Imaging Data Crosstalk Correction Using a Trained Coefficient Matrix
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Solution Overview
Problem
Existing methods for correcting inter-element crosstalk artifacts in imaging data are expensive and lack accuracy or applicability across different imaging devices.
Innovation Solution
A trained artifact correction model is developed using sample sets with detector unit arrays of the same dimension, enabling accurate correction of inter-element crosstalk artifacts in imaging data, applicable to various imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an anti-crosstalk apparatus is installed on an imaging detector, then inter-element crosstalk artifacts are corrected, but the cost increases
Solution Approach 1:
The patent uses a trained artifact correction model (software copy) to replicate the function of physical anti-crosstalk apparatus without the associated hardware costs. The model learns from training data to predict and correct crosstalk artifacts, providing the same correction capability through computational means rather than physical modification of the detector.
Solution Approach 2:
The patent replaces the mechanical/physical anti-crosstalk apparatus with a computational artifact correction model. Instead of using physical structures to prevent or correct crosstalk at the hardware level, the system uses machine learning algorithms to process and correct the imaging data, substituting mechanical correction with computational processing.
2Ease of manufacture
If a smoothing algorithm is applied to imaging data, then inter-element crosstalk artifacts are reduced, but the accuracy of correction is insufficient
Solution Approach 1:
The patent transforms the simple smoothing parameter approach into a complex, multi-parameter machine learning model. The artifact correction model learns optimal correction parameters from training data, adjusting multiple parameters simultaneously to achieve accurate crosstalk correction while preserving image details, unlike simple smoothing that applies uniform parameter changes.
Solution Approach 2:
The patent performs preliminary training of the artifact correction model using training datasets that include images with known crosstalk characteristics. This preliminary learning phase allows the model to pre-adapt to various crosstalk patterns before actual correction, enabling more accurate correction compared to applying smoothing algorithms without prior adaptation to the specific imaging conditions.
3Reliability
If existing correction methods are used, then artifact correction is achieved, but applicability is limited to certain imaging detectors
Solution Approach 1:
The patent creates a universal artifact correction model that can be applied to multiple types of imaging detectors (e.g., different detector arrays, pixel configurations, and imaging modalities). The model is trained on diverse training data representing various detector types and crosstalk patterns, enabling it to generalize and provide effective correction across different imaging systems without requiring detector-specific customization.
Data Source
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AI summary
A method for image processing may be provided. The method may include obtaining imaging data of an original image and a correction coefficient matrix of the array dimension. The imaging data may have elements arranged in an array of an array dimension. The imaging data may include an artifact caused by inter-element crosstalk. The correction coefficient matrix may be of the array dimension and is determined based on a trained artifact correction model. The method may also include determining processed image data based on the correction coefficient matrix and the imaging data. The method may further include determining an artifact corrected image of the original image based on the processed image data.