Medical Imaging Bed Positioning for Automated FOV Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional medical image diagnostic apparatuses require manual setting of the field of view (FOV) and may take time for operations before imaging, and the bed height adjustment is not optimized based on the subject's height, leading to potential alignment issues and artifacts.
Innovation Solution
A medical image diagnostic apparatus that includes an acquisition unit to gather physical characteristics data and imaging target information, using a trained model to automatically set the bed position based on these data, reducing manual intervention and optimizing FOV alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual operation is used to set the field of view (FOV), then the operator can control the imaging parameters, but it requires time for operations before imaging
Solution Approach 1:
The system performs preliminary calculation of the optimal FOV based on pre-stored subject height information and body shape classification before the actual imaging operation. The FOV is automatically determined in advance based on the subject's characteristics, eliminating the need for manual adjustment during the imaging process.
Solution Approach 2:
The imaging apparatus automatically determines the appropriate FOV by referencing pre-stored information about subject heights and body shapes. The system serves itself by using its own pre-processed data to make the FOV setting without requiring external manual intervention from the operator.
2Manufacturing precision
If bed height is not adjusted according to subject height, then the apparatus structure remains simple, but alignment issues and artifacts occur
Solution Approach 1:
The system calculates and sets the optimal bed height in advance based on pre-stored subject height information and body shape classification before imaging begins. This preliminary adjustment ensures proper alignment of the subject with the imaging field of view, preventing artifacts while maintaining a relatively simple mechanical structure.
Solution Approach 2:
The bed height parameter is automatically changed according to the subject's height and body shape classification. The system adjusts this critical parameter based on pre-stored reference data, ensuring optimal imaging geometry without requiring complex mechanical adjustment mechanisms.
3Productivity
If automated bed position setting is implemented, then operational efficiency improves, but the system complexity increases
Solution Approach 1:
The automation system uses pre-stored subject height information and body shape classification data that are prepared in advance. By referencing this pre-processed information, the system can automatically determine bed position and FOV settings without requiring complex real-time measurement and calculation systems, thus improving productivity while limiting the increase in system complexity.
Solution Approach 2:
The system uses pre-stored reference data about subject heights and body shapes as copies or templates to determine the appropriate imaging settings. Instead of requiring complex real-time measurement systems, the apparatus references pre-existing data models to automatically set bed position and FOV, achieving automation with minimal additional complexity.
Data Source
AI summary
A medical image diagnostic apparatus of embodiments includes an acquisition unit and a processing unit. The acquisition unit acquires physical characteristics data of an examination subject and information about an imaging target portion. The processing unit is configured to output bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the physical characteristics data and the information about the imaging target portion acquired by the acquisition unit to a trained model which is configured to output bed position information on the basis of physical characteristics data and information about a imaging target portion.