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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 1Rethinking the Optimization Process for Self-supervised Model-Driven MRI Reconstruction
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    Chapter 2NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data
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    Chapter 3Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations
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    Chapter 4High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors
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    Chapter 5Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases
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    Chapter 6Segmentation-Aware MRI Reconstruction
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    Chapter 7MRI Reconstruction with Conditional Adversarial Transformers
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    Chapter 8A Noise-Level-Aware Framework for PET Image Denoising
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    Chapter 9DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction
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    Chapter 10Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects
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    Chapter 11PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction
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    Chapter 12Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging
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    Chapter 13DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction
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    Chapter 14MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising
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    Chapter 15Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior
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Title
Machine Learning for Medical Image Reconstruction
Published by
Springer International Publishing, January 2022
DOI10.1007/978-3-031-17247-2
ISBNs
978-3-03-117246-5, 978-3-03-117247-2
Editors

Nandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo

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