Bone Mineral Density Analyzer Using Phantom CT Histograms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing bone mineral density analysis methods using CT image data are prone to user error in setting regions of interest, leading to unstable and objective analysis results, with a high workload for users in setting these regions.
Innovation Solution
An analyzer that automatically determines the correspondence between CT values and bone mineral density using known data from a phantom, producing a histogram of region numbers relative to CT values and displaying significant peaks, thereby simplifying the process of obtaining a calibration curve without manual operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setting of regions of interest is performed by user, then flexibility in analysis is improved, but operation complexity and error rate increase
Solution Approach 1:
The system automatically identifies and sets regions of interest by analyzing CT image data itself, without requiring manual user input. The processor extracts anatomical structures and defines ROIs based on image features, enabling the system to serve itself in the ROI setting task and eliminating user-related errors and variability
Solution Approach 2:
The manual mechanical operation of drawing and setting regions by user is replaced with an automated image processing algorithm. The processor uses computational methods to identify anatomical structures and automatically define regions of interest, substituting human manual operation with an automated digital system
2Productivity
If manual setting of regions of interest is performed by user, then adaptability to different cases is improved, but time consumption increases
Solution Approach 1:
The system performs automated ROI setting without requiring user time and effort. The processor independently completes the entire workflow of identifying anatomical structures and defining regions of interest, freeing users from this time-consuming task and significantly improving productivity
Solution Approach 2:
The system pre-identifies anatomical structures and automatically defines regions of interest before the user needs to perform analysis. This preliminary automated action prepares the data in advance, eliminating the need for users to spend time on manual ROI setting during the actual analysis workflow
3Reliability
If automated determination of correspondence between CT value and bone mineral density is performed, then objectivity and stability of analysis are improved, but device complexity increases
Solution Approach 1:
The complex manual process of establishing calibration curves and determining correspondence between CT values and bone mineral density is replaced with automated image processing algorithms. The processor uses computational methods to automatically extract features, compare with reference data, and determine the correspondence relationship, achieving objectivity while managing complexity through software automation
4Measurement precision
If manual setting of regions of interest is performed, then ease of operation is worsened, but measurement precision can be maintained through user expertise
Solution Approach 1:
The system automatically performs ROI setting with consistent precision across all cases. By using standardized image processing algorithms, the system eliminates variability introduced by different users while maintaining high measurement precision through automated feature extraction and region definition
Solution Approach 2:
The system changes the approach from manual parameter setting to automated parameter extraction. Instead of relying on user expertise to manually define regions, the processor automatically extracts anatomical features and determines optimal region parameters through image analysis algorithms, achieving both precision and ease of operation
Data Source
AI summary
To make a user easily obtain an objective and stable analysis result of bone mineral density. An analyzer 100 of bone mineral density using CT image data of a phantom having a known bone mineral density includes: a known data storage part 105 that stores known data of bone mineral density for a phantom; a histogram production part 102 that produces a histogram of region number relative to a CT value for three-dimensional CT image data of the phantom; a correspondence determination part 106 that determines correspondence between a CT value and a bone mineral density by correlating CT values showing respective peaks of the produced histogram with the known data of the phantom; and an analysis part 109 that decides a bone mineral density for three-dimensional CT image data of a subject using the determined correspondence.