Alena‐Kathrin Golla

About

Alena‐Kathrin Golla is a rising figure in medical imaging, whose work centers on advancing cone-beam computed tomography (CBCT) for interventional settings. Her research tackles a critical bottleneck: enabling fast, high-quality reconstruction from non-circular C-arm orbits—trajectories that can improve field-of-view and reduce interference during procedures but are computationally demanding. In her most-cited work (2022, 3 citations), Golla pioneered the use of convolutional neural networks to achieve rapid CBCT reconstruction for arbitrary robotic C-arm paths, directly addressing the need for speed in time-sensitive interventional imaging. This contribution bridges deep learning and practical radiology, offering a pathway to more flexible, patient-specific scans without sacrificing reconstruction efficiency. While her citation count is still growing, the novelty of her approach signals a promising trajectory: she is laying the groundwork for AI-driven, real-time 3D imaging that could transform how clinicians visualize anatomy during minimally invasive surgeries. For students and researchers, Golla’s work exemplifies how integrating machine learning with hardware constraints can solve real-world clinical challenges.

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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