Noninvasive determination of body segment parameters of the hind limb in Labrador Retrievers with and without cranial cruciate ligament disease

Chantal A. Ragetly Small Animal Clinic, Department of Small Animal Surgery, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802.

Search for other papers by Chantal A. Ragetly in
Current site
Google Scholar
PubMed
Close
 DMV
,
Dominique J. Griffon Small Animal Clinic, Department of Small Animal Surgery, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802.

Search for other papers by Dominique J. Griffon in
Current site
Google Scholar
PubMed
Close
 DMV, PhD
,
Jason E. Thomas Department of Mechanical Science and Engineering, College of Engineering, University of Illinois, Urbana, IL 61802.

Search for other papers by Jason E. Thomas in
Current site
Google Scholar
PubMed
Close
 BS
,
Ayman A. Mostafa Small Animal Clinic, Department of Small Animal Surgery, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802.

Search for other papers by Ayman A. Mostafa in
Current site
Google Scholar
PubMed
Close
 BVSc, PhD
,
David J. Schaeffer Small Animal Clinic, Department of Veterinary Biosciences, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802.

Search for other papers by David J. Schaeffer in
Current site
Google Scholar
PubMed
Close
 PhD
,
Gerald J. Pijanowski Small Animal Clinic, Department of Veterinary Biosciences, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802.

Search for other papers by Gerald J. Pijanowski in
Current site
Google Scholar
PubMed
Close
 DVM, PhD
, and
Elizabeth T. Hsiao-Wecksler Department of Mechanical Science and Engineering, College of Engineering, University of Illinois, Urbana, IL 61802.

Search for other papers by Elizabeth T. Hsiao-Wecksler in
Current site
Google Scholar
PubMed
Close
 PhD

Abstract

Objective—To determine mass, center of mass (COM), and moment of inertia (ie, body segment parameters [BSPs]) of hind limb segments by use of a noninvasive method based on computerized tomography (CT) in Labrador Retrievers with and without cranial cruciate ligament (CCL) disease and to provide regression equations to estimate BSPs of normal, CCL-deficient, and contralateral hind limbs.

Animals—14 clinically normal and 10 CCL-deficient Labrador Retrievers.

Procedures—Bone, muscle, and fat areas were identified via CT. Mass, COM, and moment of inertia were determined on the basis of tissue densities in the thigh, crus, and foot segments. Regression models were developed to determine predictive equations to estimate BSP on the basis of simple morphometric measurements.

Results—The thigh and crus of CCL-deficient limbs weighed less than in contralateral segments. Thighs weighed less in CCL-deficient than in normal limbs. The thigh moment of inertia was less in CCL-deficient than in contralateral limbs. The crural COM was located more distally in normal limbs, compared with other limbs. Predictive equations to estimate BSP varied by parameter, body segment, and limb status.

Conclusions and Clinical Relevance—BSPs of the thigh and crus varied with segment and status of the hind limb in Labrador Retrievers with or without CCL disease. Equations to estimate BSP on the basis of simple morphometric measurements were proposed, providing a basis for nonterminal studies of inverse dynamics of the hind limbs in Labrador Retrievers. This approach may offer new strategies to investigate the pathogenesis of nontraumatic joint diseases.

Abstract

Objective—To determine mass, center of mass (COM), and moment of inertia (ie, body segment parameters [BSPs]) of hind limb segments by use of a noninvasive method based on computerized tomography (CT) in Labrador Retrievers with and without cranial cruciate ligament (CCL) disease and to provide regression equations to estimate BSPs of normal, CCL-deficient, and contralateral hind limbs.

Animals—14 clinically normal and 10 CCL-deficient Labrador Retrievers.

Procedures—Bone, muscle, and fat areas were identified via CT. Mass, COM, and moment of inertia were determined on the basis of tissue densities in the thigh, crus, and foot segments. Regression models were developed to determine predictive equations to estimate BSP on the basis of simple morphometric measurements.

Results—The thigh and crus of CCL-deficient limbs weighed less than in contralateral segments. Thighs weighed less in CCL-deficient than in normal limbs. The thigh moment of inertia was less in CCL-deficient than in contralateral limbs. The crural COM was located more distally in normal limbs, compared with other limbs. Predictive equations to estimate BSP varied by parameter, body segment, and limb status.

Conclusions and Clinical Relevance—BSPs of the thigh and crus varied with segment and status of the hind limb in Labrador Retrievers with or without CCL disease. Equations to estimate BSP on the basis of simple morphometric measurements were proposed, providing a basis for nonterminal studies of inverse dynamics of the hind limbs in Labrador Retrievers. This approach may offer new strategies to investigate the pathogenesis of nontraumatic joint diseases.

Ground reaction forces and kinematic data in humans are routinely combined with morphometric measurements to calculate torque and power patterns across joints through an inverse dynamics approach.1 This approach characterizes a limb as a series of connected linkages and integrates morphometric data that provide information on inertial properties (eg, mass, location of COM, and mass moment of inertia) of each limb segment. Results of such analyses have greatly advanced knowledge about the pathogenesis of joint diseases in humans. For example, results from inverse dynamics analyses have revealed that differences in joint position, mobilization, and relative strength of quadriceps and caudal thigh muscles are factors that predispose women athletes to CCL disease.2–5

The use of inverse dynamics to compute joint forces, torques, and power in veterinary medicine has been limited because of a number of factors, including a lack of morphometric measures for dogs. Body segment parameters in humans were initially measured on cadavers,6–11 but more recently, they have been determined on live subjects via mechanical techniques12–14 or medical imaging technology.15–22 Currently, BSPs in humans are commonly calculated by use of regression equations derived from anthropometric measurements of cadavers, images, or geometric models of body segments.1,9,16,23–33

Reports34–36 of morphometric data in horses and dogs are limited by the number of samples included and number of parameters evaluated. For example, morphometric data on canine hind limb segments in 1 study36 were generated from cadavers of 3 Labrador Retrievers and 4 Greyhounds and included only mass and COM. Regression equations allow approximation of BSP from easily obtainable morphometric measurements but are only available for horses.34 Consequently, none of the noninvasive methods has been proposed to calculate BSP in dogs in a representative sample, thereby requiring direct measurements for each dog after it has been euthanatized. Investigators in 1 study36 extrapolated BSP data measured from a small number of cadavers to other dogs of the same breeds. However, their small number of samples may not accurately represent the BSP in these breeds. Obtaining BSP values from cadavers is a time-consuming, expensive process that may restrict the canine population being investigated.

