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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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This book provides a comprehensive survey of the pharmacokinetic models used for the quantitative interpretation of contrast-enhanced imaging. It discusses all the available imaging technologies and the problems related to the calibration of the imaging system and accuracy of the estimated physiological parameters. Enhancing imaging modalities using contrast agents has opened up new opportunities for going beyond morphological information and enabling minimally invasive assessment of tissue and organ functionality down to the molecular level. In combination with mathematical modeling of the contrast agent kinetics, contrast- enhanced imaging has the potential to provide clinically valuable additional information by estimating quantitative physiological parameters. The book presents the broad spectrum of diagnostic possibilities provided by quantitative contrast-enhanced imaging, with a particular focus on cardiology and oncology, as well as novel developments in the area of quantitative molecular imaging along with their potential clinical applications. Given the variety of available techniques, the choice of the appropriate imaging modality and the most suitable pharmacokinetic model is often challenging. As such, the book provides a valuable technical guide for researchers, clinical scientists, and experts in the field who wish to better understand and properly apply tracer-kinetic modeling for quantitative contrast-enhanced imaging.

Contrast-enhanced magnetic resonance imaging. --- Contrast media (Diagnostic imaging) --- Contrast agents (Diagnostic imaging) --- Contrast media --- Diagnostic imaging --- Contrast-enhanced MRI --- Magnetic resonance imaging --- Equipment and supplies --- Radiology, Medical. --- Oncology. --- Imaging / Radiology. --- Signal, Image and Speech Processing. --- Cancer Research. --- Biological and Medical Physics, Biophysics. --- Tumors --- Clinical radiology --- Radiology, Medical --- Radiology (Medicine) --- Medical physics --- Radiology. --- Signal processing. --- Image processing. --- Speech processing systems. --- Cancer research. --- Biophysics. --- Biological physics. --- Biological physics --- Biology --- Medical sciences --- Physics --- Cancer research --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Radiological physics --- Radiation

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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.

Research & information: general --- 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

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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.

Research & information: general --- 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