Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Ideal for study, review, or self-assessment, 'The Essential Physics of Medical Imaging Study Guide' is an easy-to-use, image-rich resource for learning and retaining core information in the physics of medical imaging, radiation protection, and radiation biology. Based on 'The Essential Physics of Medical Imaging, 4th Edition' textbook (widely regarded as the cornerstone text in the field), the Study Guide can be used as a supplement to the textbook or as a stand-alone, concise review tool to focus on the most important aspects of the field. The Study Guide includes numerous vibrant full-color charts, graphs, and superb illustrations that reinforce central concepts. It's a must-have resource for medical imaging professionals, teachers, and students in medical physics and biomedical engineering, especially radiology residents preparing for certification exams. The Study Guide is perfect for anyone who needs a reliable, up-to-date review of the physics behind current medical imaging modalities and medical informatics, key concepts in radiation protection, regulatory requirements, dose optimization, and an up-to-date review of the biological effects of radiation exposure. The Study Guide's 21 chapter mirror the topics in the 4th edition textbook and contain key features organized in three sections for helpful study and review. Section 1 : Distills key concepts for each chapter in the 4th edition textbook in concise sentence and outline format. Icons indicating the corresponding page numbers for figures, tables, and equations make it easy to reference additional information on any topic in the 4th edition textbook. Section 2 : Includes more then 1,000 image-rich questions and explanatory answers with reference to page numbers in the 4th edition textbook for further detail. Section 3 : Consolidates Key Equations and Symbols for each chapter with pages numbers from the main text where the equation is introduced and discussed."--taken from back cover.

Diagnostic imaging.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is designed to consider the recent advancements in hospitals to diagnose various diseases accurately using AI-supported detection procedures. The book also includes several chapters on machine learning, convoluted neural networks, segmentation, and deep learning-assisted two-class and multi-class classification.

Artificial intelligence. --- Diagnostic imaging.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.

Heart --- Imaging. --- Cardiac diagnostic imaging --- Cardiac imaging --- Diagnostic cardiac imaging --- Imaging of the heart --- Diseases --- Imaging

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Diagnostic imaging --- Artificial intelligence --- Data processing --- Medical applications

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more.

E-books --- Medical imaging equipment industry. --- Diagnostic imaging. --- Medical informatics. --- Computer networks --- Diagnostic Imaging --- Deep Learning --- Image Interpretation, Computer-Assisted --- Image Processing, Computer-Assisted --- Design.