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Rapid patient-specific neural networks for X-ray to volume registration

Research Medical/Healthcare AI

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TL;DR - A Nature paper introduces xvr, a rapid patient-specific neural-network approach for rigidly registering 2D X-rays with 3D volumes. It aims to make this capability available across anatomical regions to broader clinical and research communities.

  • Performs patient-specific 2D/3D X-ray-to-volume registration.
  • Targets rigid alignment across multiple anatomical regions.
  • Emphasizes rapid operation and broad accessibility.
  • The provided excerpt does not include quantitative results or methodological details.

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Rapid patient-specific neural networks for X-ray to volume registration

Nature Vivek Gopalakrishnan, David-Dimitris Chlorogiannis, Andrew Abumoussa, Anna M. Larson, Nazim Haouchine, Darren B. Orbach, Sarah Frisken, Neel Dey, Polina Golland 2026-09-16 doi:10.1038/s41586-026-11045-x
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:18:45.018672 UTC

TL;DR - A Nature paper introduces xvr, a rapid patient-specific neural-network approach for rigidly registering 2D X-rays with 3D volumes. It aims to make this capability available across anatomical regions to broader clinical and research communities.

  • Performs patient-specific 2D/3D X-ray-to-volume registration.
  • Targets rigid alignment across multiple anatomical regions.
  • Emphasizes rapid operation and broad accessibility.
  • The provided excerpt does not include quantitative results or methodological details.
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