CT Visualization Adjustment via HU Distribution Segmentation
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Solution Overview
Problem
In X-ray computed tomography (CT) imaging, comparing images acquired at different energy levels is challenging due to variations in gray scale renderings, making it difficult for healthcare practitioners to distinguish and diagnose tissues, especially with spectral or multi-energy CT which narrows the distribution of Hounsfield Units (HU) values, reducing precision and differentiation between tissues.
Innovation Solution
An apparatus and method that adjusts visualization settings based on a pixel-value distribution analysis of Hounsfield Units (HU) values, using a mask to selectively map HU values to gray scale values, ensuring consistent gray scale rendering across different energy levels and improving tissue differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If spectral or multi-energy CT is used to expand the range of HU values, then the ability to highlight different materials is improved, but the precision and differentiation of tissues around bi-modal peaks is reduced
Solution Approach 1:
The patent segments the HU value distribution into multiple modes or peaks, identifying distinct tissue types (e.g., iodine-enhanced tissue, non-enhanced tissue, bone) as separate clusters. By detecting bi-modal or multi-modal distributions and treating each mode as a separate entity, the system preserves tissue differentiation precision while maintaining the ability to highlight multiple materials simultaneously.
Solution Approach 2:
The patent applies different visualization settings (window level and window width) to different regions or modes of the HU distribution. Instead of using a single global mapping, the system tailors the gray scale mapping to local characteristics of each tissue type, ensuring optimal differentiation for each material while maintaining overall versatility.
2Ease of operation
If images are displayed using the same reference settings at different energy levels, then the display process is simplified, but the gray scale renderings become inconsistent making tissue comparison difficult
Solution Approach 1:
The patent dynamically adjusts the reference settings (window level and window width) based on the detected HU value distribution characteristics at each energy level. Rather than using static, fixed settings, the system adapts the visualization parameters to match the specific distribution patterns observed, ensuring consistent gray scale rendering across varying energy levels while maintaining operational simplicity through automated adjustment.
Solution Approach 2:
The system implements a feedback mechanism where the HU value distribution is analyzed, and the detected characteristics (bi-modal peaks, distribution range) are used to automatically adjust the reference settings for display. This closed-loop approach ensures that tissue comparison remains accurate across different energy levels without requiring manual intervention.
3Adaptability or versatility
If a large window width is used to map a large HU distribution, then the full range of values is visible, but the precision and differentiation of tissues around bi-modal peaks is reduced
Solution Approach 1:
The patent segments the large HU distribution into multiple distinct ranges, each corresponding to a specific tissue type or material. By identifying bi-modal or multi-modal peaks and creating separate window width settings for each mode, the system maintains both the broad adaptability to show all tissues and the precision needed to differentiate tissues within each mode.
Solution Approach 2:
The system applies different window width values to different regions of the HU distribution. Instead of using a single uniform window width, the patent tailors the mapping precision to local requirements - narrower windows for regions requiring high differentiation (around bi-modal peaks) and wider windows for regions where broad coverage is more important.
Data Source
AI summary
A computed tomography (CT) image display system (10) includes a mapping unit (32), which receives reconstructed volumetric image data (28) of a subject with values in Hounsfield Units (HU), and a set of reference settings (34), adjusts the set of reference settings (34) to an adjusted set of reference settings (35) according to a pixel-value distribution analysis of the HU values selected according to the reference settings (34), and maps the values in HU to gray scale values according to the adjusted reference settings (35).