The Authors are grateful to Dr. Federico Della Vecchia and Dr. Alessandro Cairo (University of Turin) for their valuable support with the manuscript preparation. This work was supported by grants from Fondazione CRT (Turin, Italy), the University of Turin (Fondo per la Ricerca Locale - ex-60%), and the National Institutes of Health (grant R01DK109008, Shape UP! Adults).

The protocol follows the research integrity guidelines of the Polytechnic of Turin21. The acquisition of optical images was performed within research studies approved by the local ethics committees (data are reported in the legends of Figure 1 and Figure 2) and the investigated subjects gave their written consent (for study participation and publication of anonymized images).
1. Subject preparation
NOTE: All prescanning preparations described in this section are similar between testing procedures #1 and #2.
<ol>
	<li>Ask the subject to be dressed in undergarments or to wear minimal form-fitting garments (shorts if male and shorts and sports bra if female), to remove socks, shoes, and accessories, and to wear a swim cap for hair coverage.</li>
</ol>
2. Subject registration for the testing procedure #1
<ol>
	<li>Measure the height of the subject using a standard stadiometer.</li>
	<li>Launch the app installed on the tablet (app #1 and tablet #1 in the Table of Materials) of system #1 that consists of a rotating platform with a weight measurement plate, three infrared cameras, and the tablet built into a tower (Figure 1A,B).</li>
	<li>Fill in the subject registration form (Supplemental Figure S1), including the first name, last name, email address, password, gender (select male or female), units of measurement (select US or metric), ethnicity (select one of the following groups: unspecified, Hispanic/Latino, American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, Caucasian), birth date.</li>
	<li>Flag the three checkboxes (acceptance of terms of service agreement, privacy policy, liability waiver), then tap the Submit button.</li>
	<li>Verify that the subject is wearing the swim cap properly, then tap the Next button.</li>
	<li>Verify that the subject is wearing proper attire, then tap the Next button.</li>
	<li>Verify that the scanner area is clear (i.e., keep clothing outside the scanning area and ensure that there is no sunlight or reflective material in the view of the scanner), then tap the Next button.</li>
	<li>Fill in the height measurement field, then tap the Next button.</li>
	<li>Fill in the fields related to optional additional metrics (body fat % [BF%], heart rate, systolic and diastolic blood pressure, intracellular and extracellular water), then tap the Submit button.</li>
</ol>
3. Testing procedure #1
<ol>
	<li>Ask the subject to step on the scale and stand as still as possible on the footprints (with upper limbs and hands on the two sides, without touching the telescoping handles) for 10 s to capture the body weight (and its distribution).</li>
	<li>Ask the subject to stand upright in a standardized A-pose (with shoulder relaxed and arms positioned straight and abducted from the torso) while grasping the telescoping handles to perform the body scanning according to the following instructions (Figure 1A,B). 
	NOTE: A full body scan takes ~45 s during which light-coding depth sensors capture the 3D shape as the platform rotates once around.
	<ol>
		<li>Lift the handles until the arms and legs are straight.</li>
		<li>Stay as still as possible.</li>
		<li>Keep the head still with eyes forward.</li>
		<li>Press and hold the buttons of the handles until the scan is complete.</li>
		<li>Step off the scale when the scan is complete.</li>
	</ol>
	</li>
	<li>After the subject steps off the scale, tap the Sign out button.</li>
</ol>
4. Subject registration for the testing procedure #2
<ol>
	<li>Measure the height and weight of the subject using a standard scale with a stadiometer.</li>
	<li>Using any modern web browser installed on a desktop or laptop computer, go to the dashboard of system #2 (registration dashboard in the Table of Materials).</li>
	<li>Fill in the subject registration form (Supplemental Figure S2), including the first name, last name, phone number, email address, age, gender (select male or female), weight, and height.</li>
	<li>Flag the checkbox (acceptance of terms of use and privacy policy), then tap or click the Sign up button to complete the account setup.</li>
	<li>Visualize the account view page showing the subject&#39;s unique quick response (QR) code, phone number, and email address.</li>
	<li>Take a picture (e.g., with a mobile phone) of the QR code.</li>
</ol>
5. Testing procedure #2
<ol>
	<li>Launch the app (app #2 in the Table of Materials) installed on the tablet of system #2 that consists of a tablet mounted on a holder (tablet #2 and floor stand in the Table of Materials).</li>
	<li>Tap the screen of the tablet and show the QR code.</li>
	<li>Tap the Start button.</li>
	<li>Ask the subject to listen to the audio instructions and watch the guide provided on the screen.</li>
	<li>Ask the subject to stand over a carpet at a standardized distance from the tablet, with the feet in the feet guide (black oval symbol over the carpet) aligned with the green feet marker shown on the screen (Figure 2A).</li>
	<li>Ask the subject to assume a &#34;front A-pose&#34; (and to maintain the pose without movements that can cause a malformed avatar)10,22 with legs separated, arms abducted from the torso at an approximate 45&#176; angle, and hands closed into fists to capture the frontal image (Figure 2B).</li>
	<li>After the frontal image capture, ask the subject to assume a &#34;side pose&#34; with feet together, arms/hands placed straight on the sides (i.e., arms/hands aligned with and against the trunk/thighs), and face straight forward to capture the lateral image (Figure 2C).</li>
	<li>After the lateral image capture, inform the subject the scan is complete (the app displays a Thank you screen).</li>
</ol>

