Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh

Association between lumbar muscle size and bone mineral density in nonfractured postmenopausal women with and without osteoporosis

OBJECTIVE

Estrogen deficiency in postmenopausal women is associated with bone loss and a decline in muscle mass. However, the associations between lumbar muscle size and bone mineral density (BMD) in postmenopausal women with and without osteoporosis remain unclear. The aim of this study was to investigate the associations between lumbar muscle size and BMD in nonfractured postmenopausal women with osteoporosis and those with osteopenia.

METHODS

A total of 89 postmenopausal women with osteopenia (n = 53) and osteoporosis (n = 36) were retrospectively enrolled in this study from 2014 to 2022. All participants underwent lumbar magnetic resonance imaging and dual-energy absorptiometry within a month. The lean lumbar muscle sizes at different lumbar levels were quantitatively evaluated on axial T1-weighted images. The associations between lumbar muscle size and BMD were analyzed using Pearson's correlation analysis.

RESULTS

The osteoporosis group had significantly smaller lean psoas muscle sizes than the osteopenia group. Based on the correlation analysis, the erector spinae and multifidus muscle sizes were significantly associated with lumbar and femoral neck BMDs in the osteoporosis group. However, no significant association was found between lean psoas muscle size and BMDs in the osteopenia group. Thus, the associations between lumbar muscle decline and bone loss differed between postmenopausal women with osteoporosis and those with osteopenia.

CONCLUSIONS

The study findings suggest differences in the associations between BMD and lumbar muscle size between postmenopausal women with osteoporosis and those with osteopenia.

The Clinical Development of Taldefgrobep Alfa: An Anti-Myostatin Adnectin for the Treatment of Duchenne Muscular Dystrophy

INTRODUCTION

Duchenne muscular dystrophy (DMD) is a genetic muscle disorder that manifests during early childhood and is ultimately fatal. Recently approved treatments targeting the genetic cause of DMD are limited to specific subpopulations of patients, highlighting the need for therapies with wider applications. Pharmacologic inhibition of myostatin, an endogenous inhibitor of muscle growth produced almost exclusively in skeletal muscle, has been shown to increase muscle mass in several species, including humans. Taldefgrobep alfa is an anti-myostatin recombinant protein engineered to bind to and block myostatin signaling. Preclinical studies of taldefgrobep alfa demonstrated significant decreases in myostatin and increased lower limb volume in three animal species, including dystrophic mice.

METHODS

This manuscript reports the cumulative data from three separate clinical trials of taldefgrobep alfa in DMD: a phase 1 study in healthy adult volunteers (NCT02145234), and two randomized, double-blind, placebo-controlled studies in ambulatory boys with DMD-a phase 1b/2 trial assessing safety (NCT02515669) and a phase 2/3 trial including the North Star Ambulatory Assessment (NSAA) as the primary endpoint (NCT03039686).

RESULTS

In healthy adult volunteers, taldefgrobep alfa was generally well tolerated and resulted in a significant increase in thigh muscle volume. Treatment with taldefgrobep alfa was associated with robust dose-dependent suppression of free myostatin. In the phase 1b/2 trial, myostatin suppression was associated with a positive effect on lean body mass, though effects on muscle mass were modest. The phase 2/3 trial found that the effects of treatment did not meet the primary endpoint pre-specified futility analysis threshold (change from baseline of ≥ 1.5 points on the NSAA total score).

CONCLUSIONS

The futility analysis demonstrated that taldefgrobep alfa did not result in functional change for boys with DMD. The program was subsequently terminated in 2019. Overall, there were no safety concerns, and no patients were withdrawn from treatment as a result of treatment-related adverse events or serious adverse events.

TRIAL REGISTRATION

NCT02145234, NCT02515669, NCT03039686.

Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T2 analysis

MRI's T₂ relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T₂ relaxation time. In this proof-of-concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T₂ values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary-based technique that offers accurate and reproducible mapping of T₂ relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T₂ and proton-density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T₂ value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.

Multi-echo MR thermometry in the upper leg at 7 T using near-harmonic 2D reconstruction for initialization

PURPOSE

The aim of this work is the development of a thermometry method to measure temperature increases in vivo, with a precision and accuracy sufficient for validation against thermal simulations. Such an MR thermometry model would be a valuable tool to get an indication on one of the major safety concerns in MR imaging: the tissue heating occurring due to radiofrequency (RF) exposure. To prevent excessive temperature rise, RF power deposition, expressed as specific absorption rate, cannot exceed predefined thresholds. Using these thresholds, MRI has demonstrated an extensive history of safe usage. Nevertheless, MR thermometry would be a valuable tool to address some of the unmet needs in the area of RF safety assessment, such as validation of specific absorption rate and thermal simulations, investigation of local peak temperatures during scanning, or temperature-based safety guidelines.

METHODS

The harmonic initialized model-based multi-echo approach is proposed. The method combines a previously published model-based multi-echo water/fat separated approach with an also previously published near-harmonic 2D reconstruction method. The method is tested on the human thigh with a multi-transmit array at 7 T, in three volunteers, and for several RF shims.

RESULTS

Precision and accuracy are improved considerably compared to a previous fat-referenced method (precision: 0.09 vs. 0.19°C). Comparison of measured temperature rise distributions to subject-specific simulated counterparts show good relative agreement for multiple RF shim settings.

CONCLUSION

The high precision shows promising potential for validation purposes and other RF safety applications.

Longitudinal Changes in MRI Muscle Morphometry and Composition in People With Inclusion Body Myositis

BACKGROUND AND OBJECTIVES

Limited data suggest that quantitative MRI (qMRI) measures have potential to be used as trial outcome measures in sporadic inclusion body myositis (sIBM) and as a noninvasive assessment tool to study sIBM muscle pathologic processes. Our aim was to evaluate changes in muscle structure and composition using a comprehensive multiparameter set of qMRI measures and to assess construct validity and responsiveness of qMRI measures in people with sIBM.

METHODS

This was a prospective observational cohort study with assessments at baseline (n = 30) and 1 year (n = 26). qMRI assessments include thigh muscle volume (TMV), inter/intramuscular adipose tissue (IMAT), muscle fat fraction (FF), muscle inflammation (T2 relaxation time), IMAT from T2* relaxation (T2*-IMAT), intermuscular connective tissue from T2* relaxation (T2*-IMCT), and muscle macromolecular structure from the magnetization transfer ratio (MTR). Physical performance assessments include sIBM Physical Functioning Assessment (sIFA), 6-minute walk distance, and quantitative muscle testing of the quadriceps. Correlations were assessed using the Spearman correlation coefficient. Responsiveness was assessed using the standardized response mean (SRM).

RESULTS

After 1 year, we observed a reduction in TMV (6.8%, p < 0.001) and muscle T2 (6.7%, p = 0.035), an increase in IMAT (9.7%, p < 0.001), FF (11.2%, p = 0.030), connective tissue (22%, p = 0.995), and T2*-IMAT (24%, p < 0.001), and alteration in muscle macromolecular structure (ΔMTR = -26%, p = 0.002). A decrease in muscle T2 correlated with an increase in T2*-IMAT (r = -0.47, p = 0.008). Deposition of connective tissue and IMAT correlated with deterioration in sIFA (r = 0.38, p = 0.032; r = 0.34, p = 0.048; respectively), whereas a decrease in TMV correlated with a decrease in quantitative muscle testing (r = 0.36, p = 0.035). The most responsive qMRI measures were T2*-IMAT (SRM = 1.50), TMV (SRM = -1.23), IMAT (SRM = 1.20), MTR (SRM = -0.83), and T2 relaxation time (SRM = -0.65).

DISCUSSION

Progressive deterioration in muscle quality measured by qMRI is associated with a decline in physical performance. Inflammation may play a role in triggering fat infiltration into muscle. qMRI provides valid and responsive measures that might prove valuable in sIBM experimental trials and assessment of muscle pathologic processes.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that qMRI outcome measures are associated with physical performance measures in patients with sIBM.

Quantitative magnetic resonance imaging measures as biomarkers of disease progression in boys with Duchenne muscular dystrophy: a phase 2 trial of domagrozumab

Duchenne muscular dystrophy (DMD) is a progressive, neuromuscular disorder caused by mutations in the DMD gene that results in a lack of functional dystrophin protein. Herein, we report the use of quantitative magnetic resonance imaging (MRI) measures as biomarkers in the context of a multicenter phase 2, randomized, placebo-controlled clinical trial evaluating the myostatin inhibitor domagrozumab in ambulatory boys with DMD (n = 120 aged 6 to < 16 years). MRI scans of the thigh to measure muscle volume, muscle volume index (MVI), fat fraction, and T2 relaxation time were obtained at baseline and at weeks 17, 33, 49, and 97 as per protocol. These quantitative MRI measurements appeared to be sensitive and objective biomarkers for evaluating disease progression, with significant changes observed in muscle volume, MVI, and T2 mapping measures over time. To further explore the utility of quantitative MRI measures as biomarkers to inform longer term functional changes in this cohort, a regression analysis was performed and demonstrated that muscle volume, MVI, T2 mapping measures, and fat fraction assessment were significantly correlated with longer term changes in four-stair climb times and North Star Ambulatory Assessment functional scores. Finally, less favorable baseline measures of MVI, fat fraction of the muscle bundle, and fat fraction of lean muscle were significant risk factors for loss of ambulation over a 2-year monitoring period. These analyses suggest that MRI can be a valuable tool for use in clinical trials and may help inform future functional changes in DMD.Trial registration: ClinicalTrials.gov identifier, NCT02310763; registered December 2014.

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation

Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

Reliability and Validity of Abdominal Skeletal Muscle Area Measurement Using Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

Skeletal muscle mass measurement is the most important element for diagnosing sarcopenia. MRI has an excellent soft-tissue contrast, which can non-invasively assess abdominal skeletal muscle area (SMA) as well as CT. This study aimed to assess the validity and reliability of abdominal SMA measurement by comparing CT and MRI based on the fat image of IDEAL-IQ sequence at the lumbar level mid-L3.

MATERIALS AND METHODS

CT and MRI images of 32 patients diagnosed with various kidney diseases were used to analyze intra-observer variability among abdominal SMA measurements. This was done to evaluate the correlation of SMA between CT and fat images of MRI. SMA images were segmented using Materialise Mimics software before quantification. Interobserver reliability and validation of measurements was evaluated by two independent investigators. Abdominal SMA reproducibility and correlation between CT and MRI were then assessed using the intraclass correlation coefficient (ICC), coefficient of variation (CV), Bland-Altman plot, and Pearson's correlation coefficient respectively.

RESULTS

The interobserver reliability of MRI was excellent. The CV value was 2.82% while the ICC values ranged between 0.996 and 0.999. Validity was high (CV was 1.7% and ICC ranged between 0.986 and 0.996) for measurements by MRI and CT. Bland Altman analysis revealed an average difference of 2.2% between MRI and CT. The Pearson's correlation coefficient was 0.995 (p < 0.0001). This result revealed that there was a strong correlation between the two technologies.

CONCLUSION

MRI exhibited good interobserver reliability and excellent agreement with CT for quantification of abdominal SMA.

