A study on the influence of radiation dose on device tracking accuracy during continuous-sweep limited angle fluoroscopy

Abstract

Continuous-sweep limited angle (CLA) fluoroscopy is a proposed technique for real-time 3D device guidance during catheter-based procedures, where the x-ray C-arm continuously rotates back-and-forth within a limited angle during fluoroscopic image acquisition. The 3D device reconstruction relies on accurate and robust tracking of the device centerline in the 2D projection images. The purpose of this study was to investigate the influence of radiation dose per frame on deep learning-based device segmentation and tracking accuracy. A convolutional neural network was trained based on simulated and manually annotated images from retrospectively analyzed animal studies. After binarization, images were postprocessed using topology preserving thinning and a graph-based path search was used to extract the device centerline. To evaluate the accuracy of the approach, a porcine study was conducted, where a microcatheter and guidewire were navigated through the hepatic arteries. Image sequences with three different detector dose levels were acquired (80nGy/frame, 170nGy/frame, and 380nGy/frame at the detector). The deep learning-based device tracking results were compared to manually annotated reference device centerlines. The average root mean squared distance (RMSD) between reference and automatic segmentation was 0.45±0.07mm and 0.50±0.07mm for the two higher dose levels and 2.75±7.71mm for the lowest level. A significant difference was only detected for the lowest dose level (p<0.00001). However, when only the first 1.5cm from the tip of the device was evaluated, no significant differences were found for all dose levels with average RMSDs between 0.56 and 0.62mm. This suggests that accurate device-tip tracking is possible even at the lowest dose level.