Low-dose acetylsalicylic acid reduces local inflammation and tissue perfusion in dense breast tissue in postmenopausal women

PURPOSE

One major risk factor for breast cancer is high mammographic density. It has been estimated that dense breast tissue contributes to ~ 30% of all breast cancer. Prevention targeting dense breast tissue has the potential to improve breast cancer mortality and morbidity. Anti-estrogens, which may be associated with severe side-effects, can be used for prevention of breast cancer in women with high risk of the disease per se. However, no preventive therapy targeting dense breasts is currently available. Inflammation is a hallmark of cancer. Although the biological mechanisms involved in the increased risk of cancer in dense breasts is not yet fully understood, high mammographic density has been associated with increased inflammation. We investigated whether low-dose acetylsalicylic acid (ASA) affects local breast tissue inflammation and/or structural and dynamic changes in dense breasts.

METHODS

Postmenopausal women with mammographic dense breasts on their regular mammography screen were identified. A total of 53 women were randomized to receive ASA 160 mg/day or no treatment for 6 months. Magnetic resonance imaging (MRI) was performed before and after 6 months for a sophisticated and continuous measure breast density by calculating lean tissue fraction (LTF). Additionally, dynamic quantifications including tissue perfusion were performed. Microdialysis for sampling of proteins in vivo from breasts and abdominal subcutaneous fat, as a measure of systemic effects, before and after 6 months were performed. A panel of 92 inflammatory proteins were quantified in the microdialysates using proximity extension assay.

RESULTS

After correction for false discovery rate, 20 of the 92 inflammatory proteins were significantly decreased in breast tissue after ASA treatment, whereas no systemic effects were detected. In the no-treatment group, protein levels were unaffected. Breast density, measured by LTF on MRI, were unaffected in both groups. ASA significantly decreased the perfusion rate. The perfusion rate correlated positively with local breast tissue concentration of VEGF.

CONCLUSIONS

ASA may shape the local breast tissue microenvironment into an anti-tumorigenic state. Trials investigating the effects of low-dose ASA and risk of primary breast cancer among postmenopausal women with maintained high mammographic density are warranted. Trial registration EudraCT: 2017-000317-22.

Magnetic resonance study of visceral, subcutaneous, liver and pancreas fat changes after 12 weeks intermittent fasting in obese participants with prediabetes

BACKGROUND

It is not clear whether there are differences in proportions of fat loss from visceral:subcutaneous depots by probiotic supplementation, ethnicity or sex during weight loss; or whether visceral/pancreatic fat depot changes are related to changes in HbA1c. Our objective is to investigate whether weight loss from different fat depots is related to these factors during weight loss achieved by intermittent fasting.

METHOD

Prediabetes participants on 5:2 intermittent fasting were randomized 1:1 to either daily probiotic or placebo for 12 weeks. Twenty-four patients had magnetic resonance imaging data at baseline and 12 weeks.

RESULTS

After 12 weeks of intermittent fasting, subcutaneous fat (%) changed from 35.9 ± 3.1 to 34.4 ± 3.2, visceral fat (%) from 15.8 ± 1.3 to 14.8 ± 1.2, liver fat (%) from 8.7 ± 0.8 to 7.5 ± 0.7 and pancreatic fat (%) from 7.7 ± 0.5 to 6.5 ± 0.5 (all p< 0.001). Changes in weight, HbA1c, SAT, VAT, LF and PF did not differ significantly between probiotic and placebo groups.

CONCLUSION

Overall weight loss was correlated with fat loss from subcutaneous depots. Losses from different fat depots did not correlate with changes in HbA1c or differ by probiotic supplementation, ethnicity or sex.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

BACKGROUND

The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents.

METHODS

474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards.

RESULTS

For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS).

CONCLUSION

These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition

Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h²= .25 vs. .13, p = 1.8x10^-7). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r_g= .49, p = 2.7x10^-22). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.

Ectopic fat is associated with cardiac remodeling—A comprehensive assessment of regional fat depots in type 2 diabetes using multi-parametric MRI

Background

Different regional depots of fat have distinct metabolic properties and may relate differently to adverse cardiac remodeling. We sought to quantify regional depots of body fat and to investigate their relationship to cardiac structure and function in Type 2 Diabetes (T2D) and controls.

Methods

From the SCAPIS cohort in Linköping, Sweden, we recruited 92 subjects (35% female, mean age 59.5 ± 4.6 years): 46 with T2D and 46 matched controls. In addition to the core SCAPIS data collection, participants underwent a comprehensive magnetic resonance imaging examination at 1.5 T for assessment of left ventricular (LV) structure and function (end-diastolic volume, mass, concentricity, ejection fraction), as well as regional body composition (liver proton density fat fraction, visceral adipose tissue, abdominal subcutaneous adipose tissue, thigh muscle fat infiltration, fat tissue-free thigh muscle volume and epicardial adipose tissue).

