Abstract
Accurately capturing the 3D geometry of the brain’s blood vessels is critical in helping neuro-interventionalists to identify and treat neurovascular disorders, such as stroke and aneurysms. Currently, the gold standard for obtaining a 3D representation of angiograms is through the process of 3D rotational angiography, a timely process requiring expensive machinery, which is also associated with high radiation exposure to the patient. In this research, we propose a new technique for reconstructing 3D volumes from 2D angiographic images, thereby reducing harmful X-ray radiation exposure. Our approach involves parameterizing the input data as a back-projected noisy volume from the images, which is then fed into a 3D denoising autoencoder. Through this method, we have achieved clinically relevant reconstructions with varying amounts of 2D projections from 49 patients. Additionally, our 3D denoising autoencoder outperformed previous generative models in biplane reconstruction by 15.51% for intersection over union (IOU) and 3.5% in pixel accuracy due to keeping a semi-accurate input with back projection. This research highlights the significant role of back-projection in achieving relative visual correspondence in the input space to reconstruct 3D volumes from 2D angiograms. This approach has the potential to be deployed in future neurovascular surgery, where 3D volumes of the patient’s brain blood vessels can be visualized with less X-ray radiation exposure time.
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Acknowledgements
We would like to thank the Keck Foundation for their grant to Pepperdine University to support our Data Science program and this research.
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Wu, S., Kaneko, N., Mendoza, S., Liebeskind, D.S., Scalzo, F. (2023). 3D Reconstruction from 2D Cerebral Angiograms as a Volumetric Denoising Problem. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_30
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