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This issue of Radiologic Clinics focuses on Advanced Neuroimaging in Brain Tumors and is edited by Dr. Sangam Kanekar. Articles will include: Imaging findings of new entities and patterns in brain tumor: IDH mutant, IDH wildtype, Codeletion, and MGMT methylation; CT and MR perfusion imaging in neuro-oncology; Application of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) in the pre- and post-surgical evaluation of brain tumor; Clinical applications of magnetic resonance spectroscopy (MRS) in of brain tumors: grading and recurrence; Cellular and molecular imaging with PET and SPECT in brain tumors; Role of Functional MRI (fMRI) in the presurgical mapping of brain tumor; Imaging surveillance of gliomas: role of advanced imaging techniques; Neoplastic meningitis and paraneoplastic syndrome—role of imaging; Imaging of neurologic injury following oncologic therapy; RadioGenomics of brain tumor; Imaging mimics of brain tumors; Imaging of tumor syndromes; and more!
This issue of Radiologic Clinics of North America focuses on Neuroradiology and is edited by Drs. Jacqueline A. Bello and Shira Slasky. Articles will include: CT Perfusion in Acute Stroke; The Role of DTI and fMRI Prior to Brain Tumor Surgery; Pediatric Primary Neoplasms; Radiomics of Glioma: Genotypes and their Imaging Correlates; MR Spectroscopy and MR Perfusion of Brain Neoplasms; Etiologies of Acute Stroke: A Patterned Approach; Recent Hot Topics: RCVS and PRES, Venous Occlusive Disease; CNS Lesions in Immunocompromised Patients; Imaging Glioblastoma Post-treatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis; Imaging of Acute Stroke: Current State; Adult Primary Brain Neoplasms: (Including 2016 WHO classification); Large and Small Vessel Vasculopathies; and more!
Editor Hersh Chandarana, MD and authors review Advanced MR Imaging in Clinical Practice. Articles will include: Current Status of Diffusion Weighted Imaging; Current Status of Perfusion Weighted Imaging; Non-gadolinium Enhanced MR Angiography; Pearls and Pitfalls of 3 T imaging; Implementing MR Neurography in Clinical Practice; Imaging around Hardware and Metal; Recent Advances in T1- and T2-Weighted Imaging of the Abdomen and Pelvis; Recent Advances in Neuro and Spine Imaging; Advances in MR Hardware and Software, and more!
This book describes the basics, the challenges and the limitations of state of the art brain tumor imaging and examines in detail its impact on diagnosis and treatment monitoring. It opens with an introduction to the clinically relevant physical principles of brain imaging. Since MR methodology plays a crucial role in brain imaging, the fundamental aspects of MR spectroscopy, MR perfusion and diffusion-weighted MR methods are described, focusing on the specific demands of brain tumor imaging. The potential and the limits of new imaging methodology are carefully addressed and compared to conventional MR imaging. In the main part of the book, the most important imaging criteria for the differential diagnosis of solid and necrotic brain tumors are delineated and illustrated in examples. A closing section is devoted to the use of MR methods for the monitoring of brain tumor therapy. The book is intended for radiologists, neurologists, neurosurgeons, oncologists and other scientists in the biomedical field with an interest in neuro-oncology.
Approx.240 pagesApprox.240 pages
This issue of Neuroimaging Clinics of North America focuses on fMRI for the Diagnosis, Treatment and Management of Brain Tumors and is edited by Dr. Andrei I. Holodny. Articles will include: Introduction to fMRI; How to start your clinical fMRI program; Diffusion tractography; The problem of neurovascular uncoupling; Dynamic Resting State fMRI; Resting State fMRI of language function; Methods of fMRI analysis; Patient preparation and fMRI paradigm design; Functional brain anatomy; Neurosurgical Applications of fMRI; and more!
This book offers practical guidelines for performing efficient and cost-effective MRI examinations. By adopting a practical protocol-based approach the work-flow in a MRI unit can be streamlined and optimized. All chapters have been thoroughly reviewed, and new techniques and figures are included. There is a new chapter on MRI of the chest. This book will help beginners to implement the protocols and will update the knowledge of more experienced users.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
This coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.