Note: Intro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging -- 1 Introduction -- 1.1 Background -- 1.2 Motivation -- 2 Related Work -- 3 Methods -- 3.1 Imaging Methodology -- 3.2 Transfer Learning Training for Dual-Contrast DESS -- 4 Results -- 5 Discussion and Conclusion -- References -- ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network -- 1 Introduction -- 2 Method -- 2.1 Network Architectures -- 2.2 Training Environment -- 2.3 Quantitative Evaluation -- 3 Results -- 4 Discussion -- References -- Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Network Architecture -- 3.2 Implementation Details -- 4 Experimental Results -- 4.1 K-space Corruption for Synthetic Data -- 4.2 Quantitative Results on Synthetic Dataset -- 4.3 Qualitative Results on Real Motion Artefact Case -- 5 Discussion and Conclusion -- References -- Complex Fully Convolutional Neural Networks for MR Image Reconstruction -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Network Architecture -- 2.3 Model Learning and Optimization -- 3 Results and Discussion -- 3.1 Experimental Settings and Evaluation -- 3.2 Results -- 4 Conclusion and Future Work -- References -- Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 MRF and Parametric Map Acquisition -- 2.2 Spatiotemporal CNN MRF Reconstruction -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References -- Improved Time-Resolved MRA Using k-Space Deep Learning -- 1 Introduction -- 2 Theory -- 2.1 Problem Formulation. , 2.2 From ALOHA to Deep Neural Network -- 3 Method -- 4 Result -- 5 Conclusion -- References -- Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image -- 1 Introduction -- 1.1 Related Work -- 2 Methods -- 2.1 Unsupervised Cardiac Motion Estimation from Undersampled MR Image -- 2.2 Joint Cardiac Motion Estimation and Segmentation from Undersampled MR Image -- 3 Experiments and Results -- 4 Conclusion -- References -- Bayesian Deep Learning for Accelerated MR Image Reconstruction -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- Deep Learning for Computed Tomography -- Sparse-View CT Reconstruction Using Wasserstein GANs -- 1 Introduction -- 2 Method -- 2.1 Experimental Setup -- 3 Results -- 4 Discussion and Conclusion -- References -- Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees -- 1 Introduction -- 2 Method -- 2.1 X-Ray Invariant Anatomical Landmark Detection -- 2.2 Training -- 2.3 Landmark Estimation -- 3 Experiments and Results -- 4 Conclusion and Outlook -- References -- A U-Nets Cascade for Sparse View Computed Tomography -- 1 Introduction -- 1.1 Sparse View Computed Tomography -- 2 Proposed Network Architecture -- 2.1 Data Consistency Layer -- 2.2 U-Nets Cascade -- 3 Numerical Experiments -- 3.1 Dataset -- 3.2 Network Architectures and Training -- 3.3 Conclusion -- References -- Deep Learning for General Image Reconstruction -- Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction -- 1 Introduction -- 2 Forward and Inverse Models -- 2.1 Photoacoustic Tomography -- 2.2 Fast Approximate Forward and Inverse Models -- 3 Learned Reconstruction with Approximate Models -- 3.1 Learned Iterative Reconstruction -- 3.2 An Iterative Gradient Network -- 4 Computational Results for In-Vivo Measurements. , 4.1 Data Acquisition and Preparation -- 4.2 Training of Proposed Network -- 4.3 Reconstructions of In-Vivo Measurements -- 4.4 Discussion -- 5 Conclusions -- References -- Deep Learning Based Image Reconstruction for Diffuse Optical Tomography -- 1 Introduction -- 2 Methodology -- 2.1 Generating Training Data for DOT Reconstruction -- 2.2 Reconstructing Images from DOT Measurements -- 3 Experiments and Results -- 4 Conclusion -- References -- Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging -- 1 Introduction -- 2 Methods -- 2.1 Variational Network -- 3 Results -- 4 Discussion -- References -- Towards Arbitrary Noise Augmentation-Deep Learning for Sampling from Arbitrary Probability Distributions -- 1 Introduction -- 2 Conventional Sampling Methods -- 2.1 Inversion Sampling -- 2.2 Rejection Sampling -- 2.3 Mixture of Gaussians -- 2.4 Markov-Chain-Monte-Carlo -- 2.5 FCNN Sampling -- 3 Results -- 4 Conclusion -- References -- Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networks -- 1 Introduction and Related Work -- 2 Methods -- 2.1 Reconstruction Scenarios: Using Sphere Intersection vs An Atria Path -- 3 Experiments and Results -- 3.1 Sphere Intersection -- 3.2 Synthetic Catheter Path Reconstruction -- 3.3 Laboratory Phantom -- 4 Conclusions and Future Work -- References -- High Quality Ultrasonic Multi-line Transmission Through Deep Learning -- 1 Introduction -- 2 Methods -- 3 Experimental Evaluation -- 4 Conclusion -- References -- Author Index.

