Binary classification of dead detector elements in flat panel detectors using convolutional neural networks.

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Abstract

This work aims to provide a novel deep learning technique that can be used to generate dead detector maps for flat panel detectors in the absence of ground truth maps. These maps are useful in monitoring the overall health of a flat panel detector, and in many cases are not readily available to the medical physicist responsible for quality assurance.
Approach: We greatly expand upon a previous work by providing a novel technique for classifying dead detector elements at single pixel resolution. We also demonstrate that this technique can be trained on one detector, and then tested and validated on another with moderate success, which demonstrates some ability to generalize to different detectors. The technique requires 3 flat field, or “noise”, images to be taken to predict the dead detector element maps for the system.
Main Results: Models using only for-processing pixel data were unable to successfully generalize from one detector to the other. Models preprocessed using the standard deviation across three for-processing images were able to classify dead detector element maps with an F1 score ranging from 0.4527 to 0.8107 and recall ranging from 0.5420 to 0.9303 with better performance, on average, observed using the low exposure data set. 
Significance: Many physicists do not have access to the dead detector maps for their diagnostic systems. CNNs are capable of predicting the dead detector maps of flat panel detectors with single pixel resolution. Physicists can implement this tool by acquiring three flat field images and then inputting it into the model. Model performance saw a marginal increase when trained on the low exposure set data, as opposed to the high exposure set data, indicating high exposure, low relative noise images may not be necessary for optimal performance. Model performance across detectors manufactured by different vendors requires further investigation. &#xD.© 2024 IOP Publishing Ltd.