A novel forward projection-based metal artifact reduction method for flat-detector computed tomography.
Metallic implants generate streak-like artifacts in flat-detector computed tomography (FD-CT) reconstructed volumetric images. This study presents a novel method for reducing these disturbing artifacts by inserting discarded information into the original rawdata using a three-step correction procedure and working directly with each detector element. Computation times are minimized by completely implementing the correction process on graphics processing units (GPUs). First, the original volume is corrected using a three-dimensional interpolation scheme in the rawdata domain, followed by a second reconstruction. This metal artifact-reduced volume is then segmented into three materials, i.e. air, soft-tissue and bone, using a threshold-based algorithm. Subsequently, a forward projection of the obtained tissue-class model substitutes the missing or corrupted attenuation values directly for each flat detector element that contains attenuation values corresponding to metal parts, followed by a final reconstruction. Experiments using tissue-equivalent phantoms showed a significant reduction of metal artifacts (deviations of CT values after correction compared to measurements without metallic inserts reduced typically to below 20 HU, differences in image noise to below 5 HU) caused by the implants and no significant resolution losses even in areas close to the inserts. To cover a variety of different cases, cadaver measurements and clinical images in the knee, head and spine region were used to investigate the effectiveness and applicability of our method. A comparison to a three-dimensional interpolation correction showed that the new approach outperformed interpolation schemes. Correction times are minimized, and initial and corrected images are made available at almost the same time (12.7 s for the initial reconstruction, 46.2 s for the final corrected image compared to 114.1 s and 355.1 s on central processing units (CPUs)).
Prell
Daniel
D
Institute of Medical Physics, University of Erlangen-Nürnberg, Henkestrasse 91, Erlangen, Germany. daniel.prell@imp.uni-erlangen.de
Kyriakou
Yiannis
Y
Beister
Marcel
M
Kalender
Willi A
WA
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging; D002102:Cadaver; D003196:Computer Graphics; D003201:Computers; D006801:Humans; D008670:Metals / Q000737:chemistry*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000295:instrumentation* / Q000379:methods*
D008670:Metals