The first objective of the study reported here was to determine BSPs for the hind limb segments in live Labrador Retrievers with or without CCL deficiency. The second objective was to develop regression equations on the basis of easily obtainable morphometric measurements to estimate those BSPs. We hypothesized that BSPs would differ between normal, CCL-deficient, and contralateral limbs. Consequently, we hypothesized that regression equations would differ for BSPs among these 3 groups of limbs in Labrador Retrievers.

Materials and Methods

Animals—Twenty-four client-owned purebred Labrador Retrievers were used in the study. Informed consent was obtained from owners prior to enrollment of dogs into the study. The Institutional Animal Care and Use Committee of the University of Illinois approved all study procedures.

Fourteen clinically normal dogs (28 normal hind limbs) were used in the study. Each dog was > 6 years old and did not have a history of orthopedic disease of the stifle joint and was considered currently free of orthopedic disease of the stifle joint on the basis of results of physical examination, radiography, and CT. An additional 10 adult dogs were examined at the Veterinary Teaching Hospital at the University of Illinois because of weight-bearing lameness attributable to nontraumatic unilateral CCL disease. The duration of lameness before examination was determined on the basis of the history in the medical records. The diagnosis of CCL disease was confirmed by arthroscopy or exploratory arthrotomy at the time of surgical correction of the CCL disease. For these dogs, the 10 CCL-deficient limbs comprised a group of limbs, whereas the 10 normal contralateral limbs of the CCL-deficient dogs comprised another group of limbs. Each limb in the study was analyzed separately.

Procedures—Signalment and body mass were recorded. Complete physical and orthopedic examinations were performed on each dog. Additional procedures were performed as part of another study. Joint angles with each dog in a standing position and range of motion (flexion and extension) of the hip, stifle, and tarsal joints were obtained. Radiographs of the pelvis, femur, and tibia and DEXAa of the entire body of each dog were obtained, which was followed by a gait analysis.b

Morphometric measurements were obtained in triplicate; mean values were calculated and used for analysis. Length of the thigh was defined as the distance between the greater trochanter and the most distal point of the lateral femoral epicondyle. Length of the crus was defined as the distance between the most distal point of the lateral femoral epicondyle and the lateral malleolus of the fibula. Length of the foot was defined as the distance between the lateral malleolus of the fibula and the fifth metatarsophalangeal joint. Lengths were measured by use of motion-capture softwarec as the distance between markers designed for kinematic studies that had been affixed to the skin at designated anatomic landmarks. Measurements were performed after kinematic data were recorded from each dog as part of another study. Each length measurement consisted of the mean length of the segment during a complete gait cycle. Circumference of the thigh (ie, thigh girth) was measured in the midshaft region of the femur at a point midway between the greater trochanter and lateral femoral epicondyle. Circumference of the crus (ie, crus girth) was measured at the level of the tibial crest. Circumferences were measured by use of a single standard plastic nonstretchable metric tape with the dogs positioned in lateral recumbency. Lateromedial widths of the stifle, tarsal, and metatarsophalangeal joints were measured with a caliper at the level of the lateral femoral epicondyle, lateral malleolus of the fibula, and fifth metatarsophalangeal joint, respectively.

Length of the foot was also measured on DEXA images. Total area, bone mineral content, bone mineral density, lean mass, and total mass of the foot were also determined by use of DEXA analysis.

Dogs were anesthetized, and the pelvis and both hind limbs of each anesthetized dog were imaged by use of a 3-D helical CT system.d Dogs were positioned in dorsal recumbency with the stifle and tarsal joints extended in both hind limbs. All CT scans were associated with a coordinate system in the x-, y-, and z-axes, with a common reference origin at the bottom of the field of view. Because the dogs were in dorsal recumbency with the hind limbs extended parallel to the z-axis, the x-, y-, and z-axes of the CT images were approximately aligned with the transverse, craniocaudal, and longitudinal directions, respectively, of the hind limb segments. Each image was subsequently analyzed by use of softwaree to yield data on the cross-sectional area of bone, muscle, and fat tissues. Pixel intensity values of every image were correlated with tissue densities by the software.e The operator (CAR) adjusted these ranges of pixel values for each tissue by selecting different points for each tissue and comparing the pixel intensity recorded with the range of pixel intensities provided by the software for the selected tissue (Figure 1). The range of pixel intensities for each tissue type was held constant throughout the study.

Figure 1—
Figure 1—

Three-dimensional CT images of the hip joint (transverse view [A]) and midfemur (frontal view [B] and parasagittal view [C]) obtained from a representative Labrador Retriever. Notice the outline of fat (yellow outline [yellow arrow]), muscle (red outline [red arrow]), and bone (dark gray outline [white arrow]) tissues as determined on the basis of their respective pixel values by use of software.e

Citation: American Journal of Veterinary Research 69, 9; 10.2460/ajvr.69.9.1188

Following 3-D reconstruction of CT images, each limb was divided into thigh, crus, and foot segments. A transverse plane was defined as parallel to the transverse and craniocaudal axes (x- and y-axes, respectively) and perpendicular to the longitudinal axis (z-axis) of the segment. Proximal boundary of the thigh was defined by the transverse plane that crossed through the proximal end of the greater trochanter. The pelvic bones and pelvic inlet, tail, and prepuce were excluded. Distal boundary of the thigh was defined by the transverse plane that crossed the most distal point of the lateral femoral condyle. The crus segment extended from the transverse plane that crossed the most distal point of the lateral femoral condyle to the transverse plane that crossed the distal point of the fibular malleolus. Tarsal elements proximal to the fibular malleolus (with the tarsal joint in extension) were included in the crus segment. The foot segment extended from the transverse plane that crossed the distal point of the fibular malleolus to the tip of the most distal phalanx among all digits.

