Medical Image Processing Reducing Blooming Effect
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical imaging systems, such as CT, MRI, and PET, face challenges in removing artifacts that degrade image quality for diagnostic purposes, particularly the 'blooming' effect where bright objects obscure nearby lower-intensity objects due to resolution limitations, leading to inaccurate representations of calcifications and lumens in arteries.
Innovation Solution
A model-based image processing approach that partitions image structures based on their behavior, using a combination of energy functions to minimize blurring and characterize ideal images as compositions of distinct components, allowing for post-processing or reconstruction methods that enhance image fidelity and reduce blooming effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging systems are used, then image acquisition is straightforward, but blooming effects obscure nearby lower-intensity objects
Solution Approach 1:
The patent segments the image into multiple intensity ranges by creating separate image data sets corresponding to different intensity thresholds. This allows the bright object and nearby lower-intensity objects to be processed separately, preventing the blooming effect from obscuring diagnostic information about adjacent structures.
Solution Approach 2:
The patent extracts the bright object from the overall image by creating a mask that isolates high-intensity regions. This extracted bright object can then be processed separately through deconvolution and iterative reconstruction techniques, while the remaining image data preserves the integrity of nearby lower-intensity objects.
2Object-affected harmful factors
If low-dose imaging is used, then radiation exposure is reduced, but image quality deteriorates
Solution Approach 1:
The patent applies preliminary action by performing iterative reconstruction and deconvolution processes on the low-dose image data before final diagnosis. This preliminary processing enhances the image quality extracted from low-dose data, making the reduced radiation exposure worthwhile by preserving diagnostic accuracy.
Solution Approach 2:
The patent employs feedback mechanisms through iterative reconstruction algorithms that continuously refine the image based on the measured data. This feedback process allows the system to extract maximum information from low-dose imaging, improving image quality without requiring higher radiation doses.
3Illumination intensity
If bright objects are present in the image, then certain structures are visible, but nearby lower-intensity objects are obscured
Solution Approach 1:
The patent segments the image into multiple intensity ranges by creating separate image data sets corresponding to different intensity thresholds. This allows the bright object and nearby lower-intensity objects to be processed separately, preventing the blooming effect from obscuring diagnostic information about adjacent structures.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw image data and the final diagnostic image. This intermediary involves iterative deconvolution and reconstruction algorithms that act as a mediator to restore the brightness and detail of nearby structures that would otherwise be obscured by the blooming effect.
Data Source
AI summary
In one aspect, the invention is a method to reduce a blooming effect of a bright object in a medical image generated from a low-dose imaging system. The method includes characterizing an ideal image as a composition of a first component and a second component. The first component is characterized by a first function and the second component is characterized by a second function. The method also includes minimizing the first function and the second function using a smoothing process and generating the medical image based on the minimizing.