Note: Intro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Generative Adversarial Networks (GAN) -- 2.3 Proposed Reconstruction Global-Local GAN (Recon-GLGAN) -- 2.4 Network Architecture -- 2.5 Loss Function -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- 1 Introduction -- 2 Method -- 2.1 Data Acquisition -- 2.2 The Reconstruction Pipeline -- 2.3 Self-supervised RNN -- 2.4 Super-Resolution Reconstruction (SR-net) -- 2.5 Implementation Details -- 3 Results -- 3.1 Simulated Experiment -- 3.2 Real Data Reconstructions -- 4 Discussion and Conclusion -- References -- Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network -- 1 Introduction -- 2 Proposed Approach -- 2.1 Problem -- 2.2 Proposed Network -- 2.3 Dataset Generation -- 3 Experimental Results -- 4 Conclusion -- References -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Conventional Parallel Imaging Reconstruction griswold2002generalized -- 2.2 APIR-Net Reconstruction -- 3 Experiments -- 3.1 Evaluation with Phantom Acquisition -- 3.2 Comparison to RAKI -- 3.3 Evaluation with In-Vivo Acquisitions -- 4 Results -- 4.1 Evaluation with Phantom Acquisition -- 4.2 Comparison to RAKI -- 4.3 Evaluation with In-Vivo Acquisitions -- 5 Discussion and Conclusion -- References -- Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network -- 1 Introduction -- 2 Methods. , 2.1 Accelerated MRI with Deep Generative Model -- 2.2 Network Architecture -- 2.3 Loss Function -- 2.4 Sampling Patterns -- 2.5 Datasets -- 2.6 Evaluation Metrics -- 3 Results -- 4 Discussion and Conclusions -- References -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- 1 Introduction -- 2 Methods -- 2.1 Formulation -- 2.2 Probabilistic Decimation Simulator -- 2.3 Deep Learning Framework -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Evaluation on Fixed SNR Data Sets -- 3.3 Evaluation on Variable SNR Data Sets -- 3.4 Test on Patient Data -- 4 Discussion and Conclusion -- References -- Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition -- 2.2 Variational Network -- 2.3 Network Training -- 2.4 Evaluation of Reconstructed Images -- 3 Results -- 4 Discussion -- 5 Conclusion -- Acknowledgements -- References -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- 1 Introduction -- 2 The Proposed Approach -- 2.1 Discriminative Dictionary Learning -- 2.2 Training Algorithm -- 2.3 Classification -- 3 Experiments -- 3.1 Prediction of Brain Age -- 3.2 Exploring the Cortical Brain Development -- 4 Conclusion -- References -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Objective Function -- 3.2 Network Architecture -- 3.3 Training Data -- 3.4 Conditioning Vector -- 4 Experiments -- 4.1 Datasets and Data Augmentation -- 4.2 Results -- 5 Conclusion -- References -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- 1 Introduction -- 2 Method. , 2.1 The U-Net Architecture -- 2.2 Data Consistent Artifact Reduction -- 2.3 Experimental Setup -- 3 Results -- 4 Discussion and Conclusion -- References -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- 1 Background -- 2 Methodology -- 2.1 Data -- 2.2 Quality Estimation with Gradient Structural Similarity -- 2.3 Image Normalization -- 2.4 Convolutional Neural Network for Image Quality Score Estimation -- 2.5 Heatmap Regression -- 3 Experiments and Results -- 3.1 Comparison with Conventional Quality Metrics -- 3.2 Qualitative Results -- 4 Discussion -- 5 Conclusion -- References -- Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results -- 1 Introduction -- 2 Proposed Method -- 2.1 Network Architectures -- 2.2 Training the Network -- 3 Results -- 3.1 Implicitly Learned Segmentation -- 3.2 Inpainting Results -- 4 Discussion -- 5 Conclusion -- References -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 3.1 Initial Experiments -- 3.2 Exp1: ADNI MRI and Thorax CT -- 3.3 Exp2: Fetal Brain Template Volume -- 4 Conclusion and Discussion -- References -- Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation -- 1 Introduction -- 1.1 Survey of Existing Works -- 1.2 Proposed Method -- 2 Methods -- 2.1 Mathematical Background -- 2.2 General Approach -- 2.3 Motion Estimation -- 2.4 Reconstruction -- 2.5 Full Algorithm -- 2.6 Complexity -- 3 Results -- 3.1 Derenzo Phantom -- 3.2 Methods Without Motion Correction -- 3.3 Proposed Method -- 3.4 Implementation Details -- 4 Perspectives -- References -- Stain Style Transfer Using Transitive Adversarial Networks -- 1 Introduction -- 2 Methodology -- 2.1 The Framework. , 2.2 Network Architectures -- 3 Experiments and Results -- 3.1 Dateset and Details -- 3.2 Results with Different Levels of Downsampling -- 3.3 Comparisons of Results Using Different Generators -- 3.4 Comparison with State-of-the-Art Method -- 4 Discussion and Conclusion -- References -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- 1 Introduction -- 2 Theory -- 2.1 Loss Function -- 2.2 Multi patchGANs in CycleGAN -- 3 Network Architecture -- 4 Method -- 5 Experimental Results -- 6 Discussion and Conclusion -- References -- Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors -- 1 Introduction -- 1.1 Positron Emission Tomography -- 1.2 Deep Learning in PET Imaging -- 2 Materials and Method -- 2.1 Generation of 3D Shapes and Radionuclide Distribution -- 2.2 Network Architecture -- 2.3 Testing the Procedure -- 3 Results -- 3.1 Normalization -- 3.2 Different Spatial Resolutions -- 3.3 Physical Phantom PET Scans -- 4 Discussion and Conclusion -- References -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- 1 Introduction -- 2 Proposed Method: Task-GAN -- 2.1 Designs -- 2.2 Formulation -- 3 Experiments -- 3.1 Ultra-low-dose Amyloid PET Reconstruction Task -- 3.2 Multi-contrast MR Reconstruction Task -- 4 Discussion -- 5 Conclusion -- References -- Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data -- 1 Introduction -- 2 Methods and Materials -- 3 Results -- 4 Discussion and Conclusion -- References -- Neural Denoising of Ultra-low Dose Mammography -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 The LC-NLM Algorithm -- 2.2 The Convolutional LC-NLM (CLC-NLM) Algorithm -- 2.3 Enforcing Local-Consistency -- 3 Experiments -- 3.1 Dataset -- 3.2 Setup -- 3.3 Comparison with State-of-the-Art -- 3.4 Ablation Study -- 4 Conclusions. , References -- Image Reconstruction in a Manifoldpg of Image Patches: Applicationpg to Whole-Fetus Ultrasound Imaging -- 1 Introduction -- 2 Method -- 2.1 Image Patch Fusion with Classical Manifold Embedding -- 2.2 Image Patch Fusion with a Variational Autoencoder -- 3 Materials and Experiments -- 3.1 Materials -- 3.2 Experiments -- 4 Results -- 5 Discussion and Conclusions -- References -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- 1 Introduction -- 2 Method -- 3 Results and Discussion -- 4 Conclusion -- References -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- 1 Introduction -- 2 Previous Works -- 3 Method -- 3.1 Architecture of Generator -- 3.2 Loss and Training -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Results and Discussion -- 5 Conclusion -- References -- PredictUS: A Method to Extend the Resolution-Precision Trade-Off in Quantitative Ultrasound Image Reconstruction -- 1 Introduction -- 2 Method -- 2.1 ACE Computation -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Data -- 3.2 Training and Testing -- 3.3 RF Data Processing and Analysis -- 3.4 Performance Metrics -- 3.5 Results -- 4 Conclusion -- References -- Correction to: Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data -- Correction to: Chapter "Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data" in: F. Knoll et al. (Eds.): Machine Learning for Medical Image Reconstruction, LNCS 11905, https://doi.org/10.1007/978-3-030-33843-5_19 -- Author Index.

