Evaluation of MRI and cannabinoid type 1 receptor PET templates constructed using DARTEL for spatial normalization of rat brains. Issue 12 (11th November 2015)

• Record Type: Journal Article
• Authors: Kronfeld, Andrea; Buchholz, Hans‐Georg; Maus, Stephan; Reuss, Stefan; Müller‐Forell, Wibke; Lutz, Beat; Schreckenberger, Mathias; Miederer, Isabelle
• Is Part Of: Medical physics. Volume 42:Issue 12(2015)
• Page Start: 6875
• Page End: 6884
• Publication Date: 2015-11-11
• Subjects: biological tissues -- biomedical MRI -- expectation‐maximisation algorithm -- image reconstruction -- image registration -- medical image processing -- positron emission tomography; MRI: anatomic, functional, spectral, diffusion -- Positron emission tomography (PET) -- Magnetic resonance imaging -- Reconstruction -- Registration; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Scintigraphy -- Measuring half‐life of a radioactive substance; DARTEL -- spatial normalization -- PET/MRI template -- cannabinoid type 1 receptor -- rat; Brain -- Medical magnetic resonance imaging -- Image registration -- Image reconstruction -- Positron emission tomography -- Medical image reconstruction -- Flow visualization -- Tissues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4934825
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Image registration is one prerequisite for the analysis of brain regions in magnetic‐resonance‐imaging (MRI) or positron‐emission‐tomography (PET) studies. Diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) is a nonlinear, diffeomorphic algorithm for image registration and construction of image templates. The goal of this small animal study was (1) the evaluation of a MRI and calculation of several cannabinoid type 1 (CB1) receptor PET templates constructed using DARTEL and (2) the analysis of the image registration accuracy of MR and PET images to their DARTEL templates with reference to analytical and iterative PET reconstruction algorithms. Methods: Five male Sprague Dawley rats were investigated for template construction using MRI and [ 18 F]MK‐9470 PET for CB1 receptor representation. PET images were reconstructed using the algorithms filtered back‐projection, ordered subset expectation maximization in 2D, and maximum a posteriori in 3D. Landmarks were defined on each MR image, and templates were constructed under different settings, i.e., based on different tissue class images [gray matter (GM), white matter (WM), and GM + WM] and regularization forms ("linear elastic energy, " "membrane energy, " and "bending energy"). Registration accuracy for MRI and PET templates was evaluated by means of the distance between landmark coordinates. Results: The best MRI template was constructed based on gray and white matter images and the regularization form linear elastic energy. In this case, most distances between landmark coordinates were <1 mm. Accordingly, MRI‐based spatial normalization was most accurate, but results of the PET‐based spatial normalization were quite comparable. Conclusions: Image registration using DARTEL provides a standardized and automatic framework for small animal brain data analysis. The authors were able to show that this method works with high reliability and validity. Using DARTEL templates together with nonlinear registration algorithms allows for accurate spatial normalization of combined MRI/PET or PET‐only studies.