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Introduction to Diffusion Tensor Imaging, by Susumu Mori
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The concept of Diffusion Tensor Imaging (DTI) is often difficult to grasp, even for Magnetic Resonance physicists. Introduction to Diffusion Tensor Imaging uses extensive illustrations (not equations) to help readers to understand how DTI works. Emphasis is placed on the interpretation of DTI images, the design of DTI experiments, and the forms of application studies. The theory of DTI is constantly evolving and so there is a need for a textbook that explains how the technique works in a way that is easy to understand - Introduction to Diffusion Tensor Imaging fills this gap.
* Uses extensive illustrations to explain the concept of Diffusion Tensor Imaging
* Easy to understand, even without a background in physics
* Includes sections on image interpretation, experimental design and applications
- Sales Rank: #2583959 in Books
- Published on: 2007-05-31
- Original language: English
- Number of items: 1
- Dimensions: 12.00" h x 8.50" w x .50" l, 2.34 pounds
- Binding: Hardcover
- 190 pages
Most helpful customer reviews
7 of 7 people found the following review helpful.
Excellent but Brief!
By Youssef Ragab
Diffusion Tensor Imaging (DTI), which is introduced in this book, is a magnetic resonance based technique for non-invasive imaging of neural connectivity.
Core diffusion imaging concepts are very well explained. Issues such as motion artefact handling, data visualization, 3D tract reconstruction, quantification algorithms and medical applications are presented. There is also a chapter about novel non-DTI techniques. The book is packed with insightful figures and ends with a comprehensive list or resources.
Although most of the information in the book is readily available in research papers (a considerable portion of the book is derived from articles that appeared in the 2002 edition of NMR Biomed. 15, 468-480 and from a paper published in Neuron, 51, 527-540 in 2006) it is great to have it all neatly arranged and elaborately explained in one comprehensive volume.
On the down side, the book is a bit too brief, the offered mathematical treatment is quite shallow and there is nothing much for someone with even a basic knowledge of the field.
BOTTOM LINE: If you are a student, researcher or radiologist who is just looking for a basic introduction to the field, then you have come to the right place. I can't think of a better introduction to the principles and important issues of DTI. If you are already aware of the principles and are looking for a comprehensive reference on the history, physical background or mathematical treatment involved in DTI then I would suggest you look elsewhere.
4 of 5 people found the following review helpful.
Excellent introduction to DTI.
By Andrew J. Degnan
As a researcher in the neurosciences/radiology, this text served me well as my first introduction to the topic of Diffusion Tensor Imaging. It covers everything from the fundamental basics as to what DTI is exactly to the more nuanced aspects of processing and interpretation. This book is in many ways similar to Mori's seminal review articles on DTI and uses many of the same figures. Nevertheless, here is one unified text on a novel technique in imaging that has already shown great utility and promise for the future. This book is a must for anyone conducting DTI studies to teach others about the basics of DTI or to refer to occasionally for the more fundamental discussion of the technique.
6 of 12 people found the following review helpful.
Very basic DTI
By Cyrille Velasco
I just took a look at this book at ISMRM in Berlin. It has (a lot of) very nice pictures, and I think it is a good introduction to diffusion TENSOR imaging. I guess for somebody who wants a first, simplified look into diffusion is alright, but not if you are into "real" diffusion.
It explains what diffusion is, how you can measure it with MRI, different pulse sequences, what b-values are, some math about tensors, how to measure the tensor, etc. He also acknowledges that some people look beyond that, with more complicated models which require more complicated analysis schemes.
However, since diffusion in the brain is proven to be non-Gaussian, why bother lerning about diffusion tensor imaging?
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