Removal of bone in CT angiography of the cervical arteries by piecewise matched mask bone elimination.
In maximum intensity projection (MIP) images of CT angiography (CTA) scans, the arteries are often obscured by bone. A bone removal method is presented that uses an additional, nonenhanced scan to create a mask of the bone by thresholding and dilation. After registration of the CTA scan and the additional scan, the bone in the CTA scan is masked. As the cervical area contains bones that can move with respect to each other, these bones are separated first using a watershed algorithm, and then registered individually. A phantom study was performed to evaluate and quantify the tradeoff between the removal of the bone and the preservation of the arteries contiguous to the bone. The influence of algorithm parameters and scan parameters was studied. The method was clinically evaluated with data sets of 35 patients. Best results were obtained with a threshold of 150 HU and a dilation of 8 in-plane voxels and two out-of-plane voxels. The mean width of the soft tissue layer, which is also masked, was approximately 1 mm. The mAs value of the nonenhanced scan could be reduced from 250 mAs to 65 mAs without a loss of quality. In 32 cases the bones were registered correctly and removed completely. In three cases the bone separation was not completely successful, and consequently the bone was not completely removed. The piecewise matched mask bone elimination method proved to be able to obtain MIP images of the cervical arteries free from overprojecting bone in a fully automatic way and with only a slight increase of radiation dose.
van Straten
Marcel
M
Department of Medical Physics, Academic Medical Center, Amsterdam, The Netherlands. m.van.straten@solstitium.net
Venema
Henk W
HW
Streekstra
Geert J
GJ
Majoie
Charles B L M
CB
den Heeten
Gerard J
GJ
Grimbergen
Cornelis A
CA
D000465:Algorithms; D000784:Aortic Dissection / Q000000981:diagnostic imaging*; D015901:Angiography, Digital Subtraction / Q000379:methods*; D001185:Artificial Intelligence; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging*; D002533:Cerebral Angiography / Q000379:methods*; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012886:Skull / Q000000981:diagnostic imaging*