Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle

Age-related variations in trunk composition and patterns of regional bone and soft tissue changes in adult Caucasian women by DXA

We explored the regional variations in body composition with advancing age in healthy Caucasian females living in the Mediterranean area. The objectives of this study were to establish body composition values for the trunk in healthy women of a Greek origin and to evaluate the effects of aging on the distribution of truncal bone mass, fat mass (FM) and lean mass (LM). Body composition of the trunk and detailed analysis of its anatomical components-the ribs, the thoracic spine, the lumbar spine and the pelvis, and FM and LM ratios--were calculated in 330 women aged 20-85 years, using DXA. Peak bone mineral density (BMD) of the trunk was attained between ages 30 and 33. The overall truncal BMD reduction with age was 20.7% (p < 0.001). Peak %LM of the trunk was achieved at age 20. The overall reduction of %LM with age for the trunk was 9.8% (p < 0.001). Peak %FM of the trunk was attained between ages 68 and 73, and the overall %FM reduction with age was 2.8% (p > 0.05). Multiple comparative analyses showed that the 51-60 years age group was the landmark age for significant changes of truncal bone mass measures across all age groups (p = 0). For truncal LM and FM metrics, multigroup comparative analysis showed the turning point of significant changes in soft tissue was the 41-50 age bracket (p = 0 and p = 0, respectively). In Greek women, truncal %LM exceeded by far %FM across all ages (p = 0). Our results suggest that aging affects body composition of the trunk in ambulatory healthy women of a Greek origin differently, leading to menopausal loss of bone mass, senior adulthood loss of lean mass, and middle-age storage of fat mass. In adult women, these age-related associations between bone and soft tissue metrics on DXA exams carry implications for the attainment of optimal peak values and shifts in body composition overtime, impacting lifelong skeletal health.

Assisted annotation in Deep LOGISMOS: Simultaneous multi-compartment 3D MRI segmentation of calf muscles

BACKGROUND

Automated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability.

PURPOSE

In this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n-D with limited annotation cost.

METHODS

Deep LOGISMOS combines deep-learning-based pre-segmentation of objects of interest provided by our convolutional neural network, FilterNet+, and our 3D multi-objects LOGISMOS framework (layered optimal graph image segmentation of multiple objects and surfaces) that uses newly designed trainable machine-learned cost functions. In the paradigm of assisted annotation, multi-object JEI for efficient editing of automated Deep LOGISMOS segmentation was employed to form a new larger training set with significant decrease of manual tracing effort.

RESULTS

We have evaluated our method on 350 lower leg (left/right) T1-weighted MR images from 93 subjects (47 healthy, 46 patients with muscular morbidity) by fourfold cross-validation. Compared with the fully manual annotation approach, the annotation cost with assisted annotation is reduced by 95%, from 8 h to 25 min in this study. The experimental results showed average Dice similarity coefficient (DSC) of96.56 ± 0.26 % $96.56\pm 0.26 \%$ and average absolute surface positioning error of 0.63 pixels (0.44 mm) for the five 3D muscle compartments for each leg. These results significantly improve our previously reported method and outperform the state-of-the-art nnUNet method.

CONCLUSIONS

Our proposed approach can not only dramatically reduce the expert's annotation efforts but also significantly improve the segmentation performance compared to the state-of-the-art nnUNet method. The notable performance improvements suggest the clinical-use potential of our new fully automated simultaneous segmentation of calf muscle compartments.

Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study

Purpose

Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI).

Approach

Patients with lipedema (n = 15 ) and controls (n = 13 ) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney U test applied to compare metrics in each decade between groups (significance: two-sided P < 0.05 ).

Results

Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema (P < 0.01 ), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated (P < 0.001 ) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh (r = 0.76 ).

Conclusions

The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.

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.

Ultrasound and magnetic resonance imaging as diagnostic tools for sarcopenia in immune-mediated rheumatic diseases (IMRDs)

Sarcopenia is characterized by loss of muscle mass, altered muscle composition, fat and fibrous tissue infiltration, and abnormal innervation, especially in older individuals with immune-mediated rheumatic diseases (IMRDs). Several techniques for measuring muscle mass, strength, and performance have emerged in recent decades. The portable dynamometer and gait speed represent the most frequently used tools for the evaluation of muscle strength and physical efficiency, respectively. Aside from dual-energy, X-ray, absorptiometry, and bioelectrical impedance analysis, ultrasound (US) and magnetic resonance imaging (MRI) techniques appear to have a potential role in evaluating muscle mass and composition. US and MRI have been shown to accurately identify sarcopenic biomarkers such as inflammation (edema), fatty infiltration (myosteatosis), alterations in muscle fibers, and muscular atrophy in patients with IMRDs. US is a low-cost, easy-to-use, and safe imaging method for assessing muscle mass, quality, architecture, and biomechanical function. This review summarizes the evidence for using US and MRI to assess sarcopenia.

