Cranial computed tomography angiography with automated bone subtraction: a feasibility study.
OBJECTIVE
Computed tomography angiography (CTA) is becoming an increasingly accepted noninvasive alternative to catheter angiography. To display continuous vessel contours without superimposed bone in a similar manner, postprocessing of the CTA data is necessary. Threshold-based techniques often fail in regions of close vessel-bone contact, which frequently requires user interaction to optimize the results. This may be laborious and time consuming. The aim of this study was to assess the feasibility of a fully automated algorithm for bone subtraction in cranial CT angiography.


METHODS
Forty-nine patients underwent cranial CT angiography on 64 slice CT systems. Two scans were performed, one before (mask) and one after (CTA) contrast agent administration. Images were processed with commercially available subtraction software. Two examiners rated the success of overall bone removal and image quality of different vessel segments (n = 34) with a 4-point scale (1 = poor; 4 = excellent); original CTA data served as the reference.


RESULTS
Subtraction was performed successfully in all cases, overall subtraction quality was high (82% of cases were rated good or excellent). Only 50 of 1666 arterial segments (3%) were rated nondiagnostic. No relevant artificial stenoses of the internal carotid artery (ICA) or vertebral artery along their way through the skull base were detected when comparing CTA source images to bone subtraction computed tomography angiography (BSCTA) images.


CONCLUSIONS
Automatic subtraction provides diagnostic image quality for 3D visualization of intracranial vessels, free from over projecting bone in a fully automatic way. Aneurysms can be evaluated from all angles, and ICA stenoses at the skull base were easily visualized.
Lell
Michael M
MM
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. michael.lell@uk-erlangen.de
Ruehm
Stefan G
SG
Kramer
Manuel
M
Panknin
Christoph
C
Habibi
Reza
R
Klotz
Ernst
E
Villablanca
Pablo
P
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D002533:Cerebral Angiography / Q000379:methods*; D002561:Cerebrovascular Disorders / Q000000981:diagnostic imaging*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012886:Skull / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*