Adaptive CT Artifact Reduction via Subregion Segmentation
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Solution Overview
Problem
Computed tomography (CT) image data reconstruction often results in artifacts, particularly metal artifacts, which require additional processing steps for reduction, increasing time and computational effort, and are not effectively addressed in real-time.
Innovation Solution
An adaptive method and device for generating CT image data that acquires projection measurement data, determines artifact-affected subregions, and performs artifact-reduced reconstructions only in those areas, combining uncorrected and corrected data to produce high-quality image data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If artifact-reduced reconstruction is performed on the entire image data, then image quality is improved, but processing time and computational effort are significantly increased
Solution Approach 1:
The image data is divided into multiple subregions, and artifact-reduced reconstruction is selectively applied only to subregions containing artifacts rather than processing the entire image. This segmentation approach maintains image quality where needed while significantly reducing overall processing time and computational resources.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on local characteristics. Artifact-reduced reconstruction is applied locally only to subregions identified as containing artifacts, while other regions undergo standard reconstruction, optimizing the balance between image quality and processing efficiency.
2Productivity
If visual inspection is performed to determine need for artifact reduction, then processing is avoided when not needed, but additional time and manual effort are required
Solution Approach 1:
The system automatically identifies subregions containing artifacts through analysis of the reconstructed image data without requiring manual visual inspection by a user. The method self-determines which areas need artifact reduction, eliminating the need for time-consuming manual assessment while maintaining processing efficiency.
Solution Approach 2:
The reconstructed image data is automatically analyzed to provide feedback on the presence and location of artifacts, which then guides the selective application of artifact-reduced reconstruction. This automated feedback loop replaces manual inspection and enables efficient, adaptive processing.
3Manufacturing precision
If additional artifact-reduced reconstruction is requested, then image quality in artifact regions is improved, but data volume and computational outlay are significantly increased
Solution Approach 1:
The computational workload is segmented by applying artifact-reduced reconstruction only to specific subregions containing artifacts rather than processing the entire image data volume. This reduces memory requirements and computational outlay while maintaining image quality where artifacts are present.
Solution Approach 2:
Instead of applying full artifact-reduced reconstruction to the entire image, the method applies the computationally intensive process partially only to affected subregions. This partial action approach achieves the necessary image quality improvement while significantly reducing computational resources and data volume requirements.
Data Source
AI summary
An adaptive method for generating CT image data is described. In the method, projection measurement data of an examination region of an examination object is acquired. Furthermore, uncorrected image data of the examination region is generated. Artifact-affected subregions of the examination region are determined on the basis of at least one part of the uncorrected image data. An artifact-reduced image reconstruction is carried out in the artifact-affected subregions of the examination region. Only artifact-reduced subimage data of the artifact-affected subregions is generated. Finally, artifact-reduced image data of the entire examination region is generated by combining at least one part of the uncorrected image data and the artifact-reduced subimage data. A reconstruction device is also described. Moreover, a computed tomography system is described.