CT Hard Tissue Removal Imaging for Rapid Beam Hardening Correction
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Solution Overview
Problem
Traditional methods for removing hard tissue from CT images and correcting beam hardening artifacts are time-consuming when dealing with CT images having different reconstruction parameters.
Innovation Solution
A system and method that generates bone information images with varying thicknesses and increments based on reference images, allowing for efficient correction of hard tissue artifacts by stacking multiple reference images to create bone information images with different thicknesses and increments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional hard tissue removal methods are used, then hard tissue can be removed from CT images, but the process consumes much time when multiple CT images with different reconstruction parameters need to be generated and corrected
Solution Approach 1:
The system pre-generates bone information images with various thicknesses and increments before the actual correction process. These pre-computed bone information images serve as templates that can be quickly applied to correct multiple CT images with different reconstruction parameters without performing time-consuming calculations for each image individually
Solution Approach 2:
The system creates copies of reference bone information images by stacking multiple reference images with different thicknesses and increments. These copied bone information images are then used to correct corresponding CT images, avoiding the need to recalculate bone information from scratch for each different reconstruction parameter set
2Adaptability or versatility
If multiple CT images with different reconstruction parameters are generated, then comprehensive image information can be obtained, but the complexity of processing increases significantly
Solution Approach 1:
The system creates a universal bone information image generation framework that can handle multiple CT images with different reconstruction parameters through a single unified process. The same reference image stacking methodology applies to all images regardless of their specific thickness or increment parameters, simplifying the overall processing architecture
Solution Approach 2:
The system segments the correction process into independent modules: generating reference images, stacking reference images to create bone information images with different parameters, and applying corrections. This segmentation allows each module to be optimized independently and simplifies the handling of multiple images with different parameters
Data Source
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AI summary
A system and a method for CT image reconstruction are provided. The method includes: obtaining raw data set related to an object (510); generating a first image set based on the raw data set (710, 520, 530), wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image (720); generating a first bone information image based on the one or more reference images (730); generating a second image set based on the raw data set (750), wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images (760); correcting beam hardening artifact of the second full quality image based on the second bone information image to generate a beam hardening artifact corrected image (770).