Imaging Artifact Reduction via Local Bias Field Segmentation
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Solution Overview
Problem
Imaging systems, particularly in medical CT and other modalities, face challenges in reducing motion artifacts, especially from involuntary patient movements like bowel gas, which existing methods struggle to address due to computational demands and assumptions about object motion.
Innovation Solution
A method that segments reconstructed imaging data to identify and define artifact-containing regions without estimating motion vectors, allowing for post-reconstruction processing to reduce motion artifacts and improve image quality, applicable to irregularly-shaped objects like bowel gas pockets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion-compensated reconstruction is used to reduce motion artifacts, then image quality is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the imaging data processing into two distinct phases: reconstruction phase (where data is collected but not processed for motion artifacts) and post-reconstruction phase (where motion artifact reduction is applied). This segmentation allows the system to avoid computationally intensive motion estimation during reconstruction while still achieving motion artifact reduction through subsequent processing of the reconstructed images.
Solution Approach 2:
The patent performs preliminary reconstruction of the imaging data without motion compensation, creating a baseline reconstructed image set. This preliminary action allows subsequent motion artifact reduction algorithms to work with already-reconstructed data, separating the reconstruction computation from the artifact reduction computation and enabling more efficient processing.
2Manufacturing precision
If motion estimation is performed to reduce motion artifacts, then artifact reduction is achieved, but processing time increases
Solution Approach 1:
The patent extracts motion artifact reduction from the real-time reconstruction process and applies it as a separate post-processing step. This extraction allows the system to use simplified motion detection algorithms that work on reconstructed images rather than raw projection data, significantly reducing computational time while maintaining artifact reduction effectiveness.
Solution Approach 2:
The patent applies motion artifact reduction selectively to regions of the image where motion is detected, rather than processing the entire image volume with full motion compensation. This partial action approach reduces overall processing time while still achieving artifact reduction where it is most needed.
3Device complexity
If assumptions about object rigidity and periodicity are made for motion compensation, then computational load is reduced, but accuracy decreases for irregular objects like bowel gas
Solution Approach 1:
The patent enables the imaging system to automatically detect and adapt to the specific characteristics of moving structures in the patient's body. Rather than requiring pre-defined assumptions about object properties, the system analyzes the reconstructed images to identify motion patterns specific to each patient's anatomy, including irregular structures like bowel gas pockets, and applies appropriate artifact reduction tailored to those specific patterns.
Data Source
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AI summary
A method and system for reducing localized artifacts in imaging data, such as motion artifacts and bone streak artifacts, are provided. The method includes segmenting the imaging data to identify one or more suspect regions in the imaging data near which localized artifacts are expected to occur, defining an artifact-containing region of interest in the imaging data around each suspect region, and applying a local bias field within the artifact-containing regions to correct for the localized artifacts.