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. 2024 Sep 26;246(1):98–107. doi: 10.1111/joa.14136

Sex differences in quantitative ultrasonographic measurements of the rectus femoris in children

Carlos Cruz‐Montecinos 1,2,✉, Matheus D Pinto 3, Ronei S Pinto 4
PMCID: PMC11684384  PMID: 39325922

Abstract

The distribution and amount of intramuscular fat and fibrous tissue can be influenced by biological sex and impact muscle quality in both the functional (force‐generating capacity) and morphological (muscle composition) domains. While ultrasonography (US) has proven effective in assessing age‐ or sex‐related differences in muscle quality, limited information is available on sex differences in children. Quantitative ultrasonographic measurements, such as echo intensity (EI), EI bands (number of pixels across 50‐unit intervals) and texture, may offer a comprehensive framework for identifying sex differences in muscle composition. The aim of our study was to examine the effect of sex on the rectus femoris (RF) muscle quality in children. We used EI (mean and bands) and texture as muscle quality estimates derived from B‐mode US. We hypothesised that RF muscle quality differs significantly between girls and boys. Additionally, we also hypothesised that there is a significant correlation between EI bands and texture. Forty‐four non‐active healthy children were recruited (n = 22 girls, 12.8 ± 1.5 years; and n = 22 boys, 13.5 ± 1.2 years). RF was assessed using EI mean, EI bands, and texture analysis (homogeneity and correlation) using the Gray‐Level Co‐Occurrence Matrix. The results revealed significant (p < 0.05) sex differences in RF EI bands and texture. Boys displayed higher values in the 0–50 EI band and had more homogeneous muscle texture than girls. Conversely, girls displayed greater values in the 51–100 EI band and had less homogenous texture compared to boys (p < 0.05). A positive correlation was observed between the 0–50 EI band and muscle homogeneity. However, the 51–100 EI band correlated negatively with homogeneity (p < 0.05), particularly for girls. In conclusion, our study revealed sex‐specific differences in mean EI, EI bands, and texture of the RF muscle in children. The variations in the correlations between the first and second EI bands and texture reveal different levels of homogeneity in each band. This indicates that distinct muscle tissue constituents, such as intramuscular fat and/or connective tissue, may be reflected in EI bands. Overall, the methods used in this study may be useful for examining muscle quality in healthy children and those with medical conditions.

Keywords: echo intensity, echo intensity bands, intramuscular fat, muscle composition, muscle quality, texture analysis

This study examined sex differences in the rectus femoris (RF) muscle quality in children. We used B‐mode ultrasound‐derived echo intensity (EI, mean, and bands) and texture as estimates of muscle quality. Boys had higher values in the 0–50 EI band and showed a more homogeneous muscle texture, whereas girls had higher values in the 51–100 EI band and a less homogeneous texture. These findings suggest differences in muscle tissue constituents, such as intramuscular fat and/or connective tissue. A representative example of the RF sonographic characteristics is shown, with unique colors distinguishing EI bands and their distribution. Specifically, yellow (band 0–50) encompasses the largest area in the boy's RF, while orange (band 51–100) is more predominant in the girl's RF. RF homogeneity also differs between these two examples; the girl shows less homogeneity, while the boy exhibits greater homogeneity. The black arrowhead indicates an area with band 0–50, while the unfilled arrow points to an area with band 51–100.

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1. INTRODUCTION

There is growing interest in understanding the differences and similarities between sexes in skeletal muscle characteristics, such as muscle quality (Belzunce et al., 2023; Cruz‐Montecinos et al., 2023; Hogrel et al., 2015; Kim & Won, 2022; Nuzzo, 2024). Muscle quality is typically assessed in two main domains: functional and morphological. The functional domain involves evaluation of muscle strength and muscle mass and is characterised by specific tension, which is the ratio of muscle force to its mass (de Lucena Alves et al., 2023). The morphological domain involves the assessment of muscle composition and its changes, such as the presence of non‐contractile elements like intramuscular fat and connective tissues (de Lucena Alves et al., 2023). Research suggests that both domains of muscle quality are influenced by biological sex in children and adults (Canon & Crimmins, 2011; Kasai et al., 2015; Kołodziej & Czajka, 2022; Rosa‐Caldwell & Greene, 2019; Stock et al., 2020).

