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Machine Learning for Medical Image Reconstruction

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Cover of 'Machine Learning for Medical Image Reconstruction'

Table of Contents

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    Book Overview
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    Chapter 1 Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
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    Chapter 2 ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network
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    Chapter 3 Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction
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    Chapter 4 Complex Fully Convolutional Neural Networks for MR Image Reconstruction
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    Chapter 5 Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
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    Chapter 6 Improved Time-Resolved MRA Using k -Space Deep Learning
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    Chapter 7 Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
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    Chapter 8 Bayesian Deep Learning for Accelerated MR Image Reconstruction
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    Chapter 9 Sparse-View CT Reconstruction Using Wasserstein GANs
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    Chapter 10 Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees
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    Chapter 11 A U-Nets Cascade for Sparse View Computed Tomography
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    Chapter 12 Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction
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    Chapter 13 Deep Learning Based Image Reconstruction for Diffuse Optical Tomography
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    Chapter 14 Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging
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    Chapter 15 Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributions
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    Chapter 16 Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networks
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    Chapter 17 High Quality Ultrasonic Multi-line Transmission Through Deep Learning
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Chapter title
High Quality Ultrasonic Multi-line Transmission Through Deep Learning
Chapter number 17
Book title
Machine Learning for Medical Image Reconstruction
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-00129-2_17
Book ISBNs
978-3-03-000128-5, 978-3-03-000129-2
Authors

Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, Diana Gaitini, Vedula, Sanketh, Senouf, Ortal, Zurakhov, Grigoriy, Bronstein, Alex, Zibulevsky, Michael, Michailovich, Oleg, Adam, Dan, Gaitini, Diana

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 4 15%
Student > Master 4 15%
Student > Bachelor 3 12%
Researcher 3 12%
Student > Ph. D. Student 3 12%
Other 3 12%
Unknown 6 23%
Readers by discipline Count As %
Engineering 11 42%
Computer Science 4 15%
Agricultural and Biological Sciences 1 4%
Neuroscience 1 4%
Physics and Astronomy 1 4%
Other 0 0%
Unknown 8 31%