Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

microCT is a cost-effective, uninvasive technique for analysis of body composition. It's advantageous in muscle physiology studies, allowing during interventions. microCT provides data from the same animal multiple times and improving analysis quality and reducing animal use.

Photon-counting x-ray detectors are innovation in microCT that will enable the quantitative differentiation of tissues using different contrast agents associated with nanoparticle platforms. This can improve the identification of different anatomical areas with a barium solution. Training poses a significant challenge in microCT data analysis, and also the lack of consistent protocols for preclinical CT can complicate image acquisition, especially when dealing with biomaterials or tumor images, as the anatomical regions can exhibit varying Hounsfield values.

Our protocol provide a step-by-step pathway, allows trained and untrained user to perform microCT data acquisition and analysis with similar results. We are now focusing our attention on analyze mouse spontaneous compartment associated with skeletal muscle function and social interaction. We are also interested in study skeletal cell differentiation in vitro using 2D and 3D models.

To begin, position the anesthetized mouse supine on the microCT scanner using a specialized mouse bed. Secure the mouse with a nose cone and tape to minimize movement during the scan. Then insert the mouse into the gantry of the microCT scanner.

Acquire body microCT scans using a high-resolution preclinical imaging system. Capture a total of 1, 024 projections with an exposure time of 470 milliseconds each using fly mode rotation at a voltage of 60 kilovolts and a current of 480 microamperes. Set the system to 1.25 magnification, resulting in a 94.72-millimeter field of view for a total acquisition time of 8.02 minutes.

Capture images with a binning of one by one, producing a resolution of 2, 368 by 2, 240 pixels. Perform a scan with the same parameters on an acrylic cylindrical phantom, and extract Hounsfield unit or HU values for air and water using the referenced software. Convert the images into DICOM files and correct the HU values.

After acquiring microCT images of the mouse, open the image analysis software and locate the 3D slicer menu in the top left area of the interface, highlighted in a grayish-blue color. Click on Add Data. When the window with two options appears, select the first option and choose directory to add.

Then navigate to the folder containing the target DICOM images and click on it. Observe the images displayed across three screens, representing different anatomical planes, coronal as green, sagittal as yellow, and transverse as red. In the upper tab under Modules, select the segment editor to open the segmenting options.

Next, click on the green plus Add button to create new segments, defining the HU range for each tissue type. Now double click on each segment to name and color it according to the desired settings. Set the HU range for each segment using the threshold function.

For each tissue type, enter the HU values, lean tissue minus 29 to 225, adipose tissue minus 190 to minus 30, and bone 500 to 5, 000. Click the Apply button. After setting the HU ranges, click Show 3D to generate a 3D rendering of the segmented tissues.

In the Segmentation menu, select the scissors tool to remove unwanted objects. For anatomical planes, click the maximize view button on the colored bar of the desired plane, and use the mouse scroll to navigate through the CT scan. For 3D rendering, use the left mouse button to rotate and the right mouse button to zoom.

Next, use the scissors tool to highlight the unwanted object and encircle it to remove it from the image. Click the restore view layout icon to return to the four-window layout. After segmentation, navigate to Quantification and Segment Statistics to calculate volumes for each segment.

Click Apply and wait for the software to generate a table with values for each segmentation, showing both label map and closed surface volumes. Use the volume measurements provided by the software in cubic centimeters to convert these volumes into tissue mass. Apply the appropriate density for each tissue type, 0.95 grams per cubic centimeter for adipose tissue, 1.05 grams per cubic centimeter for lean tissue, and 1.92 grams per cubic centimeter for skeletal tissue.

For bone length measurements, return to the segment editor menu and hide the adipose and lean tissue segments by clicking on the eye icon next to each segment. Next, select the toolbar option in the upper corner of the main menu. Click the Create New Line button to measure the bone length in the 3D rendering.

Identify the bone in the 3D reconstruction. Click on one end of the bone, and then click on the other end to allow the software to measure its length. Segmentation of skeletal, adipose, and lean tissues was presented through sequential coronal, sagittal, and transverse planes, effectively demonstrating clear tissue distinction.

3D renderings revealed detailed anatomical structures, with blue representing bone, yellow representing adipose tissue, and red representing lean tissue. Elderly subjects displayed a higher body fat percentage and reduced lean mass compared to adult subjects, illustrating age-related changes in body composition.