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy

Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance of 2D, 3D, and hybrid deep learning models for automatic segmentation of 11 lower leg muscles and two bones from MRI scans of children with and without cerebral palsy. All six models were trained and evaluated on manually segmented T₁ -weighted MRI scans of the lower legs of 20 children, six of whom had cerebral palsy. The segmentation results were assessed using the median Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and volume error (VError) of all 13 labels of every scan. The best performance was achieved by H-DenseUNet, a hybrid model (DSC 0.90, ASSD 0.5 mm, and VError 2.6 cm³ ). The performance was equivalent to the inter-rater performance of manual segmentation (DSC 0.89, ASSD 0.6 mm, and VError 3.3 cm³ ). Models trained with the Dice loss function outperformed models trained with the cross-entropy loss function. Near-optimal performance could be attained using only 11 scans for training. Segmentation performance was similar for scans of typically developing children (DSC 0.90, ASSD 0.5 mm, and VError 2.8 cm³ ) and children with cerebral palsy (DSC 0.85, ASSD 0.6 mm, and VError 2.4 cm³ ). These findings demonstrate the feasibility of fully automatic segmentation of individual muscles and bones from MRI scans of children with and without cerebral palsy.

Quantitative muscle MRI biomarkers in Duchenne muscular dystrophy: cross-sectional correlations with age and functional tests

Aim: Using baseline data from a clinical trial of domagrozumab in Duchenne muscular dystrophy, we evaluated the correlation between functional measures and quantitative MRI assessments of thigh muscle. Patients & methods: Analysis included timed functional tests, knee extension/strength and North Star Ambulatory Assessment. Patients (n = 120) underwent examinations of one thigh, with MRI sequences to enable measurements of muscle volume (MV), MV index, mean T2 relaxation time via T2-mapping and fat fraction. Results: MV was moderately correlated with strength assessments. MV index, fat fraction and T2-mapping measures had moderate correlations (r ∼ 0.5) to all functional tests, North Star Ambulatory Assessment and age. Conclusion: The moderate correlation between functional tests, age and baseline MRI measures supports MRI as a biomarker in Duchenne muscular dystrophy clinical trials. Trial registration: ClinicalTrials.gov, NCT02310763; registered 4 November 2014.

Effects of 16 months of high intensity resistance training on thigh muscle fat infiltration in elderly men with osteosarcopenia

Osteosarcopenia is characterized by a progressive decline in muscle function and bone strength and associated with muscle fat accumulation. This study aimed to determine the effect of long-term high intensity resistance training (HIRT) on thigh muscle fat infiltration in older men with osteosarcopenia. Forty-three community-dwelling men (72 years and older) were randomly assigned to either an exercise group (EG, n = 21) or an inactive control group (CG, n = 22). EG participants performed a supervised single-set exercise training with high effort two times per week. Participants of both groups were individually provided with dietary protein to reach a cumulative intake of 1.5-1.6 g/kg/day or 1.2-1.3 g/kg/day (EG/CG), respectively, and Up to 10,000 IE/week of Vitamin-D were supplemented in participants with 25 OH Vitamin-D 3 levels below 100 nmol/l. Magnetic resonance (MR) imaging was performed to determine muscle and adipose tissue volume and fat fraction of the thigh. At baseline, there were no significant differences between the two groups. After 16 month,, there were significant training effects of 15% (p = 0.004) on intermuscular adipose tissue (IMAT) volume, which increased in the CG (p = 0.012) and was stable in the EG. In parallel, fat fraction within the deep fascia of the thigh (Baseline, EG: 18.2 vs CG: 15.5, p = 0.16) significantly differed between the groups (Changes, EG: 0.77% vs. CG: 7.7%, p = 0.009). The study confirms the role of fat infiltration of the muscles as an advanced imaging marker in osteosarcopenia and the favorable effects of HIRT on adipose tissue volume of the thigh, in men with osteosarcopenia.

Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

Both muscle quantity and quality are predictors of waiting list mortality in patients with end-stage liver disease

BACKGROUND AND AIMS

Malnutrition is highly prevalent in patients with end-stage liver disease (ESLD) and associated with impaired clinical outcome. Previous studies focused on one component of body composition and not in combination with nutritional intake, while both are components of the nutritional status. We aimed to evaluate the most important risk factors regarding body composition (muscle mass, muscle quality and fat mass) and nutritional intake (energy and protein intake) for waiting list mortality in patients with ESLD awaiting liver transplantation (LTx).

METHODS

Consecutive patients with ESLD listed for LTx between 2007 and 2014 were investigated. Muscle mass quantity (Skeletal Muscle Mass Index, SMI), and muscle quality (Muscle Attenuation, MA), and various body fat compartments were measured on computed tomography using SliceOmatic. Nutritional intake (e.g. energy and protein intake) was assessed. Multivariable stepwise forward Cox regression analysis was used for statistical analysis.

RESULTS

261 Patients (mean age 54 years, 74.7% male) were included. Low SMI and MA were found to be statistically significant predictors of an increased risk for waiting list mortality in patients with ESLD, with a HR of 2.580 (95%CI 1.055-6.308) and HR of 9.124 (95%CI 2.871-28.970), respectively. No association between percentage adipose tissue, and protein and energy intake with waiting list mortality was found in this study.

CONCLUSION

Both low muscle quantity and quality, and not nutritional intake, were independent risk factors for mortality in patients with ESLD.

Skeletal Muscle Magnetic Resonance Biomarkers in GNE Myopathy

OBJECTIVE

To characterize muscle involvement and evaluate disease severity in patients with GNE myopathy using skeletal muscle MRI and proton magnetic resonance spectroscopy (¹H-MRS).

METHODS

Skeletal muscle imaging of the lower extremities was performed in 31 patients with genetically confirmed GNE myopathy, including T1-weighted and short tau inversion recovery (STIR) images, T1 and T2 mapping, and ¹H-MRS. Measures evaluated included longitudinal relaxation time (T1), transverse relaxation time (T2), and ¹H-MRS fat fraction (FF). Thigh muscle volume was correlated with relevant measures of strength, function, and patient-reported outcomes.

