Magnetic Resonance Image Reconstruction

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. It discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. Magnetic Resonance Image Reconstruction: Theory, Methods and Applications is a unique resource suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction as well as the latest research Gives example code of some of the methods presented Includes the latest developments such as compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Produk Detail:

  • Author : Mariya Ivanova Doneva
  • Publisher : Academic Press
  • Pages : 375 pages
  • ISBN : 9780128227268
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMagnetic Resonance Image Reconstruction

Magnetic Resonance Image Reconstruction

Magnetic Resonance Image Reconstruction
  • Author : Mariya Ivanova Doneva,Mehmet Akcakaya,Claudia Prieto
  • Publisher : Academic Press
  • Release : 15 November 2021
GET THIS BOOKMagnetic Resonance Image Reconstruction

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. It discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. Magnetic Resonance Image Reconstruction: Theory, Methods and Applications is a unique resource suitable for physicists, engineers, technologists and clinicians with an interest in

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
  • Author : Bhabesh Deka,Sumit Datta
  • Publisher : Springer
  • Release : 29 December 2018
GET THIS BOOKCompressed Sensing Magnetic Resonance Image Reconstruction Algorithms

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

MRI

MRI
  • Author : Angshul Majumdar,Rabab Kreidieh Ward
  • Publisher : CRC Press
  • Release : 03 September 2018
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The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly

Regularized Image Reconstruction in Parallel MRI with MATLAB

Regularized Image Reconstruction in Parallel MRI with MATLAB
  • Author : Joseph Suresh Paul,Raji Susan Mathew
  • Publisher : CRC Press
  • Release : 05 November 2019
GET THIS BOOKRegularized Image Reconstruction in Parallel MRI with MATLAB

Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
  • Author : Nandinee Haq,Patricia Johnson,Andreas Maier,Tobias Würfl,Jaejun Yoo
  • Publisher : Springer Nature
  • Release : 29 September 2021
GET THIS BOOKMachine Learning for Medical Image Reconstruction

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Magnetic Resonance Imaging

Magnetic Resonance Imaging
  • Author : Rajinikanth V.,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 15 February 2022
GET THIS BOOKMagnetic Resonance Imaging

Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
  • Author : Farah Deeba,Patricia Johnson,Tobias Würfl,Jong Chul Ye
  • Publisher : Springer Nature
  • Release : 21 October 2020
GET THIS BOOKMachine Learning for Medical Image Reconstruction

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
  • Author : Florian Knoll,Andreas Maier,Daniel Rueckert,Jong Chul Ye
  • Publisher : Springer Nature
  • Release : 24 October 2019
GET THIS BOOKMachine Learning for Medical Image Reconstruction

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

Magnetic Resonance Imaging

Magnetic Resonance Imaging
  • Author : Vadim Kuperman
  • Publisher : Elsevier
  • Release : 15 March 2000
GET THIS BOOKMagnetic Resonance Imaging

This book is intended as a text/reference for students, researchers, and professors interested in physical and biomedical applications of Magnetic Resonance Imaging (MRI). Both the theoretical and practical aspects of MRI are emphasized. The book begins with a comprehensive discussion of the Nuclear Magnetic Resonance (NMR) phenomenon based on quantum mechanics and the classical theory of electromagnetism. The first three chapters of this book provide the foundation needed to understand the basic characteristics of MR images, e.g.,image

Advances in Biomedical Photonics and Imaging

Advances in Biomedical Photonics and Imaging
  • Author : Qingming Luo
  • Publisher : World Scientific
  • Release : 24 May 2022
GET THIS BOOKAdvances in Biomedical Photonics and Imaging

This unique volume contains selected papers presented at the 6th International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2007), held on November 4OCo6, 2007 at Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P R China.PIBM is designed to bring together scientists, engineers and clinical researchers from a variety of disciplines engaged in applying optical science, photonics and imaging technologies to problems in biology and medicine. The scope of this conference ranges from basic

Advanced Image Processing in Magnetic Resonance Imaging

Advanced Image Processing in Magnetic Resonance Imaging
  • Author : Luigi Landini,Vincenzo Positano,Maria Santarelli
  • Publisher : CRC Press
  • Release : 03 October 2018
GET THIS BOOKAdvanced Image Processing in Magnetic Resonance Imaging

The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the

Principles of Magnetic Resonance Imaging

Principles of Magnetic Resonance Imaging
  • Author : Zhi-Pei Liang,Paul C. Lauterbur
  • Publisher : Wiley-IEEE Press
  • Release : 01 November 1999
GET THIS BOOKPrinciples of Magnetic Resonance Imaging

In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic

Magnetic Resonance Imaging

Magnetic Resonance Imaging
  • Author : Robert W. Brown,E. Mark Haacke,Y.-C. Norman Cheng,Michael R. Thompson,Ramesh Venkatesan
  • Publisher : John Wiley & Sons
  • Release : 23 June 2014
GET THIS BOOKMagnetic Resonance Imaging

Preceded by Magnetic resonance imaging: physical principles and sequence design / E. Mark Haacke ... [et al.]. c1999.

Medical Image Reconstruction

Medical Image Reconstruction
  • Author : Gengsheng Zeng
  • Publisher : Springer Science & Business Media
  • Release : 28 December 2010
GET THIS BOOKMedical Image Reconstruction

"Medical Image Reconstruction: A Conceptual Tutorial" introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection