Predictive value of adductor pollicis muscle thickness for ultrasound-based sarcopenia in older adults.

BACKGROUND: The adductor pollicis muscle thickness (APMT) is least affected by subcutaneous adipose tissue and can be measured externally. The aim of this study is to identify the predictive value of APMT for the diagnosis of ultrasonography-based sarcopenia. METHODS: A total of 245 outpatients who were aged ≥65 years (49 with sarcopenia and 196 without sarcopenia) were included in this cross-sectional study. Patients with knee or hip replacement, advanced dementia, cerebrovascular disease, neurodegenerative disease, or decompensated heart failure and those using drugs that affect muscle functions, such as steroids, were excluded. Comprehensive geriatric assessments and anthropometric measurements of the patients were performed. APMT was assessed with a skinfold caliper. The diagnosis of sarcopenia was based on a low Sonographic Thigh Adjustment Ratio (STAR) index (anterior thigh muscle thickness by ultrasonography/body mass index [BMI] ratio, cutoff points are <1.4 for men and <1.0 for women) and low handgrip strength. The predictive ability of APMT for ultrasound-based sarcopenia diagnosis was examined. RESULTS: There was no significant difference in terms of arm and calf circumference and BMI between the sarcopenic and nonsarcopenic groups. APMT, handgrip strength, anterior thigh muscle thickness, and gait speed were significantly lower in the patients with sarcopenia. The best cutoff points of APMT for sarcopenia were ≤16.5 for women and ≤21.5 for men with areas under the curve of 0.70 and 0.66, respectively, and negative predictive values of 91.2 and 94.3, respectively (P < 0.001). CONCLUSION: APMT can be a useful anthropometric marker for ultrasound-based sarcopenia diagnosis.

Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.

Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat infiltration. These features are pivotal to measuring the state of muscle functional health and in tracking the response of the body to musculoskeletal and neuromusculoskeletal disorders. The gold standard approach to perform muscle segmentation requires manual processing of large numbers of images and is associated with significant operator repeatability issues and high time requirements. Deep learning-based techniques have been recently suggested to be capable of automating the process, which would catalyse research into the effects of musculoskeletal disorders on the muscular system. In this study, three convolutional neural networks were explored in their capacity to automatically segment twenty-three lower limb muscles from the hips, thigh, and calves from magnetic resonance images. The three neural networks (UNet, Attention UNet, and a novel Spatial Channel UNet) were trained independently with augmented images to segment 6 subjects and were able to segment the muscles with an average Relative Volume Error (RVE) between -8.6% and 2.9%, average Dice Similarity Coefficient (DSC) between 0.70 and 0.84, and average Hausdorff Distance (HD) between 12.2 and 46.5 mm, with performance dependent on both the subject and the network used. The trained convolutional neural networks designed, and data used in this study are openly available for use, either through re-training for other medical images, or application to automatically segment new T1-weighted lower limb magnetic resonance images captured with similar acquisition parameters.

Effects of neuromuscular control and strengthening exercises on MRI-measured thigh tissue composition and muscle properties in people with knee osteoarthritis - an exploratory secondary analysis from a randomized controlled trial.

OBJECTIVE: To investigate the effects of adding strength training to neuromuscular control exercises on thigh tissue composition and muscle properties in people with radiographic-symptomatic knee osteoarthritis (KOA). METHODS: In this exploratory secondary analysis of a randomized controlled trial, using a complete-case approach, participants performed 12 weeks of twice-weekly neuromuscular control exercise and patient education (NEMEX, n = 34) or NEMEX plus quadriceps strength training (NEMEX+ST, n = 29). Outcomes were MRI-measured inter- and intramuscular adipose tissue (InterMAT, IntraMAT), quadriceps muscle cross-sectional area (CSA), knee-extensor strength, specific strength (strength/lean CSA) and 30 s chair-stands. Between-group effects were compared using a mixed model analysis of variance. RESULTS: At 12 weeks, responses to NEMEX+ST overlapped with NEMEX for all outcomes. Both groups reduced InterMAT (NEMEX+ST=25 %, NEMEX=21 %); between-group difference: 0.8cm2 (95 % CI: -0.1, 1.7). NEMEX+ST decreased IntraMAT (2 %) and NEMEX increased IntraMAT (4 %); between-group difference 0.1 %-points (-0.3, 0.5). Both groups increased quadriceps CSA and lean CSA (CSA minus IntraMAT), improved knee-extensor strength and specific strength, and improved chair-stand performance with a trend towards greater effects in NEMEX+ST. CONCLUSION: Adding strength training to 12 weeks of neuromuscular control exercises provided largely similar effects to neuromuscular control exercises alone in decreasing InterMAT and IntraMAT, in improving knee-extensor strength, CSA and in improving performance-based function in KOA persons, with a trend towards greater effects with additional strength training. Notably, both groups substantially reduced InterMAT and improved specific strength (an index of muscle quality). Our hypothesis-generating work warrants exploration of the roles played by InterMAT and IntraMAT in exercise effects in KOA.

Precise individual muscle segmentation in whole thigh CT scans for sarcopenia assessment using U-net transformer.

The study aims to develop a deep learning based automatic segmentation approach using the UNETR(U-net Transformer) architecture to quantify the volume of individual thigh muscles(27 muscles in 5 groups) for Sarcopenia assessment. By automating the segmentation process, this approach improves the efficiency and accuracy of muscle volume calculation, facilitating a comprehensive understanding of muscle composition and its relationship to Sarcopenia. The study utilized a dataset of 72 whole thigh CT scans from hip fracture patients, annotated by two radiologists. The UNETR model was trained to perform precise voxel-level segmentation and various metrics such as dice score, average symmetric surface distance, volume correlation, relative absolute volume difference and Hausdorff distance were employed to evaluate the model's performance. Additionally, the correlation between Sarcopenia and individual thigh muscle volumes was examined. The proposed model demonstrated superior segmentation performance compared to the baseline model, achieving higher dice scores (DC = 0.84) and lower average symmetric surface distances (ASSD = 1.4191 ± 0.91). The volume correlation between Sarcopenia and individual thigh muscles in the male group. Furthermore, the correlation analysis of grouped thigh muscles also showed negative associations with Sarcopenia in the male participants. This thesis presents a deep learning based automatic segmentation approach for quantifying individual thigh muscle volume in sarcopenia assessment. The results highlights the associations between Sarcopenia and specific individual muscles as well as grouped thigh muscle regions, particularly in males. The proposed method improves the efficiency and accuracy of muscle volume calculation, contributing to a comprehensive evaluation of Sarcopenia. This research enhances our understanding of muscle composition and performance, providing valuable insights for effective interventions in Sarcopenia management.

[A prospective study of super-thin anterolateral thigh flap harvesting assisted by high-frequency color Doppler ultrasound in detecting perforators in deep adipose layers].

Objective: To investigate the clinical application of high-frequency color Doppler ultrasound (HFCDU) in detecting perforators in the deep adipose layers for harvesting super-thin anterolateral thigh flap (ALTF). Methods: Between August 2019 and January 2023, 45 patients (46 sides) with skin and soft tissue defects in the foot and ankle were treated, including 29 males and 16 females, aged from 22 to 62 years, with an average of 46.7 years. The body mass index ranged from 19.6 to 36.2 kg/m 2, with an average of 23.62 kg/m 2. The causes of injury included traffic accident injury in 15 cases, heavy object crush injury in 20 cases, mechanical injury in 8 cases, heat crush injury in 1 case, and chronic infection in 1 case. There were 20 cases on the left side, 24 cases on the right side, and 1 case on both sides. After thorough debridement, the wound size ranged from 5 cm×4 cm to 17 cm×11 cm. All patients underwent free super-thin ALTF transplantation repair. HFCDU was used to detect the location of the perforators piercing the deep and superficial fascia, as well as the direction and branches of the perforators within the deep adipose layers before operation. According to the preoperative HFCDU findings, the dimensions of the super-thin ALTF ranged from 6 cm×4 cm to 18 cm×12 cm. The donor sites of the flaps were directly sutured. Results: A total of 55 perforators were detected by HFCDU before operation, but 1 was not found during operation. During operation, a total of 56 perforators were found, and 2 perforators were not detected by HFCDU. The positive predictive value of HFCDU for identifying perforator vessels was 98.2%, and the sensitivity was 96.4%. Among the 54 perforators accurately located by HFCDU, the orientation of the perforators in the deep adipose layers was confirmed during operation. There were 21 perforators (38.9%) traveled laterally and inferiorly, 12 (22.2%) traveled medially and inferiorly, 14 (25.9%) traveled laterally and superiorly, 5 (9.3%) traveled medially and superiorly, and 2 (3.7%) ran almost vertically to the body surface. Among the 54 perforators accurately located by HFCDU, 35 were identified as type 1 perforators and 12 as type 2 perforators (HFCDU misidentified 7 type 2 perforators as type 1 perforators). The sensitivity of HFCDU in identifying type 1 perforators was 100%, with a positive predictive value of 83.3%. For type 2 perforators, the sensitivity was 63.2%, and the positive predictive value was 100%. The surgeries were successfully completed. The super-thin ALTF had a thickness ranging from 2 to 6 mm, with an average of 3.56 mm. All super-thin ALTF survived, however, 1 flap experienced a venous crisis at 1 day after operation, but it survived after emergency exploration and re-anastomosis of the veins; 1 flap developed venous crisis at 3 days after operation but survived after bleeding with several small incisions; 3 flaps had necrosis at the distal edge of the epidermis, which healed after undergoing dressing changes. All 45 patients were followed up 6-18 months (mean, 13.6 months). Three flaps required secondary defatting procedures, while the rest had the appropriate thickness, and the overall appearance was satisfactory. Conclusion: Preoperative application of HFCDU to detect the perforator in the deep adipose layers can improve the success and safety of the procedure by facilitating the harvest of super-thin ALTF.

The profunda artery perforators: Anatomical study and radiological findings using computed tomography angiography in patients undergoing PAP flap breast reconstruction.

BACKGROUND: The popularity of the profunda femoris artery perforator (PAP) flap is increasing; however, knowledge concerning the standardization of radiological findings and their clinical implications is limited. We evaluated the radiological architecture of posterior thigh perforators using Computed Tomography Angiography (CTA) to identify landmarks to facilitate flap dissection. METHODS: A retrospective study was conducted on 35 patients who underwent unilateral breast reconstruction with a PAP flap. The preoperative CTA scans were analyzed, and the perforator characteristics were evaluated. The perforators were mapped using a Cartesian coordinate system. Data were normalized by anatomical landmarks and overlapped. Perioperative and postoperative results were analyzed. Radiological and intraoperative were compared. RESULTS: Two CTA scans were excluded; 66 thighs were examined. The mean perforator number was 3.2. The mean diameter of chosen perforators was 2.7 mm (DS ± 0.6 mm) at the origin, 2.2 mm (DS ± 0.4 mm) at the adductor space midpoint, and 1.7 mm (DS ± 0.3 mm) at the deep fascia. The mean adipose tissue thickness was 3.35 cm (DS ± 0.94) at the deep fascia and 3.59 cm (DS ± 1.19) at the adductor space midpoint. Intraoperatively, the perforator was located 3.22 cm (DS ± 0.87) from the posterior border of the gracilis muscle and 8.98 cm (DS ± 1.44) from the inferior gluteal crease. A radiological area located 9.33 cm (DS ± 4.81) from the y-axis and 7.48 cm (DS ± 1.88) from the x-axis was identified. CONCLUSIONS: CTA using the volume-rendering technique is a valuable method to study in vivo the radiological anatomy of the posterior thigh perforators.

Semi-quantitative thigh magnetic resonance imaging scores in assessing disease activity and determining long-term clinical outcome in idiopathic inflammatory myopathies: a causal mediation analysis.

