Tomosynthesis Rib Suppression via Iterative Edge Detection
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Solution Overview
Problem
Current methods for generating tomographic images of the chest cavity struggle with suppressing rib content while preserving lung and organ image quality, often resulting in ripple artifacts and requiring significant computational resources, which limits diagnostic utility and is not robust enough to handle variations in rib shape and image quality.
Innovation Solution
A method involving a computer system that processes a set of unsegmented projection images by detecting rib features, generating a predictive model, and iteratively refining rib detection to suppress rib content in each projection image, allowing for accurate rib edge identification and subtraction, thereby improving image quality and reducing ripple artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the projection density is increased to suppress ripple artifacts, then the diagnostic quality of reconstructed images is improved, but the radiation dose and scan time increase
Solution Approach 1:
The patent applies parameter changes by modifying the projection images through mathematical operations. Specifically, it uses edge detection algorithms to identify rib structures and applies suppression techniques that alter the density parameters of projected images, thereby reducing ripple artifacts without requiring increased physical projection density
Solution Approach 2:
The patent replaces the mechanical approach of increasing projection density with a computational method. Instead of physically acquiring more projections, it uses image processing algorithms including edge detection, rib identification, and selective suppression to achieve the same diagnostic quality improvement
2Object-affected harmful factors
If low pass filtering is applied to suppress ripple artifacts, then the artifacts are reduced, but the reconstruction becomes blurred and diagnostic utility is limited
Solution Approach 1:
The patent applies local quality by selectively suppressing rib structures in specific regions of the projection images while preserving other anatomical structures. The edge detection and rib identification algorithms target only high-contrast rib edges, applying suppression locally rather than globally, thus maintaining image sharpness in non-rib regions while reducing ripple artifacts
Solution Approach 2:
The patent segments the projection images by identifying and separating rib structures from other anatomical features using edge detection algorithms. This segmentation allows selective processing of rib regions to suppress ripple artifacts while leaving the rest of the image unaffected, preserving overall diagnostic quality
3Object-affected harmful factors
If template matching or rib edge detection is used to suppress ribs, then rib visibility is reduced, but the methods are not robust enough to handle variations in rib shape and image quality
Solution Approach 1:
The patent applies dynamics by using adaptive algorithms that can adjust to variations in rib shape and image quality. The edge detection and rib identification processes are designed to dynamically adapt to different anatomical variations, making the suppression method more robust and reliable across diverse patient populations and imaging conditions
Data Source
AI summary
A method for rib suppression in a volume chest x-ray image, executed at least in part by a computer captures a first set of unsegmented projection images, each at a corresponding angle, and forms a second set of segmented projection images by detecting rib features in a first projection image to form a first segmented projection image and generating a base model according to the detected rib features for the first projection image. Each of one or more additional projection images from the first set is processed to add members to the second set by a repeated sequence of generating a predictive model; detecting rib features using the predictive model; adjusting the base model according to detected rib features; and correcting rib detection in one or more members of the second set. The volume chest x-ray image is reconstructed according to the segmented projection images and is displayed.