Single-view X-ray depth recovery: toward a novel concept for image-guided interventions.

Abstract

X-ray imaging is widely used for guiding minimally invasive surgeries. Despite ongoing efforts in particular toward advanced visualization incorporating mixed reality concepts, correct depth perception from X-ray imaging is still hampered due to its projective nature. In this paper, we introduce a new concept for predicting depth information from single-view X-ray images. Patient-specific training data for depth and corresponding X-ray attenuation information are constructed using readily available preoperative 3D image information. The corresponding depth model is learned employing a novel label-consistent dictionary learning method incorporating atlas and spatial prior constraints to allow for efficient reconstruction performance. We have validated our algorithm on patient data acquired for different anatomy focus (abdomen and thorax). Of 100 image pairs per each of 6 experimental instances, 80 images have been used for training and 20 for testing. Depth estimation results have been compared to ground truth depth values. We have achieved around [Formula: see text] and [Formula: see text] mean squared error on abdomen and thorax datasets, respectively, and visual results of our proposed method are very promising. We have therefore presented a new concept for enhancing depth perception for image-guided interventions.