Results

Compared to the control group, the T2D group had increased: visceral adipose tissue volume index (P &lt; 0.001), liver fat percentage (P &lt; 0.001), thigh muscle fat infiltration percentage (P = 0.02), LV concentricity (P &lt; 0.001) and LV E/e'-ratio (P &lt; 0.001). In a multiple linear regression analysis, a negative association between liver fat percentage and LV mass (St Beta −0.23, P &lt; 0.05) as well as LV end-diastolic volume (St Beta −0.27, P &lt; 0.05) was found. Epicardial adipose tissue volume and abdominal subcutaneous adipose tissue volume index were the only parameters of fat associated with LV diastolic dysfunction (E/e'-ratio) (St Beta 0.24, P &lt; 0.05; St Beta 0.34, P &lt; 0.01, respectively). In a multivariate logistic regression analysis, only visceral adipose tissue volume index was significantly associated with T2D, with an odds ratio for T2D of 3.01 (95% CI 1.28–7.05, P &lt; 0.05) per L/m2 increase in visceral adipose tissue volume.

Conclusions

Ectopic fat is predominantly associated with cardiac remodeling, independently of type 2 diabetes. Intriguingly, liver fat appears to be related to LV structure independently of VAT, while epicardial fat is linked to impaired LV diastolic function. Visceral fat is associated with T2D independently of liver fat and abdominal subcutaneous adipose tissue.

The effect on precision and T1 bias comparing two flip angles when estimating muscle fat infiltration using fat-referenced chemical shift-encoded imaging

Investigation of the effect on accuracy and precision of different parameter settings is important for quantitative MRI. The purpose of this study was to investigate T1 bias and precision for muscle fat infiltration (MFI) measurements using fat-referenced chemical shift MFI measurements at flip angles of 5° and 10°. The fat-referenced measurements were compared with fat fractions, which is a more commonly used measure of MFI. This retrospective study was performed on data from a clinical intervention study including 40 postmenopausal women. Test and retest images were acquired with a 3-T scanner using four-point 3D spoiled gradient multiecho acquisition. Postprocessing included T2* correction and fat-referenced calibration, where the fat signal was calibrated using adipose tissue as reference. The mean MFI was calculated in six different muscle regions using both the fat-referenced fat signal and the fat fraction, defined as the fat signal divided by the sum of the fat and water signals. Both methods used the same fat and water images as input. The variance of the difference between mean MFI from test and retest was used as the measure of precision. The signal-to-noise ratio (SNR) characteristics were analyzed by measuring the full width at half maximum (FWHM) of the fat signal distribution. There was no difference in the mean MFI at different flip angles for the fat-referenced technique (p = 0.66), while the measured fat fractions were 3.3 percentage points larger for 10° compared with 5° (p < 0.001). No significant difference in the precision was found in any of the muscles analyzed. However, the FWHM of the fat signal distribution was significantly (p = 0.01) lower at 10°. This strenghtens the hypothesis that fat-referenced MFI is insensitive to flip angle-induced T1 bias in CSE-MRI, enabling usage of a higher and more SNR-effective flip angle. The lower FWHM in fat-referenced MFI at 10° indicates that high flip angle acquisition is advantageous even although no significant differences in precision were observed comparing 5° and 10°.

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies

BACKGROUND

There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of obesity is crucial for profiling and management of obesity, as different fat-subcutaneous adipose tissue depots (SAT) and visceral adipose tissue depots (VAT) have various degrees of influence on metabolic syndrome and morbidities. Manual segmentation is time consuming and laborious. Study focuses on the development of a deep learning-based, complete data processing pipeline for MRI-based fat analysis, for large cohort studies which include (1) data augmentation and preprocessing (2) model zoo (3) visualization dashboard, and (4) correction tool, for automated quantification of fat compartments SAT and VAT.

METHODS

Our sample comprised 190 healthy community-dwelling older adults from the Geri-LABS study with mean age of 67.85 ± 7.90 years, BMI 23.75 ± 3.65 kg/m², 132 (69.5%) female, and mainly Chinese ethnicity. 3D-modified Dixon T1-weighted gradient-echo MR images were acquired. Residual global aggregation-based 3D U-Net (RGA-U-Net) and standard 3D U-Net were trained to segment SAT, VAT, superficial and deep subcutaneous adipose tissue depots (SSAT and DSAT). Manual segmentation from 26 subjects was used as ground truth during training. Data augmentations, random bias, noise and ghosting were carried out to increase the number of training datasets to 130. Segmentation accuracy was evaluated using Dice and Hausdorff metrics.