Note: Intro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- Rethinking the Optimization Process for Self-supervised Model-Driven MRI Reconstruction -- 1 Introduction -- 2 Theory -- 2.1 Model Based Deep Learning Network -- 2.2 Self-supervised MRI Reconstruction -- 2.3 Derivation of K-Space Calibration -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data -- 1 Introduction -- 2 Methods -- 2.1 MRI Reconstruction -- 2.2 Non-parametric Bayesian MRI Reconstruction -- 2.3 The Reconstruction Network -- 3 Experiments -- 3.1 Database -- 3.2 Experimental Setup -- 3.3 Results -- 4 Conclusions -- References -- Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusion -- References -- High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 Reconstruction Framework -- 2.3 Objective Function -- 3 Experiment -- 3.1 Comparison Results -- 3.2 Ablation Studies on Model Components -- 3.3 Ablation Studies on Bottleneck Design in DC Blocks -- 4 Conclusions and Discussion -- References -- Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 Multi-contrast SEMAC Acceleration -- 2.3 Implementation Details -- 3 Experiment -- 3.1 Results of SEMAC Acceleration -- 3.2 Results of SEMAC/Phase-Encoding Acceleration -- 4 Discussion and Conclusion -- References -- Segmentation-Aware MRI Reconstruction. , 1 Introduction -- 2 Methods -- 2.1 Proposed Framework -- 2.2 Stabilization -- 2.3 Model Architectures -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- MRI Reconstruction with Conditional Adversarial Transformers -- 1 Introduction -- 2 Theory -- 2.1 Deep MRI Reconstruction -- 2.2 Conditional Adversarial Transformers -- 3 Methods -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Learning for General Image Reconstruction -- A Noise-Level-Aware Framework for PET Image Denoising -- 1 Introduction -- 2 Noise-Level-Aware Framework -- 2.1 Quantification of Local Relative Noise Level -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Implementation Details -- 3.3 Results and Analysis -- 4 Conclusion -- References -- DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction -- 1 Introduction and Motivation -- 2 Method -- 2.1 Network Architecture -- 3 Experimental Results -- 3.1 Ablation Study and Analysis -- 3.2 Sparse-View CT Reconstruction Analysis -- 4 Conclusion -- References -- Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects -- 1 Introduction -- 2 Methods -- 2.1 Overall Architecture -- 2.2 Parallel Warping -- 2.3 Self-attention -- 2.4 Optimization of the Network -- 3 Experimental Results -- 3.1 Dataset Acquisition -- 3.2 Experimental Setup -- 3.3 Performance Comparison with State-of-the-Art Methods -- 3.4 Ablation Study -- 4 Discussion and Conclusion -- References -- PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction -- 1 Introduction -- 2 Background -- 2.1 MPI Signal Model -- 2.2 MPI Image Reconstruction -- 3 Methods -- 3.1 Plug-and-Play MPI Reconstruction (PP-MPI) -- 3.2 Analyses -- 4 Results -- 5 Discussion -- References -- Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging. , 1 Introduction -- 2 Active Learning for US Beamforming -- 3 Results -- 4 Discussion and Conclusions -- References -- DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction -- 1 Introduction -- 2 Method -- 2.1 The DPDudo Algorithm -- 2.2 The DPDudoNet -- 2.3 Interpretability of the DPDudoNet -- 2.4 Training Loss -- 3 Experimental Results -- 3.1 Clinical Data -- 3.2 Evaluation Metrics -- 3.3 Training Details -- 3.4 Performance Evaluation -- 3.5 Ablation Study -- 4 Conclusion -- References -- MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising -- 1 Introduction -- 1.1 Deep Denoiser -- 1.2 Multi-task Learning -- 2 Methods -- 2.1 Multi-task Discriminator -- 2.2 Non-difference Suppression Loss and Consistency Loss -- 2.3 FFT-Generator -- 3 Experiments and Results -- 3.1 Experiments Settings -- 3.2 Comparison Results -- 4 Conclusion -- References -- Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior -- 1 Introduction and Related Work -- 2 Methods -- 3 Data and Experiments -- 4 Results and Discussion -- 5 Conclusion -- References -- Author Index.

Note: Intro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- 1 Introduction -- 2 Background -- 2.1 Amortized Optimization of CS-MRI -- 2.2 Hypernetworks -- 3 Proposed Method -- 3.1 Regularization-Agnostic Reconstruction Network -- 3.2 Training -- 4 Experiments -- 4.1 Hypernetwork Capacity and Hyperparameter Sampling -- 4.2 Range of Reconstructions -- 5 Conclusion -- References -- Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Self-supervised Loss Function -- 2.3 Enhancement Mask (EM) -- 3 Experiments -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- .26em plus .1em minus .1emEvaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge*-6pt -- 1 Introduction -- 2 Methods -- 2.1 Image Perturbations -- 2.2 Description of 2019 fastMRI Approaches -- 3 Results -- 4 Discussion and Conclusion -- References -- Self-supervised Dynamic MRI Reconstruction -- 1 Introduction -- 2 Theory -- 2.1 Dynamic MRI Reconstruction -- 2.2 Self-supervised Learning -- 3 Methods -- 4 Experimental Results -- 5 Conclusion -- References -- A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction -- 1 Introduction -- 2 Method -- 2.1 DCE-MRI Data Acquisition -- 2.2 Pharmacokinetics Model Analysis and Simulation -- 2.3 MR Acquisition Simulation -- 2.4 Testing with ML Reconstruction -- 3 Result -- 4 Discussion -- 5 Conclusion -- References -- Deep MRI Reconstruction with Generative Vision Transformers -- 1 Introduction -- 2 Theory -- 2.1 Deep Unsupervised MRI Reconstruction -- 2.2 Generative Vision Transformers -- 3 Methods. , 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans -- 1 Introduction -- 2 Background -- 2.1 Distortion Removal Framework -- 2.2 EDSR Architecture -- 3 Distortion Removal and Deblurring of EPI-DWI -- 3.1 Data -- 3.2 Distortion Removal Using Structural Images -- 3.3 Pre-processing for Super-Resolution -- 3.4 Data Augmentation -- 3.5 Architectures Explored for EPI-DWI Deblurring -- 4 Experiments and Results -- 4.1 Computer Hardware Details -- 4.2 Training Details -- 4.3 Baselines -- 4.4 Evaluation Metrics -- 4.5 Results -- 5 Conclusion -- References -- One Network to Solve Them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline -- 1 Introduction -- 2 Method -- 2.1 SampNet: The Sampling Pattern Learning Network -- 2.2 ReconNet: The Reconstruction Network -- 2.3 SegNet: The Segmentation Network -- 2.4 SemuNet: The Sequential Multi-task Joint Learning Network Framework -- 3 Experiments and Discussion -- 3.1 Experimental Details -- 3.2 Experiments Results -- 4 Limitation, Discussion and Conclusion -- References -- Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI -- 1 Introduction -- 2 Methods -- 2.1 Conventional FPP-CMR Reconstruction -- 2.2 Supervised Learning Reconstruction: MoDL -- 2.3 SECRET Reconstruction -- 2.4 Dataset -- 2.5 Implementation Details -- 3 Results and Discussion -- 4 Conclusion -- References -- Deep Learning for General Image Reconstruction -- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data -- 1 Introduction and Related Work -- 2 Methods -- 3 Experiments -- 3.1 MRI -- 3.2 Microscopy -- 4 Discussion -- 5 Conclusion -- References -- Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging -- 1 Introduction. , 1.1 Background -- 1.2 Our Contributions -- 2 Methods -- 2.1 Data -- 2.2 Training Pair Simulation -- 2.3 Denoising Model -- 2.4 Model Training -- 3 Experiments -- 3.1 Reader Study -- 3.2 Video Quality -- 3.3 Runtime -- 4 Conclusion -- References -- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Frequency Domain Analysis -- 3.2 Frequency Domain Loss -- 4 Experiments -- 4.1 Dataset -- 4.2 Training Details -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Semi- and Self-supervised Multi-view Fusion of 3D Microscopy Images Using Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Existing Methods for Comparison -- 4.3 CNN-Based Multi-View Deconvolution and Fusion -- 5 Conclusions -- References -- Author Index.