3D Optical Imaging for Rapid Body Size and Composition Assessment

A.P. and S.B.H. are on the medical advisory board of Tanita Corporation. The remaining authors have no conflicts of interest to declare.

The procedures presented in this article can be used to evaluate body size, shape, and composition through two commercially available solutions for 3D optical imaging that have been previously developed and validated9,10,11,12,13,14,15,16,17,18,19,20. These solutions are simple to operate, and valid data can be quickly collected and automatically organized into a report. Moreover, the presented systems enable the collection of reproducible data (as suggested by the comparison of the results from the two scans performed with both systems in our two representative cases and documented by previous studies)9,10,11,12,13,14,15,16,17,18,19,20 and can therefore be used to monitor the training- or diet-induced changes.
As system #2 has a limited weight (~4 kg in total for tablet and holder), it is easily portable. However, a limitation of system #2 is that the generation of a 3D avatar from 2D images can produce 3D reconstructions that are less accurate than those obtained with system #1, especially in persons with obesity (as shown in the representative example of Figure 3 C,D) or in patients presenting localized abnormalities of the body shape (e.g., patients after bariatric surgery presenting troublesome skin excess or cancer patients with unilateral upper or lower limb lymphedema).
The availability of adequate space is critical for the scan acquisition with both systems: a clear area of 157 x 198 cm for system #1 and of 86 x 166 cm for system #2 is required. Moreover, system #2 requires the subject to be placed close to a blank wall without mirrors, glossy posters, or windows. Both systems require that no natural sunlight and no reflective surfaces should be in view of the cameras. Both systems also require a constant and consistent wi-fi internet connection to process scans effectively.
The main limitation of the above-described procedures is that they require the investigated subject to be able to assume the standing position. Therefore, these approaches cannot be used in severely ill patients (such as seriously impaired neurological patients or critically ill patients) who are unable to get out of bed. Moreover, the investigated subjects must be able to maintain the standing position (i.e., A-pose and side pose) without movements that can change the shape of the avatar10,22 and bias the estimation of body circumferences.
A limitation of the above-described parameters is that they are obtained using proprietary device-specific algorithms: this implies that the body size, shape, and composition measurements are unique to the particular scanning system. Therefore, comparing or pooling data acquired with different systems is precluded by analytical (i.e., between scanners) variability. Consistently, circumference measurements obtained in our representative two subjects shown in Figure 3 differed between the two systems. However, device-agnostic solutions have already been developed to overcome this limitation: these solutions reformat and edit the 3D mesh, then automatically detect different landmarks (such as armpits, crotch, and feet) and then calculate body size measurements28,29,30,31,32,33,34,35. Another limitation of the above-described body composition parameters is that they are obtained through conventional anthropometrics-based prediction models. However, recent studies showed that body shape-based models could be required to capture information about body composition beyond conventional anthropometric measurements36,37.
Despite some limitations, the digital anthropometric approach must be considered ready to be used in the clinical setting. 3D imaging systems provide non-invasive measurements that can be more acceptable compared to manual (tape-based and/or caliper-based) measurements that are based on the identification of anatomical landmarks through observation and palpation. Moreover, 3D optical scanning is also faster compared to other investigations (e.g., magnetic resonance imaging and dual-energy X-ray absorptiometry) commonly adopted in research and clinical settings for body size and composition assessment. In addition, as it is relatively inexpensive and radiation-free, it is safe to be used for subsequent scans (e.g., the image acquisition can easily and quickly be repeated if the experimenter notices body movements or an improper limb placement that can produce changes in the shape of the avatar) and for repeated investigations38 as well as safe to be used in special populations (such as children, adolescents, and pregnant women)35,39.
Clinicians could therefore implement the acquisition of innovative and useful biomarkers (&#34;e-tape&#34; measurements and derived body composition estimations) in routine evaluations of healthy subjects (e.g., athletes) to assist in predicting and characterizing their physical performance and injury risk40,41,42,43 as well as to monitor injury recovery.&#160;For example, leg strength and lean mass symmetry influence physical performance and (re-)injury risk44. Therefore, the recovery of a normal symmetry of the thigh/calf circumferences can be included among the general goals to consider for returning to play45.&#160;The routine evaluation of patients also could be improved by the integration of digital anthropometry into healthcare. The assessment of body circumferences and shape (that is driven by the internal distribution of soft and fat tissues) can be useful to detect the low mass muscle (e.g., in patients suspected to be sarcopenic), to predict the metabolic disease risk46, to assess the outcome of a surgical procedure, as well as to monitor the patient progress following an intervention38. Patients with diseases that have nutritional components as key contributors to their pathophysiology can specifically benefit from longitudinal monitoring of body size and composition to reduce symptoms and co-existing conditions47. For example, in the case of diet- and/or drug-based management of obesity, it may not be appropriate to only monitor weight because the well-known &#34;25/75 rule of thumb&#34; (i.e., the general assumption that weight loss is typically 25% fat-free mass loss and 75% fat loss) may not accurately describe intervention efficacy38 that could be unraveled by anthropometry-based assessment of the relative amount of muscle and fat loss. Furthermore, digital anthropometry, integrated into healthcare, has the potential to expand healthcare services to remote locations, thereby improving patient assistance and adherence and reducing healthcare costs.

