CT HU Window Switching Using Cursor-Based Organ Classification
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Solution Overview
Problem
Conventional CT imaging systems require manual operator input to set HU windows for different anatomical structures, which is inefficient and lacks consistency among users.
Innovation Solution
An automated system determines HU windows based on the position of a cursor within the image using an organ classification model, allowing seamless adaptation to different anatomical structures by tracking the cursor position and applying appropriate HU windows in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operator input is used to set HU windows, then operator control and customization are improved, but efficiency and consistency are worsened
Solution Approach 1:
The system automatically determines appropriate HU windows by detecting the cursor position and classifying the underlying anatomical structure, eliminating the need for manual operator input. The system serves itself by autonomously selecting display parameters based on the viewed anatomical region, thereby improving efficiency while maintaining operator control through cursor-based interaction.
Solution Approach 2:
The system dynamically changes HU window parameters (center and width) based on the classified anatomical structure at the cursor position. Different anatomical structures automatically trigger different HU window settings, allowing the system to adapt display parameters in real-time without manual intervention, thus resolving the contradiction between automation and operator control.
2Adaptability or versatility
If manual operator input is used to set HU windows, then customization for different structures is improved, but time consumption and lack of consistency are worsened
Solution Approach 1:
The system automatically adapts HU window settings to different anatomical structures by detecting cursor position and performing classification, eliminating the time-consuming manual adjustment process. The system maintains consistency across different operators by autonomously selecting appropriate windows based on the viewed structure rather than relying on individual operator preferences.
Solution Approach 2:
The system performs preliminary classification of anatomical structures and pre-determines appropriate HU windows before the operator needs to view the image. By having the classification model ready and automatically applying the correct window settings based on cursor position, the system eliminates the time lag that would otherwise occur during manual adjustment.
3Productivity
If automated HU window determination is implemented, then efficiency and consistency are improved, but system complexity is worsened
Solution Approach 1:
The system introduces an organ classification model as an intermediary between the CT image data and the HU window determination process. This intermediary component automatically classifies the anatomical structure at the cursor position and translates it into appropriate HU window parameters, adding computational complexity but eliminating the need for complex manual operator interactions and multiple separate control systems.
4Reliability
If automated HU window determination is implemented, then consistency across operators is improved, but manual control is reduced
Solution Approach 1:
The system autonomously determines HU window settings based on the classified anatomical structure, ensuring consistent application across all operators. While manual control is reduced to simple cursor movement, the system maintains operator agency by allowing them to navigate to regions of interest, with the automated system handling the technical parameter selection to ensure consistency.
Data Source
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AI summary
Systems and methods include determining first pixel intensities based on Hounsfield Unit (HU) values of a first set of a plurality of voxels of a volume and a first HU window, presenting pixels having the first pixel intensities on a display, determining a position of a cursor on the display, determining an anatomical structure corresponding to the position of the cursor, determining a second HU window based on the anatomical structure, the second HU window being different from the first HU window, determining second pixel intensities of the pixels based on the HU values of the first set of the plurality of voxels and the second HU window, and changing the first pixel intensities of the displayed pixels to the second pixel intensities.