Hybrid cone-beam tomographic reconstruction: Incorporation of prior anatomical models to compensate for missing data

Ofri Sadowsky, Junghoon Lee, E. Grant Sutter, Simon J. Wall, Jerry L. Prince, Russell H. Taylor

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


We propose a method for improving the quality of cone-beam tomographic reconstruction done with a C-arm. C-arm scans frequently suffer from incomplete information due to image truncation, limited scan length, or other limitations. Our proposed hybrid reconstruction method injects information from a prior anatomical model, derived from a subject-specific computed tomography (CT) or from a statistical database (atlas), where the C-arm X-ray data is missing. This significantly reduces reconstruction artifacts with little loss of true information from the X-ray projections. The methods consist of constructing anatomical models, fast rendering of digitally reconstructed radiograph (DRR) projections of the models, rigid or deformable registration of the model and the X-ray images, and fusion of the DRR and X-ray projections, all prior to a conventional filtered back-projection algorithm. Our experiments, conducted with a mobile image intensifier C-arm, demonstrate visually and quantitatively the contribution of data fusion to image quality, which we assess through comparison to a ground truth CT. Importantly, we show that a significantly improved reconstruction can be obtained from a C-arm scan as short as 90° by complementing the observed projections with DRRs of two prior models, namely an atlas and a preoperative same-patient CT. The hybrid reconstruction principles are applicable to other types of C-arms as well.

Original languageEnglish (US)
Article number5523953
Pages (from-to)69-83
Number of pages15
JournalIEEE transactions on medical imaging
Issue number1
StatePublished - Jan 2011


  • Anatomical atlas
  • C-arm
  • computed tomography (CT)
  • cone-beam reconstruction
  • hybrid reconstruction

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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