Magnetic Resonance Microscopy (MRM) has created new approaches for high-throughput morphological phenotyping of mouse models of diseases. Transgenic and knockout mice serve as a test bed for validating hypotheses that link genotype to the phenotype of diseases, as well as developing and tracking treatments. We describe here a Markov Random Fields based segmentation of the actively-stained mouse brain, as a prerequisite for morphological phenotyping. Active staining achieves higher signal to noise ratio (SNR) thereby enabling higher resolution imaging per unit time than obtained in previous formalin-fixed mouse brain studies. The segmentation algorithm was trained on isotropic 43-micron T1-and T2-weighted MRM images. The mouse brain was segmented into 33 structures, including the hippocampus, amygdala, hypothalamus, thalamus, as well as fiber tracts and ventricles. Probabilistic information used in the segmentation consisted of a) intensity distributions in the T1-and T2-weighted data, b) location, and c) contextual priors for incorporating spatial information. Validation using standard morphometric indices showed excellent consistency between automatically-and manually-segmented data. The algorithm has been tested on the widely used C57BL/6J strain, as well as on a selection of six recombinant inbred BXD strains, chosen especially for their largely-variant hippocampus.
A 3D Carr-Purcell-Meiboom-Gill (CPMG) sequence was implemented to obtain enhanced T 2 contrast in actively stained (perfusion with fixative and contrast agent) mouse brains at 9.4 T. Short interecho spacing was used to minimize diffusion and susceptibility losses. The sequence produced 16 3D volumes with an interecho spacing of 7 ms for isotropic 43--resolution images of the mouse brains in a scan time of 4 hr. To enhance the signal-to-noise ratio (SNR) and contrast, the multiecho frequency domain image contrast (MEFIC) method was applied, resulting in a composite image with T 2 -weighted contrast. The high SNR and contrast thus achieved revealed aspects of mouse brain morphology, such as multiple cortical layers, groups of thalamic nuclei, layers of the inferior and superior colliculus, and molecular and granular layers of the cerebellum, with a high degree of definition and contrast that was not previously achieved in Magnetic resonance histology (MRH) (1) has become a vital tool for a wide range of applications in pathology, neurobiology, and developmental biology. The use of superconducting magnets at high field strengths, specialized RF coils, and fast switching gradients has enabled resolution of Ն50 in the rodent brain (2,3). MRH offers an attractive complement to conventional histology, which is labor-and time-intensive. 3D images from MRH allow sectioning to be performed along any plane without the need for alignment and matching. The high soft-tissue contrast and signal-to-noise ratio (SNR) that it provides in the brain makes MRH particularly appealing for our specific application, i.e., morphometry in the mouse brain. Neuromorphometry includes volumetric analysis, shape information, and spatial organization of structures, which form the basis of morphological phenotyping of mouse models.A prerequisite for morphometry studies in the brain, however, is the ability to visually discriminate one structure from another. MRH exploits the inherent tissue properties of proton density, diffusion, spin-lattice relaxation (T 1 ), and spin-spin relaxation (T 2 ) to emphasize differences among tissue types and indicate the presence of pathology. An accurate assessment of these properties at high fields can guide the selection of scan parameters for maximum differential contrast of structures in the rodent brain. The high magnetic field strengths used in MRH ensure an SNR increase by a power in the range of 1-1.75 of the magnetic field strength (4). However, an imagequality metric of greater utility in the differentiation of tissues is the contrast-to-noise ratio (CNR) (5,6). The CNR in MRI is driven by tissue properties of T 1 , T 2 , proton density, and diffusion. However, the behavior of these properties at high magnetic fields makes it difficult to achieve optimal contrast between tissues. Previous studies (7-9) showed an increase in tissue T 1 s and a decrease in tissue T 2 s as the strength of the magnetic field increased. The long T 1 s necessitate inordinately long repetition times (TRs) to obtain truly T 2 ...
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