RESULTS

The cohort was representative of a wide range of disease progression. Contractile thigh muscle volume ranged from 5.51% to 62.95% and correlated with thigh strength (r = 0.91), the 6-minute walk test (r = 0.82), the adult myopathy assessment tool (r = 0.83), the activities-specific balance confidence scale (r = 0.65), and the inclusion body myositis functional rating scale (r = 0.62). Four stages of muscle involvement were distinguished by qualitative (T1W and STIR images) and quantitative methods: stage I: unaffected muscle (T1 = 1,033 ± 74.2 ms, T2 = 40.0 ± 1.9 ms, FF = 7.4 ± 3.5%); stage II: STIR hyperintense muscle with minimal or no fat infiltration (T1 = 1,305 ± 147 ms, T2 = 50.2 ± 3.5 ms, FF = 27.6 ± 12.7%); stage III: fat infiltration and STIR hyperintensity (T1 = 1,209 ± 348 ms, T2 = 73.3 ± 12.6 ms, FF = 57.5 ± 10.6%); and stage IV: complete fat replacement (T1 = 318 ± 39.9 ms, T2 = 114 ± 21.2 ms, FF = 85.6 ± 4.2%). ¹H-MRS showed a significant decrease in intramyocellular lipid and trimethylamines between stage I and II, suggesting altered muscle metabolism at early stages.

CONCLUSION

MRI biomarkers can monitor muscle involvement and determine disease severity noninvasively in patients with GNE myopathy.

CLINICALTRIALSGOV IDENTIFIER

NCT01417533.

A Valid and Precise Semiautomated Method for Quantifying Intermuscular Fat Intramuscular Fat in Lower Leg Magnetic Resonance Images

The accumulation of INTERmuscular fat and INTRAmuscular fat (IMF) has been a hallmark of individuals with diabetes, those with mobility impairments such as spinal cord injuries and is known to increase with aging. An elevated amount of IMF has been associated with fractures and frailty, but the imprecision of IMF measurement has so far limited the ability to observe more consistent clinical associations. Magnetic resonance imaging has been recognized as the gold standard for portraying these features, yet reliable methods for quantifying IMF on magnetic resonance imaging is far from standardized. Previous investigators used manual segmentation guided by histogram-based region-growing, but these techniques are subjective and have not demonstrated reliability. Others applied fuzzy classification, machine learning, and atlas-based segmentation methods, but each is limited by the complexity of implementation or by the need for a learning set, which must be established each time a new disease cohort is examined. In this paper, a simple convergent iterative threshold-optimizing algorithm was explored. The goal of the algorithm is to enable IMF quantification from plain fast spin echo (FSE) T1-weighted MR images or from water-saturated images. The algorithm can be programmed into Matlab easily, and is semiautomated, thus minimizing the subjectivity of threshold-selection. In 110 participants from 3 cohort studies, IMF area measurement demonstrated a high degree of reproducibility with errors well within the 5% benchmark for intraobserver, interobserver, and test-retest analyses; in contrast to manual segmentation which already yielded over 20% error for intraobserver analysis. This algorithm showed validity against manual segmentations (r > 0.85). The simplicity of this technique lends itself to be applied to fast spin echo images commonly ordered as part of standard of care and does not require more advanced fat-water separated images.

Segmentation of the fascia lata and reproducible quantification of intermuscular adipose tissue (IMAT) of the thigh

Objective

To develop a precise semi-automated segmentation of the fascia lata (FL) of the thigh to quantify IMAT volume in T₁w MR images and fat fraction (FF) in Dixon MR images.

Materials and methods

A multi-step segmentation approach was developed to identify fibrous structures of the FL and combining them into a closed 3D surface. 23 healthy young men with low and 50 elderly sarcopenic men with moderate levels of IMAT were measured by T₁w and 6pt Dixon MRI at 3T. 20 datasets were used to determine reanalysis precision errors. IMAT volume was compared using the new FL segmentation versus an easier to segment but less accurate, tightly fitting envelope of the thigh muscle ensemble.

Results

The segmentation was successfully applied to all 73 datasets and took about 7 min per 28 slices. In particular, in elderly subjects, it includes a large amount of adipose tissue below the FL typically not accounted for in other segmentation approaches. Inter- and intra-operator RMS-CVs were 0.33% and 0.14%, respectively, for IMAT volume and 0.04% and 0.02%, respectively, for FF_MT.

Discussion

The FL segmentation is an important step to quantify IMAT with high precision and may be useful to investigate effects of aging and treatment on changes of IMAT and FF. ClinicalTrials.gov identifier NCT2857660, August 5, 2016.

Trial registration

ClinicalTrials.gov identifier NCT2857660, August 5, 2016.

A Comparison of Peripheral Imaging Technologies for Bone and Muscle Quantification: A Review of Segmentation Techniques

Musculoskeletal science has developed many overlapping branches, necessitating specialists from 1 area of focus to often require the expertise in others. In terms of imaging, this means obtaining a comprehensive illustration of bone, muscle, and fat tissues. There is currently a lack of a reliable resource for end users to learn about these tissues' imaging and quantification techniques together. An improved understanding of these tissues has been an important progression toward better prediction of disease outcomes and better elucidation of their interaction with frailty, aging, and metabolic disorders. Over the last decade, there have been major advances into the image acquisition and segmentation of bone, muscle, and fat features using computed tomography (CT), magnetic resonance imaging (MRI), and peripheral modules of these systems. Dedicated peripheral quantitative musculoskeletal imaging systems have paved the way for mobile research units, lower cost clinical research facilities, and improved resolution per unit cost paid. The purpose of this review was to detail the segmentation techniques available for each of these peripheral CT and MRI modalities and to describe advances in segmentation methods as applied to study longitudinal changes and treatment-related dynamics. Although the peripheral CT units described herein have established feasible standardized protocols that users have adopted globally, there remain challenges in standardizing MRI protocols for bone and muscle imaging.

