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Machine Learning for Medical Image Reconstruction de Farah Deeba

Machine Learning for Medical Image Reconstruction: Lecture Notes in Computer Science, cartea 12450

Editat de Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Ye
en Limba Engleză Paperback – 20 oct 2020
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually.
The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
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Specificații

ISBN-13: 9783030615970
ISBN-10: 3030615979
Pagini: 172
Ilustrații: VIII, 163 p. 76 illus., 48 illus. in color.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.27 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Deep Learning for Magnetic Resonance Imaging.- 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI.- Deep Parallel MRI Reconstruction Network Without Coil Sensitivities.- Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data.- Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI.- Model-based Learning for Quantitative Susceptibility Mapping.- Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks.- Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping.- Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction.- Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI.- AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis.- Deep Learning for General Image Reconstruction.- A deep prior approach to magnetic particle imaging.- End-To-End Convolutional NeuralNetwork for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images.- Cellular/Vascular Reconstruction using a Deep CNN for Semantic Image Preprocessing and Explicit Segmentation.- Improving PET-CT Image Segmentation via Deep Multi-Modality Data Augmentation.- Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning.