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[null,null,"Titolo",null,"Machine learning for medical image reconstruction : 4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / / edited by Nandinee Haq [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-88552-6"]
[null,null,"Descrizione fisica",null,"1 online resource (147 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.12964"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Diagnostic imaging - Data processing"]
[{"$nulls":4},"Artificial intelligence - Medical applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"DCE-MRI Reconstruction -- 1 Introduction -- 2 Method -- 2.1 DCE-MRI Data Acquisition -- 2.2 Pharmacokinetics Model Analysis and Simulation -- 2.3 MR Acquisition Simulation -- 2.4 Testing with ML Reconstruction -- 3 Result -- 4 Discussion -- 5 Conclusion -- References -- Deep MRI Reconstruction with Generative Vision Transformers -- 1 Introduction -- 2 Theory -- 2.1 Deep Unsupervised MRI Reconstruction -- 2.2 Generative Vision Transformers -- 3 Methods."]
[{"$nulls":4},"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."]
[{"$nulls":4},"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."]