To determine the inertial properties of each segment by use of 3-D CT scans, tissue densities of muscle, fat, and bone were fixed at values of 1.06, 0.95, and 1.8 g/cm3, respectively.37 The density of each tissue type within a segment was assumed to be uniform. Muscles, tendons, and ligaments were not delineated in this analysis because they have similar densities. Dermal tissues were treated as the layer in closest contact to muscle or fat. The 3-D CT volumes obtained by use of solid modeling softwaref for each tissue section were multiplied by the density value for each tissue to determine the mass of each tissue within each segment. The segment mass was then calculated by summing the values for each of the tissue masses and expressed as %BM. Location of the COM was represented along the longitudinal axis of each segment, and the distance of the estimated COM from the proximal joint of the segment was determined by use of solid modeling softwaref and expressed as %L. The mass moment of inertia of each segment about a transverse (mediolateral) axis through its estimated COM was calculated by use of solid modeling softwaref and adjusted on the basis of body mass (Figure 2).

Figure 2—
Figure 2—

Three-dimensional image of the left thigh of a representative Labrador Retriever. Volumes of fat (yellow), muscle (red), and bone (gray) tissues determined by use of solid modeling softwaref were used to calculate BSPs. The location of the center of mass (arrow) and the 3 axes (x-, y-, and z-axis, respectively) are indicated.

Citation: American Journal of Veterinary Research 69, 9; 10.2460/ajvr.69.9.1188

Statistical analysis—Statistical analysis was performed on the data obtained. Regression models were developed to estimate BSPs.

DEMOGRAPHICS AND BSPS

Clinically normal and CCL-deficient dogs were compared for sex distribution by use of an exact χ2 test.g Body mass was logarithmically transformed to achieve normality. Differences among groups with regard to age and logarithmically transformed body mass were determined by use of a 1-way ANOVA followed by use of the Tukey HSD test.

Analysis of BSP measurements for the thigh and crus required special methods because the CCL-deficient and contralateral limbs were paired within each dog, whereas normal limbs were paired within each clinically normal dog but were not related to limbs in the CCL-deficient or contralateral groups. Limbs were not independent; rather, they were considered repeated measurements on each dog. To account for within-dog pairing of limbs, a cluster-correlated ANOVA (mixed regression) followed by pairwise tests of group differences (Tukey HSD test) was performed.h Segment mass was expressed as %BM. Location of the COM was expressed as %L. Mass moment of inertia was standardized by dividing it by the body mass. Because data for the standardized mass moment of inertia were not normally distributed, inverse transformation was required. Differences among groups for adjusted segment mass (ie, %BM), COM location, and inversely transformed standardized moment of inertia were determined by use of a 1-way ANOVA followed by the Tukey HSD test. A paired t test was used when the comparison of BSP involved only right and left sides for crus and thigh in normal limbs.

Because of technical difficulties, the distal extremity of the foot segment was scanned in its entirety in only 20 limbs (18 normal, 1 CCL-deficient, and 1 contralateral limb). Morphometric measurements obtained during physical examination and DEXA of the 48 feet included in the study (28 normal, 10 CCL-deficient, and 10 contralateral limbs) were analyzed by use of mixed regression analysis and the Tukey HSD testh to validate extrapolation of BSPs of the foot in normal limbs to the BSPs of the foot in CCL-deficient and in contralateral limbs. Because the data of some variables of the foot were not normally distributed, logarithmic transformation was required for those variables. For all analyses, values of P < 0.05 were considered significant.

REGRESSION MODELS

Estimation equations were constructed from morphometric dimensions to predict mass, location of the COM, and mass moment of inertia for the thigh, crus, and foot of normal, CCL-deficient, and contralateral limbs in Labrador Retrievers. These equations were tested by use of stepwise forward and backward regressionh to determine the most adequate model for each BSP; values of α = 0.15 were used to enter or be removed from the regression. Independent variables (predictors) consisted of the morphometric measurements and body mass. The model with the highest R2 was selected for the mass, location of the COM, and mass moment of inertia for the foot segment. For the crus and thigh segments, different models were selected in accordance with the highest R2 for each limb status (ie, normal, CCL-deficient, and contralateral limbs) for the mass, location of the COM, and mass moment of inertia.27,31,38 Each R2 was higher than 0.85. The SEE was evaluated to help assess models, as described elsewhere.9,23,34,38,39 To ensure that equations were appropriate, they were placed in context to all-subsets regression by use of a statistical program.i Then, accuracy of the model was tested by use of a criterion (ie, percentage of error was < 10.5%, with percentage of error = absolute value of [{estimated value – measured value} × 100]/measured value). The R2 and SEE reflect the precision of predictive equations, and percentage of error reflects the accuracy.38 Finally, residuals and estimates were compared against measurements. When these analyses did not confirm that the estimation requirements of the linear models were met, models from the all-subsets regression were examined to identify a potentially better model and to help identify ways in which we could improve the estimation, such as incorporation of nonlinear terms.

Results

Animals—The 14 clinically normal Labrador Retrievers were significantly older (mean ± SD, 100.6 ± 23.0 months) than the 10 dogs with CCL disease (56.4 ± 20.1 months). Mean mass did not differ significantly between the 14 clinically normal dogs (36.9 ± 9.1 kg) and the 10 dogs with CCL disease (37.5 ± 7.4 kg); mean body mass for all 24 dogs was 36.2 ± 8.1 kg. The sex distribution did not differ significantly between the clinically normal dogs (5 spayed females, 3 sexually intact females, 3 castrated males, and 3 sexually intact males) and the dogs with CCL disease (6 spayed females and 4 castrated males). Mean duration of lameness for the dogs with CCL disease was 3 months (range, 1 week to 1 year).

