MRI Mouse Brain Data of Ischemic Lesion after Transient Middle Cerebral Artery Occlusion

In this data report we make available to the community a highly variable longitudinal MRI mouse brain data set of ischemic lesion after transient middle cerebral artery occlusion (tMCAo). Together with the provided semi-automated and automated segmentations, these data can be used to further improve the method proposed by Mulder et al. (2017) and also to serve as a benchmark for comparison between different approaches to segment ischemic lesions in MRI mouse brain data. It can also be used to develop and validate algorithms that further classify the stroke area into core and penumbra.• The data were collected from mice: (i) of different ages, (ii) of two different strains, (iii) at different time points after the ischemic infarct induction, (iv) from two laboratories, (v) using two different MRI systems, and (vi) using three different sets of acquisition parameters.• Segmentations of the ischemic lesions are provided as well. These were obtained by: (i) two observers using a semi-automated method and (ii) using the novel automated segmentation approach described by Mulder et al. (2017).• Type/format of data: raw files, MetaImage files, text/Excel files, analyzed data.• The following set of images associated with each of the 121 scans is included: raw Bruker MRI data (reference scan, T2 scan with all echoes, calculated T2-weighted map), automated segmentations of the ischemic lesions and semi-automated segmentations by two observers.• For 99 of these scans, an accompanying set of Bruker MR diffusion maps, containing the Diffusion-Weighted Image (DWI) and calculated Apparent Diffusion Coefficient (ADC) maps, is included.• Acquisition hardware: small-animal Bruker MRI systems (7 T and 11.7 T).• Experimental set-up: infarct was induced in male mice of different age and background, using the tMCAo model. After that, MRI scans at different time points after infarct induction were acquired.• Data sources: Leiden, Netherlands; Cologne, Germany.• Data accessibility: all related data sets (121 T2 scans + template + 99 diffusion scans) were deposited in the public Dryad Digital Repository (https://doi.org/10.5061/dryad.1m528).Pattern Recognition and Bioinformatic

Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion

Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.Pattern Recognition and Bioinformatic

Interactive local super-resolution reconstruction of whole-body MRI mouse data: A pilot study with applications to bone and kidney metastases

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.ImPhys/Imaging PhysicsApplied Science