Optimization of PET–MR registrations for nonhuman primates using mutual information measures: A Multi-Transform Method (MTM)

An important step in PET brain kinetic analysis is the registration of functional data to an anatomical MR image. Typically, PET–MR registrations in nonhuman primate neuroreceptor studies used PET images acquired early post-injection, (e.g., 0–10min) to closely resemble the subject's MR image....

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Vydáno v:	NeuroImage (Orlando, Fla.) Ročník 64; s. 571 - 581
Hlavní autoři:	Sandiego, Christine M., Weinzimmer, David, Carson, Richard E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier Inc 01.01.2013 Elsevier Elsevier Limited
Témata:	Algorithms Animals Biological and medical sciences Brain - anatomy & histology Brain - diagnostic imaging Brain research Entropy Eye and associated structures. Visual pathways and centers. Vision Fundamental and applied biological sciences. Psychology Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Intermodality Macaca mulatta Magnetic Resonance Imaging - methods Nonhuman primate Normalized mutual information Optimization Papio Pattern Recognition, Automated - methods Positron-Emission Tomography - methods Registration Reproducibility of Results Sensitivity and Specificity Studies Subtraction Technique Transformation Vertebrates: nervous system and sense organs Transformation Normalized mutual information Intermodality Registration Nonhuman primate Human Central nervous system Blocking Algorithm Nuclear magnetic resonance imaging Optimization Encephalon Information use Vertebrata Mammalia Register Distribution Primates Early Time interval Achievement Emission tomography
ISSN:	1053-8119, 1095-9572, 1095-9572
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Popis
Shrnutí:	An important step in PET brain kinetic analysis is the registration of functional data to an anatomical MR image. Typically, PET–MR registrations in nonhuman primate neuroreceptor studies used PET images acquired early post-injection, (e.g., 0–10min) to closely resemble the subject's MR image. However, a substantial fraction of these registrations (~25%) fail due to the differences in kinetics and distribution for various radiotracer studies and conditions (e.g., blocking studies). The Multi-Transform Method (MTM) was developed to improve the success of registrations between PET and MR images. Two algorithms were evaluated, MTM-I and MTM-II. The approach involves creating multiple transformations by registering PET images of different time intervals, from a dynamic study, to a single reference (i.e., MR image) (MTM-I) or to multiple reference images (i.e., MR and PET images pre-registered to the MR) (MTM-II). Normalized mutual information was used to compute similarity between the transformed PET images and the reference image(s) to choose the optimal transformation. This final transformation is used to map the dynamic dataset into the animal's anatomical MR space, required for kinetic analysis. The chosen transforms from MTM-I and MTM-II were evaluated using visual rating scores to assess the quality of spatial alignment between the resliced PET and reference images. One hundred twenty PET datasets involving eleven different tracers from 3 different scanners were used to evaluate the MTM algorithms. Studies were performed with baboons and rhesus monkeys on the HR+, HRRT, and Focus-220. Successful transformations increased from 77.5%, 85.8%, to 96.7% using the 0–10min method, MTM-I, and MTM-II, respectively, based on visual rating scores. The Multi-Transform Methods proved to be a robust technique for PET–MR registrations for a wide range of PET studies. ► A PET–MR registration algorithm is proposed for nonhuman primate brain studies. ► Transforms from our original method often resulted in misaligned PET–MR images. ► The algorithm was tested in 120 datasets, and transforms were assessed visually. ► Successful transforms improved from 78% to 97% with the new registration method.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1053-8119 1095-9572 1095-9572
DOI:	10.1016/j.neuroimage.2012.08.051