| 书目名称 | Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms |
| 副标题 | A Convex Optimizatio |
| 编辑 | Bhabesh Deka,Sumit Datta |
| 视频video | |
| 概述 | Basics of compressed sensing MRI reconstruction.Covers recently developed reconstruction algorithms.Presents experimental results both graphically and visually.Includes comparative analyses of results |
| 丛书名称 | Springer Series on Bio- and Neurosystems |
| 图书封面 |  |
| 描述 | .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) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; 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 benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need forthe CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly usef |
| 出版日期 | Book 2019 |
| 关键词 | Rapid magnetic resonance image reconstruction; k-space undersampling; Compressed sensing MRI; Fast L1-n |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-13-3597-6 |
| isbn_ebook | 978-981-13-3597-6Series ISSN 2520-8535 Series E-ISSN 2520-8543 |
| issn_series | 2520-8535 |
| copyright | Springer Nature Singapore Pte Ltd. 2019 |
发表于 2025-3-21 19:24:46