BSPs—The BSPs of the thigh, crus, and foot segments were determined for the normal, CCL-deficient, and contralateral limbs (Table 1). Because of technical difficulties, the distal portion of the foot segment was scanned in its entirety in only 18 normal, 1 CCL-deficient, and 1 contralateral limb. Hence, mass, location of the COM, and mass moment of inertia values of the foot were only determined for 20 limbs. Morphometric measurements obtained during physical examination and DEXA of the 48 feet included in the study were compared to validate extrapolation of BSPs of the foot in normal limbs to those in CCL-deficient and contralateral limbs. Morphometry and composition of the foot were similar for normal, CCL-deficient, and contralateral limbs for this sample of Labrador Retrievers because there was not a significant difference among normal, CCL-deficient, and contralateral limbs for foot length; mediolateral tarsal width; mediolateral metatarsophalangeal width; foot length measured on DEXA images; or total area, bone mineral content, bone mineral density, lean mass, and total masses of the foot measured on DEXA images. These findings supported the extrapolation of BSPs for the foot segment between healthy, CCL-deficient, and contralateral limbs in our population of Labrador Retrievers.

Table 1—

Mean ± SD values for BSPs of the foot, crus, and thigh of normal, CCL-deficient, and contralateral hind limbs of Labrador Retrievers with or without unilateral CCL disease.

SegmentGroupMassMass moment of inertiaCOM
g%BMg × cm2I/BMcm%L
ThighNormal (n = 28)2,231 ± 5816.05 ± 0.5?92,875 ± 44,2942,423± 561a,b9.0 ± 1.042 ± 5
CCL-deficient (n = 10)1,939 ± 3985.48 ± 0.34b79,074 ± 24,5962,199 ± 276a9.1 ± 0.445 ± 4
Contralateral (n = 10)2,192 ± 3746.22 ± 0.30a87,981 ± 23,7952,459 ± 210b9.1 ± 0.444± 3
CrusNormal (n = 28)516 ± 1131.41 ± 0.16a,b16,242 ± 6,778433 ± 356.3 ± 0.731 ± 2a
CCL-deficient (n = 10)484 ± 1121.36 ± 0.09a15,174 ± 5,699421 ± 806.3 ± 0.528 ± 1b
Contralateral (n = 10)512 ± 1161.44± 0.11b15,941 ± 6,210441 ± 356.4 ± 0.428 ± 1b
FootAll groups (n = 20)*249 ± 250.70 ± 0.097,228 ± 1,223203 ± 248.7 ± 0.447 ± 3

Represents 18 normal, 1 CCL-deficient, and 1 contralateral limb.

I/BM = Mass moment of inertia standardized on the basis of body mass.

For each segment, values in a column with different superscript letters differ significantly (P < 0.05).

None of the BSPs differed between left and right hind limbs in clinically normal dogs. The adjusted masses of the thigh and crus of CCL-deficient limbs (expressed as %BM) were less than in the contralateral limbs (Table 1). The thigh weighed less in CCL-deficient limbs than in normal limbs. The standardized mass moment of inertia of the thigh was less in CCL-deficient limbs than in the contralateral limbs. Position of the crural COM (expressed as %L) was more proximal in CCL-deficient and contralateral limbs, compared to the position in normal limbs.

Regression equations—Morphometric dimensions were determined for the limbs (Table 2). Range, mean, and SD were determined for each dimension measured in each group.

Table 2—

Descriptive statistics for all independent variables included in the regression analysis for 28 normal, 10 CCL-deficient, and 10 contralateral hind limbs of Labrador Retrievers with or without unilateral CCL disease.

VariableGroupRangeMeanSD
Body mass (kg)Normal25.9–55.736.98.9
CCL-deficient26.8–50.035.46.8
Contralateral26.8–50.035.46.8
Thigh length (cm)Normal17.9–24.921.32.1
CCL-deficient18.2–23.720.41.7
Contralateral19.3–23.320.91.4
Crus length (cm)Normal17.1–24.220.21.9
CCL-deficient17.2–21.019.61.2
Contralateral16.9–21.219.61.3
Foot length (cm)Normal8.4–10.89.50.6
CCL-deficient8.0–11.09.30.9
Contralateral8.0–10.59.00.8
Thigh girth (cm)Normal37.5–50.042.53.4
CCL-deficient35.5–47.540.23.7
Contralateral37.5–53.042.44.8
Crus girth (cm)Normal22.0–31.025.12.1
CCL-deficient23.0–25.824.40.9
Contralateral23.0–27.525.21.3
Stifle width (cm)Normal4.6–6.75.60.6
CCL-deficient5.0–6.55.60.5
Contralateral4.8–6.55.50.5
Tarsal width (cm)Normal3.0–4.63.70.4
CCL-deficient3.1–3.93.50.3
Contralateral3.1–4.03.50.3
Metatarsal width (cm)Normal2.9–4.53.80.4
CCL-deficient3.1–4.13.80.3
Contralateral3.0–4.03.50.3

Regression equations were generated for the thigh, crus, and foot segments, respectively, with corresponding R2 values, absolute percentage of error, and SEE (Tables 3–5). The percentage of error varied between 1.0% and 5.7% for the mass of various segments, between 1.6% and 6.5% for the location of the COM, and between 3.4% and 10.4% for the mass moment of inertia. Mean SEE of all regression equations was 4.7% for the mass of the segment, 3.8% for the location of the COM, and 7.9% for the mass moment of inertia. Regression equations accounted for > 86% of the variance in the dependent variables.

Table 3—

Regression equations generated from morphometric measurements and body mass to predict the mass, location of the COM, and mass moment of inertia of the thigh in 28 normal, 10 CCL-deficient, and 10 contralateral hind limbs of Labrador Retrievers with or without unilateral CCL disease.

