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Published online before print July 24, 2003, 10.1148/radiol.2283020095
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(Radiology 2003;228:857-863.)
© RSNA, 2003

Technical Developments

Determination of Height, Weight, Body Mass Index, and Body Surface Area with a Single Abdominal CT Image1

Estella M. Geraghty, MD, MS and John M. Boone, PhD

1 From the Department of Radiology, University of California Davis Medical Center, 4701 X St, Sacramento, CA 95817-2205. Received February 21, 2002; revision requested April 18; final revision received October 29; accepted November 5. Address correspondence to J.M.B. (e-mail: jmboone@ucdavis.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 Materials and Methods
Results
 Discussion
 REFERENCES
 
Techniques for estimation of an individual’s height, weight, body mass index (BMI), and body surface area (BSA) with a single abdominal computed tomographic (CT) image were developed. Eighty-seven abdominal CT examinations performed in adult humans were analyzed. Anatomic structures were outlined on the CT section that included L1. Multiple linear regression analysis was used to derive sex-specific predictive equations. Correlation for height was good (r > 0.65). Relationship between predicted weight and actual weight was good (r > 0.93). For BMI and BSA, r was greater than 0.893 and greater than 0.895, respectively. In this study, predictive equations for height, weight, BMI, and BSA were generated.

© RSNA, 2003

Index terms: Abdomen, CT, 70.1211 • Computed tomography (CT), utilization, 70.1211


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
Materials and Methods
Results
 Discussion
 REFERENCES
 
The technology for image acquisition in computed tomography (CT) continues to improve, and as a result, use of CT is increasing. The large image data sets hold enormous potential for computer-automated extraction of information relevant to the radiologist’s interpretation and diagnosis. In recognition of this potential, many researchers are working on the development of algorithms to locate organs in situ, to detect their boundaries, and to calculate their volumes (1–4).

Organ volume is an important diagnostic criterion because a change in volume is often associated with abnormality (5–8). The use of CT to measure various organ sizes has been shown to be accurate (6,8–10). However, organ volumes must be related to an individual’s age, sex, and body habitus for a more precise interpretation of abnormality (8,11–14). In CT data sets, age and sex are usually included in the Digital Imaging and Communications in Medicine header information. Though the Digital Imaging and Communications in Medicine standard allows entry of a patient’s physical characteristics, such as height and weight, this is not typically done, and these measures must be determined subjectively as the radiologist views the images.

In this study, our purpose was to develop techniques for estimation of an individual’s height, weight, body mass index (BMI), and body surface area (BSA) with a single abdominal CT image.


   Materials and Methods
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 REFERENCES
 
Subject Selection
By using the picture archiving and communication system (Isite Radiology; Stentor, Brisbane, Calif) at our institution, 256 human abdominal CT examinations were consecutively selected to provide a large enough sample size after anticipated exclusions. These CT examinations were performed for routine clinical evaluation, and we used the patient’s images with proper institutional review board authority. On the basis of this institutional review board protocol, patient informed consent was not required. The patient population included inpatients and outpatients who were being examined for a number of maladies ranging from trauma to cancer.

A retrospective chart review was performed to obtain each patient’s height and weight and the date on which these parameters were recorded. It was not known whether the height and weight values were measured, estimated, or provided by the patient or the patient’s family. When available, multiple recordings were sought to assess the closeness of the numbers and add validity to the data. The numbers recorded nearest the date that the patient’s CT scan was obtained were used, and these numbers were converted to their metric equivalents (90% range for height = 267 days, median = 9 days; 90% range for weight = 118 days, median = 5 days). From these numbers, BMI and BSA were calculated (15) as follows: BMI = weight/height2, where BMI is measured in kilograms per square meter, weight is measured in kilograms, and height is measured in meters, and BSA = weight0.425 x height0.725 x 0.007184, where BSA is measured in square meters, weight is measured in kilograms, and height is measured in centimeters. Patients who did not have both height and weight recorded in their charts were excluded from the study. Patients younger than 18 years old, whose axial skeletons may still have been growing, were also excluded. Patients whose CT scans showed abnormal features, such as skin defects, or had bilaterally cut off edges that could influence the various measurements of this study were also eliminated. On the basis of these selection criteria, 87 subjects remained in the study.

CT Examinations
All patients underwent scanning with either a multisection scanner (Lightspeed QX; GE Medical Systems, Waukesha, Wis) or a single-section scanner (CT/i; GE Medical Systems). Scanning parameters varied. In all patients, scanning was performed at 120 kV and 160–630 mAs, and the field of view ranged from 30.2 to 49.9 cm. Section thickness also varied from 5.0 to 7.5 mm in all subjects. Seventy-eight of the examinations were contrast material enhanced, whereas nine were not.