Activin Type II Receptor Blockade for Treatment of Muscle Depletion in Chronic Obstructive Pulmonary Disease. A Randomized Trial

RATIONALE

Bimagrumab is a fully human monoclonal antibody that blocks the activin type II receptors, preventing the activity of myostatin and other negative skeletal muscle regulators.

OBJECTIVES

To assess the effects of bimagrumab on skeletal muscle mass and function in patients with chronic obstructive pulmonary disease (COPD) and reduced skeletal muscle mass.

METHODS

Sixty-seven patients with COPD (mean FEV₁, 1.05 L [41.6% predicted]; aged 40-80 yr; body mass index < 20 kg/m² or appendicular skeletal muscle mass index ≤ 7.25 [men] and ≤ 5.67 [women] kg/m²), received two doses of either bimagrumab 30 mg/kg intravenously (n = 33) or placebo (n = 34) (Weeks 0 and 8) over 24 weeks.

MEASUREMENTS AND MAIN RESULTS

We assessed changes in thigh muscle volume (cubic centimeters) as the primary endpoint along with 6-minute-walk distance (meters), safety, and tolerability. Fifty-five (82.1%) patients completed the study. Thigh muscle volume increased by Week 4 and remained increased at Week 24 in bimagrumab-treated patients, whereas no changes were observed with placebo (Week 4: +5.9% [SD, 3.4%] vs. 0.0% [3.3%], P < 0.001; Week 8: +7.0% [3.7%] vs. -0.7% [2.8%], P < 0.001; Week 16: +7.8% [5.1%] vs. -0.9% [4.5%], P < 0.001; Week 24: +5.0% [4.9%] vs. -1.3% [4.3%], P < 0.001). Over 24 weeks, 6-minute-walk distance did not increase significantly in either group. Adverse events in the bimagrumab group included muscle-related symptoms, diarrhea, and acne, most of which were mild in severity.

CONCLUSIONS

Blocking the action of negative muscle regulators through the activin type II receptors with bimagrumab treatment safely increased skeletal muscle mass but did not improve functional capacity in patients with COPD and low muscle mass. Clinical trial registered with www.clinicaltrials.gov (NCT01669174).

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93±0.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population.

Domain-specific data augmentation for segmenting MR images of fatty infiltrated human thighs with neural networks

BACKGROUND

Fat-fraction has been established as a relevant marker for the assessment and diagnosis of neuromuscular diseases. For computing this metric, segmentation of muscle tissue in MR images is a first crucial step.

PURPOSE

To tackle the high degree of variability in combination with the high annotation effort for training supervised segmentation models (such as fully convolutional neural networks).

STUDY TYPE

Prospective.

SUBJECTS

In all, 41 patients consisting of 20 patients showing fatty infiltration and 21 healthy subjects. Field Strength/Sequence: The T₁ -weighted MR-pulse sequences were acquired on a 1.5T scanner.

ASSESSMENT

To increase performance with limited training data, we propose a domain-specific technique for simulating fatty infiltrations (i.e., texture augmentation) in nonaffected subjects' MR images in combination with shape augmentation. For simulating the fatty infiltrations, we make use of an architecture comprising several competing networks (generative adversarial networks) that facilitate a realistic artificial conversion between healthy and infiltrated MR images. Finally, we assess the segmentation accuracy (Dice similarity coefficient).

STATISTICAL TESTS

A Wilcoxon signed rank test was performed to assess whether differences in segmentation accuracy are significant.

RESULTS

The mean Dice similarity coefficients significantly increase from 0.84-0.88 (P < 0.01) using data augmentation if training is performed with mixed data and from 0.59-0.87 (P < 0.001) if training is conducted with healthy subjects only.

DATA CONCLUSION

Domain-specific data adaptation is highly suitable for facilitating neural network-based segmentation of thighs with feasible manual effort for creating training data. The results even suggest an approach completely bypassing manual annotations.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3.

Myosteatosis predicts survival after surgery for periampullary cancer: a novel method using MRI

BACKGROUND

Myosteatosis, characterized by inter- and intramyocellular fat deposition, is strongly related to poor overall survival after surgery for periampullary cancer. It is commonly assessed by calculating the muscle radiation attenuation on computed tomography (CT) scans. However, since magnetic resonance imaging (MRI) is replacing CT in routine diagnostic work-up, developing methods based on MRI is important. We developed a new method using MRI-muscle signal intensity to assess myosteatosis and compared it with CT-muscle radiation attenuation.

METHODS

Patients were selected from a prospective cohort of 236 surgical patients with periampullary cancer. The MRI-muscle signal intensity and CT-muscle radiation attenuation were assessed at the level of the third lumbar vertebra and related to survival.

RESULTS

Forty-seven patients were included in the study. Inter-observer variability for MRI assessment was low (R² = 0.94). MRI-muscle signal intensity was associated with short survival: median survival 9.8 (95%-CI: 1.5-18.1) vs. 18.2 (95%-CI: 10.7-25.8) months for high vs. low intensity, respectively (p = 0.038). Similar results were found for CT-muscle radiation attenuation (low vs. high radiation attenuation: 10.8 (95%-CI: 8.5-13.1) vs. 15.9 (95%-CI: 10.2-21.7) months, respectively; p = 0.046). MRI-signal intensity correlated negatively with CT-radiation attenuation (r=-0.614, p < 0.001).

CONCLUSIONS

Myosteatosis may be adequately assessed using either MRI-muscle signal intensity or CT-muscle radiation attenuation.

