Single-view X-ray depth recovery: toward a novel concept for image-guided interventions
Albarqouni S, Konrad U, Wang L, Navab N, Demirci S (2016)
Publication Type: Journal article
Publication year: 2016
Journal
Book Volume: 11
Pages Range: 873-880
Journal Issue: 6
DOI: 10.1007/s11548-016-1360-0
Abstract
Purpose: 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. Methods: 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. Results: 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. Conclusion: We have achieved around 4.40%±2.04 and 11.47%±2.27 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.
Involved external institutions
Germany (DE)How to cite
APA:
Albarqouni, S., Konrad, U., Wang, L., Navab, N., & Demirci, S. (2016). Single-view X-ray depth recovery: toward a novel concept for image-guided interventions. International Journal of Computer Assisted Radiology and Surgery, 11(6), 873-880. https://doi.org/10.1007/s11548-016-1360-0
MLA:
Albarqouni, Shadi, et al. "Single-view X-ray depth recovery: toward a novel concept for image-guided interventions." International Journal of Computer Assisted Radiology and Surgery 11.6 (2016): 873-880.
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