Frequency-Split Iterative Reconstruction for ROI Tomography
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Solution Overview
Problem
Conventional iterative reconstruction methods for tomographic imaging require reconstructing the entire volume to avoid data truncation artifacts, making them computationally intensive and inefficient for reconstructing a region of interest (ROI), especially in non-invasive imaging technologies like CT scans.
Innovation Solution
The frequency-split iterative reconstruction approach separates high and low-frequency components of the ROI, allowing iterative reconstruction to be performed only on high-frequency constituents using local projection data, while low-frequency components are obtained through analytic or simplified methods, eliminating the need to model non-ROI objects and reducing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative reconstruction is performed on the entire volume to avoid data truncation artifacts, then image quality is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the reconstruction process into two segments: (1) analytic reconstruction of the entire volume to obtain low-frequency components, and (2) iterative reconstruction only of the region of interest to obtain high-frequency components. This segmentation allows the computationally intensive iterative process to be applied only where needed, reducing processing time while maintaining image quality.
Solution Approach 2:
The patent extracts and separates high-frequency components from low-frequency components in the reconstruction process. By extracting only the high-frequency components through iterative reconstruction and combining them with analytically derived low-frequency components, the method avoids the need to perform iterative reconstruction on the entire volume, thereby reducing computational cost and processing time.
2Measurement precision
If iterative reconstruction is performed on the entire volume to avoid data truncation artifacts, then image quality is improved, but device complexity and computational resources increase
Solution Approach 1:
The reconstruction process is segmented into analytic and iterative components, with iterative reconstruction applied only to the ROI. This reduces the complexity of the computational system by eliminating the need to process entire volume data through the complex iterative algorithm.
Solution Approach 2:
High-frequency components are extracted through iterative reconstruction and combined with low-frequency components from analytic reconstruction. This extraction approach simplifies the overall computational complexity by avoiding full-volume iterative processing while maintaining image quality.
3Productivity
If analytic reconstruction is used on all projection data, then processing speed is improved, but image quality and detail resolution deteriorate
Solution Approach 1:
The patent merges the advantages of analytic reconstruction (fast processing speed) and iterative reconstruction (high image quality) by combining low-frequency components from analytic reconstruction with high-frequency components from iterative reconstruction. This hybrid approach achieves both fast processing and high image quality.
Solution Approach 2:
The patent applies different reconstruction methods to different frequency components: analytic reconstruction for low-frequency components (providing overall image structure) and iterative reconstruction for high-frequency components (providing fine details). This local quality approach ensures optimal processing speed and image quality for each frequency range.
Data Source
AI summary
The present approaches relate to frequency-split iterative reconstruction approaches. In some embodiment, such approaches provide for the combination of the low frequency components of an analytical reconstruction (e.g., a filtered back projection) and the high frequency components of an iterative reconstruction. In certain embodiments, frequency-split iterative reconstruction is used for generating region of interest images.