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.

CMC-Net: 3D calf muscle compartment segmentation with sparse annotation

Accurate 3D segmentation of calf muscle compartments in volumetric MR images is essential to diagnose as well as assess progression of muscular diseases. Recently, good segmentation performance was achieved using state-of-the-art deep learning approaches, which, however, require large amounts of annotated data for training. Considering that obtaining sufficiently large medical image annotation datasets is often difficult, time-consuming, and requires expert knowledge, minimizing the necessary sizes of expert-annotated training datasets is of great importance. This paper reports CMC-Net, a new deep learning framework for calf muscle compartment segmentation in 3D MR images that selects an effective small subset of 2D slices from the 3D images to be labelled, while also utilizing unannotated slices to facilitate proper generalization of the subsequent training steps. Our model consists of three parts: (1) an unsupervised method to select the most representative 2D slices on which expert annotation is performed; (2) ensemble model training employing these annotated as well as additional unannotated 2D slices; (3) a model-tuning method using pseudo-labels generated by the ensemble model that results in a trained deep network capable of accurate 3D segmentations. Experiments on segmentation of calf muscle compartments in 3D MR images show that our new approach achieves good performance with very small annotation ratios, and when utilizing full annotation, it outperforms state-of-the-art full annotation segmentation methods. Additional experiments on a 3D MR thigh dataset further verify the ability of our method in segmenting leg muscle groups with sparse annotation.

Role of anatomical location, cellular phenotype and perfusion of adipose tissue in intermediary metabolism: A narrative review

It is well-established that adipose tissue accumulation is associated with insulin resistance through multiple mechanisms. One major metabolic link is the classical Randle cycle: enhanced release of free fatty acids (FFA) from hydrolysis of adipose tissue triglycerides impedes insulin-mediated glucose uptake in muscle tissues. Less well studied are the different routes of this communication. First, white adipose tissue depots may be regionally distant from muscle (i.e., gluteal fat and diaphragm muscle) or contiguous to muscle but separated by a fascia (Scarpa's fascia in the abdomen, fascia lata in the thigh). In this case, released FFA outflow through the venous drainage and merge into arterial plasma to be transported to muscle tissues. Next, cytosolic triglycerides can directly, i.e., within the cell, provide FFA to myocytes (but also pancreatic ß-cells, renal tubular cells, etc.). Finally, adipocyte layers or lumps may be adjacent to, but not anatomically segregated, from muscle, as is typically the case for epicardial fat and cardiomyocytes. As regulation of these three main delivery paths is different, their separate contribution to substrate competition at the whole-body level is uncertain. Another important link between fat and muscle is vascular. In the resting state, blood flow is generally higher in adipose tissue than in muscle. In the insulinized state, fat blood flow is directly related to whole-body insulin resistance whereas muscle blood flow is not; consequently, fractional (i.e., flow-adjusted) glucose uptake is stimulated in muscle but not fat. Thus, reduced blood supply is a major factor for the impairment of in vivo insulin-mediated glucose uptake in both subcutaneous and visceral fat. In contrast, the insulin resistance of glucose uptake in resting skeletal muscle is predominantly a cellular defect.

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.

Automatic hip abductor muscle fat fraction estimation and association with early OA cartilage degeneration biomarkers

The aim of this study was to develop an automatic segmentation method for hip abductor muscles and find their fat fraction associations with early stage hip osteoarthritis (OA) cartilage degeneration biomarkers. This Institutional Review Board approved, Health Insurance Portability and Accountability Act compliant prospective study recruited 61 patients with evidence of hip OA or Femoroacetabular Impingement (FAI). Magnetic resonance (MR) images were acquired for cartilage segmentation, T_1ρ and T₂ relaxation times computation and grading of cartilage lesion scores. A 3D V-Net (Dice loss, Adam optimizer, learning rate = 1e^-4 , batch size = 3) was trained to segment the three muscles (gluteus medius, gluteus minimus, and tensor fascia latae). The V-Net performance was measured using Dice, distance maps between manual and automatic masks, and Bland-Altman plots of the fat fractions and volumes. Associations between muscle fat fraction and T_1ρ , T₂ relaxation times values were found using voxel based relaxometry (VBR). A p < 0.05 was considered significant. The V-Net had a Dice of 0.90, 0.88, and 0.91 (GMed, GMin, and TFL). The VBR results found associations of fat fraction of all three muscles in early stage OA and FAI patients with T_1ρ , T₂ relaxation times. Using an automatic, validated segmentation model, the associations derived between OA biomarkers and muscle fat fractions provide insight into early changes that occur in OA, and show that hip abductor muscle fat is associated with markers of cartilage degeneration.

