Digital Image Quality Score for Radiographic CNR Assessment
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Solution Overview
Problem
Digital radiography systems face challenges in accurately assessing image quality, particularly contrast-to-noise ratio (CNR), which can vary by anatomical region and spatial frequency, leading to misleading quality assessments and potential negative impacts on diagnosis due to inadequate image processing.
Innovation Solution
A method using a computer system to identify regions of interest (ROIs) within digital radiographic images, compute CNR values specifically for these regions, and derive a Digital Image Quality Score (DIQS) to provide timely feedback for improving image acquisition and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional CNR estimation methods are used to assess image quality, then the assessment process is simplified, but the measurement precision deteriorates because CNR varies by anatomical region and spatial frequency
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) based on anatomical structures and computes CNR separately for each region. This segmentation allows precise measurement of CNR in specific anatomical areas rather than using a single overall CNR value, directly resolving the contradiction between simplified assessment and precise measurement.
Solution Approach 2:
The patent applies different CNR evaluation criteria and processing to different anatomical regions within the image. Each ROI receives localized quality assessment tailored to its specific diagnostic requirements, enabling precise measurement of local CNR characteristics while maintaining a structured overall assessment framework.
2Ease of operation
If digital radiography systems operate with relaxed exposure techniques due to ease of operation, then the ease of operation improves, but the image quality deteriorates leading to insufficient diagnostic value
Solution Approach 1:
The patent implements automated CNR measurement and quality assessment that provides immediate feedback to technologists about image quality. This feedback loop allows technologists to adjust exposure techniques while maintaining ease of operation, ensuring that diagnostic value is preserved through real-time quality monitoring and guidance.
Solution Approach 2:
The system performs preliminary quality assessment and CNR measurement before the image is sent for diagnosis. This preliminary action identifies quality issues early, allowing corrective measures to be taken before the image reaches the radiologist, thereby ensuring diagnostic reliability without complicating the operational workflow.
3Device complexity
If overall CNR is used to represent image quality, then the assessment process is simplified, but the diagnostic accuracy deteriorates because CNR varies across different anatomical regions
Solution Approach 1:
The patent segments the image into multiple anatomical regions and computes separate CNR values for each segment. This allows precise measurement of diagnostic quality in each anatomical area while maintaining an organized assessment structure that balances complexity and precision.
Solution Approach 2:
The patent transitions from a single overall CNR metric to a multi-dimensional assessment that includes regional CNR values, anatomical location, and spatial frequency characteristics. This dimensional expansion enables precise diagnostic quality measurement across different anatomical regions while maintaining a structured framework for comprehensive evaluation.
Data Source
AI summary
A method for determining image quality of a digital radiographic image. The method is executed at least in part by a computer system. The method obtains image data for the digital radiographic image, identifies one or more regions of interest in the digital radiographic image, derives an image quality score that indicates the image quality of the digital radiographic image by computing at least a contrast-to-noise value for image data content within the one or more regions of interest, and reports the derived image quality score for the image.