Analysis of Images
The abdominal CT images obtained in the 87 subjects were downloaded from our research picture archiving and communication system (eFilm; University of Toronto, Ontario, Canada) and were transferred to a personal computer. Studies were viewed with this picture archiving and communication system for retrieval of Digital Imaging and Communications in Medicine header information (ie, age, sex, display field of view, and section thickness), which was subsequently recorded by one of us (E.M.G.) on a spreadsheet. The image files were saved sequentially with the extension "*.dcm." Images were displayed on a computer monitor with a resolution of 1,280 x 1,024. Custom mouse-and-cursor software, written in C (C/C++ 5.0; Microsoft, Redmond, Wash) with a software program platform (Windows 2000, Microsoft), enabled hand outlining of the regions of interest (ROIs). Each image was magnified by a factor of two during the outlining process. Window width and window level settings were changeable in the custom software, but settings were typically close to a window width of 400 HU and a window level of 30 HU. All outlining was performed by one investigator (E.M.G.) who was trained by a radiologist to recognize the relevant ROIs.

The first lumbar vertebra, or L1, was located on each image. The centralmost section through L1 was found. All outlining took place at this level (on this single image) by using 10-pixel-long (5.92–9.77 mm) line segments to trace anatomic boundaries (Fig 1). Karantanas et al (16) showed that a statistically significant correlation exists between somatometric parameters (ie, height and weight) and various vertebral indices. We chose to circumscribe L1, with a dorsal cutoff through the pedicles at the widest diameter of the spinal canal. The areas of these two ROIs were calculated by using known pixel dimensions for each image. From the outline data, the analytic software computed the greatest vertebral anteroposterior diameter, or L1APD, and greatest transverse diameter, or L1TD.



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  		Figure 1. Diagrammatic representation of outlined ROIs and other measured anthropometric parameters. Outermost tracing represents the circumference of the body. Interior to that is the outline defined as intraabdominal area. Spinal canal is circumscribed, and its widest transverse dimension is used as the dorsal cutoff for tracing of the first lumbar vertebral body. BAPD = anteroposterior body diameter, BTD = transverse body diameter, L1APD = greatest L1 vertebral anteroposterior diameter, L1TD = greatest L1 transverse diameter.

 
Published research in the anthropometric literature pertaining to body habitus led us to also consider other potential ROIs (17–21). The outer body perimeter was traced, which led to an automated calculation of body area, or BA, as well as body circumference, or BC. Since body shapes are unique, circumference was determined by using the trace length as a metric distance around the patient’s body. To determine the area of the subcutaneous fat, or SQA, an intraabdominal region, or IAA, was circumscribed by hand tracing between the abdominal musculature and its fatty perimeter. The subcutaneous fat area was computed as the difference between the total body area and the intraabdominal area, or SQA = BA - IAA. An additional calculation was performed to obtain the ratio of subcutaneous fat area to total body area, or SQR, and this calculation was as follows: SQR = SQA/BA. Again, with the outline data, additional measurements were achieved. The patient’s anteroposterior body diameter, or BAPD, and transverse diameter, or BTD, at their greatest distances were thereby recorded. Table 1 summarizes the measured parameters just described.


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  		TABLE 1. Measured and/or Calculated Anthropometric Parameter Definitions

 
Seven of the studies contained images that were slightly cut off on one side. In these cases, the perimeter was estimated by the observer who was outlining the images, with the assumption that both sides were symmetric.

Reproducibility
Reproducibility in outlining ROIs was tested. Images obtained in five CT examinations were reevaluated, and new ROIs were traced on the images. These data were used to assess the precision (reproducibility) of the manual outlining procedure.

To estimate the accuracy involved in outlining ROIs, scanning was performed with the multisection CT scanner and three polymethyl methacrylate phantoms that were 20, 25, and 32 cm in diameter and that were already available in our laboratory. Each phantom contained a central 13-mm-diameter hole. Both the outer perimeters of the cylinder and the margin of the hole were outlined (E.M.G.), and circumferences were calculated. Scanning parameters were the same for each phantom: 120 kV, 200 mA, and 1.0-second exposure time. The field of view was adjusted for the size of each phantom. Transverse images were obtained with no table increment and were reconstructed by using a standard reconstruction filter, which was part of the multisection CT scanner.

