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001440091 001__ 1440091
001440091 003__ OCoLC
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001440091 019__ $$a1287763215
001440091 020__ $$a9783030885526$$q(electronic bk.)
001440091 020__ $$a3030885526$$q(electronic bk.)
001440091 020__ $$z9783030885519
001440091 0247_ $$a10.1007/978-3-030-88552-6$$2doi
001440091 035__ $$aSP(OCoLC)1272989641
001440091 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dOCLCF$$dDCT$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001440091 049__ $$aISEA
001440091 050_4 $$aRC78.7.D53$$bM595 2021eb
001440091 08204 $$a006.3/1$$223
001440091 1112_ $$aMLMIR (Workshop)$$n(4th :$$d2021 :$$cOnline)
001440091 24510 $$aMachine learning for medical image reconstruction :$$b4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /$$cNandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (eds.).
001440091 2463_ $$aMLMIR 2021
001440091 260__ $$aCham :$$bSpringer,$$c2021.
001440091 300__ $$a1 online resource (147 pages)
001440091 336__ $$atext$$btxt$$2rdacontent
001440091 337__ $$acomputer$$bc$$2rdamedia
001440091 338__ $$aonline resource$$bcr$$2rdacarrier
001440091 347__ $$atext file
001440091 347__ $$bPDF
001440091 4901_ $$aLecture notes in computer science ;$$v12964
001440091 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
001440091 500__ $$a4 Limitation, Discussion and Conclusion.
001440091 500__ $$aIncludes author index.
001440091 5050_ $$aIntro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- 1 Introduction -- 2 Background -- 2.1 Amortized Optimization of CS-MRI -- 2.2 Hypernetworks -- 3 Proposed Method -- 3.1 Regularization-Agnostic Reconstruction Network -- 3.2 Training -- 4 Experiments -- 4.1 Hypernetwork Capacity and Hyperparameter Sampling -- 4.2 Range of Reconstructions -- 5 Conclusion -- References -- Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation -- 1 Introduction
001440091 5058_ $$a2 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
001440091 5058_ $$a2 Theory -- 2.1 Dynamic MRI Reconstruction -- 2.2 Self-supervised Learning -- 3 Methods -- 4 Experimental Results -- 5 Conclusion -- References -- A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction -- 1 Introduction -- 2 Method -- 2.1 DCE-MRI Data Acquisition -- 2.2 Pharmacokinetics Model Analysis and Simulation -- 2.3 MR Acquisition Simulation -- 2.4 Testing with ML Reconstruction -- 3 Result -- 4 Discussion -- 5 Conclusion -- References -- Deep MRI Reconstruction with Generative Vision Transformers -- 1 Introduction -- 2 Theory
001440091 5058_ $$a2.1 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
001440091 5058_ $$a4.1 Computer Hardware Details -- 4.2 Training Details -- 4.3 Baselines -- 4.4 Evaluation Metrics -- 4.5 Results -- 5 Conclusion -- References -- One Network to Solve Them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline -- 1 Introduction -- 2 Method -- 2.1 SampNet: The Sampling Pattern Learning Network -- 2.2 ReconNet: The Reconstruction Network -- 2.3 SegNet: The Segmentation Network -- 2.4 SemuNet: The Sequential Multi-task Joint Learning Network Framework -- 3 Experiments and Discussion -- 3.1 Experimental Details -- 3.2 Experiments Results
001440091 506__ $$aAccess limited to authorized users.
001440091 520__ $$aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
001440091 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 7, 2021).
001440091 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001440091 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
001440091 650_0 $$aMachine learning$$vCongresses.
001440091 650_6 $$aImagerie pour le diagnostic$$xInformatique$$vCongrès.
001440091 650_6 $$aIntelligence artificielle en médecine$$vCongrès.
001440091 650_6 $$aApprentissage automatique$$vCongrès.
001440091 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440091 655_7 $$aConference papers and proceedings.$$2lcgft
001440091 655_7 $$aActes de congrès.$$2rvmgf
001440091 655_0 $$aElectronic books.
001440091 7001_ $$aHaq, Nandinee.
001440091 7001_ $$aJohnson, Patricia.
001440091 7001_ $$aMaier, Andreas.
001440091 7001_ $$aWürfl, Tobias.
001440091 7001_ $$aYoo, Jaejun.
001440091 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(24th :$$d2021 :$$cOnline)
001440091 77608 $$iPrint version:$$aHaq, Nandinee.$$tMachine Learning for Medical Image Reconstruction.$$dCham : Springer International Publishing AG, ©2021$$z9783030885519
001440091 830_0 $$aLecture notes in computer science ;$$v12964.
001440091 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001440091 852__ $$bebk
001440091 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-88552-6$$zOnline Access$$91397441.1
001440091 909CO $$ooai:library.usi.edu:1440091$$pGLOBAL_SET
001440091 980__ $$aBIB
001440091 980__ $$aEBOOK
001440091 982__ $$aEbook
001440091 983__ $$aOnline
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