Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Name: Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
Brand: Springer Verlag, Singapore
SKU: 9789811335969
Price: 50117 HUF
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A Convex Optimization Approach

Szerző: Bhabesh Deka, Sumit Datta

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer Verlag, Singapore

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

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This book presents a comprehensive review of the recent developments in fast L1-norm regularization-...

Információk a könyvről

Szerző

Bhabesh Deka, Sumit Datta

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2019

oldal

122

EAN

9789811335969

Enbook ID

20565879

Kiadó

Springer Verlag, Singapore

Súly

411

Méretek

155 x 235 x 13

Teljes leírás

This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI), introduced in 2007, is able to reduce the scan time of MRI considerably as it makes it possible to reconstruct MR images from only a few measurements in the k-space, which is far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the standards for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of MR imaging to understand the need for CS in MRI and how it could revolutionize the soft tissue imaging technology to benefit healthcare without making major changes in the existing scanner hardware. It is particularly useful for researchers who have just entered this exciting field and would like to quickly grasp the developments to date without having to dive into the detailed mathematical analysis. Lastly, it discusses recent trends and suggests future research directions that will help consolidate this research for implementation in clinical practice.

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

A Convex Optimization Approach

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