Statistical Analysis
The strength of the association between height and weight data from patient charts (and their calculated BMI and BSA) and the various measurements on the CT image was evaluated by using multiple linear regression analysis. A commercial statistical software program (Sigma Stat; Jandel Scientific, Corte Madera, Calif) was used for this purpose. The best subset regression approach was used for this research. The variance inflation factor was used as a measure of multicollinearity, and subset models were selected that reduced multicollinearity to the extent possible. In this manner, predictive equations for height, weight, BMI, and BSA were developed. We initially attempted to combine data sets for men and women, with a binary input parameter that indicated sex. This approach did not produce satisfactory results, and thus we performed the analysis independently for each sex.

To estimate the error in the predictive equations for height, weight, BMI, and BSA, the leave-one-out cross-validation technique was used. In this method, the data were divided into n subsets, where n - 1 data were designated as a training set and the left-out independent value was used for testing. The process was repeated so that each data point was included in the testing. The resulting data were used to derive the SD from the mean.


   Results
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
Discussion
 REFERENCES
 
The reproducibility of our manual outlining technique was confirmed to a maximum error of 1.35%, which was calculated as (1,369 x 100)/101,440, for the total body area. The calculated outer circumferences of the polymethyl methacrylate cylinders ranged in error from 0.14%, which was calculated as (0.088 x 100)/62.83, to 0.44%, which was calculated as (0.350 x 100)/78.89, for circumferences ranging from 20 to 32 cm. For the much smaller 13-mm-diameter hole, the calculated circumference had an error of 2.47%, which was calculated as (0.101 x 100)/4.08407.

The mean, SD, ranges, and sex comparisons for the physical characteristics of the 87 subjects in this study are shown in Table 2. As expected, men were, on average, taller and heavier than were women and had a greater BSA. However, the mean BMI between the sexes was equivalent (26.1 kg/m2 for men vs 26.4 kg/m2 for women). The groups were also well balanced with regard to age.


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  		TABLE 2. Physical Characteristics of 87 Adult Subjects

 
The results of multiple linear regression analysis showed, in general, that the more parameters used in forming predictive equations, the better the correlation. It became evident that to facilitate usability of the equations, and in consideration of future automation of the process, a smaller subset of parameters was more practical. Therefore, we obtained the results of a best subset multiple linear regression analysis in the form of predictive equations for height, weight, BMI, and BSA. These equations are shown with their corresponding graphs in Figures 2 –5. Addition of other anthropomorphic parameters did not significantly reduce variance.



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  		Figure 2a. Scatterplots show the relationship between the patient’s actual height and calculated height derived from predictive equations (black squares). Calculated values where the balance of the data comprised the training set (gray circles) are also represented. Correlation coefficient from the multiple linear regression analysis is reported. Line of identity is depicted, and the SD is presented as calculated from the leave-one-out analysis. (a) Graph shows height in women. Predictive equation for height in women, or heightW, is as follows: heightW = -0.103177 + (0.0254 · SCA) - (0.000696 · age) + (0.015441 · BTD) + (0.196566 · L1TD) + (0.117566 · L1APD), where height is measured in meters. (b) Graph shows height in men. Predictive equation for height in men, or heightM, is as follows: heightM = 1.134047 + (0.083556 · L1A) + (0.143212 · SCA) - (0.004139 · age) - (0.171034 · L1TD), where height is measured in meters.

 


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  		Figure 2b. Scatterplots show the relationship between the patient’s actual height and calculated height derived from predictive equations (black squares). Calculated values where the balance of the data comprised the training set (gray circles) are also represented. Correlation coefficient from the multiple linear regression analysis is reported. Line of identity is depicted, and the SD is presented as calculated from the leave-one-out analysis. (a) Graph shows height in women. Predictive equation for height in women, or heightW, is as follows: heightW = -0.103177 + (0.0254 · SCA) - (0.000696 · age) + (0.015441 · BTD) + (0.196566 · L1TD) + (0.117566 · L1APD), where height is measured in meters. (b) Graph shows height in men. Predictive equation for height in men, or heightM, is as follows: heightM = 1.134047 + (0.083556 · L1A) + (0.143212 · SCA) - (0.004139 · age) - (0.171034 · L1TD), where height is measured in meters.

