CT Image Reconstruction Combining Analytic and Iterative Methods
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Solution Overview
Problem
Current CT image reconstruction methods face challenges in reducing processing time and artifacts near the boundary of the region of interest, particularly in combining analytic and iterative reconstruction techniques effectively.
Innovation Solution
A system and method that combines analytic image reconstruction with iterative reconstruction algorithms, processing the low and high-frequency components of CT images separately using different methods, and applying filters to reduce artifacts, allowing for efficient processing and improved image quality within the region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative reconstruction methods are used to achieve better resolution, then image quality is improved, but processing time increases
Solution Approach 1:
The patent divides the image reconstruction process into two distinct stages: an analytical reconstruction stage that produces an initial image quickly, followed by an iterative reconstruction stage that refines specific regions of interest. This segmentation allows the system to benefit from both fast analytical methods and high-quality iterative methods without承受ing the full computational burden of iterative reconstruction across the entire image.
Solution Approach 2:
The patent applies iterative reconstruction selectively to regions of interest rather than uniformly across the entire image. By identifying and prioritizing specific anatomical regions or areas with abnormalities, the system concentrates computational resources where they provide the most diagnostic value, thereby improving image quality in critical areas while maintaining acceptable processing times.
2Loss of time
If analytic reconstruction is applied to obtain initial images quickly, then processing time is reduced, but image quality and resolution deteriorate
Solution Approach 1:
The patent performs analytical reconstruction as a preliminary step to generate an initial image that provides a rough but quick overview of the scanned object. This initial image serves as a foundation that guides subsequent iterative refinement, allowing the system to quickly eliminate regions that do not require further processing while focusing computational effort on areas needing higher resolution.
Solution Approach 2:
The initial analytical image acts as an intermediary between the raw projection data and the final high-resolution iterative reconstruction. It provides a intermediate representation that facilitates the identification of regions of interest and serves as a starting point for the iterative algorithm, bridging the gap between speed and quality.
3Measurement precision
If iterative reconstruction is applied to the entire image, then image quality is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts and isolates regions of interest from the full image dataset before applying iterative reconstruction. By separating the diagnostically critical regions from the rest of the image, the system applies computationally intensive iterative methods only where necessary, thereby maintaining high image quality in important areas while preserving overall processing efficiency.
4Productivity
If analytic reconstruction is used for the entire field of view, then processing is efficient, but artifacts appear near the boundary of the region of interest
Solution Approach 1:
The patent applies different reconstruction methodologies to different spatial regions: analytical reconstruction is used for areas where speed is critical and artifacts are acceptable, while iterative reconstruction is applied to regions of interest where boundary artifacts would compromise diagnostic accuracy. This local differentiation eliminates artifacts in critical areas while maintaining processing efficiency elsewhere.
Data Source
AI summary
The present disclosure provides a system and method for CT image reconstruction. The method may include combining an analytic image reconstruction technique with an iterative reconstruction algorithm of CT images. The image reconstruction may be performed on or near a region of interest.