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SliceOmatic comes with a very user-friendly interface and includes technical support, but contains costly start-up and yearly licensing fees. In addition, SliceOmatic allows the user to define HU ranges to delineate tissues such as fat, muscle, and bone. On the other hand, NIH Image J is freely available, but is limited to online developer resources making it difficult for a new user to effectively use the software. The NIH Image J website offers an online instruction manual for the various functions found within the software but none to our knowledge related to muscle size and composition determination.

Typically, investigators using Image J must segment the region of interest by carefully tracing along the perimeter.


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Raw data pixel counts from the bit user-traced region of interest is then exported into an excel program with automated area calculations for the determination of each tissue type based on pre-defined HU ranges using the histogram function 7—9. However, details of this process have not been fully described nor standardized. Here, we provide a step-by-step process for analyzing single-slice CT images of the mid-thigh using NIH Image J for the measurement of muscle and fat areas as well as mean attenuation in each soft tissue depot. This technique was chosen for three reasons: 1 to essentially eliminate inter-observer error; 2 to ensure correct and proper image calibrations within Image J rather than calculating it in an excel file; and 3 to threshold or segment HU ranges in an automated fashion within the program similar to SliceOmatic.

Previously reported inter-observer coefficients of reliability between softwares are very high with values normally ranging from 0. By utilizing the wand tool as long as there is separation between the thighs , manual tracing differences between investigators are eliminated. Furthermore, Image J should automatically calibrate images to the proper scale based on the pixel width and pixel area; this eliminates the need to change pixel width in pre-set excel files as this could drastically affect calculated CSA data if it is not corrected for the field of view FOV of the obtained image.

While we specifically measure the total mid-thigh region, this tutorial can be applied to individual muscle groups of the thigh, such as the vastus lateralis.

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More research is required to determine if this protocol will accurately quantify other anatomical regions of interest. Large standardized reference data sets for both CSA and attenuation values of various anatomical regions across different populations according to age, sex, and race are needed. We provide standard reference CSA and attenuation values of a large dataset of relatively healthy older adults at the mid-thigh. Our data seem to be in line with those that have previously reported total thigh areas and total thigh muscle densities in a large older population cohort [ 14 ].

Additionally, we present the data in such a way that provides context to methodological considerations and discrepancies found among different investigations in hopes that a standardized procedure and consensus can be developed. Aubrey et al. Others have defined skeletal muscle within a smaller range of 0— HU [ 6 , 15 — 17 ].

This method ultimately leaves the HU range of to -1 undefined and unmeasured which, in the abdomen, may account for as much as In our sample, we found that to -1 HU range accounted for roughly 3. Dennis et al. However, this HU range seems to be in locations of transition such as around the edges of the skin and the muscle fascia calling into question the usefulness of including this transitional range.

However, HDM is significantly impacted by resistance training providing a rationale for including this as muscle area [ 8 ]. Higher percentages may be seen in younger, more athletic populations and needs further investigation. Mean attenuation of the muscle from CT has become a vital indicator of health related outcomes and prognosis. Goodpaster et al. Since then, several factors, such as age, obesity, diabetes, and surgical procedures are associated with reductions in muscle attenuation by 3—15 HU, but can be improved through strength and endurance training [ 9 ]. More recently, skeletal muscle attenuation has been examined in clinical populations, related to cancer progression and survival [ 18 , 19 ].

The utility of muscle attenuation measured by CT to provide insight into physical function, metabolism, and disease requires further study. A limitation of this study is that CT scans were obtained from an older population that was generally healthy, predominately white, and had mostly normal or overweight BMIs. Generalizations to younger adults, or older adults with health conditions associated with obesity should be taken with caution as higher fat depots introduce measurement error.

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However, significant bias was not seen with abdominal visceral fat measures in abdominally obese individuals using either software package [ 12 ]. In addition, a general weakness of performing a single slice CT scan is that a detailed and precise standard operating procedure must be in place to position the scanner in order to get reliable, longitudinal values. To increase reliability, a scout scan should be performed so that anatomical landmarks of the femur can be used to determine the thigh midpoint.

The tissue density range used to define muscle and fat varies between studies and areas of intermediate density are often omitted. This CT tutorial provides valuable step-by-step instructions for using free, but technically challenging, software to quantify both muscle size and composition in the mid-thigh, which was validated against a robust commercially available software. Image J and SliceOmatic generate similar results for the measurement of muscle and fat of all densities at the mid-thigh.

We show that by evaluating individual HU sub-ranges, as much as 7. Thus, we recommend all individual HU ranges be evaluated as interests dictate to be the most inclusive of skeletal muscle and to determine responses due to aging and diseases processes, nutritional intervention, or exercise training. Browse Subject Areas?

Click through the PLOS taxonomy to find articles in your field. CT acquisition CT images were obtained without contrast using a soft tissue algorithm, kVp and mA, x matrix, and a field of view FOV adjusted to capture the entire thigh of both legs. Establishment of Hounsfield Unit HU ranges CT scanners are calibrated under the assumption that the HU attenuation value of 0 and are water and air, respectively.

Download: PPT. Fig 1. Graphical representation of various Hounsfield Unit ranges used for determining fat and skeletal muscle.

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Statistical analysis Statistics were performed on subjects with baseline CT images to assess the mean, standard deviation, and range of mid-thigh CT characteristics as well as age, BMI, and percent body fat determined by dual energy x-ray absorptiometry DXA. Table 1. Baseline demographics and computed tomography characteristics of study subjects. Percent contribution of each Hounsfield Unit HU range to the total mid-thigh CSA Table 2 shows the relative contribution of individually defined soft tissue depots.


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Table 2. Percent contribution of defined HU ranges to the overall total area of the mid-thigh.

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NIH Image J vs. Fig 2.

Bland-Altman plots of the individual subject differences between softwares for various cross sectional areas. Table 3. Discussion CT quantifies a variety of unique body composition characteristics in physiology research. Limitations A limitation of this study is that CT scans were obtained from an older population that was generally healthy, predominately white, and had mostly normal or overweight BMIs.

Conclusion The tissue density range used to define muscle and fat varies between studies and areas of intermediate density are often omitted. Supporting information. S1 Table. Step-by-step methods for utilizing Image J to assess cross-sectional area and mean attenuation of the thigh.

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S2 Table. Generation of a macro to run semi-automated analysis of body composition. S1 File. CT Image. S2 File. CT analysis of S1 file. S3 File. NIH Image J dataset. References 1. Programs focus on classical physics and modern physics, with topics ranging from relativity to antimatter, quantum mechanics, physical uncertainty, quantum computing and more.

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