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

Summary

This protocol uses micro-computed tomography to enable the cost-effective quantification of lean mass, including skeletal muscle and visceral tissue, adipose tissue, and skeletal tissue in small animals. It distinguishes between lean and adipose tissue, which offers significant advantages for biomedical research, particularly in translational research in small animals.

Abstract

Skeletal muscle size, mass, and composition are critical properties for studying metabolic and muscle-related diseases, as they directly impact the understanding of disease progression and treatment outcomes. Quantifying a live animal's lean, adipose, and skeletal mass is important in metabolic, physiology, pharmacologic, and geroscience studies. However, obtaining accurate body composition measurements, especially of lean mass, remains challenging due to the inherent limitations of conventional assessment techniques. Micro-computed tomography (micro-CT) is a non-invasive radiological technique that enables high-resolution visualization of internal structures in small animal models. A standardized micro-CT method can significantly enhance translational research with more reliable and impactful results, particularly during aging studies or at different time points within the same animal. Despite its potential, the lack of standardization in image acquisition and analysis methods significantly hinders the comparability of results across different studies. Herein, we present a comprehensive and detailed low-cost protocol for lean mass analysis using micro-CT to address these challenges and promote consistency in research involving small animal models.

Introduction

Size, mass, and composition are crucial skeletal muscle properties for studying muscle-related and metabolic disease mechanisms¹. Sarcopenia, cachexia, atrophy, and myopathies share common phenotypes: reduction in mass, alteration in composition, and impaired muscle function^2,3,4,5. However, quantifying the body composition in a living animal remains highly complex and technically challenging⁶.

The primary methodologies for in vivo imaging and body composition analysis are dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI). These methods are primarily employed to screen and monitor diseases that lead to lean mass reduction^7,8,9,10,11. DXA is the gold standard for body composition analysis due to its lower cost. However, DXA has a significant disadvantage compared to MRI and CT: its inability to spatially resolve muscle and adipose tissue¹.

MRI uses strong magnetic fields and radio waves to generate detailed images of the body's internal structures¹². One of its main advantages over CT is its superior contrast resolution, allowing excellent differentiation between distinct soft tissue types^1,13,14. Unlike CT, MRI does not use ionizing radiation, making it safer for repeated use^15,16. However, MRI is more expensive and less accessible, with longer scan times and higher maintenance costs^13,17. Thus, MRI instruments adapted for small animal analysis are not usually available.

Micro-CT is similar to conventional CT but tailored for small structures and biomedical research¹⁸. Micro-CT is an advanced, non-invasive radiological assessment technique that enables detailed visualization of internal structures in small animal models. Micro-CT uses X-rays to create detailed images of the body's internal structures, relying on the differential attenuation of X-rays by various tissue types. During a micro-CT scan, the mouse lies on a table that moves slowly through a circular gantry. Inside the gantry, an X-ray tube rotates around the mouse, emitting X-rays from various angles. Detectors on the opposite side capture these X-rays after they pass through the body¹⁸.

The micro-CT scanner's software processes the data from these multiple angles to reconstruct two-dimensional cross-sectional images (slices) of the body. Through reconstruction, these slices can be combined to represent the internal anatomy comprehensively¹⁹. The images produced by micro-CT scans are based on the varying degrees of X-ray attenuation by different tissues within the body. This attenuation is quantified using Hounsfield Units (HU), a scale that standardizes radiodensity^20,21. The HU scale is fundamental for segmentation, as each structure has a slightly different value.

In the present article, we used HU values to accurately differentiate between bone, lean tissue, and adipose tissue¹. By referencing established HU ranges, we ensured precise analysis and comparison of body composition. Herein, we demonstrate how to acquire images using micro-CT and its application in visualizing and quantifying lean, fat, and skeletal mass.

Protocol

All methods were approved by the Institutional Animal Care and Use Committee of the Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro (IACUC - UFRJ; A16/23-025-20). Scans were performed on male C57BL/6 mice aged 6 and 22 months.

1. Animal preparation

1. Anesthetize mice using 2% isoflurane in a mixture of oxygen delivered via vaporization. Use a precision vaporizer for both induction and maintenance with a mixture of 100% oxygen using a precision vaporizer.
2. Position the anesthetized animal supine on the micro-CT scanning using a mouse bed. Secure the mouse with tape to minimize movement during the scan and then insert it into the gantry.
3. Throughout the experiment, which lasts approximately 10 min, ensure that the animal wears a mask to keep it under the anesthetic plane while receiving the maintenance dose as needed.