Precision of MRI-based body composition measurements of postmenopausal women

OBJECTIVES

To determine precision of magnetic resonance imaging (MRI) based fat and muscle quantification in a group of postmenopausal women. Furthermore, to extend the method to individual muscles relevant to upper-body exercise.

MATERIALS AND METHODS

This was a sub-study to a randomized control trial investigating effects of resistance training to decrease hot flushes in postmenopausal women. Thirty-six women were included, mean age 56 ± 6 years. Each subject was scanned twice with a 3.0T MR-scanner using a whole-body Dixon protocol. Water and fat images were calculated using a 6-peak lipid model including R2*-correction. Body composition analyses were performed to measure visceral and subcutaneous fat volumes, lean volumes and muscle fat infiltration (MFI) of the muscle groups' thigh muscles, lower leg muscles, and abdominal muscles, as well as the three individual muscles pectoralis, latissimus, and rhomboideus. Analysis was performed using a multi-atlas, calibrated water-fat separated quantification method. Liver-fat was measured as average proton density fat-fraction (PDFF) of three regions-of-interest. Precision was determined with Bland-Altman analysis, repeatability, and coefficient of variation.

RESULTS

All of the 36 included women were successfully scanned and analysed. The coefficient of variation was 1.1% to 1.5% for abdominal fat compartments (visceral and subcutaneous), 0.8% to 1.9% for volumes of muscle groups (thigh, lower leg, and abdomen), and 2.3% to 7.0% for individual muscle volumes (pectoralis, latissimus, and rhomboideus). Limits of agreement for MFI was within ± 2.06% for muscle groups and within ± 5.13% for individual muscles. The limits of agreement for liver PDFF was within ± 1.9%.

CONCLUSION

Whole-body Dixon MRI could characterize a range of different fat and muscle compartments with high precision, including individual muscles, in the study-group of postmenopausal women. The inclusion of individual muscles, calculated from the same scan, enables analysis for specific intervention programs and studies.

Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy

The muscular dystrophies are rare orphan diseases, characterized by progressive muscle weakness: the most common and well known is Duchenne muscular dystrophy which affects young boys and progresses quickly during childhood. However, over 70 distinct variants have been identified to date, with different rates of progression, implications for morbidity, mortality, and quality of life. There are presently no curative therapies for these diseases, but a range of potential therapies are presently reaching the stage of multi-centre, multi-national first-in-man clinical trials. There is a need for sensitive, objective end-points to assess the efficacy of the proposed therapies. Present clinical measurements are often too dependent on patient effort or motivation, and lack sensitivity to small changes, or are invasive. Quantitative MRI to measure the fat replacement of skeletal muscle by either chemical shift imaging methods (Dixon or IDEAL) or spectroscopy has been demonstrated to provide such a sensitive, objective end-point in a number of studies. This review considers the importance of the outcome measures, discusses the considerations required to make robust measurements and appropriate quality assurance measures, and draws together the existing literature for cross-sectional and longitudinal cohort studies using these methods in muscular dystrophy.

Combined quantification of fatty infiltration, T 1-relaxation times and T 2*-relaxation times in normal-appearing skeletal muscle of controls and dystrophic patients

OBJECTIVES

To evaluate the combination of a fat-water separation method with an automated segmentation algorithm to quantify the intermuscular fatty-infiltrated fraction, the relaxation times, and the microscopic fatty infiltration in the normal-appearing muscle.

MATERIALS AND METHODS

MR acquisitions were performed at 1.5T in seven patients with facio-scapulo-humeral dystrophy and eight controls. Disease severity was assessed using commonly used scales for the upper and lower limbs. The fat-water separation method provided proton density fat fraction (PDFF) and relaxation times maps (T ₂* and T ₁). The segmentation algorithm distinguished adipose tissue and normal-appearing muscle from the T ₂* map and combined active contours, a clustering analysis, and a morphological closing process to calculate the index of fatty infiltration (IFI) in the muscle compartment defined as the relative amount of pixels with the ratio between the number of pixels within IMAT and the total number of pixels (IMAT + normal appearing muscle).

RESULTS

In patients, relaxation times were longer and a larger fatty infiltration has been quantified in the normal-appearing muscle. T ₂* and PDFF distributions were broader. The relaxation times were correlated to the Vignos scale whereas the microscopic fatty infiltration was linked to the Medwin-Gardner-Walton scale. The IFI was linked to a composite clinical severity scale gathering the whole set of scales.

CONCLUSION

The MRI indices quantified within the normal-appearing muscle could be considered as potential biomarkers of dystrophies and quantitatively illustrate tissue alterations such as inflammation and fatty infiltration.

Association of Quadriceps Muscle Fat With Isometric Strength Measurements in Healthy Males Using Chemical Shift Encoding-Based Water-Fat Magnetic Resonance Imaging

Magnetic resonance–based assessment of quadriceps muscle fat has been proposed as surrogate marker in sarcopenia, osteoarthritis, and neuromuscular disorders. We presently investigated the association of quadriceps muscle fat with isometric strength measurements in healthy males using chemical shift encoding-based water-fat magnetic resonance imaging. Intermuscular adipose tissue fraction and intramuscular proton density fat fraction correlated significantly (P < 0.05) with isometric strength (up to r = −0.83 and −0.87, respectively). Reproducibility of intermuscular adipose tissue fraction and intramuscular proton density fat fraction was 1.5% and 5.7%, respectively.

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.