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.

Isokinetic strength and degeneration of lower extremity muscles in patients with myotonic dystrophy; an MRI study

Our aim was to determine isokinetic strength and degeneration of lower extremity muscles in patients with Myotonic Dystrophy (DM1). In 19 patients with DM1 and 19 matched controls, strength measured by isokinetic dynamometry was expressed as percentage of expected strength (ePct), adjusted for age, height, weight and gender. MRI of the hip, thigh and calf muscles were obtained. Fat fraction (FF), mean contractile cross-sectional area (cCSA) and specific strength (Nm/cm²) were calculated. Patients' ankle plantar flexors, knee flexors and extensors had higher FF (Δ: 0.08 - 0.42) and lower cCSA (Δ: 3.2 -17.1 cm²) compared to controls (p ≤ 0.005). EPct (Δ: 19.5 - 41.6%) and specific strength (Δ: 0.27 - 0.96 Nm/cm²) were lower in the majority of patients muscle groups (p˂0.05). Close correlations were found for patients when relating ePct to; FF for plantar flexors (R²=0.742, p<0.001) and knee extensors (R²=0.732, p<0.001), cCSA for plantar flexors (R²=0.696, p<0.001) and knee extensors (R²=0.633, p<0.001), and specific strength for dorsal flexors (ρ=0.855, p = 0.008). In conclusion, patients had weaker lower extremity muscles with higher FF, lower cCSA and specific strength compared to controls. Muscle degeneration determined by quantitative MRI strongly correlated to strength supporting its feasibility to quantify muscle dysfunction in DM1.

Fully automated 3D segmentation of MR-imaged calf muscle compartments: Neighborhood relationship enhanced fully convolutional network

Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing quantitative biomarkers for muscular disease progression and its prediction. Achieving clinically acceptable results is a challenging task due to large variations in muscle shape and MR appearance. In this paper, we present a novel fully convolutional network (FCN) that utilizes contextual information in a large neighborhood and embeds edge-aware constraints for individual calf muscle compartment segmentations. An encoder-decoder architecture is used to systematically enlarge convolution receptive field and preserve information at all resolutions. Edge positions derived from the FCN output muscle probability maps are explicitly regularized using kernel-based edge detection in an end-to-end optimization framework. Our method was evaluated on 40 T1-weighted MR images of 10 healthy and 30 diseased subjects by fourfold cross-validation. Mean DICE coefficients of 88.00-91.29% and mean absolute surface positioning errors of 1.04-1.66 mm were achieved for the five 3D muscle compartments.

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.

MRI adipose tissue and muscle composition analysis-a review of automation techniques

MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM

Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm³ with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

Strategies towards rapid generation of forefoot model incorporating realistic geometry of metatarsals encapsulated into lumped soft tissues for personalized finite element analysis

Use of finite element (FE) foot model as a clinical diagnostics tool is likely to improve the specificity of foot injury predictions in the diabetic population. Here we proposed a novel workflow for rapid construction of foot FE model incorporating realistic geometry of metatarsals encapsulated into lumped forefoot's soft tissues. Custom algorithms were implemented to perform unsupervised segmentation and mesh generation to directly convert CT data into a usable FE model. The automatically generated model provided higher efficiency and comparable numerical accuracy when compared to the model constructed using a traditional solid-based mesh process. The entire procedure uses MATLAB as the main platform, and makes the present approach attractive for creating personalized foot models to be used in clinical 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.

Muscle Tissue Labeling of Human Lower Limb in Multi-Channel mDixon MR Imaging: Concepts and Applications

With increasing resolutions and number of acquisitions, medical imaging more and more requires computer support for interpretation as currently not all imaging data is fully used. In our work, we show how multi-channel images can be used for robust air masking and reliable muscle tissue detection in the human lower limb. We exploit additional channels that are usually discarded in clinical routine. We use the common mDixon acquisition protocol for MR imaging. A series of thresholding, morphological, and connectivity operations is used for processing. We demonstrate our fully automated approach on four subjects and present a comparison with manual labeling. We discuss how this work is used for advanced and intuitive visualization, the quantification of tissue types, pose estimation, initialization of further segmentation methods, and how it could be used in clinical environments.

Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: Application to weight-loss in obesity

PURPOSE

To develop a fully automatic algorithm for abdominal organs and adipose tissue compartments segmentation and to assess organ and adipose tissue volume changes in longitudinal water-fat magnetic resonance imaging (MRI) data.

