Automated method for small-animal PET image registration with intrinsic validation.
OBJECTIVE
We propose and compare different registration approaches to align small-animal PET studies and a procedure to validate the results by means of objective registration consistency measurements.


METHODS
We have applied a registration algorithm based on information theory, using different approaches to mask the reference image. The registration consistency allows for the detection of incorrect registrations. This methodology has been evaluated on a test dataset (FDG-PET rat brain images).


RESULTS
The results show that a multiresolution two-step registration approach based on the use of the whole image at the low resolution step, while masking the brain at the high resolution step, provides the best robustness (87.5% registration success) and highest accuracy (0.67-mm average).


CONCLUSIONS
The major advantages of our approach are minimal user interaction and automatic assessment of the registration error, avoiding visual inspection of the results, thus facilitating the accurate, objective, and rapid analysis of large groups of rodent PET images.
Pascau
Javier
J
Unidad de Medicina y Cirugía Experimental, Hospital General Universitario Gregorio Marañón, C/ Doctor Esquerdo 46, 28007, Madrid, Spain. jpascau@mce.hggm.es
Gispert
Juan Domingo
JD
Michaelides
Michael
M
Thanos
Panayotis K
PK
Volkow
Nora D
ND
Vaquero
Juan José
JJ
Soto-Montenegro
Maria Luisa
ML
Desco
Manuel
M
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism; D001923:Brain Chemistry; D019788:Fluorodeoxyglucose F18 / Q000378:metabolism; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D049268:Positron-Emission Tomography / Q000379:methods*; D051381:Rats; D011924:Rats, Zucker; D015203:Reproducibility of Results
D019788:Fluorodeoxyglucose F18