DOI: 10.5281/zenodo.15684791
title: Synchrotron radiation-based micro computed tomography dataset - in vivo mouse brain - Mouse17

description: Retrospective cardiac gated imaging at ESRF ID17 beamline

This datasets features a synchrotron radiation-based micro computed tomography dataset of a C57BL/6J mouse injected with barium-based contrast agent into the right lateral cerebral ventricle. Data was recorded at the beamline ID17 of the European Synchrotron Radiation Facility in November 2021. For more information, see the associated publication, repository or the FABRIC4 portal.



Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated010ms.zipFull 3D-stack, 10 ms after the R-peak, 20 - 30 min after start of contrast agent infusion



Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated010ms_ROI.zipRegion of interest 3D-stack, subvolume of the above dataset containing the nasopharynx

Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated010ms_ROIsegmentation.zipRegion of interest 3D-stack, segmentation of the nasopharynx in the above dataset



Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated150ms.zipFull 3D-stack, 150 ms after the R-peak, 20 - 30 min after start of contrast agent infusion



Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated150ms_ROI.zipRegion of interest 3D-stack, subvolume of the above dataset containing the nasopharynx

Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_gated150ms_ROIsegmentation.zipRegion of interest 3D-stack, segmentation of the nasopharynx in the above dataset



Mouse17_male_live_23p7_0p5ulmin_5ul_60k360_h2_slice408_gated010-200ms.zip2D slice z = 408, 10-200 ms delay after R-cardiac peak, 20 - 30 min after start of contrast agent infusion, raw data for Supplementary_Video_5.mp4

Supplementary_Video_5.mp42D slice z = 408, 10-200 ms delay after R-cardiac peak, 20 - 30 min after start of contrast agent infusion


Due to an imprecise voxel size of 6.0 µm being entered in the metadata and used during reconstruction, the intensity values in the datasets provided here have to be multiplied by 6.0 µm / 6.3 µm = 0.952 for the intensity values to correspond to linear attenuation coefficients.

Methods

For the dataset shown, a male mouse (SubjectID: Mouse17) twelve weeks of age and 23.7 g body weight was first injected subcutaneously with buprenorphine (0.1 mg/kg) for analgesia. Anesthesia was induced after onset of analgesia 30 min later, via intraperitoneal injection of a cocktail of ketamine (73 mg/kg) and medetomidine (0.18 mg/kg). The depth of anesthesia was monitored by testing reflexes, and additional injections were given as needed. During imaging, this was done via an intraperitoneal infusion line (30G needle with 0.28 mm inner diameter tubing) connected to a remote-controlled syringe pump. Eye ointment was applied and the skull, neck, and upper thoracic region of the mouse were shaved to avoid potential artefacts during X-ray imaging. 2 × 0.5 ml glucose 10% was administered in two separate subcutaneous injections.

The mouse was secured in a stereotactic frame without artificial ventilation, and 5 µl of contrast agent were injected into the lateral ventricle using a 34G Hamilton syringe at a flow rate of 0.5 µl/min. Injection coordinates were 0.95 mm lateral and 0.22 mm caudal of the bregma, at 2.3 mm depth.

The animal was then transferred to the radiation hutch containing the SRµCT imaging setup described above, where it was imaged with a monochromatic beam at a photon energy of 37.95 keV. 60 000 radiographs over a rotation range of 360° were acquired with a pco.edge 5.5 camera coupled with a Hasselblad 100 mm f/2.2 lens and a 250 µm LuAG:Ce scintillator for 6.3 µm effective pixel size. The field of view was reduced to 2560 × 780 pixels due to the limited height of the X-ray beam, and recorded with 5 ms exposure time and 5 ms overhead time. Acquisition time per scan was 10 min. Sample-detector distance was 3 m.

Retrospective cardiac gating was performed using MATLAB (release R2022b; The MathWorks Inc., Natick, USA) based on Fardin et al.. The electrocardiogram (ECG) signal was denoised using a discrete wavelet transform. Detection of ECG peaks was achieved with the findpeaks function, where the parameters minimum peak height and minimum peak distance were optimized via grid search such that the error to the recorded heart rate was minimized. Following peak detection, the time delay between the recorded trigger signal of each acquired projection and the closest R-peak was calculated. The projections were then grouped according to their time delay into time bins of 10 ms width, resulting in 18 bins within the minimum cycle duration and 2 188 projections per bin. For each bin, one tomogram corresponding to a different phase in the cardiac cycle was reconstructed using GPU-accelerated filtered backprojection, implemented in the ASTRA Toolbox (version 2.1.0), and accessed through TomoPy (version 1.12.2).

For the initial analysis in 2D, the images of the most decorrelated cardiac phases, i.e. 10 ms and 150 ms after the cardiac R-peak, were subtracted to visually detect geometrical deformations of brain structures. Those structures with an image intensity difference greater than the noise floor could be identified as having moved between the two images. This inspection was performed with both raw and median-filtered images.

To quantify the detected motion in 3D, a ROI covering the nasopharynx was cropped from two scans that were cardiac-gated at 10 and 150 ms after the cardiac R-peak. The nasopharynx was segmented using a coarse mask to isolate the nasopharynx from nearby bones, thresholding, morphological operations, and connected component analysis. Specifically, the perimeters of the nasopharynx were extracted by masking the volumes: binarization with a manually selected threshold, morphological closing then opening using a ball with radius 2 pixels, removing regions with volume below 512 voxels corresponding to image noise, closing with a ball of radius 1.5 pixels, extracting the largest component using full connectivity, and finally morphological closing using a ball with radius 2.5 pixels. The resulting perimeter profiles of the nasopharynx were filled using flood filling. These operations were performed in a Python pipeline (version 3.13.1) using scikit-image (version 0.25.0). Nasopharynx surface meshes were created from these segmentations using PyVista (version 0.44.2) and smoothed with a Laplacian filter (1 000 iterations, relaxation factor 0.01) to remove pixelated edges (Fig. 5e). Surface-to-surface distances were calculated for each mesh point in the 150 ms delay dataset by finding the distance to the nearest-neighbor point in the 10 ms delays mesh with a kd-tree (SciPy, version 1.15.1).