RESULTS

The accuracy of segmentation was SSAT:0.92, DSAT:0.88 and VAT:0.9. Average Hausdorff distance was less than 5 mm. Automated segmentation significantly correlated R² > 0.99 (p < 0.001) with ground truth for all 3-fat compartments. Predicted volumes were within ± 1.96SD from Bland-Altman analysis.

CONCLUSIONS

DL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10 s.

On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank

BACKGROUND

Current consensus is to combine a functional measure with muscle quantity to assess/confirm sarcopenia. However, the proper body size adjustment for muscle quantity is debated and sarcopenia in obesity is not well described. Further, functional measures are not muscle-specific or sensitive to etiology, and can be confounded by, for example, fitness/pain. For effective detection/treatment/follow-up, muscle-specific biomarkers linked to function are needed.

METHODS

Nine thousand six hundred and fifteen participants were included and current sarcopenia thresholds (EWGSOP2: DXA, hand grip strength) applied to investigate prevalence. Fat-tissue free muscle volume (FFMV) and muscle fat infiltration (MFI) were quantified through magnetic resonance imaging (MRI) and sex-and-body mass index (BMI)-matched virtual control groups (VCGs) were used to extract each participant's FFMV/height2 z-score (FFMVVCG). The value of combining FFMVVCG and MFI was investigated through hospital nights, hand grip strength, stair climbing, walking pace, and falls.

RESULTS

Current thresholds showed decreased sarcopenia prevalence with increased BMI (underweight 8.5%/normal weight 4.3%/overweight 1.1%/obesity 0.1%). Contrary, the prevalence of low function increased with increasing BMI. Previously proposed body size adjustments (division by height2/weight/BMI) introduced body size correlations of larger/similar magnitude than before. VCG adjustment achieved normalization and strengthened associations with hospitalization/function. Hospital nights, low hand grip strength, slow walking pace, and no stair climbing were positively associated with MFI (p < .05) and negatively associated with FFMVVCG (p < .01). Only MFI was associated with falls (p < .01). FFMVVCG and MFI combined resulted in highest diagnostic performance detecting low function.

CONCLUSIONS

VCG-adjusted FFMV enables proper sarcopenia assessment across BMI classes and strengthened the link to function. MFI and FFMV combined provides a more complete, muscle-specific description linked to function enabling objective sarcopenia detection.

Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD

Background & Aims

Sarcopenia and frailty are recognised as important factors in later stages of liver disease. However, their role in non-alcoholic fatty liver disease (NAFLD) is not yet fully understood. In this study we investigate the associations of MRI-measured adverse muscle composition (AMC: low muscle volume and high muscle fat) with poor function, sarcopenia, and metabolic comorbidity within NAFLD in the large UK Biobank imaging study.

Methods

A total of 9,545 participants were included. Liver fat, fat-tissue free muscle volume, and muscle fat infiltration were quantified using a rapid MRI protocol and automated image analysis (AMRA® Researcher). For each participant, a personalised muscle volume z-score (sex- and body size-specific) was calculated and combined with muscle fat infiltration for AMC detection. The following outcomes were investigated: functional performance (hand grip strength, walking pace, stair climbing, falls) and metabolic comorbidities (coronary heart disease, type 2 diabetes). Sarcopenia was detected by combining MRI thresholds for low muscle quantity and low hand grip strength according to the European working group definition.

Results

The prevalence of sarcopenia in NAFLD (1.6%) was significantly lower (p <0.05) compared with controls without fatty liver (3.4%), whereas the prevalence of poor function and metabolic comorbidity was similar or higher. Of the 1,204 participants with NAFLD, 169 (14%) had AMC and showed 1.7–2.4× higher prevalence of poor function (all p <0.05) as well as 2.1× and 3.3× higher prevalence of type 2 diabetes and coronary heart disease (p <0.001), respectively, compared with those without AMC.

Conclusions

AMC is a prevalent and highly vulnerable NAFLD phenotype displaying poor function and high prevalence of metabolic comorbidity. Sarcopenia guidelines can be strengthened by including cut-offs for muscle fat, enabling AMC detection.

Lay summary

Today, it is hard to predict whether a patient with fatty liver disease will progress to more severe liver disease. This study shows that measuring muscle health (the patient's muscle volume and how much fat they have in their muscles) could help identify the more vulnerable patients and enable early prevention of severe liver disease.

