High-resolution MRI data of the brain of C57BL/6J and BTBR mice in three anatomical views

Ryabushkina, Yulia; Shevelev, Oleg B.; Kisaretova, Polina E.; Bobkov, Nikolai; Ayriyants, Kseniya A.; Bondar, Natalia P.; Reshetnikov, Vasiliy V.

doi:10.1016/j.dib.2021.107619

Cited by 1 publication

(3 citation statements)

References 2 publications

(1 reference statement)

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“…For the two-shot T2-weighted brain extraction network, two brain templates were generated along with their masks. One of the templates was generated from orthogonal multi-plane, high resolution data 59 which were combined to synthesize isotropic volumetric data using the B-spline fitting algorithm. 68 This algorithm is encapsulated in ants.fit_bspline_object_to_scattered_data(…) where the input is the set of voxel intensity values and each associated physical location.…”

Section: Methodsmentioning

confidence: 99%

“…The Center for Animal MRI (CAMRI) dataset 58 from the University of North Carolina at Chapel Hill consists of 16 T2-weighted MRI volumes of voxel resolution 0.16 × 0.16 × 0.16mm 3 . The second high-resolution dataset 59 comprises 88 specimens each with three spatially aligned canonical views with in-plane resolution of 0.08 × 0.08mm 2 with a slice thickness of 0.5mm. These three orthogonal views were used to reconstruct a single high-resolution volume per subject using a B-spline fitting algorithm developed in ANTsX.…”

Section: Two-shot Mouse Brain Extraction Networkmentioning

confidence: 99%

“…Herein, we introduce a mouse brain cortical thickness pipeline for T2-weighted (T2-w) MRI comprising two novel deep learning components: two-shot learning brain extraction from data augmentation of two ANTsX templates generated from two open datasets 58,59 and single-shot brain parcellation derived from the AllenCCFv3 labelings mapped to the corresponding DevCCF P56 T2-w component. Although we anticipate that this cortical thickness pipeline will be beneficial to the research community, this work demonstrates more generally how one can leverage ANTsX tools for developing tailored brain parcellation schemes using these publicly available resources.…”

Section: Introductionmentioning

confidence: 99%

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The ANTsX Ecosystem for Mapping the Mouse Brain

Tustison,

Chen,

Kronman

et al. 2024

Preprint

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Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.

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Section: Methodsmentioning

confidence: 99%

Section: Two-shot Mouse Brain Extraction Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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