In-Reconstruction Filtering for PET List Mode Iterative Reconstruction
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Solution Overview
Problem
Iterative-based reconstruction algorithms in PET and SPECT imaging face challenges with noise accumulation leading to degraded image quality and reduced signal-to-noise ratio, particularly in low-count acquisitions, which can result in compromised diagnostic usefulness due to blurring and loss of quantitative accuracy when using post-reconstruction filters.
Innovation Solution
The implementation of an in-reconstruction filtering method, such as a median filter, during iterative updates in the image domain to control noise and maintain image resolution, allowing for higher iteration counts without degrading image quality, and improving quantitative accuracy in small objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If post-reconstruction filters are applied to smooth reconstructed images, then noise levels are reduced, but image resolution and quantitative accuracy are degraded
Solution Approach 1:
The patent applies filtering operations during the iterative reconstruction process itself, rather than as a post-processing step. By incorporating filters into the forward and back projection operations during iteration, noise control is achieved before the final image is formed, preserving quantitative accuracy while reducing noise levels
Solution Approach 2:
The patent introduces filter operators as intermediary elements within the iterative reconstruction algorithm. These filter operators modify the forward projection and back projection operations, acting as mediators that control noise accumulation during the reconstruction process without directly filtering the final image
2Measurement precision
If iterative-based reconstruction algorithms are used to improve image accuracy, then noise accumulates with each iteration, but image quality and signal-to-noise ratio are reduced
Solution Approach 1:
The patent implements feedback mechanisms within the iterative reconstruction algorithm where filter operations are applied based on the current state of the reconstruction. The filtering strength and application are adjusted according to iteration number and image characteristics, creating a feedback loop that controls noise accumulation while maintaining convergence to an accurate solution
Solution Approach 2:
The patent employs dynamic filtering where the filter parameters and application strategy change during the iterative process. Filtering is applied more aggressively in early iterations when noise accumulates rapidly, and reduced or modified in later iterations as the image converges, creating a dynamic balance between noise control and accuracy preservation
Data Source
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AI summary
A system (10) and a method (100) iteratively reconstruct an image of a target volume of a subject. In each iteration of a plurality of iterations, an estimate image of the target volume (54) is forward projected (58) and compared (62) to received event data (44) to determine a discrepancy (64). The discrepancy (64) is back projected (66) and the back projection (68) updates (70) the estimate image (54). In at least one iteration, the estimate image (54) is filtered (52) in the image domain prior to being back projected.