CT Fluoroscopy Noise Reduction via Gradient Weighted Image Blending
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
CT-fluoroscopy images suffer from noise due to the inverse proportionality of image noise to the square root of the x-ray tube current, leading to poor image quality when attempting to reduce x-ray dose, and existing methods to reduce noise result in blurring due to subject movement during scanning.
Innovation Solution
A computed-tomography method involving multiple scans to generate projection data at various views, creating partial images, averaging these images to reduce noise, and using a gradient image to weight and blend second and third images for display, thereby reducing noise while maintaining image sharpness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a lower x-ray tube current is used to reduce x-ray dose, then the x-ray dose to the subject is reduced, but the image noise increases resulting in poor image quality
Solution Approach 1:
The patent segments the projection data into multiple groups of views, where each group is used to generate a separate first image. This segmentation allows for subsequent processing steps (generating second images from multiple first images, then averaging to create third images) that can reduce noise while preserving edge information through the multi-stage processing pipeline
Solution Approach 2:
The patent generates multiple first images from different groups of views, then creates multiple second images from these first images. This partial action approach processes only portions of the total data at each stage, allowing for noise reduction through averaging while maintaining computational feasibility and image quality through the progressive processing approach
2Measurement precision
If image slices are averaged to reduce image noise, then the image noise is reduced, but the edges are blurred due to subject movement during the scan
Solution Approach 1:
The patent applies different processing treatments to different regions of the image based on local characteristics. Gradient images are computed to identify edge regions, and these gradient images are used to guide the averaging process. Smooth regions are averaged more aggressively for noise reduction, while edge regions are preserved with less averaging to maintain sharpness, achieving local optimization of both noise reduction and edge preservation
Solution Approach 2:
The patent changes the weighting parameter in the averaging process based on gradient magnitude. By computing gradient images and using them to weight the contribution of different images during averaging, the system dynamically adjusts the averaging strength according to local image characteristics - applying stronger averaging in smooth regions and weaker averaging in edge regions, thus resolving the contradiction between noise reduction and edge preservation
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 noise in CT-fluoroscopy images while preserving sharp edges, enhancing image quality by blending smooth and sharp images based on gradient values, thus addressing the trade-off between noise reduction and image clarity.
Implementation Method 1
an x-ray source to expose an object with x-rays at a plurality of scans at a position of the object to obtain projection data
Data Source
AI summary
A method of computed-tomography and a computed-tomography apparatus in which x-ray projection data is acquired at a number of views for a scan of an object. Partial images are created from data for a desired number of said views. Full scan images are created from plural ones of the partial images. Non-overlapping time images are created from the full-scan images. Gradient images are also created. An improved image is created by weighting respective ones of the full scan and non-overlapping time images using the gradient image. The improved image has increased sharpness with reduced noise.