MATERIALS AND METHODS

Axial two-point Dixon images were acquired in 20 obese women (age range 24-65, BMI 34.9±3.8kg/m(2)) before and after a four-week calorie restriction. Abdominal organs, subcutaneous adipose tissue (SAT) compartments (abdominal, anterior, posterior), SAT regions along the feet-head direction and regional visceral adipose tissue (VAT) were assessed by a fully automatic algorithm using morphological operations and a multi-atlas-based segmentation method.

RESULTS

The accuracy of organ segmentation represented by Dice coefficients ranged from 0.672±0.155 for the pancreas to 0.943±0.023 for the liver. Abdominal SAT changes were significantly greater in the posterior than the anterior SAT compartment (-11.4%±5.1% versus -9.5%±6.3%, p<0.001). The loss of VAT that was not located around any organ (-16.1%±8.9%) was significantly greater than the loss of VAT 5cm around liver, left and right kidney, spleen, and pancreas (p<0.05).

CONCLUSION

The presented fully automatic algorithm showed good performance in abdominal adipose tissue and organ segmentation, and allowed the detection of SAT and VAT subcompartments changes during weight loss.

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.

3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle

PURPOSE

To develop and evaluate an automated algorithm to segment intramuscular adipose (IMAT) and connective (IMCT) tissue from musculoskeletal MRI images acquired with a dual echo Ultrashort TE (UTE) sequence.

THEORY AND METHODS

The dual echo images and calculated structure tensor images are the inputs to the multichannel fuzzy cluster mean (MCFCM) algorithm. Modifications to the basic multichannel fuzzy cluster mean include an adaptive spatial term and bias shading correction. The algorithm was tested on digital phantoms simulating IMAT/IMCT tissue under varying conditions of image noise and bias and on ten subjects with varying amounts of IMAT/IMCT.

RESULTS

The MCFCM including the adaptive spatial term and bias shading correction performed better than the original MCFCM and adaptive spatial MCFCM algorithms. IMAT/IMCT was segmented from the unsmoothed simulated phantom data with a mean Dice coefficient of 0.933 ±0.001 when contrast-to-noise (CNR) was 140 and bias was varied between 30% and 65%. The algorithm yielded accurate in vivo segmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ±0.066.

CONCLUSION

The proposed algorithm is completely automated and yielded accurate segmentation of intramuscular adipose and connective tissue in the digital phantom and in human calf data. Magn Reson Med 77:870-883, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Sarcopenia: Current Concepts and Imaging Implications

OBJECTIVE

The purpose of this article is to review the nomenclature, clinical impact, and diagnostic techniques characterizing sarcopenia.

CONCLUSION

Sarcopenia-defined as significant loss of muscle-is associated with cachexia and frailty. Specific diagnostic criteria for sarcopenia continue to evolve, but imaging can play a role in the detection and quantification of muscle depletion. Emerging evidence indicates that sarcopenia is a relevant predictor of quality and quantity of life, particularly in patients who are elderly, have cancer, or undergo surgery.

Quadriceps intramuscular fat fraction rather than muscle size is associated with knee osteoarthritis

OBJECTIVES

To compare thigh muscle intramuscular fat (intraMF) fractions and area between people with and without knee radiographic osteoarthritis (ROA); and to evaluate the relationships of quadriceps adiposity and area with strength, function and knee magnetic resonance imaging (MRI) lesions.

METHODS

Ninety six subjects (ROA: Kellgren-Lawrence (KL) > 1; n = 30, control: KL = 0, 1; n = 66) underwent 3-T MRI of the thigh muscles using chemical shift-based water/fat MRI (fat fractions) and the knee (clinical grading). Subjects were assessed for isometric/isokinetic quadriceps/hamstrings strength, function Knee injury and Osteoarthritis Outcome Score (KOOS), stair climbing test (SCT), and 6-minute walk test (6MWT). Thigh muscle intraMF fractions, muscle area and strength, and function were compared between controls and ROA subjects, adjusting for age. Relationships between measures of muscle fat/area with strength, function, KL and lesion scores were assessed using regression and correlational analyses.

RESULTS

The ROA group had worse KOOS scores but SCT and 6MWT were not different. The ROA group had greater quadriceps intraMF fraction but not for other muscles. Quadriceps strength was lower in ROA group but the area was not different. Quadriceps intraMF fraction but not area predicted self-reported disability. Aging, worse KL, and cartilage and meniscus lesions were associated with higher quadriceps intraMF fraction.

CONCLUSION

Quadriceps intraMF is higher in people with knee OA and is related to symptomatic and structural severity of knee OA, whereas the quadriceps area is not. Quadriceps fat fraction from chemical shift-based water/fat MR imaging may have utility as a marker of structural and symptomatic severity of knee OA disease process.

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.