Over the past three decades, the utilisation of ultrasonography (US) has gained popularity in examining the morphological domain of muscle quality due to its cost‐effectiveness and accessibility (Fu et al., 2023; Nijholt et al., 2017; Pillen et al., 2009; Pillen & van Alfen, 2011). Additionally, US has been used in several studies to assess muscle quality in children (García‐Alonso et al., 2022; Peeters et al., 2023; Rahmani et al., 2015; Scholten et al., 2003). Quantitative assessment often involves measuring muscle thickness, cross‐sectional area, and echo intensity (EI) (Cruz‐Montecinos et al., 2016; Fu et al., 2023; Pillen et al., 2009). EI is traditionally estimated using the average grayscale value of muscle images. Research has shown higher EI to be associated with lower muscle quality likely due to increased intramuscular fat and/or connective tissue content (Akima et al., 2016; Arts et al., 2012; Pillen et al., 2009; Young et al., 2015). Mean EI has been used to assess muscle quality differences between sexes in adults and the elderly (Monjo et al., 2023; Palmer et al., 2015; Stock et al., 2020); however, little information is available on children (García‐Alonso et al., 2023). In children, especially during maturation, girls may exhibit more intramuscular fat than boys (García‐Alonso et al., 2023), potentially due to hormonal and metabolic differences. These differences could result in sex‐specific variations in muscle quality (e.g., mean EI) (Alvim et al., 2020; García‐Alonso et al., 2022, 2023; Orsso et al., 2019; Ramos et al., 1998; Round et al., 1999). However, the mean EI potentially omits critical information for accurate US‐based measurement of muscle quality (Fu et al., 2023; Nijholt et al., 2017). The EI bands method is a recent development in quantitative muscle analysis (Pinto & Pinto, 2021) that offers a complementary approach to the EI mean. This method was proposed to distinguish tissue‐specific characteristics between different age groups and examine the impact of muscle disuse (Logeson et al., 2022; Pinto & Pinto, 2021). EI band analysis quantifies the number of pixels across a 0–255 grayscale spectrum using 50‐unit intervals (Pinto & Pinto, 2021). The pixel intensity within the 0–50 band is thought to reflect muscle tissue (and water), whereas those above the 51–100 range are thought to reflect non‐contractile tissue (e.g., fat and connective tissues) (Pinto & Pinto, 2021). This hypothesis has been partly tested in studies examining the effects of lower limb immobilisation and comparing different biological ages (e.g., young vs. older adults) (Logeson et al., 2022; Pinto & Pinto, 2021). However, whether sex‐specific variations in EI mean and bands occur in children requires further investigation.

Texture analysis has also been proposed as a quantitative US method for examining sex differences in adults (Molinari et al., 2015); however, its use has not been explored in detail in children (Paris & Mourtzakis, 2021). This method quantifies the relationships of adjacent pixels by calculating the frequency of various grayscale levels between neighbouring pixels to identify distinct texture features (Wijntjes & van Alfen, 2021). More homogeneous muscle texture has been shown to correlate with better muscle quality (Molinari et al., 2015; Paris & Mourtzakis, 2021; Tang et al., 2022; Wilkinson et al., 2024). Age‐ and sex‐related variations in muscle texture have been demonstrated in adults and are thought to be linked to differences in muscle composition (Molinari et al., 2015; Tang et al., 2022). However, it remains uncertain whether texture and EI bands are correlated and whether both can reveal sex differences in muscle quality in children (Ashir et al., 2023; Paris & Mourtzakis, 2021; Wijntjes & van Alfen, 2021).