•

The role of sarcopenia and frailty in NAFLD is not yet fully understood.

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Magnetic resonance imaging enables quantification of muscle composition.

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Myosteatosis in combination with low muscle volume characterises an adverse muscle composition.

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Adverse muscle composition is a novel NAFLD phenotype associated with poor function and metabolic comorbidity.

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Sarcopenia guidelines can be strengthened by including cut-offs for muscle fat.

Pilot study on longitudinal change in pancreatic proton density fat fraction during a weight-loss surgery program in adults with obesity

BACKGROUND

Quantitative-chemical-shift-encoded (CSE)-MRI methods have been applied to the liver. The feasibility and potential utility CSE-MRI in monitoring changes in pancreatic proton density fat fraction (PDFF) have not yet been demonstrated.

PURPOSE

To use quantitative CSE-MRI to estimate pancreatic fat changes during a weight-loss program in adults with severe obesity and nonalcoholic fatty liver disease (NAFLD). To explore the relationship of reduction in pancreatic PDFF with reductions in anthropometric indices.

STUDY TYPE

Prospective/longitudinal.

POPULATION

Nine adults with severe obesity and NAFLD enrolled in a weight-loss program.

FIELD STRENGTH/SEQUENCE

CSE-MRI fat quantification techniques and multistation-volumetric fat/water separation techniques were performed at 3 T.

ASSESSMENT

PDFF values were recorded from parametric maps colocalized across timepoints.

STATISTICAL TESTS

Rates of change of log-transformed variables across time were determined (linear-regression), and their significance assessed compared with no change (Wilcoxon test). Rates of change were correlated pairwise (Spearman's correlation).

RESULTS

Mean pancreatic PDFF decreased by 5.7% (range 0.7-17.7%) from 14.3 to 8.6%, hepatic PDFF by 11.4% (2.6-22.0%) from 14.8 to 3.4%, weight by 30.9 kg (17.3-64.2 kg) from 119.0 to 88.1 kg, body mass index by 11.0 kg/m2 (6.3-19.1 kg/m2 ) from 44.1 to 32.9 kg/m2 , waist circumference (WC) by 25.2 cm (4.0-41.0 cm) from 133.1 to 107.9 cm, HC by 23.5 cm (4.5-47.0 cm) from 135.8 to 112.3 cm, visceral adipose tissue (VAT) by 2.9 L (1.7-5.7 L) from 7.1 to 4.2 L, subcutaneous adipose tissue (SCAT) by 4.0 L (2.9-7.4 L) from 15.0 to 11.0 L. Log-transformed rate of change for pancreatic PDFF was moderately correlated with log-transformed rates for hepatic PDFF, VAT, SCAT, and WC (ρ = 0.5, 0.47, 0.45, and 0.48, respectively), although not statistically significant.

DATA CONCLUSION

Changes in pancreatic PDFF can be estimated by quantitative CSE-MRI in adults undergoing a weight-loss surgery program. Pancreatic and hepatic PDFF and anthropometric indices decreased significantly.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1092-1102.

Body Composition Profiling in the UK Biobank Imaging Study

OBJECTIVE

This study aimed to investigate the value of imaging-based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels.

METHODS

The first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modeling.

RESULTS

Matched control analysis showed that higher VAT and MFI were associated with CHD and T2D (P < 0.001). Higher liver fat was associated with T2D (P < 0.001) and lower liver fat with CHD (P < 0.05), matching on VAT. Multivariable modeling showed that lower VAT and MFI were associated with metabolic health (P < 0.001), and liver fat was nonsignificant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity.

CONCLUSIONS

Body composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.

Advanced body composition assessment: from body mass index to body composition profiling

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment.

Unexpected Fat Distribution in Adolescents With Narcolepsy

Narcolepsy type 1 is a chronic sleep disorder with significantly higher BMI reported in more than 50% of adolescent patients, putting them at a higher risk for metabolic syndrome in adulthood. Although well-documented, the body fat distribution and mechanisms behind weight gain in narcolepsy are still not fully understood but may be related to the loss of orexin associated with the disease. Orexin has been linked to the regulation of brown adipose tissue (BAT), a metabolically active fat involved in energy homeostasis. Previous studies have used BMI and waist circumference to characterize adipose tissue increases in narcolepsy but none have investigated its specific distribution. Here, we examine adipose tissue distribution in 19 adolescent patients with narcolepsy type 1 and compare them to 17 of their healthy peers using full body magnetic resonance imaging (MRI). In line with previous findings we saw that the narcolepsy patients had more overall fat than the healthy controls, but contrary to our expectations there were no group differences in supraclavicular BAT, suggesting that orexin may have no effect at all on BAT, at least under thermoneutral conditions. Also, in line with previous reports, we observed that patients had more total abdominal adipose tissue (TAAT), however, we found that they had a lower ratio between visceral adipose tissue (VAT) and TAAT indicating a relative increase of subcutaneous abdominal adipose tissue (ASAT). This relationship between VAT and ASAT has been associated with a lower risk for metabolic disease. We conclude that while weight gain in adolescents with narcolepsy matches that of central obesity, the lower VAT ratio may suggest a lower risk of developing metabolic disease.

Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population

PURPOSE

To determine age- and gender-dependent whole-body adipose tissue and muscle volumes in healthy Swiss volunteers in Dixon MRI in comparison with anthropometric and bioelectrical impedance (BIA) measurements.

METHODS

Fat-water-separated whole-body 3 Tesla MRI of 80 healthy volunteers (ages 20 to 62 years) with a body mass index (BMI) of 17.5 to 26.2 kg/m² (10 men, 10 women per decade). Age and gender-dependent volumes of total adipose tissue (TAT), visceral adipose tissue (VAT), total abdominal subcutaneous adipose tissue (ASAT) and total abdominal adipose tissue (TAAT), and the total lean muscle tissue (TLMT) normalized for body height were determined by semi-automatic segmentation, and correlated with anthropometric and BIA measurements as well as lifestyle parameters.

RESULTS

The TAT, ASAT, VAT, and TLMT indexes (TATi, ASATi, VATi, and TLMTi, respectively) (L/m²  ± standard deviation) for women/men were 6.4 ± 1.8/5.3 ± 1.7, 1.6 ± 0.7/1.2 ± 0.5, 0.4 ± 0.2/0.8 ± 0.5, and 5.6 ± 0.6/7.1 ± 0.7, respectively. The TATi correlated strongly with ASATi (r > 0.93), VATi, BMI and BIA (r > 0.70), and TAATi (r > 0.96), and weak with TLMTi for both genders (r > -0.34). The VAT was the only parameter showing an age dependency (r > 0.32). The BMI and BIA showed strong correlation with all MR-derived adipose tissue volumes. The TAT mass was estimated significantly lower from BIA than from MRI (both genders P < .001; mean bias -5 kg).

CONCLUSIONS

The reported gender-specific MRI-based adipose tissue and muscle volumes might serve as normative values. The estimation of adipose tissue volumes was significantly lower from anthropometric and BIA measurements than from MRI. Magn Reson Med 79:449-458, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging-based, Semiautomated Analysis Method

Purpose To determine the repeatability and accuracy of a commercially available magnetic resonance (MR) imaging-based, semiautomated method to quantify abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction (PDFF). Materials and Methods This prospective study was institutional review board- approved and HIPAA compliant. All subjects provided written informed consent. Inclusion criteria were age of 18 years or older and willingness to participate. The exclusion criterion was contraindication to MR imaging. Three-dimensional T1-weighted dual-echo body-coil images were acquired three times. Source images were reconstructed to generate water and calibrated fat images. Abdominal adipose tissue and thigh muscle were segmented, and their volumes were estimated by using a semiautomated method and, as a reference standard, a manual method. Hepatic PDFF was estimated by using a confounder-corrected chemical shift-encoded MR imaging method with hybrid complex-magnitude reconstruction and, as a reference standard, MR spectroscopy. Tissue volume and hepatic PDFF intra- and interexamination repeatability were assessed by using intraclass correlation and coefficient of variation analysis. Tissue volume and hepatic PDFF accuracy were assessed by means of linear regression with the respective reference standards. Results Adipose and thigh muscle tissue volumes of 20 subjects (18 women; age range, 25-76 years; body mass index range, 19.3-43.9 kg/m²) were estimated by using the semiautomated method. Intra- and interexamination intraclass correlation coefficients were 0.996-0.998 and coefficients of variation were 1.5%-3.6%. For hepatic MR imaging PDFF, intra- and interexamination intraclass correlation coefficients were greater than or equal to 0.994 and coefficients of variation were less than or equal to 7.3%. In the regression analyses of manual versus semiautomated volume and spectroscopy versus MR imaging, PDFF slopes and intercepts were close to the identity line, and correlations of determination at multivariate analysis (R²) ranged from 0.744 to 0.994. Conclusion This MR imaging-based, semiautomated method provides high repeatability and accuracy for estimating abdominal adipose tissue and thigh muscle volumes and hepatic PDFF. ^© RSNA, 2017.

Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

INTRODUCTION

Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects.

