CT Image Reconstruction Using Weighted Ramp Filter Kernel
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Solution Overview
Problem
Current CT imaging technologies face challenges in visualizing small structures within the human body due to high noise and aliasing artifacts, particularly in applications like Inner Auditory Canal examinations, where traditional high-resolution kernels enhance high-frequency regions but often result in unusable images with significant aliasing artifacts.
Innovation Solution
A high-resolution filter kernel algorithm is applied, incorporating a weighting factor that scales high-frequency regions by a factor greater than one and a windowing function to reduce aliasing artifacts, which is implemented in a CT imaging system to enhance image quality and reduce computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional high-resolution kernels are used to enhance high-frequency regions, then image sharpness is improved, but aliasing artifacts increase significantly
Solution Approach 1:
The patent modifies the filter kernel parameters by applying a polynomial weighting function to the ramp filter, creating a new kernel that balances high-frequency enhancement with aliasing suppression. The weighting function parameters are optimized to achieve both sharpness and artifact reduction simultaneously
Solution Approach 2:
The patent combines multiple filter components (ramp filter, polynomial weighting function, and windowing function) into a composite filter kernel. This composite approach integrates the edge-enhancing properties of the ramp filter with the noise-suppressing and aliasing-reducing properties of the weighting and windowing functions
2Measurement precision
If high-resolution kernels are applied to visualize small structures, then structural detail is improved, but image noise increases
Solution Approach 1:
The polynomial weighting function parameters are specifically designed to attenuate high-frequency noise while preserving structurally relevant high-frequency details. The degree and coefficients of the polynomial are optimized to differentiate between noise and meaningful structural information
Solution Approach 2:
The filter applies different weighting to different frequency components based on their local characteristics. Structurally important high-frequency regions (edges, boundaries) are enhanced while noise-dominated regions are suppressed through the polynomial weighting function
3Productivity
If conventional filtering methods are used for image reconstruction, then processing speed is maintained, but image quality deteriorates due to aliasing
Solution Approach 1:
The polynomial weighting function is pre-computed and integrated into the filter kernel before the actual reconstruction process. This preliminary preparation allows the filter to operate efficiently during reconstruction without requiring complex real-time calculations, thus maintaining processing speed while improving image quality
Data Source
AI summary
Methods and systems for a system for visualizing relatively small structures within an object are provided. The system includes an image acquisition sub-system for acquiring a dataset for a volume of interest and processor for generating image data from the acquired data wherein the processor is programmed to execute a high resolution filter kernel algorithm that includes a weighting factor applied to a ramp filter that scales relatively high frequency regions of the image dataset by a factor greater than one. The high resolution filter kernel algorithm also includes a windowing function applied to the weighted ramp filter that facilitates reducing aliasing artifacts in reconstructed images generated from the image dataset.