Purpose: To prospectively evaluate chemical shift magnetic resonance (MR) imaging for differentiating thymic hyperplasia from tumors of the thymus gland. Materials and Methods: The institutional review board approved this study; informed consent was obtained and patient confidentiality was protected. The authors assessed 41 patients (17 male, 24 female; age range, 16–78 years) in whom thymic lesions were seen at chest computed tomography. Patients were assigned to a hyperplasia group (n = 23) (18 patients with hyperplastic thymus associated with Graves disease and five with rebound thymic hyperplasia) and a tumor group (n = 18) (seven patients with thymomas, four with invasive thymomas, five with thymic cancers, and two with malignant lymphomas). T2-weighted fast spin-echo and T1-weighted in-phase and opposed-phase MR images were obtained in all patients and visually assessed. A chemical shift ratio (CSR), determined by comparing the signal intensity of the thymus gland with that of the paraspinal muscle,...

This study aimed at elucidating the relationship between the number of computed tomography (CT) images, including data concerning the accuracy of models and contrast enhancement for classifying the images. We enrolled 1539 patients who underwent contrast or noncontrast CT imaging, followed by dividing the CT imaging dataset for creating classification models into 10 classes for brain, neck, chest, abdomen, and pelvis with contrast-enhanced and plain imaging. The number of images prepared in each class were 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10,000. Accordingly, the names of datasets were defined as 0.1K, 0.5K, 1K, 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, and 10K, respectively. We subsequently created and evaluated the models and compared the convolutional neural network (CNN) architecture between AlexNet and GoogLeNet. The time required for training models of AlexNet was lesser than that for GoogLeNet. The best overall accuracy for the classification of 10 classes was 0.721 with the 10K dataset of GoogLeNet. Furthermore, the best overall accuracy for the classification of the slice position without contrast media was 0.862 with the 2K dataset of AlexNet.

Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947,p < 0.001) and abdominal datasets (ρ = 0.869,p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.

OBJECTIVE. The great potential of MRI for assessing gastrointestinal abnormalities in fetuses has been described. T1-weighted images may add additional information to T2-weighted images in diagnosing fetal gastrointestinal abnormalities. The objective of this study was to assess the performance of a 3D volumetric interpolated breath-hold sequence (VIBE) in evaluating the normal and abnormal fetal gastrointestinal tract.CONCLUSION. VIBE provides high-quality T1-weighted and 3D MR colonography images for the evaluation of the normal and abnormal gastrointestinal tract in fetuses, and 3D MR colonography provides excellent delineation of the meconium.