A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

The overall goal of this procedure is to quantify cerebral vasal spasm in mice, after induction of subarachnoid hemorrhage. This is accomplished by subjecting the animal to transcardiac perfusion and endovascular casting, using a radiopaque casting agent. The next step is to obtain cross sectional image data, by subjecting the brain to microcomputer Tomography.

Then image and data is processed. Miro software is used to virtually reconstruct the intracranial vascular tree and to calculate volumes of defined vessel segments, which represent an objective measure to quantify vessel spasm. Subarachnoid hemorrhage is induced in mice by endovascular filament perforation under anesthesia with Isoflurane.

The left external carotid artery is prepared surgically. Then a filament is inserted into the external carotid artery and advanced intra cranially through the internal carotid artery, which is perforated at the carotid t, inducing the subarachnoid hemorrhage. A rise in intra cranial pressure is taken as an indicator of successful endovascular perforation.

To analyze vasal spasm, induce anesthesia. Continue only after sufficient anesthesia level has been reached, which is confirmed by the absence of reactions to pain stimuli. Perform a transcardiac perfusion using the following solutions.

A physiological salt solution containing the physiological concentrations of sodium, potassium, calcium and magnesium at a ph of seven point four. And afterwards, a 4%PFA solution. Start the profusion with solution number one for two minutes and continue with solution number two for four minutes.

Infuse the solutions at a temperature of 37 degrees celsius and use a pressure controlled pump with a variable perfusion rate to perfuse with a constant pressure of 70 millimeter mercury. Avoid a loss of pressure when switching from solution one to solution two. After perfusion with both solutions, continue perfusion for 20 minutes at room temperature with a radiopaque casting agent at a constant rate of point two milliliter per minute.

Hold the sample for curing of the radiopaque casting material at four degrees Celsius overnight. And remove the brain and store the sample at four degrees Celsius in a 4%PFA solution until micro-setisiny. Place the brain in the middle of a plastic tube with a blunt anatomical forceps.

Choose a tube with a slightly larger diameter than the sample, to ensure that the object does not move during image acquisition. Use gauze to close the tube. Attach the plastic tube through micro stepping motor of the computer navigated control positioning system in the x-ray cabin, in which the object is rotated around its'horizontal axis.

Align the sample in the field of view under x-ray radiography. To achieve the maximum magnification place the object as close as possible to the x-ray source and maximize the distance to the detector as far as possible. Use a step and shoot image acquisition protocol with the following scan parameters.

Set exposure time to one second for each projection and use a tube voltage of 80 kilovolts to optimize the signal to noise ratio. Set the number of projections to 1, 000 for one rotation. For reconstruction of raw data, use the filtered back projection algorithm, implying the Shepp-Logan filter with a matrix of 1, 024 cubic voxels.

Import dicom data into a Miro software using the function import. Visualize the vessel tree with the function vol-run. Choose the visualization threshold to that the large cerebral arteries are depicted in sharp outlines.

It is important to use the same visualization threshold for all data sets belonging to the experimental series. Virtually dissect the basal cerebral arteries of the Circle of Willis, with a function volume added by surrounding the vessels with the cur-zon. Then virtually dissect the vessel segment to be analyzed.

Therefore, rotate the three dimensional model of the vascular tree in order to precisely separate all small branches from the main artery. It is essential for the further analysis to delete all vessels except the vessel segment to be analyzed. Apply the function auto skeleton with threshold set to visualization threshold, which generates a central line, based spatial graph.

Then apply the function spatial graph to line set, to create a line set. Divide the line set into its'single sub segments by manually choosing the single sub segments with the cursor and clicking on split. This step is crucial in order to calculate the volumes of the single sub segments.

Use the function line set to spatial graph to create a spatial graph again. Use the function spatial graph statistics to determine length, volume, and diameter of each subsegment. For color coded visualization, representing the course of the vessel diameter use the functions spatial graph view.

Set segment coloring to thickness, which correlates with the vessel diameter. It is important to choose the same color map for all data sets belonging to the experimental series. Add the lengths of the sub segments to determine which sub segments are to be included in the further analysis.

In the present study, we evaluated a vessel segment consisting of one millimeter of the internal carotid artery, proximal of the carotid t, and two point five millimeter of the cerebral artery distal of the carotid t. Then add the volumes to determine the vessel volume of the defined vessel segment. To assess the accuracy of the virtual reconstruction of the vascular tree, we performed the diameter based comparison between vessel diameters determined microscopically and from the 3-D virtual reconstructions at two anatomically defined points in ten brain samples.

For microscopic determination of vessel diameters a high resolution camera, software calibration tool, micrometer scales used, there were no significant differences between the diameters determined microscopically and virtually. Indicating an accurate virtual reconstruction of the intra cranial vascular anatomy. To quantify cerebral basal spasm, we determined the volume of a predefined representative three point five millimeter vessel segment, consisting of one millimeter IC8 and two point five millimeter MCA on the left.

In brain samples from SAH and sham operated animals. The vessel volume was significantly lower in SAH compared to sham, indicating cerebral vasal spasm. Vessel diameters were also significantly lower in SAH compared to sham.

The virtual reconstruction achieved with the method presented here reflects the vascular anatomy accurately. Entire vessel segments can be examined, presumably presenting a more objective parameter to quantify vasospasm than determination of vessel diameters at a single point. Volumetric evaluation leads to larger differences between vasospastic and non-vasospastic vessels compared to evaluation of vessel diameters only.