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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 Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction
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    Chapter 2 Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging
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    Chapter 3 Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network
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    Chapter 4 APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network
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    Chapter 5 Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network
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    Chapter 6 Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
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    Chapter 7 Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions
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    Chapter 8 Modeling and Analysis Brain Development via Discriminative Dictionary Learning
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    Chapter 9 Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval
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    Chapter 10 Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
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    Chapter 11 Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks
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    Chapter 12 Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results
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    Chapter 13 Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
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    Chapter 14 Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation
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    Chapter 15 Stain Style Transfer Using Transitive Adversarial Networks
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    Chapter 16 Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
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    Chapter 17 Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors
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    Chapter 18 Task-GAN: Improving Generative Adversarial Network for Image Reconstruction
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    Chapter 19 Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data
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    Chapter 20 Neural Denoising of Ultra-low Dose Mammography
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    Chapter 21 Image Reconstruction in a Manifold of Image Patches: Application to Whole-Fetus Ultrasound Imaging
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    Chapter 22 Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy
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    Chapter 23 TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis
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    Chapter 24 PredictUS: A Method to Extend the Resolution-Precision Trade-Off in Quantitative Ultrasound Image Reconstruction
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Title
Machine Learning for Medical Image Reconstruction
Published by
Springer International Publishing, December 2019
DOI 10.1007/978-3-030-33843-5
ISBNs
978-3-03-033842-8, 978-3-03-033843-5
Editors

Knoll, Florian, Maier, Andreas, Rueckert, Daniel, Ye, Jong Chul

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 88%
Researcher 5 63%
Other 4 50%
Student > Master 4 50%
Student > Postgraduate 3 38%
Other 4 50%
Readers by discipline Count As %
Engineering 9 113%
Computer Science 6 75%
Medicine and Dentistry 3 38%
Agricultural and Biological Sciences 2 25%
Psychology 1 13%
Other 4 50%