Implications of artefacts reduction in the planning CT originating from implanted fiducial markers.
The efficacy of metal artefact reduction (MAR) software to suppress artefacts in reconstructed computed tomography (CT) images originating from small metal objects, like tumor markers and surgical clips, was evaluated. In addition, possible implications of using digital reconstructed radiographs (DRRs), based on the MAR CT images, for setup verification were analyzed. A phantom and 15 patients with different tumor sites and implanted markers were imaged with a multislice CT scanner. The raw image data was reconstructed both with the clinically used filtered-backprojection (FBP) and with the MAR software. Using the MAR software, improvements in image quality were often observed in CT slices with markers or clips. Especially when several markers were located near to each other, fewer streak artefacts were observed than with the FBP algorithm. In addition, the shape and size of markers could be identified more accurately, reducing the contoured marker volumes by a factor of 2. For the phantom study, the CT numbers measured near to the markers corresponded more closely to the expected values. However, the MAR images were slightly more smoothed compared with the images reconstructed with FBP. For 8 prostate cancer patients in this study, the interobserver variation in 3D marker definition was similar (<0.4 mm) when using DRRs based on either FBP or MAR CT scans. Automatic marker matches also showed a similar success rate. However, differences in automatic match results up to 1 mm, caused by differences in the marker definition, were observed, which turned out to be (borderline) statistically significant (p = 0.06) for 2 patients. In conclusion, the MAR software might improve image quality by suppressing metal artefacts, probably allowing for a more reliable delineation of structures. When implanted markers or clips are used for setup verification, the accuracy may slightly be improved as well, which is relevant when using very tight clinical target volume (CTV) to planning target volume (PTV) margins for planning.
Kassim
Iskandar
I
Department of Radiation Oncology, Division of Medical Physics, Erasmus MC-Daniel den Hoed Cancer Center, Rotterdam, The Netherlands.
Joosten
Hans
H
Barnhoorn
Jaco C
JC
Heijmen
Ben J M
BJ
Dirkx
Maarten L P
ML
D016477:Artifacts; D006801:Humans; D008297:Male; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D019736:Prostheses and Implants; D011856:Radiographic Image Enhancement / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation* / Q000379:methods*