Medical Image Reconstruction : From Analytical and Iterative Methods to Machine Learning / Gengsheng Lawrence Zeng.

Format:

Book

Author/Creator:

Zeng, Gengsheng Lawrence, Author.

Series:

De Gruyter Textbook Series

De Gruyter Textbook

Language:

English

Physical Description:

1 online resource (XIV, 273 p.)

Edition:

2nd edition

Place of Publication:

Berlin ; Boston : De Gruyter, [2023]

Language Note:

In English.

Summary:

This textbook introduces the essential concepts of tomography in the field of medical imaging. The medical imaging modalities include x-ray CT (computed tomography), PET (positron emission tomography), SPECT (single photon emission tomography) and MRI. In these modalities, the measurements are not in the image domain and the conversion from the measurements to the images is referred to as the image reconstruction. The work covers various image reconstruction methods, ranging from the classic analytical inversion methods to the optimization-based iterative image reconstruction methods. As machine learning methods have lately exhibited astonishing potentials in various areas including medical imaging the author devotes one chapter to applications of machine learning in image reconstruction. Based on college level in mathematics, physics, and engineering the textbook supports students in understanding the concepts. It is an essential reference for graduate students and engineers with electrical engineering and biomedical background due to its didactical structure and the balanced combination of methodologies and applications.

Contents:

Frontmatter

Preface

Contents

1 Basic principles of tomography

2 Parallel-beam image reconstruction

3 Fan-beam image reconstruction

4 Transmission and emission tomography

5 Three-dimensional image reconstruction

6 Iterative reconstruction

7 MRI reconstruction

8 Using FBP to perform iterative reconstruction

9 Machine learning

Index

Notes:

Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Aug 2023)

Description based on publisher supplied metadata and other sources.

ISBN:

9783111055404

311105540X

OCLC:

1388361139