The role of MRI in understanding the underlying mechanisms in obesity associated diseases

Obesity and its possible association with diseases including diabetes and cardiovascular diseases have been studied for decades for its impact on healthcare. Recent studies clearly indicate the need for developing accurate and reproducible methodologies for assessing body fat content and distribution. Body fat distribution plays a significant role in developing an insight in the underlying mechanisms in which adipose tissue is linked with various diseases. Among imaging technologies including computerized axial tomography (CAT or CT), magnetic resonance imaging (MRI), and magnetic resonance spectroscopy (MRS), MRI and MRS seem to be the best emerging techniques and together are being considered as the gold standard for body fat content and distribution. This paper reviews studies up to the present time involving different methodologies of these two emerging technologies and presents the basic concepts of MRI and MRS with required novel image analysis techniques in accurate, quantitative, and direct assessment of body fat content and distribution. This article is part of a Special Issue entitled: Oxidative Stress and Mitochondrial Quality in Diabetes/Obesity and Critical Illness Spectrum of Diseases - edited by P. Hemachandra Reddy.

Localization and quantification of intramuscular damage using statistical parametric mapping and skeletal muscle parcellation

In the present study, we proposed an original and robust methodology which combines the spatial normalization of skeletal muscle images, the statistical parametric mapping (SPM) analysis and the use of a specific parcellation in order to accurately localize and quantify the extent of skeletal muscle damage within the four heads of the quadriceps femoris. T₂ maps of thigh muscles were characterized before, two (D2) and four (D4) days after 40 maximal isometric electrically-evoked contractions in 25 healthy young males. On the basis of SPM analysis of coregistrated T₂ maps, the alterations were similarly detected at D2 and D4 in the superficial and distal regions of the vastus medialis (VM) whereas the proportion of altered muscle was higher in deep muscle regions of the vastus lateralis at D4 (deep: 35 ± 25%, superficial: 23 ± 15%) as compared to D2 (deep: 18 ± 13%, superficial: 17 ± 13%). The present methodology used for the first time on skeletal muscle would be of utmost interest to detect subtle intramuscular alterations not only for the diagnosis of muscular diseases but also for assessing the efficacy of potential therapeutic interventions and clinical treatment strategies.

Multi-atlas-based fully automatic segmentation of individual muscles in rat leg

OBJECTIVE

To quantify individual muscle volume in rat leg MR images using a fully automatic multi-atlas-based segmentation method.

MATERIALS AND METHODS

We optimized a multi-atlas-based segmentation method to take into account the voxel anisotropy of numbers of MRI acquisition protocols. We mainly tested an image upsampling process along Z and a constraint on the nonlinear deformation in the XY plane. We also evaluated a weighted vote procedure and an original implementation of an artificial atlas addition. Using this approach, we measured gastrocnemius and plantaris muscle volumes and compared the results with manual segmentation. The method reliability for volume quantification was evaluated using the relative overlap index.

RESULTS

The most accurate segmentation was obtained using a nonlinear registration constrained in the XY plane by zeroing the Z component of the displacement and a weighted vote procedure for both muscles regardless of the number of atlases. The performance of the automatic segmentation and the corresponding volume quantification outperformed the interoperator variability using a minimum of three original atlases.

CONCLUSION

We demonstrated the reliability of a multi-atlas segmentation approach for the automatic segmentation and volume quantification of individual muscles in rat leg and found that constraining the registration in plane significantly improved the results.

Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization

The identification and characterization of regional body tissues is essential to understand changes that occur with aging and age-related metabolic diseases such as diabetes and obesity and how these diseases affect trajectories of health and functional status. Imaging technologies are frequently used to derive volumetric, area, and density measurements of different tissues. Despite the significance and direct applicability of automated tissue quantification and characterization techniques, these topics have remained relatively underexplored in the medical image analysis literature. We present a method for identification and characterization of muscle and adipose tissue in the midthigh region using MRI. We propose an image-based muscle quality prediction technique that estimates tissue-specific probability density models and their eigenstructures in the joint domain of water- and fat-suppressed voxel signal intensities along with volumetric and intensity-based tissue characteristics computed during the quantification stage. We evaluated the predictive capability of our approach against reference biomechanical muscle quality (MQ) measurements using statistical tests and classification performance experiments. The reference standard for MQ is defined as the ratio of muscle strength to muscle mass. The results show promise for the development of noninvasive image-based MQ descriptors.

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.

Muscle Quantitative MR Imaging and Clustering Analysis in Patients with Facioscapulohumeral Muscular Dystrophy Type 1

BACKGROUND

Facioscapulohumeral muscular dystrophy type 1 (FSHD1) is the third most common inherited muscular dystrophy. Considering the highly variable clinical expression and the slow disease progression, sensitive outcome measures would be of interest.

METHODS AND FINDINGS

Using muscle MRI, we assessed muscular fatty infiltration in the lower limbs of 35 FSHD1 patients and 22 healthy volunteers by two methods: a quantitative imaging (qMRI) combined with a dedicated automated segmentation method performed on both thighs and a standard T1-weighted four-point visual scale (visual score) on thighs and legs. Each patient had a clinical evaluation including manual muscular testing, Clinical Severity Score (CSS) scale and MFM scale. The intramuscular fat fraction measured using qMRI in the thighs was significantly higher in patients (21.9 ± 20.4%) than in volunteers (3.6 ± 2.8%) (p<0.001). In patients, the intramuscular fat fraction was significantly correlated with the muscular fatty infiltration in the thighs evaluated by the mean visual score (p<0.001). However, we observed a ceiling effect of the visual score for patients with a severe fatty infiltration clearly indicating the larger accuracy of the qMRI approach. Mean intramuscular fat fraction was significantly correlated with CSS scale (p ≤ 0.01) and was inversely correlated with MMT score, MFM subscore D1 (p ≤ 0.01) further illustrating the sensitivity of the qMRI approach. Overall, a clustering analysis disclosed three different imaging patterns of muscle involvement for the thighs and the legs which could be related to different stages of the disease and put forth muscles which could be of interest for a subtle investigation of the disease progression and/or the efficiency of any therapeutic strategy.

