Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT

Journal article

Publication Details

Author(s): Xia Y, Bauer S, Maier A, Berger M, Hornegger J
Journal: Medical Physics
Publisher: American Association of Physicists in Medicine
Publication year: 2015
Volume: 42
Journal issue: 4
Pages range: 1787-1796
ISSN: 0094-2405


Purpose: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. Methods: A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anteriorposterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. Results: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. Conclusions: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.

FAU Authors / FAU Editors

Bauer, Sebastian
Lehrstuhl für Informatik 5 (Mustererkennung)
Berger, Martin
Lehrstuhl für Informatik 5 (Mustererkennung)
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Xia, Yan
Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

Xia, Y., Bauer, S., Maier, A., Berger, M., & Hornegger, J. (2015). Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT. Medical Physics, 42(4), 1787-1796.

Xia, Yan, et al. "Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT." Medical Physics 42.4 (2015): 1787-1796.


Last updated on 2018-19-04 at 02:58