CT Projection Truncation Correction via Hybrid Fitting
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Solution Overview
Problem
Computed tomography (CT) systems face image degradation due to truncation artifacts caused by a limited field of view (FOV) in X-ray beams, leading to incomplete information and poor image quality, especially when the FOV does not completely span the object being imaged.
Innovation Solution
The method involves extrapolating X-ray attenuation values beyond the edges of the detector array by using a hybrid fitting method that combines water cylinder fitting and polynomial fitting, with a two-dimensional adaptive edge-preserving filter to smooth the padding map, ensuring accurate and jagged-edge-free virtual projection data for image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the field of view (FOV) of the X-ray beam is limited, then the imaging area is reduced and scan time is shortened, but incomplete information is lost for portions of the object outside the FOV, degrading image quality
Solution Approach 1:
The patent applies preliminary action by performing extrapolation of projection data before image reconstruction. Virtual projection data is generated by extrapolating beyond the detector array edges using a hybrid fitting method combining water cylinder fitting and polynomial fitting. This preliminary completion of incomplete data enables subsequent reconstruction algorithms to produce high-quality images without requiring extended scan times or larger FOVs.
2Area of stationary object
If the field of view (FOV) of the X-ray beam is limited, then the imaging area is reduced, but truncation artifacts appear in reconstructed images, degrading image quality
Solution Approach 1:
The patent converts the harmful effect of data truncation into a benefit by using the known geometric constraints of the imaging setup (water cylinder shape) and the polynomial trends in the measured data to generate accurate virtual projection data. The truncation boundary itself becomes a useful feature for fitting, as the hybrid method uses the water cylinder model to guide the extrapolation, transforming the artifact problem into a structured prediction task that improves image quality.
3Measurement precision
If extrapolation methods are used to fill missing projection data, then image quality improves, but jagged edges may appear in the reconstructed images
Solution Approach 1:
The patent applies parameter changes by using a two-dimensional adaptive edge-preserving filter that dynamically adjusts its smoothing parameters based on local image features. The filter strength and kernel size are adaptively modified to preserve true edges while removing jagged artifacts from the extrapolated regions. This adaptive parameter adjustment maintains measurement precision in the extrapolated areas while ensuring edge smoothness in the final reconstructed image.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces truncation artifacts, providing improved image quality by accurately filling in missing data and maintaining smooth edges in reconstructed images, even when the FOV is limited, and is applicable to both 2D and 3D reconstructions.
Implementation Method 1
The attenuation of the radiation that has passed through the body is measured by processing electrical signals received from the detector
Implementation Method 2
a two-dimensional adaptive edge-preserving filter to smooth the padding map
Data Source
AI summary
An apparatus and method are provided for computed tomography (CT) imaging to reduce truncation artifacts due to a part of an imaged object being outside the scanner field of view (FOV) for at least some views of a CT scan. After initial determining extrapolation widths to extend the projection data to fill a truncation region, the extrapolation widths are combined into a padding map and smoothed to improve uniformity and remove jagged edges. Then a hybrid material model fits the measured projection data nearest the truncation region to extrapolate projection data filling the truncation region. Smoothing the padding map is improved by the insight that in general smaller extrapolation widths are more accurate and trustworthy. Further, practical applications often include multiple inhomogeneous materials. Thus, the hybrid material model provides a better approximation than single material models, and more accurate fitting is achieved.