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Patient-bounded extrapolation using low-dose priors for
volume-of-interest imaging in C-arm CT
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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.
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 anterior–posterior (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.
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
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
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