Angular super-resolution in X-ray projection radiography using deep neural network: Implementation on rotational angiography

Abstract

Background: Rotational angiography acquires radiographs at multiple projection angles to demonstrate superimposed vasculature. However, this comes at the expense of the inherent risk of increased ionizing radiation. In this paper, building upon a successful deep learning model, we developed a novel technique to super-resolve the radiography at different projection angles to reduce the actual projections needed for a diagnosable radiographic procedure. Methods: Ten models were trained for different levels of angular super-resolution (ASR), denoted as ASRN, where for every N+2 frames, the first and the last frames were submitted as inputs to super-resolve the intermediate N frames. Results: The results show that large arterial structures were well preserved in all ASR levels. Small arteries were adequately visualized in lower ASR levels but progressively blurred out in higher ASR levels. Noninferiority of image quality was demonstrated in ASR1–4 (99.75% confidence intervals: −0.16–0.03, −0.19–0.04, −0.17–0.01, −0.15–0.05 respectively). Conclusions: ASR technique is capable of super-resolving rotational angiographic frames at intermediate projection angles.

Keywords

• Angular super-resolution
• Interpolation
• Projection radiography
• Rotational angiography
• Deep learning