2. Image acquisition and reconstruction

1. Acquire body micro-CT scans using a high-resolution preclinical imaging system. Capture a total of 1,024 projections with an exposure time of 470 ms each, using a fly mode rotation at a voltage of 60 kV and a current of 480 µA. Set the system to a 1.25 magnification, resulting in a 94.72 mm field of view (FOV) for a total acquisition time of 8.02 min. Capture images with a binning of 1 x 1, resulting in a resolution of 2,368 x 2,240 pixels.
   NOTE: The equipment used in this manuscript for micro-CT images presents limitations in obtaining head images due to the overlap with the anesthetic mask. Additionally, the entire tail length is not captured. Thus, we excluded the head and tail analysis (see section 3).
2. Perform a scan with the same parameters on an acrylic cylindrical phantom (diameter: 2.5 cm, length: 11.5 cm) half-filled with distilled water to determine the HU for air and water. Upon completion of the acquisitions, reconstruct the images using the acquisition software with settings optimized for noise reduction and the Filtered Back Projection (FBP) algorithm. Batch-reconstruct both animal and phantom images using a 147 µm isotropic voxel size and a 640 matrix.
3. Extract air and water HU from the phantom (raw data) using the referenced software. Convert the images into DICOM files and correct the HU values.

3. Image analysis

1. Uploading a file
   NOTE: Micro-CT is a technique that captures images temporally and spatially, generating an image sequence. Therefore, it is necessary to open the entire folder rather than a single image.
   1. In the top left area of the interface, find the 3D Slicer menu, highlighted in a grayish-blue color. Click on Add Data ().
   2. When a window with two options appears, select the first option: Choose Directory to Add.
   3. Navigate to the folder containing the target animal DICOM images series and click on it. This action will open the folder within the software, allowing all the images to be viewed simultaneously.
   4. Observe the images displayed across three screens. Each screen represents a different anatomical plane: coronal, sagittal, and transverse, identified by the colors green, yellow, and red, respectively.
2. Segmentation
   1. Locate the Segment Editor () in the upper tab (Modules), and click on it.
   2. Click the green + Add button to create segments, defining the HU range.
   3. Create segments for bone, lean tissue, and adipose tissue by clicking 3x each.
   4. Double-click on each segment to name and color it according to the desired settings.
   5. Set the HU range for each segment using the Threshold function, located on the left side of the window. To do this, find the Threshold Range function and enter the HU values for each tissue type: Lean tissue (-29 to 225 HU); adipose tissue (-190 to -30 HU); bone (500 to 5,000 HU). Click the Apply button.
      NOTE: Refer to Supplemental Figure S1 for HU value comparisons.
   6. Repeat the segmentation process for each tissue type.
   7. Click Show 3D to generate a 3D rendering.
      NOTE: The rendering will appear in the blue quadrant of the display.
3. Cleaning the image
   NOTE: After segmentation and 3D rendering, extraneous items must be excluded to ensure they are not included in the volumetric measurements of lean tissue, adipose tissue, and bone. Removing these artifacts is essential to ensure accurate analysis.
   1. Locate the "Scissors" tool () in the Segmentation menu.
   2. Choose either the anatomical plane or the 3D rendering for a clearer view of the object to be removed.
      1. If using an anatomical plane, click the maximize view button () on the colored bar of the plane. Inside the anatomical plane window, use the mouse scroll button to navigate forward or backward through the CT scan.
         NOTE: Each window can scroll independently.
      2. For 3D rendering, use the left mouse button to rotate along any axis and the right mouse button to zoom in.
         NOTE: Arrow keys on the keyboard can also rotate the image.
   3. With the Scissors tool selected, highlight the segmentation around the unwanted object and encircle it to remove it from the image. The software deletes the object automatically.
      NOTE: Make sure the operation Erase Inside is selected. If the equipment used generates images for any nonbiological image, such as the bead, anesthetic, or surgical equipment, it is important to remove it. Additionally, if any anatomic area is not wholly obtained, it is fundamental to establish an anatomic reference to remove these areas that are not fully obtained to ensure repeatability between experiments. Unwanted objects must be removed in each plane.
   4. Click the restore view layout icon () to return to the screen with the four windows, regardless of the window selected.
   5. Repeat the process for all windows where the object needs removal.
4. Segmentation volume
   1. After segmentation, quantify the volumes of the segments using the quantification function found in the dropdown menu.
      NOTE: All segmentations must be visible for this action.