OBJECTIVES: To evaluate the relationship of thigh MRI (t-MRI) with manual muscle testing-8 (MMT-8), muscle enzymes and autoantibodies. To determine the causal and mediating factors resulting in poor recovery of MMT-8 in inflammatory myositis (IIM). METHODS: This was a single-centre retrospective study in IIM patients. t-MRI was semi-quantitatively scored for muscle oedema, fascial oedema, muscle atrophy and fatty infiltration. Spearman correlation of t-MRI scores was done with muscle enzymes at baseline, and MMT-8 at baseline and on follow-up. Causal mediation analysis was performed with age, sex, symptom duration, autoantibodies, diabetes and BMI as independent variables, follow-up MMT-8 as dependent and t-MRI scores as mediating variables. RESULTS: Baseline evaluation was done on 59 and follow-up on 38 patients. Median follow-up of the cohort was 31 (10-57) months. Baseline MMT-8 negatively correlated with muscle oedema (r = -0755), fascial oedema (r = -0.443) and muscle atrophy (r = -0.343). Creatinine kinase (r = 0.422) and aspartate transaminase (r = 0.480) positively correlated with muscle oedema. Follow-up MMT-8 correlated negatively with baseline atrophy (r = -0.497) and fatty infiltration (r = -0.531). On follow-up, MMT-8 males had positive total effect (estimate (95%CI)) via atrophy [2.93 (0.44, 4.89)] and fatty infiltration [2.08 (0.54, 3.71)]. Antisynthetase antibody had a positive total effect via fatty infiltration [4.50 (0.37, 7.59)]. Age had a negative total effect via atrophy [-0.09 (0.19, -0.01)] and fatty infiltration [-0.07 (-0.15, -0.01)]. Disease duration had a negative total effect via fatty infiltration [-0.18 (-0.27, -0.02)]. CONCLUSION: Baseline fatty infiltration and muscle atrophy resulting from older age, female sex, longer disease duration and absent anti-synthetase antibodies, partly mediate muscle recovery in IIM.

Statin use and longitudinal changes in quantitative MRI-based biomarkers of thigh muscle quality: data from Osteoarthritis Initiative.

OBJECTIVE: To assess whether changes in MRI-based measures of thigh muscle quality associated with statin use in participants with and without/at-risk of knee osteoarthritis. METHODS: This retrospective cohort study used data from the Osteoarthritis Initiative study. Statin users and non-users were matched for relevant covariates using 1:1 propensity-score matching. Participants were further stratified according to baseline radiographic knee osteoarthritis status. We used a validated deep-learning method for thigh muscle MRI segmentation and calculation of muscle quality biomarkers at baseline, 2nd, and 4th visits. Mean difference and 95% confidence intervals (CI) in longitudinal 4-year measurements of muscle quality biomarkers, including cross-sectional area, intramuscular adipose tissue, contractile percent, and knee extensors and flexors maximum and specific contractile force (force/muscle area) were the outcomes of interest. RESULTS: After matching, 3772 thighs of 1910 participants were included (1886 thighs of statin-users: 1886 of non-users; age: 62 ± 9 years (average ± standard deviation), range: 45-79; female/male: 1). During 4 years, statin use was associated with a slight decrease in muscle quality, indicated by decreased knee extension maximum (mean-difference, 95% CI: - 1.85 N/year, - 3.23 to - 0.47) and specific contractile force (- 0.04 N/cm2/year, - 0.07 to - 0.01), decreased thigh muscle contractile percent (- 0.03%/year, - 0.06 to - 0.01), and increased thigh intramuscular adipose tissue (3.06 mm2/year, 0.53 to 5.59). Stratified analyses showed decreased muscle quality only in participants without/at-risk of knee osteoarthritis but not those with established knee osteoarthritis. CONCLUSIONS: Statin use is associated with a slight decrease in MRI-based measures of thigh muscle quality over 4 years. However, considering statins' substantial cardiovascular benefits, these slight muscle changes may be relatively less important in overall patient care.

Thigh muscle thickness on ultrasonography for diagnosing sarcopenia: The Kyoto-Kameoka study.

AIM: The Asian Working Group for Sarcopenia 2019 consensus reported that evidence for the diagnosis of sarcopenia based on ultrasonography findings is lacking. The revised European Working Group on Sarcopenia in Older People consensus stated that ultrasonography is reliable and valid for assessing muscle size in older adults. The present study aimed to determine the predictive accuracy of ultrasonography for sarcopenia in older adults in Japan. METHODS: A total of 1229 participants aged 65-91 years were included in this cross-sectional study. The thickness of the anterior compartment of the right thigh was assessed using B-mode ultrasonography. The measurement position was at the midpoint of the thigh. In addition, the grip strength, gait speed, Five-Time Sit-to-Stand Test, Short Physical Performance Battery score and skeletal muscle mass index were evaluated. Sarcopenia was diagnosed based on the Asian Working Group for Sarcopenia 2019 algorithm. We calculated the area under the receiver operating characteristic curve. RESULTS: The area under the receiver operating characteristic curves (95% confidence intervals) and cutoff values for the thigh muscle thickness in sarcopenia were 0.901 (0.856-0.946) and 4.0 cm in men, respectively, and 0.923 (0.851-0.995) and 3.1 cm in women, respectively. The area under the receiver operating characteristic curve for each subdomain of sarcopenia, such as grip strength and gait speed, ranged from 0.618 to 0.872. CONCLUSIONS: In the present study, the suggested cutoff mid-thigh muscle thicknesses on ultrasonography for predicting sarcopenia were 4.0 cm in men and 3.1 cm in women. Geriatr Gerontol Int 2024; 24: 156-161.