Anthropometry is the study of the physical measures of the human body. Height, weight, lengths, skinfold thicknesses, and circumferences are commonly used anthropometric measures that proved to be useful for investigating patients with endocrine and metabolic disorders and for monitoring growth, aging, and body size and composition adaptations elicited by diet and training in athletes1,2. For example, the assessment of waist and hip circumferences proved to be useful for the management of persons with obesity: both circumferences assess the distribution of adiposity that can be considered a predictor of all-cause mortality3.
Limb circumferences are frequently assessed in rehabilitative and sports medicine because of their usefulness for detecting and/or monitoring the decrease in appendicular lean mass (e.g., calf circumference is used as a simple and practical skeletal muscle marker for diagnosing low skeletal muscle and sarcopenia)1,2 and the inter-limb asymmetry that impacts both physical performance and risk of injuries in athletes and quality of life in patients (e.g., cancer patients with unilateral extremity swelling)1,2. Further, a large number of anthropometrics-based body composition prediction models have been proposed over the last several decades to estimate the amount of fat mass or fat-free mass from a combination of different anthropometric measures such as body circumferences or skinfold thicknesses1,2,4,5,6,7.
Because conventional anthropometric (i.e., tape-based and caliper-based) measurements may not be culturally or socially acceptable and also exhibit poor reliability8, there was the need for the development and validation of non-invasive, reproducible, and valid approaches. Recently developed three-dimensional (3D) optical imaging systems enabled to provide non-invasive, precise, and accurate measurements8,9,10,11, as well as digital consumer cameras and smartphones offer easy-to-use and widely available tools suitable to be used in clinical and non-clinical settings to evaluate both patients and healthy subjects8,9,10,11,12,13,14,15,16,17,18,19,20. The aim of the protocol reported in the following section is to describe the procedures for evaluating body size, shape, and composition through two commercially available solutions for 3D optical imaging that became pervasive during the last years both in the healthcare setting (to evaluate patients) and in non-clinical settings (to evaluate athletes).

<table><tbody><tr><td>&lt;strong&gt;System #1&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Proscanner</td><td>Fit3D Inc., San Mateo, CA, USA</td><td>Version 5</td><td>&quot;System #1&quot; in the manuscript</td></tr><tr><td>Fit3D Proscanner app</td><td>Fit3D Inc., San Mateo, CA, USA</td><td>Version 5</td><td>&quot;App #1&quot; in the manuscript</td></tr><tr><td>CHUWI tablet PC</td><td>Chuwi Technology Co., Ltd., Shenzhen, CHINA</td><td>Hi10X</td><td>&quot;Tablet #1&quot; in the manuscript</td></tr><tr><td>Fit3D dashboard</td><td>Fit3D Inc., San Mateo, CA, USA</td><td></td><td>https://dashboard.fit3d.com</td></tr><tr><td>&lt;strong&gt;System #2&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Mobile Scanner 1 (MS-1) app</td><td>Size Stream LLC, Cary, NC, USA</td><td>Version 2</td><td>&quot;System #2&quot; in the manuscript</td></tr><tr><td>iPad</td><td>Apple Inc., Cupertino, CA, USA</td><td>9th generation</td><td>&quot;Tablet #2&quot; in the manuscript</td></tr><tr><td>iPad Floor Stand</td><td>Displays2go LLC, Fall River, MA, USA</td><td>SKU: TABFLATBBK</td><td>www.displays2go.com/P-29987/Universal-Tablet-Floor-Stand-Anti-Theft-Locking-Kit</td></tr><tr><td>Size Stream registration dashboard&amp;nbsp;</td><td>Size Stream LLC, Cary, NC, USA</td><td></td><td>https://measure.mobilefit.sizestream.com</td></tr><tr><td>Size Stream data download dashboard&amp;nbsp;</td><td>Size Stream LLC, Cary, NC, USA</td><td></td><td>https://data.mobilefit.sizestream.com</td></tr></tbody></table>