Note: Intro -- Foreword -- Acknowledgments -- Contents -- List of Tables -- List of Figures -- Abbreviations -- Chapter 1: Introduction to the Central European Magdalenian: Area, Corpus, and Major Questions -- The Term "Central Europe" in This Study -- The Sites Assigned to the Central European Magdalenian -- Purpose and Conduct of the Investigation -- Part I: Methodological and Theoretical Framework -- Chapter 2: Collecting Data for Large-Scale, Literature-Based Studies -- Background Data -- Data on Site History and Documentation -- Geographical Data -- Spatial Data -- Mapping on the Plane -- Objective Determination of the Study Area and the Edge Effect -- One-to-One-Correspondence Between Objects and Events -- Precise Indication of the Location of Events -- Representative Enumeration or Census of the Objects of Interest -- Landscape-Related Data -- Radiocarbon Data -- Environmental Data -- Climate -- Vegetation -- Fauna -- Cultural Data -- Mobility Patterns -- Lithic Raw Material -- Mollusk Shells and Amber -- Tool Types -- Definition of Magdalenian Tool Types -- End Scrapers -- Burins -- Borers -- Zinken -- Backed Bladelets -- Backed Points -- Shouldered Points -- Pointed Blades -- Laterally Retouched Pieces -- Truncated Pieces -- Notched and Denticulated Pieces -- Splintered Pieces -- Magdalenian Triangles -- Organic Points -- Bâtons Percés (Perforated Batons) -- Needles -- Baguettes Demi-Rondes (Half-Round Rods) -- Magdalenian Barbed Points -- Navettes -- Blade Technology -- Recording of Technological Information -- Blanks -- Blank Dimensions and Preservation -- Cortex Cover -- Striking-Platform Remnant (SPR) Kind -- Striking-Platform Remnant (SPR) Form -- Striking-Platform Remnant (SPR) Size -- Dorsal Reduction -- Lip -- Bulb -- Bulbar Scar -- Esquillements du Bulbe -- Impact Point -- En éperon Preparation -- Flaking Angle. , Sculpted and Engraved Objects -- Chapter 3: Theoretical Framework -- Sites, Assemblages, and the Problem of Palimpsests -- Types as Analytical Units -- Facts or Fiction -- Etic Concepts with Emic Significance -- Types as Indicators for Interaction -- The Use of Ethnographic Analogies in this Study -- Territories, Territoriality, and Land Tenure -- Ethnicity -- Part II: Analyzing the Central European Magdalenian -- Chapter 4: Past Research on the Magdalenian and Its Current Implications -- The Rock Shelter of La Madeleine -- The Subdivision of the Magdalenian into Six Phases and the Critique to Which It Is Subject -- The Long Debate About Lithic Triangles -- Assemblages for Which No Radiocarbon Dates Are Available -- Assemblages for Which Radiocarbon Dates Are Available -- Conclusion -- Theories About the Internal Structure of the Central European Magdalenian -- Chapter 5: Environmental Diversity -- Climate and Vegetation -- The Last Glacial Maximum -- Between LGM and Oldest Dryas: The Late Pleniglacial Optimum -- The Oldest Dryas -- The Bølling Interstadial -- Fauna -- Horse and Reindeer Remains -- Other Animal Remains in the Faunal Record -- Chapter 6: Cultural Diversity and Regional Grouping -- Critical Review and Classification of the Recorded Assemblages -- Possible Factors for the Formation of the Large-Scale Site Pattern -- Research Activities -- Erosion -- Rivers -- Lithic Raw Material -- Procurement Pattern of Lithic Raw Materials -- The Circum-Jurassic Group -- The Danube Group -- The Meuse-Rhine Group -- The Vltava-Saale Group -- The Polish-Moravian Group -- Procurement Pattern of Mollusk Shells and Amber -- The Circum-Jurassic Group -- The Danube Group -- The Meuse-Rhine Group -- The Vltava-Saale Group -- The Polish-Moravian Group -- Morphological Observations -- Typological and Technological Analysis. , Spatial Distribution of Concepts of Lithic Tools -- End Scrapers -- Burins -- Borers -- Zinken (Beaked Borers) -- Backed Bladelets -- Truncated Pieces -- Laterally Retouched Pieces -- Splintered Pieces -- Notched and Denticulated Pieces -- Pointed Blades -- Backed Points -- Shouldered Points -- Triangles -- Concluding Remarks -- Spatial Distribution of Concepts of Organic Tools -- Points -- Needles -- Barbed Points -- Baguettes Demi-rondes -- Bâtons Percés -- Linear Discriminant Function Analysis: Multivariate Analysis of Spatial and Temporal Trends Among Concepts of Lithic Tools -- The Method -- Initial Transformation of Raw Data -- Nonparametric Test for Intergroup Variance -- Test for the Homogeneity of the Intragroup Variance/Covariance -- The Discriminant Functions -- The Standardized Canonical Discriminant Coefficient -- Wilks' Lambda -- Additional Quality Measures for Discriminant Functions -- Visualization of the Results -- The Course of Analysis -- Analysis of Spatial Grouping -- LDA of Hamburgian and Magdalenian Assemblages -- LDA of Magdalenian Assemblages -- Analysis of Temporal Grouping -- Correspondence Analysis: Multivariate Investigation of Spatial and Temporal Trends in Blade and Bladelet Production -- The Method -- Course and Corpus of the Analysis -- The Assemblages -- The Technological Features -- Results of the Technological Analysis -- The Features -- The Assemblages -- Spatial and Temporal Trends Among Technological Concepts -- Spatial Trends -- Temporal Trends -- Typological and Technological Variability -- Spatial Aspects -- Hamburgian and Magdalenian -- Circum-Jurassic Group -- Danube Group -- Meuse-Rhine Group -- Vltava-Saale Group -- Polish-Moravian Group -- Temporal Aspects -- Some Aspects of Central European Magdalenian Sculptures and Engravings -- Geometrical Representations -- Figural Representations. , Horse and Reindeer Representations -- Other Animal Representations -- Female Representations -- Phalliform Objects -- Other Noteworthy Objects -- Part III: Interpreting the Central European Magdalenian -- Chapter 7: Territories and Land-Use Patterns of the Five Regional Groups -- The Circum-Jurassic Group -- Group Stsatistics -- Landscape and Environmental Setting -- Location of Sites and Estimation of the Group's Territory -- Land-Use Pattern -- The Danube Group -- Group Statistics -- Landscape and Environmental Setting -- Location of Sites and Estimation of the Group's Territory -- Land-Use Pattern -- The Meuse-Rhine Group -- Group Statistics -- Landscape and Environmental Setting -- Location of Sites and Estimation of the Group's Territory -- Land-Use Pattern -- The Vltava-Saale Group -- Group Statistics -- Landscape and Environmental Setting -- The Northwestern Part -- The Southeastern Part -- Location of Sites and Estimation of the Group's Territory -- Land-Use Pattern -- The Polish-Moravian Group -- Group Statistics -- Landscape and Environmental Setting -- Location of Sites and Estimation of the Group's Territory -- Land-Use Pattern -- Chapter 8: The Recolonization of Central Europe -- The Current Theory and the Major Critiques to Which It Is Subject -- Toward a New Theory of the Recolonization of Central Europe -- New Light on the Recolonization of Central Europe -- The Eastward Expansion -- The Westward Expansion -- Chapter 9: Small-World Networks: Backbone of the Magdalenian Society? -- Chapter 10: Summary -- Spatial Diversity -- Regional Diversity -- Supra-Regional Diversity -- Temporal Diversity -- Subsistence -- The Bidirectional Recolonization of Central Europe -- The Central European Magdalenian: A Small-­World Network? -- Chapter 11: Zusammenfassung (German Translation of Chapter 10 Summary) -- Räumliche Diversität. , Regionale Diversität -- Überregionale Diversität -- Zeitliche Diversität -- Subsistenz -- Die bidirektionale Wiederbesiedlung Mitteleuropas -- Das Mitteleuropäische Magdalénien: ein „Small-World" Netzwerk? -- References -- Index -- Appendix.

Note: Introduction to the Central European Magdalenian-Area, Corpus and Major QuestionsPart I: Methodological and Theoretical Framework -- Collecting Data for Large-Scale, Literature-Based Studies -- Theoretical Framework -- Part II: Analyzing the Central European Magdalenian -- Past Research on the Magdalenian and its Current Implications -- Environmental Diversity -- Cultural Diversity and Regional Grouping -- Part III: Interpreting the Central European Magdalenian -- Territories and Land-Use Patterns -- The Recolonization of Central Europe -- Small-World Networks-Backbone of the Magdalenian Society? -- Summary -- Zusammenfassung: (German translation of Chapter 10: Summary) -- Appendix.

Note: Description based on publisher supplied metadata and other sources

Note: Förderkennzeichen BMBF 02NUK017A. - Verbund-Nummer 01113965 , Verfasser dem Berichtsblatt entnommen , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Sprache der Zusammenfassung: Deutsch, Englisch

Note: Förderkennzeichen BMBF 05K13GU5 , "Förderzeitraum: 01.07.2013-31.12.2016" - Startseite der Ressource , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden

Note: Förderkennzeichen BMBF 03INT506BA [richtig] - 03INT506BB [falsch] , Verbundnummer 01183475