Cross-Sectional Imaging of the Abdomen and Pelvis: A Practical Algorithmic Approach


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Description

This book offers concise descriptions of cross-sectional imaging studies of the abdomen and pelvis, supplemented with over 1100 high-quality images and discussion of state-of-the-art techniques. It is based on the most common clinical cases encountered in daily practice and uses an algorithmic approach to help radiologists arrive first at a working differential diagnosis and then reach an accurate diagnosis based on imaging features, which incorporate clinical, laboratory, and other underlying contexts.

The book is organized by anatomical organ of origin and each chapter provides a brief anatomical background of the organ under review; explores various cross-sectional imaging techniques and common pathologies; and presents practical algorithms based on frequently encountered imaging features. Special emphasis is placed on the role of computed tomography (CT) and magnetic resonance imaging (MRI). In addition to algorithmic coverage of many pathological entities in various abdominopelvic organs, unique topics are also examined, such as imaging of organ transplant (including kidney, liver and pancreas), evaluation of perianal fistula, and assessment of rectal carcinoma and prostate carcinoma by MRI. Cross-Sectional Imaging of the Abdomen and Pelvis: A Practical Algorithmic Approach is a unique and practical resource for radiologists, fellows, and residents.

Author: Khaled M. Elsayes
Publisher: Springer
Published: 03/27/2015
Pages: 1066
Binding Type: Hardcover
Weight: 6.95lbs
Size: 11.00h x 8.00w x 2.00d
ISBN13: 9781493918836
ISBN10: 1493918834
BISAC Categories:
- Medical | Diagnostic Imaging | General
- Medical | Allied Health Services | Imaging Technologies

About the Author
Khaled M. Elsayes, MD, is Associate Professor of Diagnostic Radiology at the University of Texas, MD Anderson Cancer Center and University of Texas Medical School, Houston. He formerly served as Assistant Professor at the University of Michigan, Ann Arbor.