clinical anthropometrics and body composition from 3-dimensional optical imaging

The body size and composition assessment is commonly included in the routine management of healthy athletes as well as of different types of patients to personalize the training or rehabilitation strategy. The digital anthropometric analyses described in the following protocol can be performed with recently introduced systems. These new tools and approaches have the potential to be widely used in clinical settings because they are very simple to operate and enable the rapid collection of accurate and reproducible data. One system consists of a rotating platform with a weight measurement plate, three infrared cameras, and a tablet built into a tower, while the other system consists of a tablet mounted on a holder. After image capture, the software of both systems generates a de-identified three-dimensional humanoid avatar with associated anthropometric and body composition variables. The measurement procedures are simple: a subject can be tested in a few minutes and a comprehensive report (including the three-dimensional scan and body size, shape, and composition measurements) is automatically generated.

After the image capture, the software of system #1 generates a de-identified 3D humanoid avatar (Figure 1C: point clouds are converted to a mesh connected by triangles with approximately 25,000 vertices and 50,000 faces) and automated anthropometry, which includes lengths, circumferences, volumes, surface areas, and body composition estimates. The dashboard of system #1 enables for each subject to visualize (and download a report including) the 3D scan (Figure 1C), measurements of body weight, size, and shape (i.e., body shape rating, waist circumference, waist-to-hip ratio, trunk-to-leg volume ratio), estimates of basal metabolic rate and body composition (i.e., BF%, fat mass, lean mass), and standard circumference measurements (neck, bust, waist, hips, left and right biceps, left and right forearm, left and right thigh, left and right calf).
Moreover, the results of the posture and balance assessments can also be visualized and are included in the report. The posture assessment results include the front, side, and back views of the 3D scan with associated shift (defined as a part of the body that moves into a sloping position and the rate at which it slopes in any one direction) and tilt (defined as the &#34;sliding&#34; movement forward, backward, left or right, a slight change or variation in position from the center point) measurements: i) front and back views: shift measurements toward right or left with respect to the sagittal plane (represented as a vertical line between right and left hemisomes) and tilt percentages with respect to the transverse (horizontal plane) for head, shoulder, underbust, hip, knee; ii) side view: shift measurements forward or backward with respect to the frontal (coronal) plane (represented as a vertical line up from the ankle joint) for head, shoulder, hip, knee.
The balance assessment result includes the weight distribution during standing posture for the anterior and posterior regions of the right and left feet. The dashboard of system #1 also enables for each subject to download .OBJ and .GIF image files and a .CSV file with the anthropometric measurements and body composition estimates listed in Table 1.
All body composition estimates are obtained by using proprietary algorithms, with the exceptions of the basal metabolic rate estimation and of the body shape index calculation that are obtained, respectively, according to the Mifflin-St. Jeor equation23 and the Krakauer equation24 reported in Table 2.
After the image capture, the software of system #2 generates a de-identified 3D humanoid avatar (Figure 2D: point clouds are converted to a mesh connected by triangles with approximately 50,000 vertices and 100,000 faces) and automated anthropometry, which includes lengths, circumferences, volumes, surface areas, and body composition estimates. The dashboard of system #2 (data download dashboard in the Table of Materials) enables for each subject to download .OBJ and .PNG image files and the following three .CSV files:
The &#34;App Measures.csv&#34; file reports the following anthropometric and body composition measurements: weight, body surface area, BF%, visceral adipose tissue, fitness index, arms lean mass, legs lean mass, lean body mass, total bone mineral content, shoulder width, back shoulder width (through back neck), circumferences of neck, overarm, biceps (right/left), forearm (right/left), wrist (right/left), chest, underbust, bust (with drop), stomach, waist, paint waist, hips (taken 8 inches down from small of back), seat, thigh (right/left), calf (right/left), back-neck-to-waist length, sleeve length (right/left), crotch length, inseam, outseam (right/left).
The &#34;Body Composition.csv&#34; file reports the following anthropometric and body composition measurements: body fat, body mass index, body surface area, bone mineral content, fat mass index, fitness index, height, lean body index, lean body mass, arms lean mass, legs lean mass, resting metabolic rate, stomach circumference, visceral adipose tissue, waist-to-height ratio, waist-to-hip ratio, weight.
The &#34;Core Measures.csv&#34; file reports the anthropometric measurements listed in Table 1.
All body composition estimates are obtained by using proprietary algorithms, with the exceptions of the basal metabolic rate estimation and the BF% estimation that are obtained, respectively, according to the Katch-McArdle equation25 and to the two equations previously developed and validated by Harty et al.26. These two equations (BF% Equation 1 and Equation 2 in Table 2) are adopted, respectively, for individuals with lower abdominal circumference &#60;103.5 cm (&#60;40.75 inches) and &#8805;103.5 cm (&#8805;40.75 inches). Although previous studies demonstrated the accuracy of Equation 1 for BF% estimation in healthy adults15,26, we recently found that it overestimated (with respect to dual-energy X-ray absorptiometry) the BF% in young athletes18. Therefore, we proposed the reparameterization (Equation 3) reported in Table 2 to provide an accurate estimation of BF% in young soccer players of both sexes18.