Therefore, the aim of our study was to examine the effect of sex on the muscle quality of the rectus femoris (RF) in children. We used EI (mean and bands) and texture as muscle quality estimates derived from B‐mode US. We hypothesised that RF muscle quality differs significantly between girls and boys. Additionally, we also hypothesised that there is a significant correlation between EI bands and texture.

2. METHODS

2.1. Participants

Non‐active healthy children, defined as those not achieving 60 min of moderate‐to‐vigorous intensity physical activity per day (Tremblay et al., 2017) and only participating in physical activity at school for 60 min twice a week, were recruited for participation through invitations extended to their parents. The inclusion criteria specified an age range from 10 to 15 years. Children with a body mass index (BMI) >30 kg/m2 or with neurological, respiratory, metabolic, and orthopaedic diseases were excluded. Additionally, participants were instructed to avoid participating in any physical activity 48 h before testing and to maintain their habitual hydration (Sarvazyan et al., 2005; Wong et al., 2020). This study received authorisation from the local ethical committee of the Federal University of Rio Grande do Sul (Ethics no. 1.802.716). Before recruitment, participants and their parents were informed about the measurements involved in the study, and parental consent was obtained in writing.

2.1.1. Sample size calculation

Given previously reported sex differences in mean EI in the RF muscle among young individuals, with an effect size of d = 0.79 (Caresio et al., 2015), a power of 0.8, and an alpha error of 0.05, a minimal sample size of 21 girls and 21 boys was determined.

We recruited 22 girls and 22 boys for the study. We found no significant sex differences across age [t (42) = 1.54, p = 0.131], anthropometrics [body mass: t (42) = 1.19, p = 0.237; height: t (42) = 1.76, p = 0.086; BMI: t (42) = 0.09, p = 0.932], puberty stage [Tanner stages: χ 2 (3) = 1.54, p = 0.674; age at peak height velocity stages χ 2 (2) = 1.71, p = 0.426] and fitness level outcomes [t (42) = 0.13, p = 0.897]. The comparisons included age (girls: 12.8 ± 1.5 years; boys: 13.5 ± 1.2 years), body mass (girls: 50.9 ± 8.3 kg; boys: 54.9 ± 13.1 kg), height (girls: 1.59 ± 0.08 m; boys: 1.64 ± 0.12 m), and BMI (girls: 20.1 ± 2.3 kg/m2; boys: 20.2 ± 3.3 kg/m2). Additionally, no significant differences were observed in puberty Tanner stages (I prepubertal, II early puberty, III mid‐puberty, and IV late puberty; girls: n = 1/3/14/4; boys: n = 0/5/13/4), age at peak height velocity stages (prepubertal, pubertal, post‐pubertal; girls: n = 6/11/5; boys: n = 5/8/9), and fitness level measured by VO2 max measured during cycle ergometry (girls: 51.9 ± 10.2 mL/kg/min; boys: 51.5 ± 11.3 mL/kg/min).

2.2. Ultrasonographic measurements

The US assessment included the RF muscle of the dominant leg, which was determined by asking the subjects which leg they would use to kick a ball. A Nemio XG ultrasound system (Toshiba, Japan) was used to capture transverse B‐mode US images of the RF using a 38‐mm, 9 MHz linear‐array transducer with the following setting configuration: 70 mm image depth, 90 dB general gain, and time‐gain compensation at a neutral position. The US images of the RF muscle were acquired at 50% of the distance between the lateral condyle of the femur and the greater trochanter (Pinto & Pinto, 2021; Radaelli et al., 2019). The US transducer was placed perpendicular to the axis of the limb. Participants rested in a supine position for 5 min before image acquisition (Lopez et al., 2019).

