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

Vivek Gopalakrishnan^1,2,3, David-Dimitris Chlorogiannis⁴, Andrew Abumoussa⁵, Anna M Larson^6,7, Nazim Haouchine⁴, Darren B Orbach⁷, Sarah Frisken⁴, Neel Dey^8,9,10, Polina Golland^11,12

¹Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA. vivekg@csail.mit.edu.

²Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. vivekg@csail.mit.edu.

³Department of Radiology, Harvard Medical School, Boston, MA, USA. vivekg@csail.mit.edu.

⁴Department of Radiology, Harvard Medical School, Boston, MA, USA.

⁵Saint Luke's Marion Bloch Neuroscience Institute, Kansas City, MO, USA.

⁶Pediatric Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA.

⁷Department of Interventional Neuroradiology, Boston Children's Hospital, Boston, MA, USA.

⁸Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. dey@csail.mit.edu.

⁹Department of Radiology, Harvard Medical School, Boston, MA, USA. dey@csail.mit.edu.

¹⁰Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA. dey@csail.mit.edu.

¹¹Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA. polina@csail.mit.edu.

¹²Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. polina@csail.mit.edu.

PubMed DOI

Abstract

Advanced navigation techniques in image-guided interventions and surgical robotics require the rapid and precise alignment of three-dimensional (3D) preoperative volumes (such as computed tomography and magnetic resonance imaging) to two-dimensional (2D) intraoperative images (such as X-ray fluoroscopy)1,2. However, existing 2D/3D registration methods fail to generalize across the broad spectrum o…

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