CONCLUSION

The qMRI provides a sensitive measurement of fat fraction which should also be of high interest to assess disease progression and any therapeutic strategy in FSHD1 patients.

Computer-based assessment for facioscapulohumeral dystrophy diagnosis

The paper presents a computer-based assessment for facioscapulohumeral dystrophy (FSHD) diagnosis through characterisation of the fat and oedema percentages in the muscle region. A novel multi-slice method for the muscle-region segmentation in the T1-weighted magnetic resonance images is proposed using principles of the live-wire technique to find the path representing the muscle-region border. For this purpose, an exponential cost function is used that incorporates the edge information obtained after applying the edge-enhancement algorithm formerly designed for the fingerprint enhancement. The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index, indicating a high accuracy of the proposed method. Finally, the fat and oedema are quantified from the muscle region in the T1-weighted and T2-STIR magnetic resonance images, respectively, using the fuzzy c-mean clustering approach for 10 FSHD patients.

Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space: a novel approach

PURPOSE

The quantification of body fat plays an important role in the study of numerous diseases. It is common current practice to use the fat area at a single abdominal computed tomography (CT) slice as a marker of the body fat content in studying various disease processes. This paper sets out to answer three questions related to this issue which have not been addressed in the literature. At what single anatomic slice location do the areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) estimated from the slice correlate maximally with the corresponding fat volume measures? How does one ensure that the slices used for correlation calculation from different subjects are at the same anatomic location? Are there combinations of multiple slices (not necessarily contiguous) whose area sum correlates better with volume than does single slice area with volume?

METHODS

The authors propose a novel strategy for mapping slice locations to a standardized anatomic space so that same anatomic slice locations are identified in different subjects. The authors then study the volume-to-area correlations and determine where they become maximal. To address the third issue, the authors carry out similar correlation studies by utilizing two and three slices for calculating area sum.

RESULTS

Based on 50 abdominal CT data sets, the proposed mapping achieves significantly improved consistency of anatomic localization compared to current practice. Maximum correlations are achieved at different anatomic locations for SAT and VAT which are both different from the L4-L5 junction commonly utilized currently for single slice area estimation as a marker.

CONCLUSIONS

The maximum area-to-volume correlation achieved is quite high, suggesting that it may be reasonable to estimate body fat by measuring the area of fat from a single anatomic slice at the site of maximum correlation and use this as a marker. The site of maximum correlation is not at L4-L5 as commonly assumed, but is more superiorly located at T12-L1 for SAT and at L3-L4 for VAT. Furthermore, the optimal anatomic locations for SAT and VAT estimation are not the same, contrary to common assumption. The proposed standardized space mapping achieves high consistency of anatomic localization by accurately managing nonlinearities in the relationships among landmarks. Multiple slices achieve greater improvement in correlation for VAT than for SAT. The optimal locations in the case of multiple slices are not contiguous.

Robust separation of visceral and subcutaneous adipose tissues in micro-CT of mice

One of the common practices in obesity and diabetes studies is to measure the volumes and weights of various adipose tissues, among which, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) play critical yet different physiological roles in mouse aging. In this paper, a robust two-stage VAT/SAT separation framework for micro-CT mouse data is proposed. The first stage is to distinguish adipose from other tissue types, including background, soft tissue and bone, through a robust mixture of Gaussian model. Spatial recognition relevant to anatomical locations is carried out in the second step to determine whether the adipose is visceral or subcutaneous. We tackle this problem through a novel approach that relies on evolving the abdominal muscular wall to keep VAT/SAT separated. The VAT region of interest (ROI) is also automatically set up through an atlas based skeleton matching procedure. The results of our method are compared with VAT/SAT delineations by human experts, and a high classification accuracy is demonstrated on eight micro-CT mouse volume sets.

Combined MRI and ³¹P-MRS investigations of the ACTA1(H40Y) mouse model of nemaline myopathy show impaired muscle function and altered energy metabolism

Nemaline myopathy (NM) is the most common disease entity among non-dystrophic skeletal muscle congenital diseases. Mutations in the skeletal muscle α-actin gene (ACTA1) account for ∼25% of all NM cases and are the most frequent cause of severe forms of NM. So far, the mechanisms underlying muscle weakness in NM patients remain unclear. Additionally, recent Magnetic Resonance Imaging (MRI) studies reported a progressive fatty infiltration of skeletal muscle with a specific muscle involvement in patients with ACTA1 mutations. We investigated strictly noninvasively the gastrocnemius muscle function of a mouse model carrying a mutation in the ACTA1 gene (H40Y). Skeletal muscle anatomy (hindlimb muscles and fat volumes) and energy metabolism were studied using MRI and ³¹Phosphorus magnetic resonance spectroscopy. Skeletal muscle contractile performance was investigated while applying a force-frequency protocol (from 1–150 Hz) and a fatigue protocol (80 stimuli at 40 Hz). H40Y mice showed a reduction of both absolute (−40%) and specific (−25%) maximal force production as compared to controls. Interestingly, muscle weakness was associated with an improved resistance to fatigue (+40%) and an increased energy cost. On the contrary, the force frequency relationship was not modified in H40Y mice and the extent of fatty infiltration was minor and not different from the WT group. We concluded that the H40Y mouse model does not reproduce human MRI findings but shows a severe muscle weakness which might be related to an alteration of intrinsic muscular properties. The increased energy cost in H40Y mice might be related to either an impaired mitochondrial function or an alteration at the cross-bridges level. Overall, we provided a unique set of anatomic, metabolic and functional biomarkers that might be relevant for monitoring the progression of NM disease but also for assessing the efficacy of potential therapeutic interventions at a preclinical level.