MATERIALS AND METHODS

3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics.

RESULTS

Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view.

DISCUSSION AND CONCLUSIONS

In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.

Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla

OBJECTIVES

To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE).

MATERIALS AND METHODS

The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m² (30.02 ± 6.63 kg/m²) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes.

RESULTS

Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images.

CONCLUSION

Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images

OBJECTIVES

To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.

MATERIALS AND METHODS

The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects.

RESULTS

The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively.

CONCLUSION

Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate

Background

Fruit has since long been advocated as a healthy source of many nutrients, however, the high content of sugars in fruit might be a concern.

Objectives

To study effects of an increased fruit intake compared with similar amount of extra calories from nuts in humans.

Methods

Thirty healthy non-obese participants were randomized to either supplement the diet with fruits or nuts, each at +7 kcal/kg bodyweight/day for two months. Major endpoints were change of hepatic fat content (HFC, by magnetic resonance imaging, MRI), basal metabolic rate (BMR, with indirect calorimetry) and cardiovascular risk markers.

Results

Weight gain was numerically similar in both groups although only statistically significant in the group randomized to nuts (fruit: from 22.15±1.61 kg/m² to 22.30±1.7 kg/m², p = 0.24 nuts: from 22.54±2.26 kg/m² to 22.73±2.28 kg/m², p = 0.045). On the other hand BMR increased in the nut group only (p = 0.028). Only the nut group reported a net increase of calories (from 2519±721 kcal/day to 2763±595 kcal/day, p = 0.035) according to 3-day food registrations. Despite an almost three-fold reported increased fructose-intake in the fruit group (from 9.1±6.0 gram/day to 25.6±9.6 gram/day, p<0.0001, nuts: from 12.4±5.7 gram/day to 6.5±5.3 gram/day, p = 0.007) there was no change of HFC. The numerical increase in fasting insulin was statistical significant only in the fruit group (from 7.73±3.1 pmol/l to 8.81±2.9 pmol/l, p = 0.018, nuts: from 7.29±2.9 pmol/l to 8.62±3.0 pmol/l, p = 0.14). Levels of vitamin C increased in both groups while α-tocopherol/cholesterol-ratio increased only in the fruit group.

Conclusions

Although BMR increased in the nut-group only this was not linked with differences in weight gain between groups which potentially could be explained by the lack of reported net caloric increase in the fruit group. In healthy non-obese individuals an increased fruit intake seems safe from cardiovascular risk perspective, including measurement of HFC by MRI.

Trial Registration

ClinicalTrials.gov NCT02227511

Segmentation and quantification of adipose tissue by magnetic resonance imaging

In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water-fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.

Fully automatic and nonparametric quantification of adipose tissue in fat-water separation MR imaging

Despite increasing demand and research efforts, currently there is no consensus on the protocol for automated and reliable quantification of adipose tissue (AT) and visceral adipose tissue (VAT) using MRI. The purpose of this study was to propose a novel computational method with enhanced objectiveness for the quantification of AT and VAT in fat-water separation MRI. 3T data from IDEAL were acquired for the fat-water separation. Fat tissues were separated from nonfat regions (background air, bone, water, and other nonfat tissues) using K-means clustering (K = 2). From the binary fat mask, arm regions were separated from body based on the relative size of connected component. AT was obtained from the binary body fat mask. With the initial contour as the outer boundary of body fat, the subcutaneous adipose tissue (SAT) and VAT were separated using deformable model driven by a specifically generated deformation field pointing to the inner boundary of SAT. The proposed method was tested on 16 patients with dyslipidemia and evaluated by comparing the correlation with semi-automatic segmentation results. Good robustness was also observed in the proposed method from the Bland-Altman plots. Compared to other established fat segmentation methods, the proposed method is highly objective for fat-water separation MRI with minimal variability induced by subjective parameter settings.

Characterization of brown adipose tissue by water-fat separated magnetic resonance imaging

BACKGROUND

To evaluate the possibility of quantifying brown adipose tissue (BAT) volume and fat concentration with a high resolution, long echo time, dual-echo Dixon imaging protocol.

METHODS

A 0.42 mm isotropic resolution water-fat separated MRI protocol was implemented by using the second opposite-phase echo and third in-phase echo. Fat images were calibrated with regard to the intensity of nearby white adipose tissue (WAT) to form relative fat content (RFC) images. To evaluate the ability to measure BAT volume and RFC contrast dynamics, rats were divided into two groups that were kept at 4° or 22°C for 5 days. The rats were then scanned in a 70 cm bore 3.0 Tesla MRI scanner and a human dual energy CT. Interscapular, paraaortal, and perirenal BAT (i/pa/pr-BAT) depots as well as WAT and muscle were segmented in the MRI and CT images. Biopsies were collected from the identified BAT depots.

