Iterative CT Reconstruction with Temporal Voxel Modeling
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Solution Overview
Problem
Conventional iterative algorithms in computed tomography (CT) imaging assume constant voxel values over time, leading to pronounced artifacts due to patient motion, breathing, peristalsis, and contrast agent flow, which are not effectively addressed.
Innovation Solution
A system and method that model voxel values as a function of time using kinetic parameters, allowing for the reconstruction of images based on these parameters to account for motion and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional iterative algorithms assume constant voxel values over time, then the algorithm complexity is reduced, but motion artifacts are pronounced and extend farther from the source of motion
Solution Approach 1:
The patent applies the dynamics principle by transitioning from static voxel value assumptions to dynamic temporal modeling. Each voxel is modeled as a function of time using basis functions (e.g., step functions, linear functions, or exponential functions) that capture temporal variations. This allows the algorithm to account for motion, breathing, peristalsis, and contrast agent flow, thereby reducing motion artifacts while maintaining manageable computational complexity through parameterized temporal models.
2Device complexity
If conventional iterative algorithms assume constant voxel values, then the reconstruction process is simpler, but image quality deteriorates due to unaccounted motion
Solution Approach 1:
The patent applies parameter changes by introducing temporal parameters to the voxel model. Instead of assuming constant voxel values, the algorithm models voxel values as functions of time using basis functions with temporal parameters (e.g., time points, rates, or phases). This parameterization allows the reconstruction to account for motion and temporal variations, improving image quality while the parameter estimation process remains integrated into the iterative reconstruction framework.
3Object-affected harmful factors
If temporal modeling of voxels is implemented, then motion artifacts are reduced, but computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the temporal dimension into discrete basis functions or time points. Each voxel is modeled using a set of basis functions that can be independently estimated, allowing the temporal modeling to be segmented into manageable components. This segmentation approach reduces the computational burden by avoiding the need to model every possible temporal variation simultaneously, while still capturing motion artifacts effectively.
Solution Approach 2:
The patent applies partial action by using a limited set of basis functions to model temporal variations rather than attempting to model all possible temporal behaviors. The number and complexity of basis functions can be adjusted based on the specific application (e.g., using fewer basis functions for slow motion and more for rapid motion). This partial modeling approach reduces computational requirements while still effectively reducing motion artifacts for the intended use case.
Data Source
AI summary
A system and method include acquisition of a set of image data corresponding to a time period of data acquisition, the set of image data corresponding to a plurality of voxels, wherein each of the plurality of voxels corresponds to a distinct acquisition time within the time period of data acquisition. The system and method further include the modeling of the plurality of voxels as a function of time based on a plurality of kinetic parameters associated therewith and reconstruction of an image from the set of image data based on the modeled plurality of voxels.