2.3. EI and texture analysis

The EI (mean and bands) and texture of the RF muscle were conducted using a customised algorithm developed in MATLAB software (MATLAB version 2023b, USA). This algorithm involved five steps: (i) using a rectangular selection function to identify a region of interest (ROI) representative of two‐thirds of the visible thickness and width of the muscle, with the ROI size tailored to the muscle thickness of each individual (Cruz‐Montecinos et al., 2016); (ii) positioning the rectangular ROI within the muscle, ensuring the exclusion of fascial tissue (Figure 1); (iii) calculating the mean EI through histogram analysis and counting the numbers of pixels in each band every 50th of grayscale (Pinto & Pinto, 2021) (Figure 1); (iv) determining the ratio between the target and total pixels, which resulted in values between 0 and 1; and (v) using the Image Processing Toolbox of Matlab software, we applied the Gray‐Level Co‐Occurrence Matrix (GLCM) to compute texture for each ROI at four angles (0°, 45°, 90°, and 135°), with a pixel distance of one. A matrix based on the distance and the direction between a pixel and its neighbours was used in the analysis. This matrix analyses the relationship between a pixel and its neighbours at specified angles and distances, helping to identify texture patterns. The four angles were selected based on previous studies on sex differences in adults using GLCM (Molinari et al., 2015; Paris & Mourtzakis, 2021). GLCM properties, such as correlation and homogeneity, were calculated for each angle as previously described (Molinari et al., 2015; Paris & Mourtzakis, 2021). The extracted texture features from all angles were averaged for the final analysis (Paris & Mourtzakis, 2021). Briefly, homogeneity evaluates the grey‐level uniformity, and the correlation explores the association between a pixel and its neighbour (Molinari et al., 2015; Paris & Mourtzakis, 2021; Sahinis & Kellis, 2023). Higher values for both properties indicate a more uniform texture. The ROI depth (i.e., the distance from the skin to the centre of the ROI) was similar between groups (girls: 2.4 ± 0.3 cm; boys: 2.3 ± 0.6 cm, p = 0.433) so that analyses were completed at the same image depth (Pinto et al., 2023).

FIGURE 1.

FIGURE 1

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Illustration of region of interest (ROI) selection and echo intensity band identification within the ROI. Each echo intensity band is distinguished by a unique color to demonstrate its distribution. Notably, the yellow (band 0–50) encompasses the largest area, followed by the orange (band 51–100), while the red color intensities represent the grayscale pixels greater than 101, which shows the lowest distribution.

2.4. Statistical analysis

Data are reported as mean ± standard deviation unless otherwise specified. The normality of the distribution was assessed using the Shapiro–Wilk test. Independent t‐test or the Mann–Whitney U test was applied to evaluate the differences in characteristics between groups, depending on the normal distribution of variables. The independent t‐test was used to compare the EI bands and texture between the sexes. Mixed ANOVA was performed to determine the interaction between sex (between‐group factor: boys and girls) and five bands of EI (within‐factor: 0–50, 51–100, etc.). When a significant interaction was found between factors, post‐hoc pairwise comparisons were performed with the correction of p‐values using the False Discovery Rate Method (Benjamini & Hochberg, 1995). In addition, an ANCOVA analysis was used to determine the interaction between Tanner stages (grouped as I–II and III–IV) and sex factors. Tanner stages were grouped because no boys were categorised as stage I.

Pearson and Spearman's correlations were used according to data characteristics to analyse correlations between significant EI bands between sex and texture variables.

All statistical analyses were performed using Jamovi® software 2.3.13. Correlation levels were categorised as weak (r ≤ 0.35), moderate (r = 0.36–0.67), and strong (r ≥ 0.68) (Taylor, 1990). Effect sizes were determined by calculating the partial eta squared (ηP2=0.01, small; ηP2=0.06, moderate; or ηP2=0.14, large). Additionally, Cohen's d effect sizes were computed and categorised as small (d ≥ 0.2), moderate (d ≥ 0.5), or large (d ≥ 0.8) (Fritz et al., 2012).

3. RESULTS

3.1. Mean EI

The mean EI was significantly higher in girls than in boys [t (42) = 3.2, p = 0.003, d = 0.953] (Figure 2).

FIGURE 2.

FIGURE 2

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Mean echo intensity in girls (circle) and boys (squared). Data are expressed as mean and 95% of confidence level. Arbitrary unit (a.u). *p < 0.05.