In addition to the above-listed body composition variables, the appendicular lean mass (ALM) can also be estimated for the scans performed with system #2 through the device-specific equation recently proposed by McCarthy et al.27 for sedentary subjects (ALM Equation 1 for males and ALM Equation 2 for females in Table 2) that we adapted for ALM estimation in young athletes (ALM Equation 3 for males and ALM Equation 4 for females in Table 2)18.
Figure 3 shows representative avatars obtained in a male athlete (body mass index: 26.0 kg/m2: Figure 3A,B) and a person with obesity (body mass index: 44.0 kg/m2: Figure 3C,D) with the system #1 (Figure 3A,C) and with the system #2 (Figure 3B,D). The anthropometric measurements and body composition estimates obtained by system #1 and system #2 for two scans of both subjects are reported in Table 3.
Circumference measurements obtained in the athlete differed between the scans obtained with the two systems (especially for the neck, arms, hips, and legs: data are reported in Table 3). BF% values (averages of the two scans) were 19.1% and 16.1% for the scans performed with systems #1 and #2, respectively, and lean mass values (averages of the two scans) were 69.7 kg and 72.2 kg. The ALM average value for the scans performed with system #2 was 38.4 kg.
Circumference measurements obtained in the person with obesity differed between the scans obtained with the two systems (especially for waist and legs: data are reported in Table 3). BF% values (averages of the two scans) were 44.1% and 46.3% for the scans performed with systems #1 and #2, respectively, and lean mass values (averages of the two scans) were 67.9 kg and 64.9 kg. The ALM average value for the scans performed with system #2 was 25.1 kg.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66698/66698fig01.jpg" /> 
Figure 1: Images taken with system #1. (A,B) Standardized A-pose assumed and maintained by a representative male subject during the rotation of the platform and (C) the relative 3D avatar. Acquisition of optical images was performed within a research study approved by the ethics committee of the University of Turin (protocol n. 0115311). <a href="https://www.jove.com/files/ftp_upload/66698/66698fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66698/66698fig02.jpg" /> 
Figure 2: Images taken with system #2. (A) Image of a representative male subject standing over a carpet with the feet guide symbol (black oval symbol over the carpet) aligned with the green feet marker shown on the screen of the tablet of system #2. Acquisition of the (B) frontal and (C) lateral images in the representative subject and (D) the relative 3D avatar. Acquisition of optical images was performed within a research study approved by the ethics committee of the University of Turin (protocol n. 0115311). <a href="https://www.jove.com/files/ftp_upload/66698/66698fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66698/66698fig03.jpg" /> 
Figure 3: Representative avatars obtained with systems #1 and #2. (A,B) Male athlete and (C,D) a person with obesity investigated with (A,C) system #1 and (B,D) system #2. Each subject underwent two scans, with repositioning: the avatar obtained from the first scan was shown for both subjects, while the anthropometric and body composition estimates obtained by system #1 and system #2 for two scans of both subjects are reported in Table 3. Acquisition of optical images was performed within research studies approved by the ethics committee of the University of Turin (protocol n. 0115311) and by the Territorial Ethics Committee (CET - protocol n. 0065654). <a href="https://www.jove.com/files/ftp_upload/66698/66698fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Extended measurement sets downloadable from the dashboard of systems #1 and #2. <a href="https://www.jove.com/files/ftp_upload/66698/66698_Table 1.xls" target="_blank">Please click here to download this Table.</a>
Table 2: Equations for estimation of basal metabolic rate, body fat percentage, and appendicular lean mass. Basal metabolic rate estimations: the units of measurement are kg for weight and lean mass, cm for height, and years for age. A body shape index estimation: the units of measurement are m for waist circumference and height are measured, kg/m2 for body mass index. Body fat percentage estimation: sex is coded as male = 1 and female = 0, body surface area is measured in cm2, and all remaining variables indicated as circ. are measured in cm. Muscle to stomach index is obtained as (right biceps circ. + left biceps circ. + right thigh circ. + left thigh circ. + right calf circ. + left calf circ.) / maximum stomach circ. Appendicular lean mass estimation: the units of measurement are cm for all circumferences and lengths; cm2 for surface areas; cm3 for volumes; kg for weight; years for age. Three equations of this table are from Minetto et al.18. Abbreviations: BMR = basal metabolic rate; ABSI = a body shape index; BF% = body fat percentage; circ. = circumferences; ALM = appendicular lean mass; NHOPI = Native Hawaiian and other Pacific Islander. <a href="https://www.jove.com/files/ftp_upload/66698/66698_Table 2.xls" target="_blank">Please click here to download this Table.</a>
Table 3. Circumference measurements and body composition estimates obtained by the two systems in each of the two representative subjects (one athlete and one person with obesity). <a href="https://www.jove.com/files/ftp_upload/66698/66698_Table 3.xls" target="_blank">Please click here to download this Table.</a>
Supplemental Figure S1: Subject registration form for testing procedure #1. <a href="https://www.jove.com/files/ftp_upload/66698/Supplemental file 1.zip" target="_blank">Please click here to download this File.</a>
Supplemental Figure S2: Subject registration form for testing procedure #2. <a href="https://www.jove.com/files/ftp_upload/66698/Supplemental file 2.zip" target="_blank">Please click here to download this File.</a>

