LEADER 03411oam 2200469 450 001 996418303303316 005 20210415040413.0 010 $a3-030-61598-7 024 7 $a10.1007/978-3-030-61598-7 035 $a(CKB)4100000011515579 035 $a(DE-He213)978-3-030-61598-7 035 $a(MiAaPQ)EBC6381261 035 $a(PPN)254615392 035 $a(EXLCZ)994100000011515579 100 $a20210415d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning for medical image reconstruction $ethird International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, proceedings /$fFarah Deeba [and three others] (editors) 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (VIII, 163 p. 76 illus., 48 illus. in color.) 225 1 $aLecture notes in computer science ;$v12450 300 $aIncludes index. 311 $a3-030-61597-9 327 $aDeep 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 Neural Network 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. 330 $aThis book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction. 410 0$aLecture notes in computer science ;$v12450. 606 $aDiagnostic imaging$xData processing$vCongresses 615 0$aDiagnostic imaging$xData processing 676 $a616.07540285 702 $aDeeba$b Farah 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a996418303303316 996 $aMachine Learning for Medical Image Reconstruction$91912511 997 $aUNISA