Language-related white matter tracts and their relationship to language function in typically developing children

The dorsal and ventral white matter tracts believed to connect the anterior and posterior language cortices have been investigated in previous studies, but not extensively in children and adolescents. Magnetic resonance diffusion tensor imaging (DTI) tractography was used in order to examine the asymmetry of dorsal and ventral language white matter tracts of 34 typically developing children ages 8 to 18, and the relationship of these asymmetries with language development and ability. In our sample of participants, the dorsal and ventral tracts both demonstrated lateralization to the left hemisphere in fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), but not for tract volume or axial diffusivity (AD). We found no correlations between tract asymmetries and age or language level

Quantitative Imaging of Body Composition

Body composition refers to the amount and distribution of lean tissue, adipose tissue, and bone in the human body. Lean tissue primarily consists of skeletal muscle; adipose tissue comprises mostly abdominal visceral adipose tissue and abdominal and nonabdominal subcutaneous adipose tissue. Hepatocellular and myocellular lipids are also fat pools with important metabolic implications. Importantly, body composition reflects generalized processes such as increased adiposity in obesity and age-related loss of muscle mass known as sarcopenia. In recent years, body composition has been extensively studied quantitatively to predict overall health. Multiple imaging methods have allowed precise estimates of tissue types and provided insights showing the relationship of body composition to varied pathologic conditions. In this review article, we discuss different imaging methods used to quantify body composition and describe important anatomical locations where target tissues can be measured

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

Objective: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. Materials and methods: We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. Results: The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Conclusions: Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs

OBJECTIVE: To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans. MATERIALS AND METHODS: Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone. RESULTS: Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images. CONCLUSION: A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images

Cross-sectional areas of rotator cuff muscles in males without tears on shoulder MRI

OBJECTIVE To establish reference values of rotator cuff (RC) cross sectional area (CSA) in males. MATERIALS AND METHODS We retrospectively analyzed shoulder MRIs from 500 patients aged 13-78 years, grouped as follows (N=100 in each): 50 years. All examinations were reviewed to exclude prior surgery, tears, or significant RC pathology. We segmented a standardized T1 sagittal MR image in each case to obtain CSA of supraspinatus (SUP), infraspinatus/teres minor (INF), and subscapularis (SUB) muscles. Across age groups, we recorded individual and total muscle CSA. We also performed ratios between individual muscle CSA and total CSA to examine total muscle mass contribution over age groups. We tested for differences between age groups controlled for BMI. RESULTS CSAs for SUP, INF, SUB, and total RC CSA were lower in subjects >50 years compared to all other groups (P0.32). INF CSA relative to total RC CSA increased with age, whereas SUB decreased (P50 years showed lower SUP (-15%), INF (-6%), and SUB (-21%) CSA, when compared to mean CSAs of all subjects <50 years. Total RC CSA significantly correlated with age (r=-0.34, P<0.001), persisting after controlling for BMI (r=-0.42, P<0.001). CONCLUSION RC muscles in male subjects with no tears on MRI show decreasing CSA with age, independent of BMI