One-Gate Reconstruction for SPECT Motion Correction
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Solution Overview
Problem
Current SPECT imaging modalities face challenges due to patient movement during data acquisition, leading to motion artifacts and reduced image quality, as existing methods average motion over the entire acquisition period, neglecting inter-view motion and resulting in noisy gated datasets with lower statistics.
Innovation Solution
A one-gate reconstruction method that generates a motion-corrected data model by forward projecting image estimates using a motion estimate, calculating an image update factor by comparing gates to the model, and iteratively updating the image estimate to correct for inter-view and inter-gate motion errors, effectively merging data from multiple gates into a single reconstructed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data acquisition is performed over a long duration to achieve sufficient image statistics, then image quality improves, but patient motion artifacts increase
Solution Approach 1:
The patent segments the acquired projection data into multiple gates based on respiratory phase, creating separate datasets for different respiratory states. This allows motion correction by processing each gate independently with its own motion estimate, rather than treating the entire acquisition as a single static dataset.
Solution Approach 2:
The patent performs preliminary motion estimation for each view and gate before reconstruction. Motion estimates are calculated in advance using registration techniques comparing projections from different views, allowing the reconstruction algorithm to compensate for motion artifacts during the image formation process.
2Object-affected harmful factors
If gating is applied to reduce motion impact, then motion artifacts decrease, but statistics per gate are reduced leading to increased noise
Solution Approach 1:
The patent merges information from multiple gates during reconstruction by incorporating motion-corrected projections from all gates into a single unified image. The reconstruction algorithm combines the segmented gate data with appropriate weighting and motion compensation, achieving both motion correction and sufficient statistics.
Solution Approach 2:
The patent changes the reconstruction parameters by using view-specific and gate-specific motion estimates rather than a single global motion correction. The reconstruction algorithm adjusts for varying motion conditions across different views and gates, optimizing the balance between noise reduction and motion artifact suppression.
3Device complexity
If global motion correction averaging is used, then processing is simplified, but severe motion artifacts are generated due to neglecting inter-view motion
Solution Approach 1:
The patent applies local motion correction by calculating view-specific and gate-specific motion estimates rather than using a single global correction for all data. Each view and gate receives tailored motion compensation based on its specific motion characteristics, significantly improving correction accuracy while maintaining computational feasibility.
Data Source
AI summary
A set of set of first modality data is received including at least one view comprising a plurality of gates. The set of first modality data is received from a first imaging modality of an imaging system. A set of second modality data is received from a second imaging modality of the imaging system. A motion corrected model of the set of first modality data is generated by forward projecting the set of first modality data including a motion estimate. An update factor for each of the plurality of views is generated by comparing at least one of the plurality of gates to the motion corrected model. The motion corrected model is updated by the update factor to generate a motion corrected image.