   2. Navigate to Quantification | Segment Statistics. Click on Apply, and wait for the software to generate a table displaying values for each segmentation. The software provides two volume values for each segmentation: Labelmap (LM) and Closed Surface (CS).
      NOTE: LM Value is generally suitable for calculating segment volumes, using the number of voxels multiplied by the volume of a single voxel. CS Value utilizes a smoothed surface model and integrates volume based on triangular surface elements. CS aligns more closely with the anatomical contours and is recommended for accurate measurements.
   3. To upload data from a new animal, save and close the scene at the file menu.
5. Corporal composition analysis
   1. Use the volume measurements provided by the software in cm³ to convert these volumes to tissue mass and apply the appropriate density for each tissue type. Use the following tissue densities specified by the International Commission on Radiation Units and Measurements²²: 0.95 g/cm³ (adipose tissue), 1.05 g/cm³ (lean tissue), and 1.92 g/cm³ (skeletal tissue)².
      1. To compute tissue mass, multiply the volume (cm³) obtained from the software by the respective tissue density (g/cm³):
         Adipose Tissue Mass: Volume_segmentation X 0.95 = Mass_adipose
         Lean Tissue Mass: Volume_segmentation X 1.05 = Mass_lean
         Skeletal Tissue Mass: Volume_segmentation X 1.92 = Mass_skeletal
         ​Total Mass: Mass_adipose + Mass_lean + Mass_skeletal= Mass_Total
6. Bone length measurement
   NOTE: To correct tissue mass by bone length for certain analyses, it is necessary to obtain specific bone lengths.
   1. Return to the Segment Editor menu. Hide the adipose tissue and lean tissue segmentations by clicking on the eye icon () next to each segment.
      NOTE: Only the segmentation corresponding to skeletal tissue should remain visible.
   2. Select the Toggle Markups/Toolbar () option in the upper corner of the main menu. This will open a secondary menu just below the main menu. Locate the create new line button ().
   3. After clicking on the create new line button, navigate to the 3D reconstruction and identify the bone to be measured.
      NOTE: The recommended bones are the tibia or femur²².
   4. Once the bone is identified, left-click on one end of the bone and again on the other end of the tissue. This action allows the software to calculate the bone size.
      NOTE: For consistent measurements, select the same anatomical region of the bone for each animal. For example, the femoral head is used as one end and the lateral condyle as the other end of the femur.
7. Data analysis and normalization
   NOTE: The mass data can be obtained as explained in section 3.5 and used as it is. Alternatively, the lean, fat, and bone mass can be normalized in two ways.
   1. Calculate the ratio between the tissue mass and the animal's weight on the day the images are obtained.
      ​Normalization to weight: Mass_lean ÷ Body weight
   2. Calculate the ratio between mass and bone size (for instance, femur or tibia).
      Normalization to femur: Mass_lean ÷ Size_femur
      Normalization to tibia: Mass_lean ÷ Size_tibia
      NOTE: The normalization parameters are chosen based on the experimental question. If changes in bone size are expected (for example: when comparing bone remodeling) the normalization by body weight is suggested (item 3.7.1). Additionally, if changes in body weight are expected in your experimental question, the long bone size is the suggested normalization parameter (item 3.7.2).

Results

Proper anatomical positioning of the sedated animal on the imaging table ensures consistent and reproducible scan outcomes, highlighting the data acquisition effectiveness in achieving reliable results. Proper animal sedation throughout imaging, including specialized gas delivery systems and vaporizers, is fundamental for precise anatomical assessments (Figure 1).

Figure 2 illustrates segmentation and rendering in different anatomical planes, presenting sequential images in the coronal, sagittal, and transverse planes. These images demonstrate the gradual segmentation of skeletal, adipose, and lean tissue, culminating in a thoroughly segmented representation that integrates all tissue types, highlighting a clear distinction between tissues and demonstrating region-specific markings based on the identified tissue types. Despite equipment limitations precluding whole-body capture, the method provides detailed information on tissue distribution and composition across various anatomical planes.

Figure 3 showcases 3D renderings emphasizing tissue types-bone (blue), adipose tissue (yellow), and lean tissue (red)-offering a comprehensive visualization of anatomical structures. This visualization underscores the protocol's effectiveness in generating detailed 3D models essential for anatomical studies.

Table 1 details normalized tissue masses (lean mass, body fat, and skeletal mass) relative to the animal's weight on imaging day. The values presented result from multiplying the tissue volume (in cm³) by the corresponding tissue density, as detailed in step 3.5.1.1 of the methodology.

