Automatic labeling of anatomical structures in MR FastView images using a statistical atlas.
We present a method for fast and automatic labeling of anatomical structures in MR FastView localizer images, which can be useful for automatic MR examination planning. FastView is a modern MR protocol, that provides larger planning fields of view than previously available with isotropic 3D resolution by scanning during continuous movement of the patient table. Hence, full 3D information is obtained within short acquisition time. Anatomical labeling is done by registering the images to a statistical atlas created from training image data beforehand. The statistical atlas consists of a statistical model of deformation and a statistical model of grey value appearance. It is generated by non-rigid registration and principal component analysis of the resulting deformation fields and registered images. Labeling of an unseen FastView image is done by non-rigid registration of the image to the statistical atlas and propagating the labels from the atlas to the image. In our implementation, the statistical models of deformation and appearance are both implemented on the GPU (graphics processing unit), which permits computing the atlas based labeling using GPU hardware acceleration. The running times of about 10 to 30 seconds are of the same magnitude as the image acquisition itself, which allows for practical usage in clinical MR routine.
Fenchel
Matthias
M
Siemens Healthcare Sector, MR Division, Applications & Workflow, Erlangen, Germany. matthias.fenchel.ext@siemens.com
Thesen
Stefan
S
Schilling
Andreas
A
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004282:Documentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014584:User-Computer Interface