3.2. Differences in EI bands between girls and boys

The mixed ANOVA showed a significant interaction between sex and EI bands [F = 7.58(4,168), p < 0.001, ηP2=0.153] and a significant effect of EI bands [F = 510.2(4,168), p < 0.001, ηP2=0.924]. No significant main effect was found for sex [F = 0.001(4,168), p = 0.999, ηP2=0.001]. Post‐hoc analysis revealed significant differences between boys and girls for the 0–50 EI band (p = 0.022, d = 0.84) and the 51–100 band (p = 0.022, d = 0.84) (Figure 3). Boys showed a higher number of pixels (normalised by total) in the 0–50 range (the first EI band), whereas girls displayed a greater number of pixels (normalised by total) in the 51–100 range (the second EI band).

FIGURE 3.

FIGURE 3

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Echo intensity bands in girls (circle) and boys (squared). Data are expressed as mean and 95% confidence level. *p < 0.05.

3.3. Differences in texture between girls and boys

The texture analysis showed significant differences between boys and girls for both homogeneity [t (42) = 3.5, p = 0.001, d = 1.04] and correlation [t (42) = 3.4, p = 0.001, d = 1.03]. Boys showed higher homogeneity and correlation than girls, indicating a more homogeneous texture (Figure 4).

FIGURE 4.

FIGURE 4

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Texture in girls (circle) and boys (squared). Data are expressed as mean and 95% of confidence level. Arbitrary unit (a.u). *p < 0.05.

3.4. Effect of puberty stage on sex‐specific differences in EI and texture

We also tested the potential interaction between puberty stages (Tanner I–II and III–IV) and sex on the US muscle variables. The ANCOVA revealed no interaction between sex and puberty stage [mean EI, F = 0.036(1,40), p = 0.851, ηP2=0.001; EI band 0–50, F = 0.200(1,40), p = 0.657, ηP2=0.005; EI band 51–100, F = 0.091(1,40), p = 0.764, ηP2=0.002; correlation, F = 0.006(1,40), p = 0.936, ηP2=0.000; homogeneity, F = 0.310(1,40), p = 0.581, ηP2=0.008]. Additionally, there was no significant difference between puberty stage across the dependent variables [mean EI, F = 3.30(1,40), p = 0.077, ηP2=0.076; EI band 0–50, F = 2.35(1,40), p = 0.133, ηP2=0.055; EI band 51–100, F = 1.90(1,40), p = 0.176, ηP2=0.002; correlation, F = 0.217(1,40), p = 0.664, ηP2=0.005; homogeneity, F = 0.253(1,40), p = 0.119, ηP2=0.060]. However, the ANCOVA that included puberty stage as a factor revealed significant differences between sexes across the dependent variables [mean EI, F = 7.67(1,40), p = 0.008, ηP2=0.161; EI band 0–50, F = 6.43(1,40), p = 0.015, ηP2=0.138; EI band 51–100, F = 6.25(1,40), p = 0.017, ηP2=0.135; correlation, F = 7.65(1,40), p = 0.009, ηP2=0.160; homogeneity, F = 6.48(1,40), p = 0.015, ηP2=0.139].

3.5. Correlations between EI bands and texture

There was a significant, moderate to strong positive correlation between the first EI band (0–50) and both texture variables (homogeneity: r = 0.78, p < 0.001; correlation: r = 0.52, p < 0.001), suggesting that individuals with a higher distribution of pixels in the 0–50 grayscale range tend to have a more homogeneous texture in RF muscle. Contrarily, there was a significant, moderate to strong negative correlation between the second EI band (51–100) and texture variables (homogeneity: r = −0.77, p < 0.001; correlation: r = −0.56, p < 0.001), indicating that an increased distribution in the 51–100 grayscale range is associated with a less homogeneous, more varied texture in the RF muscle (Figure 5).

FIGURE 5.