Table 2 provides absolute body composition values, showing each animal's mass distribution and lean, body fat, and skeletal tissue percentage. It allows us to assess the total amount of each tissue type and how it contributes to each specimen's overall body composition.

Supplemental Table S1 shows the time each analyst takes to complete the body composition analysis for each animal and the variability in the results across analysts. It assesses whether there is significant variability between experienced (Analysts 1, 2, and 5) and inexperienced operators (Analysts 3 and 4) in measuring body composition metrics such as lean mass, body fat mass, and skeletal mass.

Figure 4 presents a comparative body composition analysis between adult and elderly subjects. It reveals a reduction in lean mass percentage and increased body fat, which aligns with typical aging patterns. These findings collectively highlight the imaging protocol's robustness and precision in delivering critical anatomical data, which is essential for advancing preclinical research methodologies.

Figure 1: Animal preparation. (A) The apparatus used for animal sedation throughout the imaging process, such as gas delivery systems and vaporizers; (B) the micro-CT scanningsystem. (C) The anesthetized animal is lying supine on a mouse bed. Proper positioning is critical to obtain consistent and reproducible results. Please click here to view a larger version of this figure.

Figure 2: Segmentation and visualization of slices in different anatomical planes. (A) Coronal plane, (B) sagittal plane, and (C) transverse plane. From left to right, the images show an unsegmented scan (gray), bone segmentation (blue), body fat segmentation (yellow), lean tissue segmentation (red), and finally, complete segmentation, including all segmentations. Please click here to view a larger version of this figure.

Figure 3: 3D renderings. From left to right, the images display the 3D rendering of the subject, highlighting different tissue types: bone in blue, body fat in yellow, lean tissue in red, and the final image shows a comprehensive view combining bone, adipose tissue, and lean tissue. Please click here to view a larger version of this figure.

Figure 4: Comparison of body composition between adult and elderly mice. The bar graph illustrates the body composition percentages of skeletal tissue (adult: 10.5 ± 0.5%; elderly: 9.9 ± 1.0%, p = 0.19), body fat (adult: 17.2 ± 5.7%; elderly: 27.9 ± 4.4%, p = 0.03), and lean mass (adult: 72.3 ± 5.7%; elderly: 62.3 ± 3.6%, p = 0.03). The data are presented as MEAN ± SD from the animals (adult: n = 3; elderly: n = 3). Statistics were calculated using an unpaired t-test with a one-tail p value (* p < 0.05). Please click here to view a larger version of this figure.

LEAN
Animal ID	Lean mass (g)	Weight (g)	Normalization (Weight)
Elderly 1	15.1	37.4	0.4
Elderly 2	14.4	44.4	0.3
Elderly 3	14.3	37.7	0.4
Adult 1	12.3	29.1	0.4
Adult 2	16.5	30.4	0.5
Adult 3	16.1	31.7	0.5
BODY FAT
Animal ID	Body fat mass (g)	Weight (g)	Normalization (Weight)
Elderly 1	6.4	37.4	0.2
Elderly 2	8.2	44.4	0.2
Elderly 3	5.3	37.7	0.1
Adult 1	4.4	29.1	0.2
Adult 2	2.7	30.4	0.1
Adult 3	3.4	31.7	0.1
SKELETAL
Animal ID	Skeletal mass (g)	Weight (g)	Normalization (Weight)
Elderly 1	2.3	37.4	0.06
Elderly 2	2.3	44.4	0.05
Elderly 3	2.4	37.7	0.06
Adult 1	2.0	29.1	0.07
Adult 2	2.3	30.4	0.08
Adult 3	2.2	31.7	0.07

Table 1: Normalization of masses by animal weight on the day of imaging. As explained in Section 3.7, the mass of the tissue can be normalized by the animal's weight on the day of the examination. This normalization has been applied to lean, adipose, and bone tissue based on the animal's weight.

Body Composition (g)
	Segmentation Volume X 1.05	Segmentation Volume X 0.95	Segmentation Volume X 1.92	Lean + Adipose + Skeletal
Animal ID	Lean mass (g)	Body fat mass (g)	Skeletal mass (g)	Total mass (g)
Elderly 1	15.1	6.4	2.3	23.8
Elderly 2	14.3	8.2	2.3	24.9
Elderly 3	14.3	5.3	2.4	22.0
Adult 1	12.3	4.4	2.0	18.6
Adult 2	16.5	2.7	2.3	21.5
Adult 3	16.1	3.4	2.2	21.7
Body Composition (%)
Animal ID	Lean mass (%)	Body fat mass (%)	Skeletal mass (%)	Total mass (%)
Elderly 1	63.6	26.8	9.6	100
Elderly 2	58.1	32.8	9.0	100
Elderly 3	64.9	24.1	11.0	100
Adult 1	65.9	23.5	10.6	100
Adult 2	76.8	12.4	10.9	100
Adult 3	74.3	15.7	9.9	100

Table 2: Body composition. (A) The body composition of the animals in grams; (B) the percentage of body composition.

