In order to overcome these artifacts, acquisitions and post-processing techniques, such as higher x-ray tube voltage, gantry / head tilting, dual-energy or dedicated metal artifact reduction algorithms have been proposed and implemented in diagnostic CT exams [12, 14 17].
The proposed metric is dedicated to artifact detection in DIBR-synthesized images by measuring the edge distortion between the multi-scale representations of the reference image and the DIBR-synthesized image using MSE.
In this paper, a new full-reference objective quality metric, 3DSwIM (3D Synthesized view Image Quality Metric), dedicated to artifacts detection in DIBR-synthesized view-points is presented.
Mean artifact volume segmented in the images without dedicated iterative metal artifact reduction was 20.8 ± 15.2 ml with a mean segmentation time of 275 ± 73.7 s (Fig. 6).
Only one patient with small number of metal dental hardware was acquired with a jaw spacer installed, showing a reduction of artifact volume by 49.3% when reconstructed with the dedicated iterative metal artifact reduction algorithm (3.4 ± 6.7 ml).
The most time and labor effective artifact reduction method in both phantom and patient model is the dedicated iterative metal artifact reduction [16].
In addition we showed that using dedicated iterative metal artifact reduction algorithm is the most promising technique to reduce the metal artifact volume and hence reducing segmentation time.
All acquisitions were reconstructed both with and without a dedicated iterative metal artifact reduction algorithm (MAR).
In addition, all images were reconstructed using a dedicated iterative metal artifact reduction algorithm (MAR) (iMAR®, Siemens Healthcare, Forchheim, Germany).
