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

Objective.Medical physicists routinely perform quality assurance on digital detection systems, part of which involves the testing of flat panel detectors. Flat panels may degrade over time as an increasing number of individual detector elements begin to malfunction. The pixels that correspond to these elements are corrected for using information elsewhere in the detector system, however these corrected elements still constitute a loss in image quality for the system as a whole. These correction methods, as well as the location and number of dead detector elements, are often only available to the vendor of the digital detection system, but not to the medical physicist responsible for the quality assurance of the system.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 F₁score 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 digital radiography 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 them 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.