Supplemental Table S1: Body composition evaluated by different operators. Lean mass, body fat mass, skeletal mass, and the time taken for analysis (in minutes) were recorded for each evaluation. Analysts 1, 2, and 5 were experienced operators, while Analysts 3 and 4 were inexperienced and followed only the protocol described in the study. Please click here to download this File.

Supplemental Figure S1: Density range of different biological and non-biological materials. The figure illustrates the density ranges of various biological tissues and non-biological substances. The values were obtained from the literature and adjusted to achieve optimal tissue differentiation. Bones exhibit the highest density, followed by lean tissue, while body fat shows a significantly lower density. The device was calibrated with water as the reference at a density of 0. Please click here to download this File.

Discussion

Evaluation through tomography is an effective and non-invasive method for obtaining detailed body composition information. Micro-CT, in particular, offers valuable outcome measures for preclinical studies. In the bone field, micro-CT has different uses, as analyzing the micro-architecture²³ and bone remodeling²⁴ are particularly interesting. Assessing the morphology of internal biological structure is also relevant in biomedical research, as analyzing the density of vasculature in specific tissue can assess its function and infer about the metabolism²⁵. For endocrine and metabolic studies of the adipose mass, the use of micro-CT provides fundamental information in live small animals^26,27. Additionally, micro-CT can be applied in other fields, such as zoology and botany, where it is used to evaluate the structures of small insects and plants^28,29.

A significant limitation of micro-CT is the high cost and limited access to image analysis software. Additionally, prior anatomical knowledge, especially of animal anatomy, is essential for correct measurements. This manuscript introduces the 3D slicer software capable of producing results comparable to commercial alternatives. This system also reduces the requirement for extensive anatomical knowledge, as it is semi-automatic³⁰.

The images obtained through micro-CT and analyzed by the software demonstrated that the body composition of mice follows a similar trajectory to that of humans during aging, characterized by an increase in total adipose tissue and a concomitant decrease in lean mass³¹. These findings were evident when comparing adult mice (6 months) to elderly mice (22 months).

Due to the equipment's limitations, the anesthesia mask blocks the head acquisition. Thus, we could not capture and accurately segment the entire head. As a result, we established a convention to use the atlas bone (C1) as an anatomical reference point, focusing our analysis on the region below this point in the equipment used herein. Therefore, all images above the C1 were selected to be removed. The equipment did not obtain the full tail length. Thus, the tail was removed from the basal area in all analyses. We suggest the user establish anatomical points for select excluded areas to maintain reproducibility between experiments.

This article presents a method for analyzing tomographic images using an accessible and cost-effective protocol. The software successfully distinguished between three tissue types: lean mass, adipose tissue, and skeletal tissue. However, it was unable to distinguish skeletal muscle from visceral organs, a common challenge in murine body composition analysis.

Micro-CT lacks the capability to differentiate visceral tissue and skeletal muscle mass. Despite this limitation, our results demonstrate the software's value for studying diseases affecting body composition, particularly those impacting skeletal muscle, such as hypertrophy or atrophy. It can be repeated as a non-invasive test, enabling longitudinal body composition monitoring. This capability helps evaluate protective treatments and develop protocols to model many metabolic diseases.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
3DSlicer, version 5.6.2	3D Slicer platform		A free and open-source software for image analysis and scientific visualization.
Amide, version 1.0.1	Amide platform		A free and open-source software used for correcting the Hounsfield Unit values in the processed DICOM images.
Amira, version 4.1	Thermo Fisher Scientific		Used to extract air and water Hounsfield Unit values from the phantom's raw data and to convert images into DICOM files.
Isoforine	Cristália		Isoflurane is a non-flammable liquid anesthetic agent for use in general inhalation anesthesia by vaporization.
Triumph XO subsystem	Gamma Medica-Ideas Flex		Advanced imaging subsystem designed for preclinical small animal imaging, offering high-resolution CT and PET capabilities for quantitative and qualitative analysis.