 


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  		Figure 3a. Scatterplots show good correlation between the actual patient weight derived from hospital medical records and the calculated weight derived from predictive equations. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows weight in women. Predictive equation for weight in women, or weightW, is as follows: weightW = -71.039024 + (1.520375 · BC) - (0.223321 · age) + (3.958301 · L1APD), where weight is measured in kilograms. (b) Graph shows weight in men. Predictive equation for weight in men, or weightM, is as follows: weightM = -54.856007 + (1.985658 · L1A) + (3.784671 · SCA) + (0.103460 · SQA) - (0.302853 · age) + (2.987843 · BTD), where weight is measured in kilograms.

 


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  		Figure 3b. Scatterplots show good correlation between the actual patient weight derived from hospital medical records and the calculated weight derived from predictive equations. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows weight in women. Predictive equation for weight in women, or weightW, is as follows: weightW = -71.039024 + (1.520375 · BC) - (0.223321 · age) + (3.958301 · L1APD), where weight is measured in kilograms. (b) Graph shows weight in men. Predictive equation for weight in men, or weightM, is as follows: weightM = -54.856007 + (1.985658 · L1A) + (3.784671 · SCA) + (0.103460 · SQA) - (0.302853 · age) + (2.987843 · BTD), where weight is measured in kilograms.

 


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  		Figure 4a. Scatterplots show correlation between actual BMI when calculated from the patient’s height and weight versus calculated BMI derived from predictive equations with CT data. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows BMI in women. Predictive equation for BMI in women, or BMIW, is as follows: BMIW = -9.163352 + (0.252992 · BC) + (10.621081 · SQR) - (0.080649 · age) + (0.597135 · BAPD), where BMI is measured in kilograms per square meter. (b) Graph shows BMI in men. Predictive equation for BMI in men, or BMIM, is as follows: BMIM = 2.069055 + (0.037443 · SQA) - (0.050594 · age) + (0.984937 · BTD) - (2.647949 · L1APD), where BMI is measured in kilograms per square meter.

 


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  		Figure 4b. Scatterplots show correlation between actual BMI when calculated from the patient’s height and weight versus calculated BMI derived from predictive equations with CT data. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows BMI in women. Predictive equation for BMI in women, or BMIW, is as follows: BMIW = -9.163352 + (0.252992 · BC) + (10.621081 · SQR) - (0.080649 · age) + (0.597135 · BAPD), where BMI is measured in kilograms per square meter. (b) Graph shows BMI in men. Predictive equation for BMI in men, or BMIM, is as follows: BMIM = 2.069055 + (0.037443 · SQA) - (0.050594 · age) + (0.984937 · BTD) - (2.647949 · L1APD), where BMI is measured in kilograms per square meter.

 


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  		Figure 5a. Scatterplots show relationship between the actual BSA as calculated from patient height and weight versus BSA derived from predictive equations. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows BSA in women. Predictive equation for BSA in women, or BSAW, is as follows: BSAW = -0.724995 + (0.019472 · BC) - (0.003817 · age) +(0.231837 · L1TD), where BSA is measured in square meters. (b) Graph shows BSA in men. Predictive equation for BSA in men, or BSAM, is as follows: BSAM = -0.615293 + (0.041699 · L1A) + (0.088085 · SCA) - (0.006687 · age) + (0.062063 · BTD), where BSA is measured in square meters.

 


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  		Figure 5b. Scatterplots show relationship between the actual BSA as calculated from patient height and weight versus BSA derived from predictive equations. Black squares are parameter calculated with equations derived from all data points, and gray circles are parameter calculated with leave-one-out method. (a) Graph shows BSA in women. Predictive equation for BSA in women, or BSAW, is as follows: BSAW = -0.724995 + (0.019472 · BC) - (0.003817 · age) +(0.231837 · L1TD), where BSA is measured in square meters. (b) Graph shows BSA in men. Predictive equation for BSA in men, or BSAM, is as follows: BSAM = -0.615293 + (0.041699 · L1A) + (0.088085 · SCA) - (0.006687 · age) + (0.062063 · BTD), where BSA is measured in square meters.

 
Figure 2 illustrates the relationship between the actual recorded height of women and men versus the height calculated from the best subset equations. The results of the leave-one-out analysis are shown for each point during its omission in the cross-validation procedure. In both men and women, height could be accurately estimated from age, spinal canal area, and the transverse diameter of the first lumbar vertebra. There was a small contribution to height prediction accuracy from the transverse diameter of the body in women. The height in men was predicted more accurately by including the cross-sectional area of L1, whereas the anteroposterior diameter of L1 was a better complementary parameter for height prediction in women.