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

The procedures for body size, shape, and composition assessment through commercially available solutions for three-dimensional optical imaging enable the rapid collection of accurate and reproducible data. Clinicians could implement the acquisition of innovative and useful biomarkers (&#34;e-tape&#34; measurements) in routine evaluations of patients to assist in characterizing&#160;their health status.

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

The body size and composition assessment is commonly included in the routine management of healthy athletes as well as of different types of patients to ...

clinical-anthropometrics-body-composition-from-3-dimensional-optical

Research

JoVE Journal

Medicine

1.1K Views.  University of Turin. The procedures for body size, shape, and composition assessment through commercially available solutions for three-dimensional optical imaging enable the rapid collection of accurate and reproducible data. Clinicians could implement the acquisition of innovative and useful biomarkers ("e-tape" measurements) in routine evaluations of patients to assist in characterizing their health status.

Video: Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Modeling and Imaging 3-Dimensional Collective Cell Invasion

A defining characteristic of cancer malignancy is invasion and metastasis 1. In some cancers (e.g. glioma 2), local invasion into surrounding healthy tissue is the root cause of disease and death. For other cancers (e.g. breast, lung, etc.), it is the process of metastasis, in which tumor cells move from a primary tumor mass, colonize distal sites and ultimately contribute to organ failure, that eventually leads to morbidity and mortality 3. It has been estimated that invasion and metastasis are responsible for 90&#37; of cancer deaths 4. As a result, there has been intense interest in identifying the molecular processes and critical protein mediators of invasion and metastasis for the purposes of improving diagnosis and treatment 5.
A challenge for cancer scientists is to develop invasion assays that sufficiently resemble the in vivo situation to enable accurate disease modeling 6. Two-dimensional cell motility assays are only informative about one aspect of invasion and do not take into account extracellular matrix (ECM) protein remodeling which is also a critical element. Recently, research has refined our understanding of tumor cell invasion and revealed that individual cells may move by elongated or rounded modes 7. In addition, there has been greater appreciation of the contribution of collective invasion, in which cells invade in strands, sheets and clusters, particularly in highly differentiated tumors that maintain epithelial characteristics, to the spread of cancer 8.
We present a refined method 9 for examining the contributions of candidate proteins to collective invasion 10. In particular, by engineering separate pools of cells to express different fluorescent proteins, it is possible to molecularly dissect the activities and proteins required in leading cells versus those required in following cells. The use of RNAi provides the molecular tool to experimentally disassemble the processes involved in individual cell invasion as well as in different positions of collective invasion. In this procedure, mixtures of fluorescently-labeled cells are plated on the bottom of a Transwell insert previously filled with Matrigel ECM protein, then allowed to invade "upwards" through the filter and into the Matrigel. Reconstruction of z-series image stacks, obtained by confocal imaging, into three-dimensional representations allows for visualization of collectively invading strands and analysis of the representation of fluorescently-labeled cells in leading versus following positions.

