Adaptive Regularization for CT Cardiac Image Reconstruction
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Solution Overview
Problem
Conventional CT cardiac imaging methods face limitations in achieving high temporal and spatial resolution due to heart motion during scans, with existing iterative reconstruction methods not improving temporal resolution effectively and requiring complete projection views, leading to longer scan times.
Innovation Solution
A method and system that iteratively apply a data-fit term and regularization terms to scan data, with the regularization terms being modified based on spatio-temporal information and voxel value changes over time, to improve image reconstruction in CT imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative reconstruction methods are used to reconstruct images from grouped datasets, then image reconstruction is performed, but temporal resolution does not improve relative to FBP because the grouping defines temporal resolution and complete projection views require longer time intervals
Solution Approach 1:
The patent applies dynamics by making the regularization parameter adaptive rather than static. The parameter changes dynamically based on the current image estimate and data fidelity, allowing the reconstruction to adjust to varying motion conditions throughout the scan, thereby improving temporal resolution without requiring longer scan times
Solution Approach 2:
The invention changes the regularization parameter during the iterative reconstruction process based on spatio-temporal information and voxel value changes over time. This parameter adaptation allows the system to optimize temporal resolution dynamically, resolving the contradiction between maintaining complete projection views and reducing scan time
2Reliability
If complete or nearly complete sets of projection views are collected, then image reconstruction quality is maintained, but longer time intervals are required over which object motion may occur
Solution Approach 1:
The patent applies partial action by using incomplete or partially overlapping projection view sets rather than requiring complete sets. The adaptive regularization compensates for the missing data, allowing reconstruction quality to be maintained while reducing the time interval required and thereby minimizing object motion during scanning
Solution Approach 2:
The invention implements feedback by using the current image estimate to dynamically adjust the regularization parameter in subsequent iterations. This feedback mechanism ensures that reconstruction quality is maintained adaptively, allowing the system to work with fewer projection views and reduce scan time while compensating for object motion
3Reliability
If data is collected within a narrow temporal window corresponding to a specific phase of the heart cycle, then motion artifacts are reduced, but temporal resolution is limited by the narrow window
Solution Approach 1:
The patent resolves this contradiction by making the regularization parameter dynamic and adaptive to the specific phase and motion characteristics of the heart cycle. This allows the system to reduce motion artifacts in each phase while maintaining high temporal resolution across the entire cardiac cycle, rather than being limited to a narrow temporal window
Solution Approach 2:
The invention changes the regularization parameter based on spatio-temporal information and voxel value changes over time, allowing different parameters to be applied to different phases of the cardiac cycle. This adaptive approach reduces motion artifacts phase-specifically while maintaining high temporal resolution across all phases
Data Source
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AI summary
A method of improving a resolution of an image using image reconstruction is provided. The method includes acquiring scan data of an object and forward projecting a current image estimate of the scan data to generate calculated projection data. The method also includes applying a data-fit term and a regularization term to the scan data and the calculated projection data and modifying at least one of the data fit term and the regularization term to accommodate spatio-temporal information to form a reconstructed image from the scan data and the calculated projection data.