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In Contrast-Enhanced Clinical Magnetic Resonance Imaging, Val M. Runge and other leading experts present an overview of the basic principles regarding MR contrast media, a review of clinical applications in the head, spine, and body, and a look at future developments. Their focus is on clinical applications, with extensive illustrations to demonstrate the use of MR in each anatomic area and to aid in film interpretation.

Contrast-enhanced magnetic resonance imaging. --- Magnetic resonance imaging. --- Contrast-enhanced MRI --- Magnetic resonance imaging --- Clinical magnetic resonance imaging --- Diagnostic magnetic resonance imaging --- Functional magnetic resonance imaging --- Imaging, Magnetic resonance --- Medical magnetic resonance imaging --- MR imaging --- MRI (Magnetic resonance imaging) --- NMR imaging --- Nuclear magnetic resonance --- Nuclear magnetic resonance imaging --- Cross-sectional imaging --- Diagnostic imaging --- Diagnostic use

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Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.

depthwise separable convolution (DSC) --- all convolutional network (ACN) --- batch normalization (BN) --- ensemble convolutional neural network (ECNN) --- electrocardiogram (ECG) --- MIT-BIH database --- cephalometric landmark --- X-ray --- deep learning --- ResNet --- registration --- electronic human-machine interface --- blindness --- gesture recognition --- inertial sensors --- IMU --- dynamic contrast-enhanced MRI --- kidney perfusion --- glomerular filtration rate --- pharmacokinetic modeling --- multi-layer perceptron --- parameter estimation --- instance segmentation --- computer vision --- retinal blood vessel image --- computer-aided diagnosis --- U-shaped neural network --- residual learning --- semantic gap --- intracranial hemorrhage --- computed tomography --- random forest --- sleep disorder --- obstructive sleep disorder --- overnight polysomnogram --- EEG --- EMG --- ECG --- HRV signals --- Electronic Medical Record (EMR) --- disease prediction --- Amyotrophic Lateral Sclerosis (ALS) --- weighted Jaccard index (WJI) --- lung cancer --- CT images --- CNN --- pulmonary fibrosis --- radiotherapy --- n/a