Models of tumor cell invasion into three-dimensional extracellular matrix better reflect the in vivo situation than two-dimensional motility assays. Using matrix invasion assays combined with confocal imaging of fluorescently-labeled cells, detailed information on invasion modes and the distinct contributions of leading versus following cells can be obtained.

A defining characteristic of cancer malignancy is invasion and metastasis 1. In some cancers (e.g. glioma 2), local invasion into surrounding healthy ...

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are associated with obesity, though the precise underlying mechanisms that contribute to the disease are currently being delineated.2,3 Several small animal models of obesity have been developed and are employed in a variety of studies.4 A critical component to these experiments involves the collection of regional and/or total animal fat content data under varied conditions.
Traditional experimental methods available for measuring fat content in small animal models of obesity include invasive (e.g. ex vivo measurement of fat deposits) and non-invasive (e.g. Dual Energy X-ray Absorptiometry (DEXA), or Magnetic Resonance (MR)) protocols, each of which presents relative trade-offs. Current invasive methods for measuring fat content may provide details for organ and region specific fat distribution, but sacrificing the subjects will preclude longitudinal assessments. Conversely, current non-invasive strategies provide limited details for organ and region specific fat distribution, but enable valuable longitudinal assessment. With the advent of dedicated small animal X-ray computed tomography (CT) systems and customized analytical procedures, both organ and region specific analysis of fat distribution and longitudinal profiling may be possible. Recent reports have validated the use of CT for in vivo longitudinal imaging of adiposity in living mice.5,6 Here we provide a modified method that allows for fat/total volume measurement, analysis and visualization utilizing the Carestream Molecular Imaging Albira CT system in conjunction with PMOD and Volview software packages.

Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are ...

Assessment of Child Anthropometry in a Large Epidemiologic Study

A high proportion of children have overweight and obesity in the United States and other countries. Accurate assessment of anthropometry is essential to understand health effects of child growth and adiposity. Gold standard methods of measuring adiposity, such as dual X-ray absorptiometry (DXA), may not be feasible in large field studies. Research staff can, however, complete anthropometric measurements, such as body circumferences and skinfold measurements, using inexpensive portable equipment. In this protocol we detail how to obtain manual anthropometric measurements from children, including standing and sitting height, weight, waist circumference, hip circumference, mid-upper arm circumference, triceps skinfold thickness, and subscapular skinfold thickness, and procedures to assess the quality of these measurements. To demonstrate accuracy of these measurements, among 1,110 school-aged children in the pre-birth cohort Project Viva we calculated Spearman correlation coefficients comparing manual anthropometric measurements with a gold standard measure of body fat, DXA fat mass1. To address reliability, we evaluate intra-rater technical error of measurement at a quality control session conducted on adult female volunteers.

In epidemiologic studies of children, well-trained research staff can accurately and precisely assess weight, height, sitting height, skinfold thicknesses, and body circumferences.

A high proportion of children have overweight and obesity in the United States and other countries. Accurate assessment of anthropometry is essential to ...

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

The developmental goal of 3D ultrasound imaging (3DUS) is to engineer a modality to perform 3D morphological ultrasound analysis of human muscles. 3DUS images are constructed from calibrated freehand 2D B-mode ultrasound images, which are positioned into a voxel array. Ultrasound (US) imaging allows quantification of muscle size, fascicle length, and angle of pennation. These morphological variables are important determinants of muscle force and length range of force exertion. The presented protocol describes an approach to determine volume and fascicle length of m.&#160;vastus lateralis and m.&#160;gastrocnemius medialis. 3DUS facilitates standardization using 3D anatomical references. This approach provides a fast and cost-effective approach for quantifying 3D morphology in skeletal muscles. In healthcare and sports, information on the morphometry of muscles is very valuable in diagnostics and/or follow-up evaluations after treatment or training.

3D ultrasound imaging (3DUS) allows fast and cost-effective morphometry of musculoskeletal tissues. We present a protocol to measure muscle volume and fascicle length using 3DUS.

