LEADER 06097nam 22007575 450 001 996466288703316 005 20200706002535.0 010 $a3-030-33843-6 024 7 $a10.1007/978-3-030-33843-5 035 $a(CKB)4100000009678335 035 $a(MiAaPQ)EBC5968246 035 $a(DE-He213)978-3-030-33843-5 035 $a(PPN)255691807 035 $a(EXLCZ)994100000009678335 100 $a20191023d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Medical Image Reconstruction$b[electronic resource] $eSecond International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /$fedited by Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (ix, 266 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11905 300 $aIncludes index. 311 $a3-030-33842-8 327 $aDeep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. 330 $aThis book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11905 606 $aArtificial intelligence 606 $aEducation?Data processing 606 $aApplication software 606 $aBioinformatics 606 $aOptical data processing 606 $aHealth informatics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputers and Education$3https://scigraph.springernature.com/ontologies/product-market-codes/I24032 606 $aComputer Appl. in Social and Behavioral Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/I23028 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 615 0$aArtificial intelligence. 615 0$aEducation?Data processing. 615 0$aApplication software. 615 0$aBioinformatics. 615 0$aOptical data processing. 615 0$aHealth informatics. 615 14$aArtificial Intelligence. 615 24$aComputers and Education. 615 24$aComputer Appl. in Social and Behavioral Sciences. 615 24$aComputational Biology/Bioinformatics. 615 24$aImage Processing and Computer Vision. 615 24$aHealth Informatics. 676 $a610.28563 702 $aKnoll$b Florian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaier$b Andreas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRueckert$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYe$b Jong Chul$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466288703316 996 $aMachine Learning for Medical Image Reconstruction$91912511 997 $aUNISA