Tomography Image Reconstruction Reducing Artifacts via Substance Estimation
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Solution Overview
Problem
Conventional image reconstruction methods in tomography, such as those using successive approximation, face challenges in reducing artifacts when the constituent substances of an imaging sample are unknown or when substances are not pure, leading to unreliable substance information and blurring in images.
Innovation Solution
An image reconstruction processing method that estimates substance information from a reconstructed image at regular intervals using a successive approximation method, allowing for reliable substance information estimation regardless of known or unknown substances, and updates images using this information to reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If substance information is given based on known constituent substances, then artifact reduction is achieved, but the method cannot be applied when substances are unknown or not pure
Solution Approach 1:
The system automatically estimates substance information from the image data itself without requiring external input about constituent substances. The estimation unit extracts substance information directly from the measured data and reconstructed images, allowing the method to serve itself and eliminating dependency on prior knowledge of sample composition.
Solution Approach 2:
The method transitions from using fixed, pre-defined substance information to dynamically estimated substance information that changes based on the actual image data. By changing the parameter source from external knowledge to internal data-driven estimation, the system adapts to unknown and mixed substances while maintaining artifact reduction capability.
2Adaptability or versatility
If substance information is estimated from initial reconstructed images, then adaptability to unknown substances is achieved, but estimation reliability is low due to image blurring and artifacts
Solution Approach 1:
The system performs preliminary rough estimation of substance information from initial reconstructed images to enable the successive approximation method to start, even though this initial estimation is imprecise. This preliminary action allows the process to begin and progressively improve through subsequent iterations.
Solution Approach 2:
The substance information estimation is performed continuously throughout the successive approximation process, not just once at the beginning. By repeatedly estimating and updating substance information as the image reconstruction progresses, the system continuously improves estimation accuracy while maintaining adaptability to unknown substances.
3Reliability
If the successive approximation method is used to improve image quality, then artifact reduction is achieved, but computational complexity increases
Solution Approach 1:
The system performs substance information estimation at selected intervals rather than at every single iteration step. This partial action approach reduces computational complexity by avoiding excessive calculations while still maintaining the benefits of updated substance information for improving image quality and reducing artifacts.
Data Source
AI summary
The image reconstruction processing method estimates, in a substance information estimation process, substance information from a reconstructed image for every image update in an image update process, and uses the estimated substance information to update images based on succeeding calculations by successive approximation formulae of a successive approximation method. Since the substance information is estimated, the present methodology can be applied regardless of whether the constituent substance of an imaging sample is known. Further, because the substance information is estimated (updated) from the reconstructed image for every image update in the image update process, reliable substance information can be estimated by avoiding the problem of continuously using the substance information estimated at a point of time where a repeat count (iteration count) by successive approximation formulae is low and at a point of time where the repeat count is high. Accordingly, artifacts can be reduced using the reliable substance information.