FIGURE 5

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Regression lines and equations of texture variables versus echo intensity bands for each sex. Girls (circle) and boys (squared). Note that only significant correlations show the regression line and equation. **p < 0.05 and **p < 0.001.

In addition, we tested the correlation separated by sex. In girls, there was a significant strong correlation between homogeneity and the first (r = 0.83, p < 0.001) and second EI bands (r = −0.81, p < 0.001) and between correlation texture and the first (r = 0.50, p = 0.018) and second EI bands (r = −0.56, p = 0.007) (Figure 5). In boys, a significant strong correlation was detected between homogeneity and the first (r = 0.81, p < 0.001) and second EI bands (r = −0.80, p < 0.001); however, no significant correlation was detected between the correlation texture and the first (r = 0.31, p = 0.164) and second EI bands (r = 0.32, p = 0.153) (Figure 5). The regression equations for analysis by sex are displayed in Figure 5. Overall, these results suggest that texture may reflect sex‐specific differences in the quantitative US characteristics of the RF muscle in children.

4. DISCUSSION

The main results of our study are (i) girls showed a higher mean EI than boys, (ii) boys showed a higher number of pixels in the first EI band, whereas girls displayed a greater number of pixels in the second EI band; (iii) boys exhibited a more homogeneous texture compared to girls; and (iv) the first EI band was positively correlated with homogeneous muscle texture, while the second EI band showed a negative association, with these correlations being more pronounced in girls. These results corroborate our initial hypothesis that sex‐specific variations in the US characteristics of the RF (muscle quality) in children can be detected based on EI (mean and bands) and muscle texture. The observed differences may indicate muscle composition variations (e.g., intramuscular fat and/or connective tissue) attributable to hormonal and metabolic differences between sexes (Alvim et al., 2020; Round et al., 1999; Stock et al., 2020), among other factors (discussed below).

4.1. Sex‐related US muscle characteristics and potential contributing factors

Our study observed differences in US muscle quantitative characteristics (such as mean EI, EI bands, and texture) between girls and boys. The lower mean EI in boys compared to girls observed in the present study aligns with previous research (García‐Alonso et al., 2022, 2023). The differences observed in EI bands may represent more specific results than mean EI alone. Specifically, the RF of boys is characterised predominantly by the first band, while the RF of girls displays greater pixel concentration in the second band. In addition, we found that the RF texture is more homogeneous in boys than girls, possibly reflecting differences in muscle composition (e.g., intramuscular fat). A recent study demonstrated that greater homogeneity in US images is associated with greater intramuscular fat infiltration, confirmed by magnetic resonance imaging (Wilkinson et al., 2024). Hence, the sex differences in EI bands and texture observed in our study may reflect different quantitative characteristics in muscle US images (as an example, see Figure 6), similar to the well‐documented differences reported in adults (Molinari et al., 2015).

FIGURE 6.

FIGURE 6

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A representative example of ultrasound characteristics of rectus femoris (RF) muscle in girls and boys. A unique color distinguishes each echo intensity band to demonstrate its distribution. Notably, the yellow (band 0–50) encompasses the largest area in the boy, and the orange (band 51–100) in the girl. The homogeneity of RF is also different between these two examples; the girl showed less homogeneity of RF, while the boy showed a higher homogeneity. The black arrowhead indicates an area with a 0–50 band, while the non‐filled arrow indicates an area with a 51–100 band.

Various factors can contribute to the sex differences in EI observed in our study. For example, hormonal and metabolic sex differences may influence muscle composition and quality (Alvim et al., 2020; Pruszkowska‐Przybylska et al., 2021; Ramos et al., 1998; Round et al., 1999), which could explain why women and prepubertal girls tend to exhibit increased intramuscular fat infiltration (Chang et al., 2018; García‐Alonso et al., 2022) and display increased mean EI (García‐Alonso et al., 2022, 2023). Females have lower lean mass and higher fat mass than males throughout their lives (Kirchengast, 2010). Sex steroids, such as testosterone and estrogen, are thought to cause these disparities (Garnett et al., 2004; Xu et al., 2021), explaining 3%–17% of the variations in body composition in prepubertal children (Garnett et al., 2004). Sex differences in body composition appear in infancy and persist until puberty, when they become more pronounced (Butte et al., 2000; Davis et al., 2019). Additionally, the combined anabolic effects of growth hormone and testosterone result in higher protein synthesis rates in boys than girls during puberty (Mauras, 2006), which may explain lower mean EI in boys (García‐Alonso et al., 2022, 2023). Muscle architecture (e.g., fascicle angle) and fibre density may also result in different EI (Pinto et al., 2023, 2024) and muscle texture (Molinari et al., 2015; Paris & Mourtzakis, 2021; Tang et al., 2022). Theoretically, differences in fascicle angle between boys and girls suggest that the angle of insonation (the angle of the US beam relative to the tissue) would have differed between sexes when images are taken in the same plane, thus affecting the strength of US signals returned to the transducer, which in turn impacts EI (Bahner et al., 2016; Pinto et al., 2023). For instance, a previous study altered muscle length (joint angle) to change the fascicle angle while simultaneously examining EI, revealing that EI increased as fascicle angle decreased (Pinto et al., 2023). However, no‐significant sex differences in the RF fascicle angle have been reported in children (O'Brien et al., 2010). Other factors, such as differences in muscle fibre density—which may change in response to alterations in intramuscular fat or connective tissue—could additionally contribute to sex‐related differences in EI and texture (Miller et al., 1993; Molinari et al., 2015; Paris & Mourtzakis, 2021). Therefore, variations in fibre density, increased intramuscular fat content, and potential differences in muscle architecture could collectively contribute to the observed higher EI and a less homogeneous texture in girls.

Differences in fitness level and puberty stage between sexes may influence muscle composition (García‐Alonso et al., 2022, 2023). However, in our study, girls and boys displayed similar VO2 max relative to total body mass, comparable to other reports [45–52 mL/kg/min boys and girls aged 11–12 years (Armstrong & Barker, 2009; Armstrong & Welsman, 1994; Silva et al., 2022)], suggesting that differences in fitness level may not account for the sex‐related differences in muscle quality observed in the present study. Chronological age and puberty stage could also be considered as factors that can impact the sex differences in muscle quality found in the present study; however, in our study, age and puberty stage (i.e., Tanner) were similar between boys and girls, and analyses revealed no interaction between puberty stage and sex, suggesting that our results likely had little influence from puberty stage. In addition, previous studies have reported that mean EI increases in children by only 1% per year (Jacobs et al., 2013).

4.2. Correlations between muscle texture and EI bands

To our knowledge, no studies have previously examined the correlation between muscle texture and EI bands. Our findings revealed a moderate to strong correlation between muscle texture and EI bands (Figure 5). For instance, children with RF muscle containing more pixels in the second EI band tend to show lower homogeneity values (or lower muscle quality). The observed sex‐specific correlations between muscle texture and EI bands may support the hypothesis that the first EI (0–50) band reflects muscle tissue concentration, while the second EI (51–100) band may indicate a greater concentration of non‐contractile tissue (i.e., fat and/or connective tissues) (Pinto & Pinto, 2021). However, more studies are needed to confirm this hypothesis.

Notably, the correlations between texture variables and EI bands were more pronounced in girls than boys (Figure 5). This distinction could be related to higher intramuscular fat in girls than boys but also to differences in muscle architecture and muscle fibre density (García‐Alonso et al., 2022, 2023; Miller et al., 1993; O'Brien et al., 2010; Siervogel et al., 2003). However, more studies are needed to understand further the factors that explain these results.

4.3. EI (mean and bands) and muscle texture in children: Potential implications

Our results may contribute to a better understanding of sex‐related muscle differences in children by integrating EI bands and texture analysis to elucidate the quantitative characteristics of muscles. Furthermore, identifying variations in muscle quality in children may assist in detecting changes in muscle composition before maturity and enable ongoing monitoring of muscle quality. This may facilitate early interventions to prevent or mitigate declines in muscle quality. However, it is crucial to consider normative values of US muscle tissue characteristics in children by sex, as presented in this study, and by muscle.

