Focal-Loss CNN Detection of Dead Detector Elements in DR Imaging
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Solution Overview
Problem
Existing digital radiographic (DR) imaging systems rely on vendor-specific software to correct for dead detector elements, limiting clinicians' knowledge and independence, and there is a need for a method to accurately detect and assess the number of dead detector elements to determine when the system needs replacement.
Innovation Solution
A convolutional neural network (CNN) is trained and validated to localize and estimate dead detector elements in DR imaging systems using processed images, employing spatially-oriented calculations like noise power spectrum and entropy, and focal loss to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vendor-specific software is used to correct dead detector elements, then the imaging system can maintain operation, but clinicians lose knowledge and independence in assessing detector quality
Solution Approach 1:
The system enables clinicians to independently detect and assess dead detector elements using the CNN model, making the system self-sufficient without relying on vendor-specific software. The clinician can directly evaluate detector quality through the automated detection system.
Solution Approach 2:
A CNN-based automated detection system serves as an intermediary between the imaging system and clinician, translating complex detector element status into interpretable visual representations that clinicians can independently assess without vendor software dependencies.
2Measurement precision
If traditional methods are used to detect dead detector elements, then the process is simple, but the precision and accuracy of detection are insufficient
Solution Approach 1:
Traditional manual or simple automated detection methods are replaced with a deep learning-based CNN system that uses noise power spectrum analysis and entropy calculations to achieve high-precision detection of dead detector elements.
Solution Approach 2:
The detection approach transitions from simple pixel-based methods to sophisticated parameter analysis including noise power spectrum and entropy calculations, fundamentally changing the detection parameters to achieve superior accuracy.
3Reliability
If no detection system is implemented, then the system remains simple, but clinicians cannot make informed decisions about detector replacement
Solution Approach 1:
The CNN-based detection system provides continuous feedback to clinicians about the status of detector elements, enabling data-driven decisions about detector replacement. The system monitors and reports detector quality metrics that directly inform replacement timing.
Data Source
AI summary
A method comprises: performing training and testing of an initial machine model to create a final machine model, wherein the training and testing use focal loss; performing detection of dead detector elements in a digital detector of a second digital radiographic (DR) imaging system using the final machine model; and determining whether to replace or keep the digital detector based on the detection. An apparatus comprises: a memory; and a processor coupled to the memory and configured to: perform training and testing of an initial machine model to create a final machine model, wherein the training and testing use focal loss; perform detection of dead detector elements in a digital detector of a second digital radiographic (DR) imaging system using the final machine model; and determine whether to replace or keep the digital detector based on the detection.