Principles of Computerized Tomographic Imaging
Avinash C. Kak,Malcolm Slaney | 1900-01-01 00:00:00 | I.E.E.E.Press | 344 | Engineering
Principles of Computerized Tomographic Imaging provides a comprehensive, tutorial-style introduction to the algorithms for reconstructing cross-sectional images from projection data and contains a complete overview of the engineering and signal processing algorithms necessary for tomographic imaging. In addition to the purely mathematical and algorithmic aspects of these algorithms, the book also discusses the artifacts caused by the nature of the various forms of energy sources that can be used for generating the projection data. Kak and Slaney go beyond theory, emphasizing real-world applications and detailing the steps necessary for building a tomographic system.
Since the fundamental aspects of tomographic reconstruction algorithms have remained virtually the same since this book was originally published, it is just as useful today as it was in 1987. It explains, among other things, what happens when there is excessive noise in the projection data; when images are formed from insufficient projection data; and when refracting or diffracting energy sources are used for imaging. Anyone interested in these explanations will find a wealth of useful information in this book.
Audience
Beginning graduate students or practitioners wishing to see the development of the algorithm from the ground up, as well as anyone interested in cross-sectional imaging for a wide variety of applications, will find this book extremely useful.
Reviews
The book is great: it explains all the way from mathematical theory to implementation details, but this reprint quality is very bad.
The images look like they were scanned/printed in black and white instead of grey shades (which is very surprising, knowing that the pdf version available on the web looks perfectly fine).
I am not sure I would have bought this book knowing how bad the print quality was beforehand, though it is still more convenient than the pdf sometimes.
Reviews
For someone with a little background and a lot of determination, this book provides a good basic grounding in the issues of tomographic reconstruction and the basic mathematical tools involved. Discussion starts slowly, with a chapter that establishes the vocabulary and notation of the signal processing involved. The next three chapters discuss non-diffracting cases, where the radiation that senses the body structures is not appreciably deflected by them, as is the case for CAT, PET, and SPECT. This includes discussion of the sensors, illuminators, and their geometries, on up to helical scans and complex sensor geometries. It also includes confounding effects, like the wavelength dependent nonlinearities in absorption of X-rays and how they affect beam transmission and the final image produced.
This chapter includes only brief menton of MRI, because of the very different physics behind it, and of ultrasonography, because of the diffractive and refractive features of the radiator and tissues being examined. Likewise, little mention is made of the reasons for different modalities or techniques for merging their results.
The final chapters address the special problems of ultrasound, digging as far in as the wave equations and the common approximations that make the wave equations at least somewhat practical as tools for solution. These chapters also address more advanced and computationally exhorbitant algorithms, though not in nearly the detail that back-projection got in the earlier chapters.
This book first appeared in 1988, which seems like centuries ago in the time scale of tomography algorithm development. Even the 2001 update is aging, and it never really went into the Feldkamp algorithms now widely in use. The discussion of sonography seems sketchier than discussion of the X-ray based modalities, and MRI newer exotica get little if any attention. That's fine, though. It's a big field, and the authors do reasonably well at defining and addressing the area they intended to cover. The working algorithm developer won't get much from this classic. The target audience today is probably a grad student or industrial practitioner who's been thrown in at the deep end. As long as its limits remain clear, this is a helpful introduction for readers with the math skills and time needed to extract its value.
-- wiredweird
Reviews
This book is one of the clearest introductions to tomography. I use it as a text in my course, and my students have also liked it.
Reviews
This is still a great text on the principles of tomographic imaging. Buy it!
Reviews
This book, which has become a classic, is a must for anyone who wishes to study tomography.
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