Lower mean EI in RF in children has been linked to better physical fitness, including cardiorespiratory fitness, jump performance, and speed agility (García‐Alonso et al., 2022; Stock et al., 2017). The integration of mean EI, EI bands, and texture US assessments can be applied to monitor the effects of physical training strategies on muscle quality in children. Muscle composition can also be linked to current nutritional status and potentially to the onset of metabolic diseases (Burrows et al., 2017; García‐Alonso et al., 2022; Gehad et al., 2023; Siervogel et al., 2003). This advancement in muscle US assessment may contribute to a better understanding of the impact of a sedentary lifestyle on muscle quality (García‐Alonso et al., 2022) and the implications of diseases on muscle health in children (Gehad et al., 2023; Pillen et al., 2003). Nonetheless, validation studies are needed to fully understand the relationship between EI bands, texture measurements, and muscle morphology (e.g., biopsies). Furthermore, future longitudinal studies are essential to understand the functional implications of these quantitative muscle characteristics in healthy and diseased paediatric populations.

4.4. Limitations

Our study has some limitations. The findings are based on a relatively small sample size but offer a new perspective on assessing sex‐related muscle characteristics using US. Analysing specific muscle tissue constituents (contractile and non‐contractile elements) using US analysis is still challenging. The EI bands and texture analysis may not be reflected as a direct measure of muscle tissue constituents (i.e., contractile and non‐contractile tissues) but as a measure of quantitative features of muscle. However, our study provides one step forward in comprehending the EI bands and texture analysis. Regarding the texture analysis, we used GLCM to compute texture for each ROI at four angles (0°, 45°, 90°, and 135°) with a common pixel distance (Molinari et al., 2015). Then, the angle values were averaged for the final analysis (Paris & Mourtzakis, 2021). Future studies should explore the effectiveness of texture analysis using various angle and distance configurations. Furthermore, we specifically examined a defined mean age (13 ± 1 years across both sexes) instead of exploring differences among various stages of maturity (e.g., Tanner stages I, II, III, and IV). Finally, we focused our analysis exclusively on the RF muscle and acquired images at 50% of the distance between the lateral condyle of the femur and the greater trochanter, as in previous studies (Pinto & Pinto, 2021; Radaelli et al., 2019). However, the composition of the RF muscle may differ from proximal to distal regions (Stock et al., 2020). Future studies should thus consider evaluating different muscles and obtaining measurements from different locations within each muscle.

5. CONCLUSIONS

Our study revealed sex‐specific differences in mean EI, EI bands, and texture of the RF muscle in children. The variations in the correlations between the first and second EI bands and texture reveal different levels of homogeneity in each band. This indicates that distinct muscle tissue constituents, such as intramuscular fat and/or connective tissue, may be reflected in EI bands. Overall, the methods used in this study may be useful for examining muscle quality in healthy children and those with medical conditions. Further research is needed to confirm the validity of the proposed methods for quantifying muscle composition differences between sexes and to fully understand their functional implications and associations with physical fitness and maturation stages in children.

AUTHOR CONTRIBUTIONS

Ronei S. Pinto: Data acquisition. Carlos Cruz‐Montecinos: Data analyses and Matlab workflow. All authors contributed to the conceptualisation and design of the study and the interpretation of data. All authors contributed to the drafting, critical revision, and approval of the final article.

FUNDING INFORMATION

No funding was received to conduct this project.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

ACKNOWLEDGEMENTS

Ronei S. Pinto was supported by CNPq via the Productivity Research Scholarship (PQ scholarship).

Cruz‐Montecinos, C. , Pinto, M.D. & Pinto, R.S. (2025) Sex differences in quantitative ultrasonographic measurements of the rectus femoris in children. Journal of Anatomy, 246, 98–107. Available from: 10.1111/joa.14136

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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