Image processing using Convolutional Neural Network (CNN) for region of interest (ROI) fluoroscopy

Patient x-ray dose during fluoroscopically-guided neuro interventions can be reduced by using a differential Region-of-Interest (ROI) x-ray attenuator. The dose in the periphery region outside the region-of-interest (ROI) treatment area is reduced while maintaining regular dose within the ROI. In this work we present a convolutional neural network to aid in restoration of image quality in dose-reduced regions. A 0.7 mm Cu attenuator with a 10 mm circular hole in the middle was used to reduce entrance dose in the periphery. A 29 layer deep CNN was developed to derive the ROI attenuator mask image from the dose-reduced images. To train the CNN, simulated ROI dose-reduced images of various backgrounds such as anthropomorphic head and chest phantoms were generated using acquired mask images of the ROI attenuator at different positions and radiological magnifications. The image quality in the dose-reduced region of the images was restored by first dividing the CNN derived mask from the dose-reduced image and then noise in the periphery region was reduced by using a combination of Gaussian and recursive temporal filtering. A total dose-area-product reduction of 70% per frame was achieved. After image processing using the CNN derived image mask of the ROI attenuator, the SNR in the dose reduced periphery regions was improved by a factor of 3. The CNN is capable of deriving the mask without any prior knowledge of ROI attenuator position or radiological magnification. Using the CNN generated mask, the image quality in the dose reduced images was restored with minimal or no boundary artifacts.