| Literature DB >> 35321151 |
Nazim Haouchine1,2, Parikshit Juvekar1,2, Xin Xiong2,3, Jie Luo1,2, Tina Kapur1,2, Rose Du1,2, Alexandra Golby1,2, Sarah Frisken1,2.
Abstract
Digital Subtraction Angiography (DSA) provides high resolution image sequences of blood flow through arteries and veins and is considered the gold standard for visualizing cerebrovascular anatomy for neurovascular interventions. However, acquisition frame rates are typically limited to 1-3 fps to reduce radiation exposure, and thus DSA sequences often suffer from stroboscopic effects. We present the first approach that permits generating high frame rate DSA sequences from low frame rate acquisitions eliminating these artifacts without increasing the patient's exposure to radiation. Our approach synthesizes new intermediate frames using a phase-aware Convolutional Neural Network. This network accounts for the non-linear blood flow progression due to vessel geometry and initial velocity of the contrast agent. Our approach out-performs existing methods and was tested on several low frame rate DSA sequences of the human brain resulting in sequences of up to 17 fps with smooth and continuous contrast flow, free of flickering artifacts.Entities:
Keywords: Biomedical Image Synthesis; Convolutional Neural Networks; Digital Subtraction Angiography; Video Interpolation
Year: 2021 PMID: 35321151 PMCID: PMC8938707 DOI: 10.1007/978-3-030-87231-1_17
Source DB: PubMed Journal: Med Image Comput Comput Assist Interv