GroupVariableRegression equation*R2Error (%)SEE
NormalMass (g)−2,723.4 + (30.5 × BM) + (51.2 × thigh girth) + (296.4 × stifle width)0.955.0127.6
COM (cm)(0.80 × foot length) + (0.30 × crus girth) − (0.26 × thigh girth) + (0.70 × stifle width) + (1.3 × tarsal width) − (1.1 × metatarsal width)0.996.50.55
Mass moment of inertia (g × cm2)10(1.52 × {log10 BM}] + [0.94 × {1 + log10 cruslength}] + [0.70 × {1 + log10 thigh length}] − [0.78 × {1 + log10 tarsal width)])0.8910.412,906
CCL-deficientMass (g)−37.7 + (55.9 × BM)0.915.5120.1
COM (cm)(−0.04 × BM) + (0.40 × crus length) + (0.09 × crus girth)1.005.40.21
Mass moment of inertia (g × cm2)9,609.1 + (3,458.7 × BM) − (15,158.0 × metatarsal width)0.966.15,356
ContralateralMass (g)−1,773.2 + (18.7 × BM) + (31.2 × crus girth) + (48.9 × thigh girth) − (156.3 × tarsal width) + (179.3 × stifle width)0.991.030.5
COM (cm)(0.40 × foot length) + (0.22 × crus girth)1.001.60.19
Mass moment of inertia (g × cm2)−66,727.5 + (2,404.8 × BM) + (1,641.6 × thigh girth)0.973.44,073

Body mass (BM) is expressed in kilograms, and segment lengths, widths, and girths are expressed in centimeters.

Table 4—

Regression equations generated from morphometric measurements and body mass to predict the mass, location of the COM, and mass moment of inertia of the crus in 28 normal, 10 CCL-deficient, and 10 contralateral hind limbs of Labrador Retrievers with or without unilateral CCL disease.

GroupVariableRegression equation*R2Error (%)SEE
NormalMass (g)−750.0 + (36.1 × crus girth) + (95.5 × metatarsal width)0.905.734.6
COM (cm)(0.20 × crus length) + (0.09 × crus girth)0.995.50.36
Mass moment of inertia (g × cm2)−68,619.6 + (2,773.4 × crus girth) × (705.8 × thigh girth) + (3,167.6 × foot length) + (4,104.6 × tarsal width)0.939.51,797
CCL-deficientMass (g)−311.0 + (11.2 × BM) + (9.9 × thigh girth)0.934.230.5
COM (cm)0.70 × foot length1.004.50.32
Mass moment of inertia (g × cm2)−31,413.4+ (506.1 × BM) + (3,095.7 × foot length)0.937.51,546
ContralateralMass (g)−374.6+ (15.6 × BM) + (37.3 × foot length)0.944.228.8
COM (cm)(0.20 × foot length) + (0.18 × crus girth) − (1.0 × metatarsal width) + (0.90 × tarsal width)1.005.60.13
Mass moment of inertia (g × cm2)−48,810.8 + (654.7 × BM) +(3,393.9 × foot length) − (4,164.4 × metatarsal width) + (7,268.8 × tarsal width)0.966.21,322

See Table 3 for key.

Table 5—

Regression equations generated from morphometric measurements and body mass to predict the mass, location of the COM, and mass moment of inertia of the foot in 18 normal, 1 CCL-deficient, and 1 contralateral hind limb of Labrador Retrievers with or without unilateral CCL disease.

GroupVariableRegression equation*R2Error (%)SEE
Mass (g)−74.5 + (2.1 × BM) + (11.9 × foot length) + (36.3 × tarsal width)0.863.18.8 
COM (cm)(0.70 × foot length) + (0.18 × crus girth) − (0.60 × metatarsal width)1.003.10.30 
Mass moment of inertia (g × cm2)−703.5+ (124.9 × BM) + (427.0 × crus length) − (217.2 × crus girth)0.884.7408 

See Table 3 for key.

Discussion

The study reported here had several results. The thigh and crus of CCL-deficient limbs weighed less than their matched contralateral segments. The thigh of CCL-deficient limbs also weighed less than that of normal limbs. The COM of the crus was located more proximally in CCL-deficient and contralateral limbs than in normal limbs. For the thigh, the mass moment of inertia standardized on the basis of body mass was lower in CCL-deficient limbs than in matched contralateral limbs. Morphometric measurements used in the regression equations to estimate BSPs (ie, mass, location of the COM, and mass moment of inertia) varied with parameter, body segment, and status of the hind limb.

The age of dogs with CCL disease in our study was consistent with the epidemiologic characteristic of this condition in large-breed dogs.40 The difference in age between clinically normal and CCL-deficient Labrador Retrievers in the study reported here was expected because the minimum age requirement for inclusion in the clinically normal group was based on the decreased risk for CCL disease reported41 in older Labrador Retrievers.

The thigh and crus segments weighed less in CCL-deficient limbs than in unaffected contralateral limbs. The mass of the thigh was also less in CCL-deficient limbs, compared with the mass of the thigh in normal limbs. These findings are consistent with muscle atrophy secondary to CCL disease.42 Although the duration of lameness before examination varied, most dogs were lame for at least 1 month; therefore, the results reported were likely to reflect discomfort and disuse of the CCL-deficient limb. The decreased mass of the thigh also explains the lower mass moment of inertia of the thigh in CCL-deficient limbs, compared with values for the contralateral limbs in dogs with CCL disease. The mass moment of inertia of a segment measures resistance to change in angular motion and correlates proportionally with the segment's distribution of mass with respect to the COM. Differences appeared more pronounced in the thigh than in the crus, which suggested that muscle atrophy secondary to CCL disease affects predominantly the muscles of the thigh, rather than those of the crus. Alternatively, the magnitude of changes in the crus may be limited by the amount of tissue naturally around the tibia, compared with the muscle mass around the femur. Mass of the crus and mass moment of inertia of the thigh differed significantly between CCL-deficient and contralateral limbs in dogs with CCL disease but not between CCL-deficient and normal limbs. This could have been attributable to the difference in age between our clinically normal and CCL-deficient dogs and a potential mild atrophy of the muscles in older dogs.43–45 However, dogs with CCL deficiency redistribute their weight so that the contralateral limb bears 87% of the body weight, compared with 66% in clinically normal dogs.46 Therefore, our results are more likely to reflect this compensatory activity, thereby magnifying differences between CCL-deficient and contralateral limbs in dogs with CCL disease.