RESULTS

The biopsies confirmed that the three depots identified with the RFC images consisted of BAT. There was a significant linear correlation (P < 0.001) between the measured RFC and the Hounsfield units from DECT. Significantly lower iBAT RFC (P = 0.0064) and significantly larger iBAT and prBAT volumes (P = 0.0017) were observed in the cold stimulated rats.

CONCLUSION

The calibrated Dixon images with RFC scaling can depict BAT and be used to measure differences in volume, and fat concentration, induced by cold stimulation. The high correlation between RFC and HU suggests that the fat concentration is the main RFC image contrast mechanism.

Whole-body MRI-based fat quantification: a comparison to air displacement plethysmography

PURPOSE

To demonstrate the feasibility of an algorithm for MRI whole-body quantification of internal and subcutaneous fat and quantitative comparison of total adipose tissue to air displacement plethysmography (ADP).

MATERIALS AND METHODS

For comparison with ADP, whole-body MR data of 11 volunteers were obtained using a continuously moving table Dixon sequence. Resulting fat images were corrected for B1 related intensity inhomogeneities before fat segmentation.

RESULTS

The performed MR measurements of the whole body provided a direct comparison to ADP measurements. The segmentation of subcutaneous and internal fat in the abdomen worked reliably with an accuracy of 98%. Depending on the underlying model for fat quantification, the resultant MR fat masses represent an upper and a lower limit for the true fat masses. In comparison to ADP, the results were in good agreement with ρ ≥ 0.97, P < 0.0001.

CONCLUSION

Whole-body fat quantities derived noninvasively by using a continuously moving table Dixon acquisition were directly compared with ADP. The accuracy of the method and the high reproducibility of results indicate its potential for clinical applications.

Quantitative proton MR techniques for measuring fat

Accurate, precise and reliable techniques for the quantification of body and organ fat distributions are important tools in physiology research. They are critically needed in studies of obesity and diseases involving excess fat accumulation. Proton MR methods address this need by providing an array of relaxometry-based (T1, T2) and chemical shift-based approaches. These techniques can generate informative visualizations of regional and whole-body fat distributions, yield measurements of fat volumes within specific body depots and quantify fat accumulation in abdominal organs and muscles. MR methods are commonly used to investigate the role of fat in nutrition and metabolism, to measure the efficacy of short- and long-term dietary and exercise interventions, to study the implications of fat in organ steatosis and muscular dystrophies and to elucidate pathophysiological mechanisms in the context of obesity and its comorbidities. The purpose of this review is to provide a summary of mainstream MR strategies for fat quantification. The article succinctly describes the principles that differentiate water and fat proton signals, summarizes the advantages and limitations of various techniques and offers a few illustrative examples. The article also highlights recent efforts in the MR of brown adipose tissue and concludes by briefly discussing some future research directions.

Measurement of subcutaneous adipose tissue thickness by near-infrared

Obesity is strongly associated with the risks of diabetes and cardiovascular disease, and there is a need to measure the subcutaneous adipose tissue (SAT) layer thickness and to understand the distribution of body fat. A device was designed to illuminate the body parts by near-infrared (NIR), measure the backscattered light, and predict the SAT layer thickness. The device was controlled by a single-chip microcontroller (SCM), and the thickness value was presented on a liquid crystal display (LCD). There were 30 subjects in this study, and the measurements were performed on 14 body parts for each subject. The paper investigated the impacts of pressure and skin colour on the measurement. Combining with principal component analysis (PCA) and support vector regression (SVR), the measurement accuracy of SAT layer thickness was 89.1 % with a mechanical caliper as reference. The measuring range was 5-11 mm. The study provides a non-invasive and low-cost technique to detect subcutaneous fat thickness, which is more accessible and affordable compared to other conventional techniques. The designed device can be used at home and in community.

Evidence for two types of brown adipose tissue in humans

The previously observed supraclavicular depot of brown adipose tissue (BAT) in adult humans was commonly believed to be the equivalent of the interscapular thermogenic organ of small mammals. This view was recently disputed on the basis of the demonstration that this depot consists of beige (also called brite) brown adipocytes, a newly identified type of brown adipocyte that is distinct from the classical brown adipocytes that make up the interscapular thermogenic organs of other mammals. A combination of high-resolution imaging techniques and histological and biochemical analyses showed evidence for an anatomically distinguishable interscapular BAT (iBAT) depot in human infants that consists of classical brown adipocytes, a cell type that has so far not been shown to exist in humans. On the basis of these findings, we conclude that infants, similarly to rodents, have the bona fide iBAT thermogenic organ consisting of classical brown adipocytes that is essential for the survival of small mammals in a cold environment.

