title: Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization.
abstract: OBJECTIVE
  Real-time 6 degrees of freedom (6DoF) pose recovery and tracking from X-ray images is a key enabling technology for many interventional imaging applications. However, real-time 2D/3D registration is a very challenging problem because of the heavy computation in iterative digitally reconstructed radiograph (DRR) generation. In this paper, we propose a real-time 2D/3D registration framework using library-based DRRs to achieve high computational efficiency.
  
  
  METHODS
  The proposed method pre-computes a library of canonical DRRs and reconstructs library-based DRRs (libDRRs) during registration without online rendering. The transformation parameters are decoupled to 2 geometry-relevant and 4 geometry-irrelevant ones so that canonical DRRs only need to cover the variation of 2 geometry-relevant parameters, making it practical to be pre-computed and stored. The 2D/3D registration using libDRRs is then solved as a hybrid optimization problem, i.e., continuous in geometry-irrelevant parameters while discrete in geometry-relevant parameters.
  
  
  RESULTS
  On 5 fluoroscopic sequences with 246 frames acquired during animal studies with a transesophageal echocardiography (TEE) probe in the field of view, 6DoF tracking of the TEE probe using the proposed method achieved a mean target registration error in the projection direction (mTREproj) of 0.81 mm, a success rate of 100 % (defined as mTREproj [Formula: see text]2.5 mm), and a registration frame rate of 23.1 fps on a pure CPU-based implementation executed in a single thread.
  
  
  CONCLUSIONS
  Using libDRRs with a hybrid optimization can significantly improve the computational efficiency (up to tenfold) for 6DoF pose recovery and tracking with little degradation in robustness and accuracy, compared to conventional intensity-based 2D/3D registration using ray casting DRRs with a continuous optimization.
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA. shun.miao@siemens.com.
authors.forename: S
authors.identifier: http://orcid.org/0000-0002-4688-7087
authors.initials: S
authors.lastname: Miao
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA.
authors.forename: A
authors.initials: A
authors.lastname: Tuysuzoglu
authors.affiliation: Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
authors.forename: Z J
authors.initials: ZJ
authors.lastname: Wang
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA.
authors.forename: R
authors.initials: R
authors.lastname: Liao
mesh_terms: D000465:Algorithms; D000818:Animals; D005471:Fluoroscopy / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011859:Radiography; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: 2D/3D registration; Digitally reconstructed radiograph; Image-guided intervention