Intravoxel incoherent motion imaging to assess the acute effects of moderate-intensity continuous training and high-intensity interval training on thigh muscles.

This study investigated the use of intravoxel incoherent motion imaging (IVIM) to compare skeletal muscle perfusion during and after high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) to determine the impact on fat oxidation outcomes. Twenty overweight volunteers were recruited for the study. Each participant received one HIIT intervention and one MICT intervention using a cycling ergometer. Participants underwent a magnetic resonance imaging scan before, immediately after, and 1 and 2 h after each intervention. The IVIM parameters (D, fD*) of the rectus femoris, vastus lateralis, and biceps femoris long head were obtained. Changes in IVIM parameters of these muscles after both exercise interventions were compared using a two-factor repeated measures analysis of variance. In the rectus femoris, the fD* increased immediately after exercise intervention (d = 0.69 × 10-3  mm2 /s, p < 0.0083) and 2 h after exercise intervention (d = 0.64 × 10-3  mm2 /s, p < 0.0083) compared with before exercise. The increase in the fD* in the HIIT group was greater than that in the MICT group (d = 0.32, p = 0.023). In the vastus lateralis, the fD* increased immediately after the exercise intervention (d = 0.53 × 10-3  mm2 /s, p < 0.001) and returned to the pre-exercise level 1 h after exercising. The increase in the fD* in the HIIT group was lower than that in the MICT group (d = -0.21, p = 0.015). For the biceps femoris long head, the fD* was not significantly different between the two exercise interventions before and after exercise. Furthermore, the fD* 60 min after the HIIT intervention correlated with maximal oxygen consumption (VO2max), whereas fD* immediately after the MICT intervention correlated with VO2max. In summary, IVIM parameters can be used to evaluate differences in muscle perfusion between HIIT and MICT, and show a correlation with VO2max.

Pilot study: is there an influence of lower limb positioning during magnetic resonance imaging on muscle cross section shape assessment in the thigh?

Positioning in an MRI can influence quantitative measures of the muscle. The goal of this pilot study was to assess the influence of different levels of knee elevation during MRI on the predicted cross-sectional muscle shape in the thigh. Data were acquired in three healthy male participants (age: 29.3 ± 5.1y, height: 181.3 ± 6.4cm, weight: 85.1 ± 3.7kg). For each participant, three MRI scans were taken by a trained radiographer with low, moderate and high knee elevation. The shape of the anatomical cross-sectional areas of the hamstrings and quadriceps in three leg positionings were compared by fitting ellipsoidal functions to the segmented MRI data and calculating the so-called J index for every image slice using the Python scripting language. Different levels of knee elevation resulted in apparent changes in J index for all muscles except vastus medialis. Thereby, the changes were overall more pronounced in the hamstrings compared to the quadriceps. Particularly, by elevating the knee from 8 to 15 degree, the percentage changes in J index were between 7.2 and 13.6% for the hamstrings and between 0.5 and 3.3% for the quadriceps, respectively. For assessing the musculoskeletal properties by means of MRI, a standardized positioning of the leg is required and the knee joint angle should be controlled.

Association of Regional Muscle Thickness and Echo Intensity with Muscle Volume, Intramuscular Adipose Tissue, and Strength of the Quadriceps Femoris.

