Fundamentals of High-Resolution Lung CT: Common Findings, Common Patterns, Common Diseases and Differential Diagnosis
ISBN: 1496389921
EAN13: 9781496389923
Language: English
Pages: 304
Dimensions: 1.00" H x 10.00" L x 7.00" W
Weight: 1.00 lbs.
Format: Paperback
Publisher:

Fundamentals of High-Resolution Lung CT: Common Findings, Common Patterns, Common Diseases and Differential Diagnosis

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Book Overview
Publisher's Note: Products purchased from 3rd Party sellers are not guaranteed by the Publisher for quality, authenticity, or access to any online entitlements included with the product. In clear and concise language, Fundamentals of High-Resolution Lung CT, provides a straightforward guide to understanding the high-resolution lung CT (HRCT) assessment of diffuse lung diseases. You will learn to accurately interpret HRCT findings, and how those findings can translate into the correct diagnosis or appropriate differential diagnosis. This volume includes over 600 images we consider classic, many of which have been updated and improved to facilitate easier reading and more precise interpretation. This new edition includes two-page summaries of each chapter, including; key diseases, facts, and findings, as well as classic images -- useful for review or to better understand CT findings and specific topics Every chapter has been updated with new content and new imagery to reflect recent advances and understanding of diseases, and to aid in diagnosis. Numerous pathology images have been added to help you better understand disease processes. Seminal images are now highlighted so you can compare to more recent findings. Covers many commonly encountered diseases like pneumonias, sarcoidosis, and connective tissue diseases, as well as rare diseases. Not just for radiologists in practice and residence; specialists in pulmonology and oncology benefit, too Enhance Your eBook Reading Experience Read directly on your preferred device(s), such as computer, tablet, or smartphone. Easily convert to audiobook, powering your content with natural language text-to-speech.