Inner Auditory Canal CT Image Enhancement via Density Segmentation
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Solution Overview
Problem
Current CT image processing systems face challenges in visualizing bones of varying densities without increasing image noise or radiation exposure, particularly in enhancing the quality of inner auditory canal structures, which suffer from lack of sharpness and aliasing artifacts.
Innovation Solution
The method involves segmenting image data into areas based on pixel CT numbers to enhance lower and higher density bones separately, applying non-linear bone gray scale stretching and deconvolution, and using a deconvolution gain function to adaptively enhance bony structures while reducing noise in surrounding soft tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher data sampling and high-pass filtering are used to achieve higher spatial resolution, then the visualization of bony structures is improved, but the radiation dose to the patient increases
Solution Approach 1:
The patent segments the image processing into different density regions (higher density bones and lower density bones/soft tissue) and applies different enhancement strategies to each segment. This allows optimization for bone visualization without uniformly increasing radiation across the entire imaging field.
Solution Approach 2:
The patent applies local enhancement techniques specifically to bone regions rather than processing the entire image uniformly. By identifying bone pixels and applying targeted enhancement algorithms, the system improves bone visualization quality without requiring global increases in radiation dose or data sampling density.
2Measurement precision
If higher data sampling is used to improve spatial resolution, then the visualization of bony structures is improved, but the equipment cost increases
Solution Approach 1:
The patent uses software-based image processing algorithms to create an enhanced copy of the bone structures from the original lower-resolution data. Instead of requiring expensive hardware upgrades to achieve higher spatial resolution, the system processes existing data through enhancement algorithms that simulate the effect of higher resolution imaging.
Solution Approach 2:
The patent changes processing parameters (enhancement factors, filtering parameters, deconvolution parameters) to optimize bone visualization from existing data. By adjusting these software parameters, the system achieves improved spatial resolution equivalent to what would require expensive hardware modifications.
3Measurement precision
If deconvolution process is applied to enhance image quality, then the sharpness of IAC structure is improved, but undershooting artifacts between bones and soft tissues occur
Solution Approach 1:
The patent applies different processing treatments to different regions: deconvolution enhancement is applied to bone regions to improve sharpness, while soft tissue regions are handled differently to prevent undershooting artifacts. This localized approach allows sharpness enhancement without the harmful side effects in tissue regions.
Solution Approach 2:
The patent uses adaptive enhancement where the processing parameters dynamically adjust based on local image characteristics. The enhancement factor and processing intensity are modulated according to whether a pixel represents bone or soft tissue, preventing fixed-parameter deconvolution from causing undershooting artifacts in tissue regions.
Data Source
AI summary
Method and apparatus for locally enhancing image data represented as pixels having CT numbers is provided. The image data includes bony structures, and the method comprises segmenting pixels within the image data into areas based on the pixel's CT number. A first area represents a combination of soft tissue and lower density bones and a second area represents higher density bones. A subset of pixels is identified within the first area representative of the lower density bones. An enhancement is applied to the subset of pixels within the first area and to the second area to create an enhanced dataset, and a locally enhanced image is generated based on the image data and the enhanced dataset.