Scatter Correction Using Distinct Single and Multiple Scatter Kernels
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Solution Overview
Problem
Current X-ray imaging technologies face challenges in effectively reducing scatter radiation, which degrades image quality and can lead to misdiagnosis, particularly in medical applications like mammography, as existing methods either rely on hardware-based anti-scatter grids or simplistic image processing that fails to distinguish between single and multiple scatter events.
Innovation Solution
An image processing system that uses distinct scatter kernels for single and multiple scatter events, allowing for more refined scatter correction by differentiating between these types based on object characteristics and employing pre-computed kernels matched to specific phantom models, including water-based phantoms, to optimize scatter correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-scatter grids are used to block scattered radiation, then scatter correction is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical anti-scatter grid system with a computational image processing system. Instead of using physical grids to block scattered radiation, the invention uses software-based scatter estimation and correction algorithms that process the acquired X-ray images to remove scatter effects, thereby reducing hardware complexity while maintaining scatter correction effectiveness
Solution Approach 2:
The patent introduces an intermediary computational processing step between image acquisition and final image formation. Scatter estimation kernels are used as intermediaries to model and estimate scatter distribution, which then serves as a basis for correcting the primary image, allowing scatter removal without direct physical intervention
2Productivity
If single scatter kernel is used for correction, then processing speed is improved, but measurement precision deteriorates due to inability to distinguish multiple scatter events
Solution Approach 1:
The patent segments the scatter correction problem into distinct components by providing separate scatter estimation kernels for single scatter events and multiple scatter events. This segmentation allows the system to model different scatter physical processes independently, improving measurement precision by capturing the nuanced differences between single and multiple scatter contributions rather than using a single averaged kernel
Solution Approach 2:
The patent changes the parameters of the scatter estimation by providing different kernel functions optimized for different scatter event types. Instead of using a single set of kernel parameters, the system selects or computes different kernel parameters based on the expected scatter regime, allowing accurate adaptation to varying imaging conditions and object characteristics
3Productivity
If pre-computed kernels are used for scatter estimation, then computational efficiency is improved, but adaptability to different object characteristics deteriorates
Solution Approach 1:
The patent introduces dynamics into the kernel selection process by allowing the system to adaptively select or compute scatter estimation kernels based on the specific characteristics of the imaged object. Rather than using fixed pre-computed kernels for all cases, the system dynamically adjusts kernel selection based on object properties, imaging geometry, and scatter conditions, maintaining computational efficiency through smart selection while achieving object-specific optimization
4Measurement precision
If scatter correction is applied to improve image quality, then diagnostic accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing scatter estimation kernels that can be rapidly applied during image processing. The scatter correction computation is prepared in advance, allowing the actual correction step during diagnostic imaging to be performed efficiently with minimal additional processing time, thus maintaining diagnostic accuracy improvement while minimizing time loss
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
The system significantly improves image quality by accurately distinguishing and correcting for single and multiple scatter events, leading to enhanced diagnostic accuracy and reduced misdiagnosis rates, particularly in medical imaging where delicate details are critical.
Implementation Method 1
Scatter is a phenomenon that occurs during X-ray imaging and is known to reduce image quality, in particular X-ray contrast
Data Source
AI summary
An image processing system (IPS) and related method. The system (IPS) comprises an input interface (IN) for receiving an image (IM) of an object (OB) acquired by an imaging apparatus (IA). A kernel provider (KP) of the system (IPS) is configured to provide respective scatter kernels for at least two scatter types. A scatter correction module (SCM) of the system (IPS) is configured to perform a correction in the image based on the provided at least two kernels.