Prediction of weight in our population of 87 subjects provided good correlation for both sexes, even though the parameters used for equations for each sex were very different. Weight in men was accurately predicted from age, L1 cross-sectional area, the spinal canal area, the subcutaneous fat area, and the transverse diameter of the body (r = 0.956). The weight equation for women included only age, body circumference, and the anteroposterior diameter of L1 to achieve a correlation of r = 0.932 (Fig 3).

For the comparison of actual versus calculated BMI and BSA values (Figs 4 and 5, respectively), which are functions of height and weight, better correlation was achieved than was achieved with height alone, but worse correlation was achieved than was achieved with weight alone. Prediction of BMI in women relied only on age and soft-tissue parameters (ie, body circumference, subcutaneous fat percentage, and body anteroposterior diameter) (r = 0.919). BMI in men included both soft-tissue and vertebral indices with age (ie, subcutaneous fat area, transverse diameter of the body, and the anteroposterior diameter of L1) (r = 0.893). The best estimation of BSA in women included age, body circumference, and the L1 transverse diameter (r = 0.897). To accurately predict BSA in men, the clinically important parameters included age, the cross-sectional area of L1, the spinal canal area, and the transverse diameter of the body (r = 0.895).

On Figures 2–5, the SD that resulted from the leave-one-out analysis is shown. P values that were based on a paired t test comparison of measured versus calculated height, weight, BMI, and BSA were also calculated, and these values indicated that the calculated values were statistically indistinguishable from the actual measurements (P < .01 in all cases). The mean error was also calculated as a part of the validation technique (Table 3).


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  		TABLE 3. Mean Error and Significance

 

   Discussion
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
REFERENCES
 
Advances in digital imaging techniques have opened the door for computerized analysis of imaging data. However, there are many challenges when heuristic medical interpretation skills are replaced with quantitative algorithms that the computer can understand.

In this study, parameters obtained with a single CT image were identified that are useful for estimation of height, weight, BMI, and BSA of both male and female patients. In addition, predictive equations were validated by using the leave-one-out technique, which indicated that the mean error was minimal and the SD showed good precision. The future automation of parameter extraction was considered, and thus simplicity was emphasized where possible. All parameters can be determined with a single CT image. Other investigators have focused on the lower lumbar vertebrae (L2–L4) (16,20,22). However, we chose L1 as our anatomic landmark for four reasons: Findings of early work on organ volume calculation, with cross-sectional imaging, indicated that normalizing data to indices that were based on L1 accounted for body habitus (14,23). L1 is easily identifiable by human observers and is likely to be of only moderate difficulty to locate automatically. Variance in the orientation of L1 changes the area and diameter results minimally (eg, a 10° change would lead to a 1.5% difference in area). Almost all abdominal CT studies include L1.

The potential complexity related to automation of the detection of ROIs was considered. With regard to L1, its consistent position and high density should make it easy to locate. Border detection is unlikely to be difficult, particularly since the dorsal boundary was defined to be at the pedicles where the transverse diameter of the spinal canal is greatest. Similarly, determination of the body circumference, defined by the contrast of air to soft tissue, should be fairly simple. As previously mentioned, the anteroposterior diameters and transverse diameters of both the body and L1 vertebra were automatically extracted from the outlined ROIs. The most difficult parameter to acquire is likely to be the subcutaneous fat area, since this boundary between fat and muscle is low in contrast.

To summarize, interest in volumetric organ data has evolved since the advent of digital cross-sectional imaging technologies (5–10,14,23). However, this information is not widely usable unless it can be normalized to an individual’s age, sex, and body habitus (11,12). In this study, sex-specific predictive equations for basic somatometric parameters that defined body habitus were developed and evaluated; these equations provided predictive ability for important physical measures that defined patient size. Although there are clear limitations in regard to the accuracy of the proposed methods because of the relatively small sample size used (51 men and 36 women evaluated retrospectively), the proposed technique, which requires just one abdominal CT image, may prove useful in patient treatment when patient height and weight are not known.


   FOOTNOTES
 
Abbreviations: BMI = body mass index, BSA = body surface area, ROI = region of interest

Author contributions: Guarantor of integrity of entire study, J.M.B.; study concepts and design, E.M.G., J.M.B.; literature research, E.M.G.; experimental studies, E.M.G., J.M.B.; data acquisition, E.M.G.; data analysis/interpretation, E.M.G., J.M.B.; statistical analysis, E.M.G., J.M.B.; manuscript preparation, E.M.G.; manuscript definition of intellectual content, editing, revision/review, and final version approval, E.M.G., J.M.B.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 REFERENCES
 

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