The location of the COM of the thigh segment did not differ among groups. Therefore, muscle atrophy appears to be uniform and maintains the repartition of mass along the longitudinal axis of the thigh. The COM of the crus was located in a more proximal position in CCL-deficient and contralateral limbs than in normal limbs. This difference in repartition of the mass along the crus segment is unlikely to be solely attributable to CCL disease because contralateral limbs had the same distribution. These contralateral limbs can be considered as predisposed to CCL deficiency on the basis of the high incidence of bilateral and contralateral CCL disease in dogs.47–49 Further studies are warranted to evaluate whether a relatively greater proportion of bone or muscle (or both) in the proximal portion of the crus is a factor that predisposes dogs to CCL disease.

To our knowledge, the only publication of breedspecific morphometric data in dogs was based on musculoskeletal disease–free cadaveric specimens of 3 Labrador Retrievers and 4 Greyhounds.36 Unfortunately, age of those dogs was not reported. In the study reported here, BSPs of the crus and thigh were measured directly on cadavers by use of a digital scale and a balance board technique.1 These techniques were the first methods used to measure BSPs in humans and provide a reference to which new methods of measurements can be compared. Medical imaging techniques, such as CT,15,19 magnetic resonance imaging,18,20 DEXA,21,22 and G mass scanning,16 have subsequently been found to provide accurate measurements of BSPs in live human subjects. In our study, the crural mass (mean ± SD, 1.41 ± 0.16 %BM) measured by use of a noninvasive approach based on CT was similar to that measured directly in the cadaver study36 of musculoskeletal disease–free Labrador Retrievers (1.32 %BM). The mass of the thigh and mass moment of inertia of the segments have not been reported, which prevents us from comparing results. The COM of the crus (31 %L from the proximal joint) and thigh (42 %L) appear to be located slightly more proximally in the normal limbs in our study than in the limbs of Labrador Retrievers described in another study36 (38 %L for the crus and 48 %L for the thigh). The importance of this difference is difficult to evaluate because direct measurements in that other study36 were obtained only on 3 Labrador Retrievers, rather than on a representative sample of the population. The location of the COM is primarily affected by the geometry of the segment; however, the discrepancies between results of the study reported here and that other study36 may also be caused by inaccuracies associated with the measurement of mass. Two potential sources of error can lead to overestimation of the segment mass for advanced imaging techniques: overestimation of the segmental volume and use of tissue densities higher than actual values.18 Overestimation of the volume (3% with the CT method and 6.3% with magnetic resonance imaging18,50) has been attributed to the difficulty in discriminating the tissue perimeter.20 In addition, bone was assigned a constant density regardless of its relative content of cancellous and compact bone, which potentially could have caused an overestimation of the bone mass in our study. We would expect this overestimation to be magnified in areas where bone predominates over soft tissues, such as the crus. On the other hand, direct measurements in cadavers are affected by postmortem changes, which can lead to an estimated 5% to 6% loss in body fluids and thereby cause underestimation of the mass of segments.37

Although CT allows determination of BSPs in live subjects, its application to gait analysis in dogs remains limited by cost and the need for anesthesia of the subjects. Data developed in the study reported here indicated that mass, location of the COM, and mass moment of inertia for hind limb segments in Labrador Retrievers can be predicted on the basis of simple morphometric measurements of the body. Regression equations generated from direct measurements on cadavers have become the most common method used to estimate BSPs in humans.1,9,27,31,33 We applied a similar approach to generate regression equations on the basis of simple morphometric measurements in Labrador Retrievers. The mass of each segment has been expressed as a percentage of the body mass and the location of the COM as a percentage of the length of the segment.6 This approach can only provide a crude estimation of parameters and assumes that all subjects have similar body proportions and mass repartitions. This assumption is not valid for subjects outside the tested range. Instead, we found that in addition to body mass, several morphometric measurements (segment length, girth, or width) were generally required to generate equations that met validation criteria recommended in other reports.9,23,34,38,39 These equations have been validated for dogs with morphometric dimensions similar to those of our population but cannot be recommended outside the range of our study. One of the 2 main sources of uncertainty in inverse dynamics solution in humans has been attributed to inaccuracies in estimated BSPs.51 These inaccuracies are partially attributable to the fact that anthropometric tables were derived from a relatively small and biased population.52,53 Similar limitations should be expected in dogs and justify the determination of breed-specific morphometric data. In fact, BSPs varied with the status of the hind limb in the study reported here, which prevented breed-specific extrapolation of BSPs for clinically normal dogs to those of dogs with orthopedic disease. Accurate determination of morphometric parameters is especially critical to the study of swing-phase mechanics in which segmental angular accelerations are the largest, particularly for distal segments.54,j

To our knowledge, the study reported here represents the first time that mass moment of inertia in hind limb segments of live clinically normal Labrador Retrievers and BSPs in Labrador Retrievers with CCL disease have been described. Computed tomography allows noninvasive determination of BSPs on live subjects whose gait can be concurrently evaluated. The application of this approach in small animals is limited by availability of CT equipment, cost, and the need to anesthetize subjects. Estimating BSPs on the basis of simple morphometric measurements provides a basis for nonterminal studies of inverse dynamics of the hind limbs in Labrador Retrievers. Regression equations were generated to estimate the mass, location of the COM, and mass moment of inertia in each segment in normal, CCL-deficient, and contralateral limbs of Labrador Retrievers. These equations were based on parameters that are technically simple and cost-effective and can be rapidly generated. This approach will facilitate clinical studies of gait mechanics in dogs, which should offer new strategies to investigate the pathogenesis of nontraumatic joint diseases.

