Partial-Angle CT Reconstruction with 360° Artifact Correction
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Solution Overview
Problem
Existing computed tomography (CT) image reconstruction methods, particularly in quick-scan techniques, suffer from cone beam artifacts and HU value fluctuations due to incomplete angular data intervals, leading to unstable image quality over time series.
Innovation Solution
A method that reconstructs intermediate and base data sets using modified parameterization to emphasize lower spatial frequencies, applies a correction algorithm modeling the acquisition process, and subtracts artifact data sets to stabilize HU values and correct cone beam artifacts in a single step, utilizing a complete data basis for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If projection images covering a complete 360° orbit are used for reconstruction, then image completeness and accuracy are improved, but temporal resolution deteriorates and computational complexity increases
Solution Approach 1:
The patent divides the 360° projection data into two separate datasets: a first dataset covering a partial angular interval (less than 360°) for rapid reconstruction, and a second dataset covering the complete 360° orbit for artifact correction. This segmentation allows the system to maintain both temporal resolution and image completeness by using each dataset for its specific purpose.
Solution Approach 2:
The patent introduces an intermediary correction algorithm that acts as a mediator between the rapid partial-angle reconstruction and the complete 360° reconstruction. This intermediary process models the acquisition and reconstruction to determine artifact data that can be subtracted from the base dataset, enabling the system to achieve accurate complete-orbit reconstruction without fully processing all projection data for each time point.
2Loss of time
If quick-scan technique with partial angular interval is used, then temporal resolution is improved, but cone beam artifacts and HU value fluctuations increase
Solution Approach 1:
The patent converts the harmful cone beam artifacts generated by quick-scan reconstruction into a correctable signal. By using the complete 360° projection data to create a base dataset and then modeling the acquisition process, the system identifies and quantifies the artifact patterns. These previously harmful artifacts become the basis for a correction algorithm that subtracts them from the rapid reconstruction, effectively converting the harmful quick-scan artifacts into a correctable reference pattern.
Solution Approach 2:
The patent implements feedback through an iterative correction process. The complete 360° projection data is used to generate a base dataset, which is then processed through forward projection and reconstruction to create artifact models. These artifact models are fed back into the system to correct the rapid reconstruction results, continuously refining the image quality while maintaining temporal resolution.
3Measurement precision
If iterative correction algorithm is applied to remove artifacts, then image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-processing the complete 360° projection data to create a base dataset and pre-calculating artifact correction models. This preliminary processing is done once or periodically, and the resulting correction algorithms can then be applied efficiently to multiple rapid reconstructions. By preparing the correction data in advance, the system avoids the need for computationally intensive iterative corrections for every single time point.
Solution Approach 2:
The patent changes parameters by switching between different reconstruction approaches based on the data availability. When complete 360° data is available, it uses a modified parameterization for the base dataset; when only partial angle data is available, it uses standard quick-scan parameters. This parameter adaptation allows the system to optimize computational complexity based on the specific reconstruction task at hand.
Data Source
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AI summary
A computer-implemented method for improving the image quality of a time series of computed tomography image datasets, wherein: - projection images of an acquisition area acquired in a single acquisition process with different projection geometries are provided as a projection image series; - the projection geometries are described by at least one projection angle in a plane perpendicular to an axis of rotation about which an X-ray source (7) of a computed tomography device (4) is rotated along an acquisition trajectory; - the projection images in the acquisition process cover a total angular interval of at least 360° in their projection angles; and - for the reconstruction of the computed tomography image datasets, first projection images are acquired successively in time, the projection angles of which cover a partial angular interval of less than 360°, in particular 180° or less.the projection image series are used, wherein, for artifact reduction, for each computed tomography image dataset, an intermediate dataset from the first projection images and a base dataset from second projection images of the projection image series, covering at least one angular interval of 360°, are reconstructed using a modified parameterization that results in a stronger weighting of lower spatial frequencies of a covered spatial frequency range with respect to at least one direction than in the reconstruction of the computed tomography image dataset; and, by applying a correction algorithm that models the acquisition process with respect to the first projection images and the reconstruction for artifact reduction with respect to at least one type of artifact, a base dataset corrected with respect to the artifact type is generated.and - the computed tomography dataset is corrected by adding a correction dataset describing artifacts, determined by subtracting the corrected basic dataset from the intermediate dataset.