Decreased muscle concentrations of ATP and PCR in the quadriceps muscle of fibromyalgia patients--a 31P-MRS study

BACKGROUND AND METHODS

Fibromyalgia (FMS) has a prevalence of approximately 2% in the population. Central alterations have been described in FMS, but there is not consensus with respect to the role of peripheral factors for the maintenance of FMS. 31P magnetic resonance spectroscopy (31P-MRS) has been used to investigate the metabolism of phosphagens in muscles of FMS patients, but the results in the literature are not in consensus. The aim was to investigate the quantitative content of phosphagens and pH in resting quadriceps muscle of patients with FMS (n = 19) and in healthy controls (CONTROLS; n = 14) using (31) P-MRS. It was also investigated whether the concentrations of these substances correlated with measures of pain and/or physical capacity.

RESULTS

Significantly lower concentrations of adenosine triphosphate (ATP) and phosphocreatinine (PCr; 28-29% lower) were found in FMS. No significant group differences existed with respect to inorganic phosphate (Pi), Pi/PCr and pH. The quadriceps muscle fat content was significantly higher in FMS than in CONTROLS [FMS: 9.0 ± 0.5% vs.

CONTROLS

6.6 ± 0.6%; (mean ± standard error); P = 0.005]. FMS had significantly lower hand and leg capacity according to specific physical test, but there were no group differences in body mass index, subjective activity level and in aerobic fitness. In FMS, the specific physical capacity in the leg and the hand correlated positively with the concentrations of ATP and PCr; no significant correlations were found with pain intensities.

CONCLUSIONS

Alterations in intramuscular ATP, PCr and fat content in FMS probably reflect a combination of inactivity related to pain and dysfunction of muscle mitochondria.

Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI

PURPOSE

To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water-fat MRI data, and to evaluate its performance against manual segmentation.

MATERIALS AND METHODS

Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water-fat pulse sequence. Adipose tissue (subcutaneous--SAT, visceral--VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water-fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF).

RESULTS

Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF).

CONCLUSION

Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation.

Automated quantification of muscle and fat in the thigh from water-, fat-, and nonsuppressed MR images

PURPOSE

To introduce and validate an unsupervised muscle and fat quantification algorithm based on joint analysis of water-suppressed (WS), fat-suppressed (FS), and water and fat (nonsuppressed) volumetric magnetic resonance imaging (MRI) of the mid-thigh region.

MATERIALS AND METHODS

We first segmented the subcutaneous fat by use of a parametric deformable model, then applied centroid clustering in the feature domain defined by the voxel intensities in WS and FS images to identify the intermuscular fat and muscle. In the final step we computed volumetric and area measures of fat and muscle. We applied this algorithm on datasets of water-, fat-, and nonsuppressed volumetric MR images acquired from 28 participants.

RESULTS

We validated our tissue composition analysis against fat and muscle area measurements obtained from semimanual analysis of single-slice mid-thigh computed tomography (CT) images of the same participants and found very good agreement between the two methods. Furthermore, we compared the proposed approach with a variant that uses nonsuppressed images only and observed that joint analysis of WS and FS images is more accurate than the nonsuppressed only variant.

CONCLUSION

Our MRI algorithm produces accurate tissue quantification, is less labor-intensive, and more reproducible than the original CT-based workflow and can address interparticipant anatomic variability and intensity inhomogeneity effects.

Automated segmentation of micro-CT images of bone formation in calcium phosphate scaffolds

In this work, we develop and validate an automated micro-computed tomography (micro-CT) image segmentation algorithm that accurately and efficiently segments bone, calcium phosphate (CaP)-based bone scaffold, and soft tissue. The algorithm enables quantitative evaluation of bone growth in CaP scaffolds in our study that includes many samples (100+) and large data sets (900 images per sample). The use of micro-CT for such applications is otherwise limited because the similarity in X-ray attenuation for the two materials makes them indistinguishable. Destructive characterization using histological techniques and scanning electron microscopy (SEM) has been the standard for CaP scaffolds, but these methods are cumbersome, inaccurate, and yield only 2D information. The proposed algorithm exploits scaffold periodicity and combines signal analysis, edge detection, and knowledge of three-dimensional spatial relationships between bone, CaP scaffold, and soft tissue to achieve fast and accurate segmentation. Application of this algorithm can lead to a new understanding of the role of CaP and scaffold internal structure on patterns and rates of bone growth.