ABBREVIATIONS

%BM

Percentage of body mass

%L

Percentage of length of the segment

3-D

Three-dimensional

BSP

Body segment parameter

CCL

Cranial cruciate ligament

COM

Center of mass

CT

Computerized tomography

DEXA

Dual-energy x-ray absorptiometry

HSD

Honestly significant difference

SEE

Standard error of the estimate

a.

QDR 4500 fan beam x-ray bone densitometer, Hologic Inc, Bedford, Mass.

b.

Human Dynamics and Controls Laboratory, College of Engineering, University of Illinois, Urbana, Ill.

c.

VICON motion systems, Oxford Metrics Ltd, Oxford, England.

d.

High-speed F/X helical scanner, General Electric, Milwaukee, Wis.

e.

Amira 4, Mercurey Computer System Inc, Chelmsford, Mass.

f.

Pro/ENGINEER, Wildfire, version 3.0, Parametric Technology Corp, Needham, Mass.

g.

StatXact, version 9, Cytel Software, Cambridge, Mass.

h.

Systat, version 12, Systat Inc, Richmond, Calif.

i.

SAS, version 9.1, SAS Institute Inc, Cary, NC.

j.

Colborne GR, Shellard LJ, Morris KD. Sensitivity of kinetic gait analysis to accuracy of morphometric variables (abstr), in Proceedings. 50th Cong Br Small Anim Vet Assoc 2007:86.

References

  • 1.

    Winter DA. Anthropometry. In: Biomechanics and motor control of human movement. 3rd ed. Hoboken, NJ: John Wiley & Sons, 2005;5985.

  • 2.

    Sigward S, Powers CM. The influence of experience on knee mechanics during side-step cutting in females. Clin Biomech (Bristol, Avon) 2006;21:740747.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Sigward SM, Powers CM. The influence of gender on knee kinematics, kinetics and muscle activation patterns during side-step cutting. Clin Biomech (Bristol, Avon) 2006;21:4148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Decker MJ, Torry MR, Wyland DJ, et al. Gender differences in lower extremity kinematics, kinetics and energy absorption during landing. Clin Biomech (Bristol, Avon) 2003;18:662669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Hewett TE, Myer GD, Ford KR. Prevention of anterior cruciate ligament injuries. Curr Womens Health Rep 2001;1:218224.

  • 6.

    Dempster WT. Space requirements of the seated operator. USAF technical report WADC-TR-55–159. Wright-Patterson Air Force Base, Ohio: Wright Air Development Center, 1955.

    • Search Google Scholar
    • Export Citation
  • 7.

    Mori M, Yamamoto T. The proportionate mass of the separate body sections of Japanese [in German]. Acta Anat (Basel) 1959;37:385388.

  • 8.

    Fujikawa K. The center of gravity in the parts of human body. Okajimas Folia Anat Jpn 1963;39:117125.

  • 9.

    Clauser CE, McConville JT, Young JW. Weight, volume and center of mass of segments of the human body. Technical report AMRL-TR-69–70 (AD-710 622). Wright-Patterson Air Force Base, Ohio: Aerospace Medical Research Laboratory, 1969;6970.

    • Search Google Scholar
    • Export Citation
  • 10.

    Liu YK, Laborde JM, Van Buskirk WC. Inertial properties of a segmented cadaver trunk: their implications in acceleration injuries. Aerosp Med 1971;42:650657.

    • Search Google Scholar
    • Export Citation
  • 11.

    Chandler RF, Clauser CE, McConville JT, et al. Investigation of inertial properties of the human body. Technical report DOT HS-801 430. Wright-Patterson Air Force Base, Ohio: Aerospace Medical Research Laboratory, 1975;74137.

    • Search Google Scholar
    • Export Citation
  • 12.

    Hatze H. A new method for the simultaneous measurement of the movement of inertia, the damping coefficient and the location of the centre of mass of a body segment in situ. Eur J Appl Physiol Occup Physiol 1975;34:217226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Peyton AJ. Determination of the moment of inertia of limb segments by a simple method. J Biomech 1986;19:405410.

  • 14.

    Kodek T, Munih M. An identification technique for evaluating body segment parameters in the upper extremity from manipulator-hand contact forces and arm kinematics. Clin Biomech (Bristol, Avon) 2006;21:710716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Huang HK, Suarez FR. Evaluation of cross-sectional geometry and mass density distributions of humans and laboratory animals using computerized tomography. J Biomech 1983;16:821832.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Zatsiorsky V, Seluyanov V. The mass and inertia characteristics of the main segments of the human body, in Proceedings. Int Cong Biomech 1983;A–B:11521159.

    • Search Google Scholar
    • Export Citation
  • 17.

    Buckley DC, Kudsk KA, Rose BS, et al. Anthropometric and computerized tomographic measurements of lower extremity lean body mass. J Am Diet Assoc 1987;87:196199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Martin PE, Mungiole M, Marzke MW, et al. The use of magnetic resonance imaging for measuring segment inertial properties. J Biomech 1989;22:367376.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19.

    Pearsall DJ, Reid JG, Livingston LA. Segmental inertial parameters of the human trunk as determined from computed tomography. Ann Biomed Eng 1996;24:198210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Cheng CK, Chen HH, Chen CS, et al. Segment inertial properties of Chinese adults determined from magnetic resonance imaging. Clin Biomech (Bristol, Avon) 2000;15:559566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Durkin JL, Dowling JJ, Andrews DM. The measurement of body segment inertial parameters using dual energy x-ray absorptiometry. J Biomech 2002;35:15751580.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Durkin JL, Dowling JJ. Analysis of body segment parameter differences between four human populations and the estimation errors of four popular mathematical models. J Biomech Eng 2003;125:515522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Dempster WT, Sherr LA, Priest JG. Conversion scales for estimating humeral and femoral lengths and the lengths of functional segments in the limbs of American Caucasoid males. Hum Biol 1964;36:246262.