Purpose: This study aimed to investigate the association of muscle thickness (MT) and echo intensity (EI) obtained at different regions along the muscle length with muscle volume (MV), intramuscular adipose tissue (IntraMAT), and muscle strength of the quadriceps femoris (QF). Patients and Methods: A total of 135 community-dwelling adults (64 men and 71 women) participated in the study. Ultrasound scanning of the rectus femoris (RF) and vastus intermedius (VI) was performed at three locations (from mid- to distal thigh). The MT of the RF and VI and EI of the RF were measured. MRI-derived MV, IntraMAT, and muscle strength of the QF were measured. Results: The correlation between RF-MT and RF-MV weakened as scanning approached the distal thigh, and the difference between the coefficients for the scanning locations was significant for women. However, the correlation of VI-MT with VI-MV and that of the combined MT of RF and VI with the MV of the whole QF and muscle strength were comparable among the scanning locations for both sexes. The correlation of RF-EI with the IntraMAT of the RF and the whole QF and muscle strength was also comparable among the scanning locations for both sexes. Conclusion: The results of this study suggest that ultrasound measurements at the distal thigh can predict MV, IntraMAT, and muscle strength of the QF to the same degree as those at the mid-thigh.

Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy.

OBJECTIVES: To validate the correlation between histopathological findings and quantitative magnetic resonance imaging (qMRI) fat fraction (FF) and water T2 mapping in patients with idiopathic inflammatory myopathy (IIM). METHODS: The study included 13 patients with histopathologically confirmed IIM who underwent dedicated thigh qMRI scanning within 1 month before open muscle biopsy. For the biopsied muscles, FF derived from the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) and T2 time from T2 mapping with chemical shift selective fat saturation were measured using a machine learning software. Individual histochemical and immunohistochemical slides were evaluated using a 5-point Likert score. Inter-reader agreement and the correlation between qMRI markers and histopathological scores were analyzed. RESULTS: Readers showed good to perfect agreement in qMRI measurements and most histopathological scores. FF of the biopsied muscles was positively correlated with the amount of fat in histopathological slides (p = 0.031). Prolonged T2 time was associated with the degree of variation in myofiber size, inflammatory cell infiltration, and amount of connective tissues (p ≤ 0.008 for all). CONCLUSIONS: Using the machine learning-based muscle segmentation method, a positive correlation was confirmed between qMRI biomarkers and histopathological findings of patients with IIM. This finding provides a basis for using qMRI as a non-invasive tool in the diagnostic workflow of IIM. RELEVANCE STATEMENT: By using ML-based muscle segmentation, a correlation between qMRI biomarkers and histopathology was found in patients with IIM: qMRI is a potential non-invasive tool in this clinical setting. KEY POINTS: • Quantitative magnetic resonance imaging measurements using machine learning-based muscle segmentation have good consistency and reproductivity. • Fat fraction of idiopathic inflammatory myopathy (IIM) correlated with the amount of fat at histopathology. • Prolonged T2 time was associated with muscle inflammation in IIM.

Consistency of muscle fibers directionality in human thigh derived from diffusion-weighted MRI.

Objective.Diffusion-weighted MR imaging (DW-MRI) is known to quantify muscle fiber directionality and thus may be useful for radiotherapy target definition in sarcomas. Here, we investigate the variability of tissue anisotropy derived from diffusion tensor (DT) in the human thigh to establish the baseline parameters and protocols for DW-MRI acquisition for future studies in sarcoma patients.Approach.We recruited ten healthy volunteers to acquire diffusion-weighted MR images of the left and right thigh. DW-MRI data were used to reconstruct DT eigenvectors within each individual thigh muscle. Deviations of the principal eigenvector from its mean were calculated for different experimental conditions.Main results.Within the majority of muscles in most subjects, the mode of the histogram of the angular deviation of the principal eigenvector of the water DT from its muscle-averaged value did not exceed 20°. On average for all subjects, the mode ranged from 15° to 24°. Deviations much larger than 20° were observed in muscles far from the RF coil, including cases with significant amounts of subcutaneous fat and muscle deformation under its own weight.Significance.Our study is a robust characterization of angular deviations of muscle fiber directionality in the thigh as determined by DW-MRI. We show that an appropriate choice of experimental conditions reduces the variability of the observed directionality. Precise determination of tissue directionality will enable reproducible models of microscopic tumor spread, with future application in defining the clinical target volume for soft tissue sarcoma.

The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.

BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. PURPOSE: Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. STUDY TYPE: Retrospective study. SUBJECTS: Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). FIELD STRENGTH/SEQUENCE: A 1.5 T. Sequences: 2D T1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence. ASSESSMENT: U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). STATISTICAL TESTS: Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20-). RESULTS: The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching -0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, -0.55 ± 1.95 msec for T2, and - 0.38 ± 3.67 cm3 for volume. The performances of HRNet, between G20- and G20+ decreased significantly. DATA CONCLUSION: HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Quantification of sarcopenia in patients with rheumatoid arthritis by measuring the cross-sectional area of the thigh muscles with magnetic resonance imaging.

PURPOSE: To determine the utility of cross-sectional area (CSA) measurements on magnetic resonance imaging (MRI), at the level of the thigh muscles, to estimate muscle mass in discriminating rheumatoid arthritis (RA) patients with sarcopenia from those without. MATERIALS AND METHODS: Consecutive female RA patients were enrolled for this cross-sectional study. Patients were assessed for disease activity, radiological damage, handgrip strength, physical performance and for the presence of sarcopenia, identified in accordance with the EWGSOP2 criteria. A 1.5 T MRI machine was used to scan the thigh muscles. A dimensional region growth algorithm (Horos™) was used to segment the muscles CSAs (in cm2) on MR images located 25 cm above the knee joint (MRI-CSA-25). The MRI-CSA-25 was obtained by summing the CSAs of the individual muscles. MRI-CSA-25 was correlated (Pearson's r) with the other variables, and its optimal cut-off point (Youden index) for sarcopenia diagnosis was identified in relation to the EWGSOP2 criteria. RESULTS: 32 RA female patients were studied, 34.4% diagnosed as sarcopenic. The mean MRI-CSA-25 was 151.00 cm2 for patient with sarcopenia, 275.57 cm2 for patient without sarcopenia (p < 0.001). MRI-CSA-25 correlated significantly with measures of physical performance, and disease activity, but not with radiological damage or age. The MRI-CSA-25 optimal cut-off point in discriminating sarcopenic patients was identified at 182.00 cm2 (AUC-ROC = 0.894). CONCLUSION: MRI-CSA-25 can differentiate sarcopenic versus non-sarcopenic RA patients, representing an imaging biomarker of this condition.

Thigh muscles are more susceptible to age-related muscle loss when compared to lower leg and pelvic muscles.

BACKGROUND: A key hallmark of aging is the progressive loss of skeletal muscle mass. Due to limitations of the various methods typically applied to assess muscle mass, only limited information is available on age-related differences between various muscle groups. This study assessed differences in individual lower body muscle group volumes between healthy young and older males. METHODS: Lower body muscle mass assessments were performed in 10 young (age: 27 ± 4 y) and 10 older (age: 71 ± 6 y) healthy, male adults using Dual-energy X-ray Absorptiometry (DXA), single slice (thigh) Computed Tomography (CT), as well as Magnetic Resonance Imaging (MRI). Muscle volumes of all individual muscle groups in the lower body were assessed by MRI. RESULTS: Leg lean mass, as assessed with DXA, was not significantly different between older (9.2 ± 1.0 kg) and young (10.5 ± 2.0 kg) men (P = 0.075). Thigh muscle cross-sectional area, as assessed with CT, was significantly lower (by 13 %) in the older (137 ± 17 cm2) compared to young (157 ± 24 cm2) participants (P = 0.044). MRI-derived lower body muscle volume was also significantly lower (by 20 %) in older (6.7 ± 0.9 L) compared to young (8.3 ± 1.3 L) men (P = 0.005). This was primarily attributed to substantial differences in thigh (24 %), rather than lower leg (12 %) and pelvis (15 %) muscle volume in the older vs the young. Thigh muscle volume averaged 3.4 ± 0.5 L in older and 4.5 ± 0.7 L in young men (P = 0.001). Of all thigh muscle groups, the quadriceps femoris showed the most profound difference (30 %) between young (2.3 ± 0.4 L) and older (1.6 ± 0.2 L) men (P < 0.001). CONCLUSIONS: The most profound differences in lower body muscle volume between young and older men are observed in the thigh. Within the thigh muscle groups, the quadriceps femoris shows the largest difference in muscle volume between young and older men. Finally, DXA appears less sensitive when compared to CT and MRI to assess age-related differences in muscle mass.

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.

PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. METHODS: A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. RESULTS: The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. CONCLUSIONS: The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.