The developmental goal of 3D ultrasound imaging (3DUS) is to engineer a modality to perform 3D morphological ultrasound analysis of human muscles. 3DUS ...

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

Bioengineering

Body Composition and Metabolic Caging Analysis in High Fat Fed Mice

Alterations to body composition (fat or lean mass), metabolic parameters such as whole-body oxygen consumption, energy expenditure, and substrate utilization, and behaviors such as food intake and physical activity can provide important information regarding the underlying mechanisms of disease. Given the importance of body composition and metabolism to the development of obesity and its subsequent sequelae, it is necessary to make accurate measures of these parameters in the pre-clinical research setting. Advances in technology over the past few decades have made it possible to derive these measures in rodent models in a non-invasive and longitudinal fashion. Consequently, these metabolic measures have proven useful when assessing the response of genetic manipulations (for example knockout or transgenic mice, viral knock-down or overexpression of genes), experimental drug/compound screening and dietary, behavioral or physical activity interventions. Herein, we describe the protocols used to measure body composition and metabolic parameters using an animal monitoring system in chow-fed and high fat diet-fed mice.

This protocol describes the use of a body composition analyzer and metabolic animal monitoring system to characterize body composition and metabolic parameters in mice. An obesity model induced by high-fat feeding is used as an example for the application of these techniques.

Alterations to body composition (fat or lean mass), metabolic parameters such as whole-body oxygen consumption, energy expenditure, and substrate ...

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinic-friendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.

Segmentation and linear measurements quantify skeletal muscle mass and adipose tissues using Computed Tomography and/or Magnetic Resonance Imaging images. Here, we outline the use of Slice-O-Matic software and Horos image viewer for rapid and accurate analysis of body composition. These methods can provide important information for prognosis and risk stratification.

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of ...

3D Cephalometric Landmark Annotation Demonstration on Human CBCT Scans

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and face. Cephalometric analysis has been traditionally conducted with the use of 2D radiographs and landmark sets and restricted to size, linear and angular measurements, and 2D relationships. The increasing use of 3D cone beam computed tomography (CBCT) scans in the dental field dictates the need for the evolution to 3D cephalometric analysis, which incorporates shape and a more realistic analysis of longitudinal development in all three planes. This study is a demonstration of 3D cephalometric analysis with the use of a validated set of skeletal tissue landmarks on human CBCT scans. Detailed instructions for the annotation of each landmark on a 3D volume are provided as part of a step-by-step protocol. The generated measurements and 3D coordinates of the landmarks can be exported and used both for clinical and research purposes. The introduction of 3D cephalometric analysis in basic and clinical craniofacial studies will lead to future advancements in the field of craniofacial growth and development.

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans  

Presented here is a detailed protocol for the conduction of three-dimensional cephalometric analysis with the use of human cone beam computed tomography scans.

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and ...

Adipo-Clear: A Tissue Clearing Method for Three-Dimensional Imaging of Adipose Tissue

Adipose tissue plays a central role in energy homeostasis and thermoregulation. It is composed of different types of adipocytes, as well as adipocyte precursors, immune cells, fibroblasts, blood vessels, and nerve projections. Although the molecular control of cell type specification and how these cells interact have been increasingly delineated, a more comprehensive understanding of these adipose-resident cells can be achieved by visualizing their distribution and architecture throughout the whole tissue. Existing immunohistochemistry and immunofluorescence approaches to analyze adipose histology rely on thin paraffin-embedded sections. However, thin sections capture only a small portion of tissue; as a result, the conclusions can be biased by what portion of tissue is analyzed. We have therefore developed an adipose tissue clearing technique, Adipo-Clear, to permit comprehensive three-dimensional visualization of molecular and cellular patterns in whole adipose tissues. Adipo-Clear was adapted from iDISCO/iDISCO+, with specific modifications made to completely remove the lipid stored in the tissue while preserving native tissue morphology. In combination with light-sheet fluorescence microscopy, we demonstrate here the use of the Adipo-Clear method to obtain high-resolution volumetric images&#160;of an entire adipose tissue.

Due to the high lipid content, adipose tissue has been challenging to visualize using traditional histological methods. Adipo-Clear is a tissue clearing technique that allows robust labeling and high-resolution volumetric fluorescent imaging of adipose tissue. Here, we describe the methods for sample preparation, pretreatment, staining, clearing, and mounting for imaging.

Adipose tissue plays a central role in energy homeostasis and thermoregulation. It is composed of different types of adipocytes, as well as adipocyte ...

Biology

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

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.

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.

Skeletal muscle size, mass, and composition are critical properties for studying metabolic and muscle-related diseases, as they directly impact the ...