    • Search Google Scholar
    • Export Citation
  • 24.

    Hanavan EP. A mathematical model of the human body. AMRL technical report, AMRL-TR-64–102. Wright-Patterson Air Force Base, Ohio: Behavioral Sciences Laboratory, 1964;64102.

    • Search Google Scholar
    • Export Citation
  • 25.

    Jensen RK. Estimation of the biomechanical properties of three body types using a photogrammetric method. J Biomech 1978;11:349358.

  • 26.

    Hatze H. A mathematical model for the computational determination of parameter values of anthropomorphic segments. J Biomech 1980;13:833843.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Hinrichs RN. Regression equations to predict segmental moments of inertia from anthropometric measurements: an extension of the data of Chandler et al (1975). J Biomech 1985;18:621624.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Morlock M, Yeadon MR. Regression equations for segmental inertia parameters, in Proceedings. North Am Cong Biomech, 1986;231234.

  • 29.

    Ackland TR, Blanksby BA, Bloomfield J. Inertial characteristics of adolescent male body segments. J Biomech 1988;21:319327.

  • 30.

    Yeadon MR, Morlock M. The appropriate use of regression equations for the estimation of segmental inertia parameters. J Biomech 1989;22:683689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Schneider K, Zernicke RF. Mass, center of mass, and moment of inertia estimates for infant limb segments. J Biomech 1992;25:145148.

  • 32.

    Pavol MJ, Owings TM, Grabiner MD. Body segment inertial parameter estimation for the general population of older adults. J Biomech 2002;35:707712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33.

    Shan G, Bohn C. Anthropometrical data and coefficients of regression related to gender and race. Appl Ergon 2003;34:327337.

  • 34.

    Buchner HH, Savelberg HH, Schamhardt HC, et al. Inertial properties of Dutch Warmblood horses. J Biomech 1997;30:653658.

  • 35.

    Nielsen C, Stover SM, Schulz KS, et al. Two-dimensional linksegment model of the forelimb of dogs at a walk. Am J Vet Res 2003;64:609617.

  • 36.

    Colborne GR, Innes JF, Comerford EJ, et al. Distribution of power across the hind limb joints in Labrador Retrievers and Greyhounds. Am J Vet Res 2005;66:15631571.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37.

    Zatsiorsky V. Inertial properties of the human body. In: Kinetics of human motion. Champaign, Ill: Human Kinetics, 2002;265364.

  • 38.

    Staniar WB, Kronfeld DS, Hoffman RM, et al. Weight prediction from linear measures of growing Thoroughbreds. Equine Vet J 2004;36:149154.

    • Search Google Scholar
    • Export Citation
  • 39.

    Young JW, Chandler RF, Snow CC. Anthropometric and mass distribution characteristics of the adult female. FAA-AM-83–16. Oklahoma City, Okla: FAA Civil Aeromedical Institute, 1983;169.

    • Search Google Scholar
    • Export Citation
  • 40.

    Whitehair JG, Vasseur PB, Willits NH. Epidemiology of cranial cruciate ligament rupture in dogs. J Am Vet Med Assoc 1993;203:10161019.

  • 41.

    Reif U, Probst CW. Comparison of tibial plateau angles in normal and cranial cruciate deficient stifles of Labrador Retrievers. Vet Surg 2003;32:385389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42.

    Monk ML, Preston CA, McGowan CM. Effects of early intensive postoperative physiotherapy on limb function after tibial plateau leveling osteotomy in dogs with deficiency of the cranial cruciate ligament. Am J Vet Res 2006;67:529536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Colman RJ, McKiernan SH, Aiken JM, et al. Muscle mass loss in rhesus monkeys: age of onset. Exp Gerontol 2005;40:573581.

  • 44.

    Marcell TJ. Sarcopenia: causes, consequences, and preventions. J Gerontol A Biol Sci Med Sci 2003;58:M911M916.

  • 45.

    Braund KG, McGuire JA, Lincoln CE. Observations on normal skeletal muscle of mature dogs: a cytochemical, histochemical, and morphometric study. Vet Pathol 1982;19:577595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Rumph PF, Kincaid SA, Visco DM, et al. Redistribution of vertical ground reaction force in dogs with experimentally induced chronic hindlimb lameness. Vet Surg 1995;24:384389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47.

    Doverspike M, Vasseur PB, Harb MF, et al. Contralateral cranial cruciate ligament rupture: incidence in 114 dogs. J Am Anim Hosp Assoc 1993;29:167170.

    • Search Google Scholar
    • Export Citation
  • 48.

    Duval JM, Budsberg SC, Flo GL, et al. Breed, sex, and body weight as risk factors for rupture of the cranial cruciate ligament in young dogs. J Am Vet Med Assoc 1999;215:811814.

    • Search Google Scholar
    • Export Citation
  • 49.

    Moore KW, Read RA. Cranial cruciate ligament rupture in the dog—a retrospective study comparing surgical techniques. Aust Vet J 1995;72:281285.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 50.

    Rodrigue D, Gagnon M. The evaluation of forearm density with axial tomography. J Biomech 1983;16:907913.

  • 51.

    Riemer R, Hsiao-Wecksler ET, Zhang X. Uncertainties in inverse dynamics solutions: a comprehensive analysis and an application to gait. Gait Posture 2007;27:578588.

    • Search Google Scholar
    • Export Citation
  • 52.

    Pearsall DJ, Costigan PA. The effect of segment parameter error on gait analysis results. Gait Posture 1999;9:173183.

  • 53.

    Rao G, Amarantini D, Berton E, et al. Influence of body segments' parameters estimation models on inverse dynamics solutions during gait. J Biomech 2006;39:15311536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 54.

    Lanovaz JL, Clayton HM. Sensitivity of forelimb swing phase inverse dynamics to inertial parameter errors. Equine Vet J Suppl 2001;33:2731.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 43 0 0
Full Text Views 680 524 19
PDF Downloads 188 110 3
Advertisement