Cone-beam CT Reconstruction Using Frequency Band Segmentation
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Solution Overview
Problem
Cone-beam computed tomography (CT) images often suffer from artifacts due to data truncation in the z-direction, mishandled data redundancy, and missing frequency information, particularly in axial scans, leading to imperfections in reconstructed images.
Innovation Solution
The method involves reconstructing an initial subset of axial projection data, filtering to preserve uniformly weighted frequencies, and repeating the process at shifted view ranges to generate partial reconstructions, which are then combined to produce an intermediate volume with complete frequency data, using advanced filtering techniques to address data redundancy and missing frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional axial scan trajectory is used in cone-beam CT, then acquisition speed is improved, but cone-beam artifacts occur due to data truncation and missing frequencies
Solution Approach 1:
The patent divides the frequency spectrum into multiple bands and processes each band separately through multiple reconstructions with different view ranges. This segmentation allows complete frequency coverage to be achieved while maintaining the speed benefits of axial scanning, as each segmented reconstruction uses only the data needed for its specific frequency band.
Solution Approach 2:
The patent transitions from processing in spatial domain to frequency domain by applying filtering operations in the frequency spectrum. This dimensional change enables the system to address missing frequency information and data truncation issues that cannot be resolved in the spatial domain, thereby improving image quality without sacrificing acquisition speed.
2Reliability
If full-scan reconstruction is performed to obtain complete frequency data, then image quality is improved, but computational complexity increases
Solution Approach 1:
The patent segments the frequency spectrum and processes different frequency bands in separate reconstruction steps. Each segmentation uses a tailored view range optimized for that specific frequency band, avoiding the computational overhead of processing all frequencies with the most demanding view range requirements. This reduces overall computational complexity while achieving complete frequency coverage.
Solution Approach 2:
The patent applies partial action by performing multiple reconstructions with progressively expanding view ranges, where each reconstruction handles only the frequency band that requires that specific view range. This avoids the excessive computational cost of performing a single full-scan reconstruction with maximum view range for all frequency bands, as each band receives only the processing it specifically needs.
3Loss of information
If data filtering is applied to preserve uniformly weighted frequencies, then frequency completeness is improved, but image processing time increases
Solution Approach 1:
The patent segments the frequency spectrum into multiple bands and applies filtering operations only to the specific frequency ranges that require them. This segmented approach avoids the time cost of applying comprehensive filtering to the entire frequency spectrum, as each segmentation receives targeted filtering only where needed, thereby reducing overall processing time while maintaining frequency completeness.
Data Source
AI summary
Algorithms are disclosed that recombine acquired data so as to generate a substantially uniform and complete set of frequency data where frequency data might otherwise be incomplete. This process, or its equivalent, may be accomplished in a computationally efficient manner using filtering steps in one or both of the reconstruction space and/or the post-processing space.