Tomography Reconstruction Algorithm for Artifact Reduction
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Solution Overview
Problem
Current tomography image reconstruction algorithms, particularly in limited data scenarios, often produce artifacts due to sparse or limited angular projection data, leading to inaccuracies in reconstructed images across various applications.
Innovation Solution
An algebraic reconstruction technique (ART) algorithm with a transformation step, where threshold values are adjusted between iterative steps to either increase or decrease based on the density relative to the surrounding medium, effectively reducing or eliminating artifacts by ensuring reconstructed values adhere to a gradually changing threshold, thereby improving image quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If filtered back projection (FBP) is used for reconstruction, then computational time is reduced, but image accuracy and artifact reduction deteriorate
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting threshold values during iterative reconstruction steps. The threshold parameter is modified between iterative steps to adapt to changing image densities, enabling better artifact reduction while maintaining computational efficiency. This resolves the contradiction by optimizing the reconstruction parameters to achieve both speed and accuracy.
Solution Approach 2:
The patent implements dynamics by making the threshold value dynamic rather than static. The threshold changes between iterative steps based on the current image state, allowing the reconstruction algorithm to adapt to different density regions. This dynamic adjustment improves image accuracy without requiring excessive computational time.
2Measurement precision
If iterative reconstruction (IR) is used for reconstruction, then image accuracy improves, but computational time increases
Solution Approach 1:
The patent reduces computational time by strategically changing threshold parameters during iteration. Instead of performing excessive iterative steps, the dynamic threshold adjustment accelerates convergence, achieving high image accuracy with fewer iterations. This resolves the time-accuracy tradeoff by optimizing the reconstruction parameters.
Solution Approach 2:
The patent incorporates feedback by using the current image density information to adjust the threshold for the next iterative step. This feedback mechanism allows the algorithm to converge faster by adapting to the actual image state, reducing the total computational time required while maintaining high accuracy.
3Loss of time
If limited angular range projection data is used, then data acquisition time is reduced, but image quality and artifact reduction deteriorate
Solution Approach 1:
The patent compensates for limited angular data by dynamically adjusting threshold parameters during reconstruction. The changing threshold adapts to the sparse data conditions, maintaining image quality without requiring extended data acquisition time. This resolves the contradiction by optimizing reconstruction parameters to work effectively with limited data.
Solution Approach 2:
The patent applies preliminary action by pre-establishing a threshold adjustment strategy before reconstruction. The method prepares the threshold modification approach in advance, enabling effective artifact reduction even with limited angular data, thus maintaining image quality without extending acquisition time.
4Device complexity
If sparse projection data is used, then measurement complexity is reduced, but artifact generation increases
Solution Approach 1:
The patent reduces artifacts from sparse data by dynamically changing threshold parameters during reconstruction. The adaptive threshold adjustment compensates for the sparsity, maintaining image quality without increasing measurement complexity. This resolves the contradiction by optimizing reconstruction parameters to handle sparse data effectively.
Data Source
AI summary
The present invention is a tomographic reconstruction algorithm, which is highly effective improving image quality and accuracy by reducing or eliminating artifacts within images produced by limited data tomography. Using algebraic reconstruction techniques (ART), depending on whether or not an object has higher or lower densities, a current threshold value is set to either a high or low threshold parameter and then decreased or increased, respectively, to reduce or eliminate the artifacts in a reconstructed image.