Dominik Bauer

About

Dominik Bauer is an emerging researcher working at the intersection of medical imaging, deep learning, and interventional radiology. His work focuses on accelerating and improving cone-beam computed tomography (CBCT) reconstruction, with a particular emphasis on leveraging convolutional neural networks to handle the computational challenges posed by non-standard, robotic C-arm acquisition geometries. His notable contribution, "Fast CBCT reconstruction using convolutional neural networks for arbitrary robotic C-arm orbits" (2022), addresses a critical bottleneck in interventional imaging: the need for rapid, high-quality image reconstruction in time-sensitive clinical environments. By developing neural network-based approaches capable of handling arbitrary orbital trajectories — rather than conventional circular paths — Bauer's research opens new possibilities for improved image quality, expanded fields of view, and reduced interference in operating room settings. While still early in his career with an accumulating citation record, his work sits at a highly relevant frontier where artificial intelligence meets clinical radiology workflows. Researchers and students interested in AI-driven medical image reconstruction, particularly within surgical or interventional contexts, will find Bauer's contributions a valuable reference for understanding how deep learning can transform real-time diagnostic imaging pipelines.

Research Focus

Key Achievements

Top Papers

1. 1
   Fast CBCT reconstruction using convolutional neural networks for arbitrary robotic C-arm orbits

   3 citations · 2022

Key Collaborators

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