process of the first image using a learning device; cause the CT apparatus to execute a generation process of a second image corresponding to a result of the analysis process; and display at least one of the first image, the result of the analysis process, or the second image on a display device.