CT Trans-Axial Truncation Compensation With Progressive FOV Refinement
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Solution Overview
Problem
Existing CT imaging methods struggle with trans-axial truncation due to patient size and limited field-of-view, leading to inaccurate data estimation and undesirable artifacts in reconstructed images.
Innovation Solution
A progressive data estimation method that reconstructs images using projection data from a virtual scan and estimated data, progressively refining pixel values to achieve a larger reconstruction field-of-view without truncation, adjusting for patient anatomy and geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional data-driven truncation compensation is used to estimate truncated data beyond the scan FOV, then the reconstruction FOV can be enlarged, but the estimation accuracy decreases with increasing distance from the truncated edge
Solution Approach 1:
The patent divides the truncated region into multiple segments or zones at different distances from the scan FOV boundary. Each segment is processed with different refinement iterations, where regions closer to the boundary receive more refinement passes. This segmentation allows the system to allocate computational resources efficiently, improving accuracy where it matters most while maintaining overall reconstruction quality.
Solution Approach 2:
The patent implements an adaptive, iterative refinement process where the number of refinement iterations dynamically adjusts based on the distance from the scan FOV boundary. Regions farther from the boundary undergo more refinement iterations, creating a dynamic processing approach that optimizes accuracy across the entire reconstructed image without uniformly increasing computational load.
2Area of stationary object
If hardware enhancements are used to increase the effective imaging FOV, then trans-axial truncation is reduced, but the device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the scan FOV boundary and uses iterative refinement to simulate the effect of a larger physical FOV. Instead of physically enlarging the detector or bore, the system computationally reconstructs the extended region by refining estimates multiple times, effectively copying the imaging capability without the physical hardware changes.
Solution Approach 2:
The patent replaces the mechanical/hardware solution (larger detector, increased bore size) with a computational/software-based solution. The iterative refinement algorithm substitutes for physical hardware enhancements, achieving the same goal of expanded FOV through mathematical processing rather than mechanical modification.
3Area of stationary object
If multiple scans with different imaging geometry are used to reduce truncation, then the coverage is improved, but the scan time and productivity decrease
Solution Approach 1:
The patent performs preliminary actions by acquiring all necessary projection data within the scan FOV first, then using iterative refinement to pre-compute and fill in the truncated regions. This preliminary data acquisition followed by computational completion avoids the need for multiple physical scans, maintaining scan efficiency while achieving complete coverage.
Solution Approach 2:
The patent maintains continuity of useful action by performing the iterative refinement process continuously after the initial scan, rather than requiring intermittent additional scans. The computational refinement operates continuously on the acquired data, transforming it into a complete reconstruction without interrupting the scanning workflow.
Data Source
AI summary
Imaging apparatuses described herein include a radiation source configured for imaging radiation, a radiation detector positioned to receive radiation from the radiation source, and an image processing system. The image processing system is configured to: receive projection data from the radiation detector, the projection data corresponding to a scan field-of-view (scanFOV), the image being trans-axially truncated; identify a final reconstruction field-of-view (reconFOV) that is larger than the scanFOV; reconstruct an image having a reconstruction field-of-view (reconFOVn), wherein reconFOVn is less than reconFOV; generate a progressive refinement for the image; reproject the image with the progressive refinement thereby generating a virtual scan vscanFOVn; refine the virtual scan vscanFOVn data; and repeat the reconstruction, generation of the progressive refinement, and reprojection for one or more subsequent reconstructions until reconFOV is reached.