Automatic intraoperative stitching of nonoverlapping cone‐beam CT acquisitions. Issue 6 (10th April 2018)

• Record Type: Journal Article
• Authors: Fotouhi, Javad; Fuerst, Bernhard; Unberath, Mathias; Reichenstein, Stefan; Lee, Sing Chun; Johnson, Alex A.; Osgood, Greg M.; Armand, Mehran; Navab, Nassir
• Is Part Of: Medical physics. Volume 45:Issue 6(2018)
• Page Start: 2463
• Page End: 2475
• Publication Date: 2018-04-10
• Subjects: cone‐beam CT -- C‐arm -- RGBD -- stitching -- tracking; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12877
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Cone‐beam computed tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While CBCT provides crucial intraoperative information, it is bounded by a limited imaging volume, resulting in reduced effectiveness. This paper introduces an approach allowing real‐time intraoperative stitching of overlapping and nonoverlapping CBCT volumes to enable 3D measurements on large anatomical structures. Methods: A CBCT‐capable mobile C‐arm is augmented with a red‐green‐blue‐depth (RGBD) camera. An offline cocalibration of the two imaging modalities results in coregistered video, infrared, and x‐ray views of the surgical scene. Then, automatic stitching of multiple small, nonoverlapping CBCT volumes is possible by recovering the relative motion of the C‐arm with respect to the patient based on the camera observations. We propose three methods to recover the relative pose: RGB‐based tracking of visual markers that are placed near the surgical site, RGBD‐based simultaneous localization and mapping (SLAM) of the surgical scene which incorporates both color and depth information for pose estimation, and surface tracking of the patient using only depth data provided by the RGBD sensor. Results: On an animal cadaver, we show stitching errors as low as 0.33, 0.91, and 1.72 mm when the visual marker, RGBD SLAM, and surface data are used for tracking, respectively. Conclusions: The proposed method overcomes one of the major limitations of CBCT C‐arm systems by integrating vision‐based tracking and expanding the imaging volume without any intraoperative use of calibration grids or external tracking systems. We believe this solution to be most appropriate for 3D intraoperative verification of several orthopedic procedures.