000145540 001__ 145540
000145540 005__ 20250522150104.0
000145540 020__ $$a978-3-030-00128-5 (print)
000145540 020__ $$a978-3-030-00129-2 (electronic)
000145540 0247_ $$2doi$$a10.1007/978-3-030-00129-2_4
000145540 0247_ $$2ISSN$$a0302-9743
000145540 0247_ $$2ISSN$$a1611-3349
000145540 037__ $$aDZNE-2020-00874
000145540 041__ $$aEnglish
000145540 1001_ $$0P:(DE-2719)2812572$$aDedmari, Muneer Ahmad$$b0$$udzne
000145540 1112_ $$aMachine Learning for Medical Image Reconstruction$$cGranada$$d2018-09-16 - 2018-09-16$$gMLMIR 2018$$wSpain
000145540 245__ $$aComplex Fully Convolutional Neural Networks for MR Image Reconstruction
000145540 260__ $$c2018
000145540 3367_ $$033$$2EndNote$$aConference Paper
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000145540 3367_ $$2BibTeX$$aINPROCEEDINGS
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000145540 3367_ $$2ORCID$$aLECTURE_SPEECH
000145540 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1747918810_22615$$xOther
000145540 520__ $$aUndersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network (CDFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. CDFNet leverages the inherently complex-valued nature of input k-space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through CDFNet in contrast to its real-valued counterparts.
000145540 536__ $$0G:(DE-HGF)POF3-345$$a345 - Population Studies and Genetics (POF3-345)$$cPOF3-345$$fPOF III$$x0
000145540 588__ $$aDataset connected to CrossRef Book Series, Journals: pub.dzne.de
000145540 7001_ $$0P:(DE-2719)2812477$$aConjeti, Sailesh$$b1$$udzne
000145540 7001_ $$0P:(DE-2719)2812449$$aEstrada Leon, Edgar Santiago$$b2$$udzne
000145540 7001_ $$0P:(DE-2719)2812222$$aEhses, Philipp$$b3$$udzne
000145540 7001_ $$0P:(DE-2719)2810538$$aStöcker, Tony$$b4$$udzne
000145540 7001_ $$0P:(DE-2719)2812134$$aReuter, Martin$$b5$$udzne
000145540 773__ $$a10.1007/978-3-030-00129-2_4
000145540 8564_ $$uhttps://link.springer.com/chapter/10.1007/978-3-030-00129-2_4
000145540 909CO $$ooai:pub.dzne.de:145540$$pVDB
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812572$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812477$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b1$$kDZNE
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812449$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b2$$kDZNE
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812222$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b3$$kDZNE
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2810538$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b4$$kDZNE
000145540 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812134$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b5$$kDZNE
000145540 9131_ $$0G:(DE-HGF)POF3-345$$1G:(DE-HGF)POF3-340$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lErkrankungen des Nervensystems$$vPopulation Studies and Genetics$$x0
000145540 9141_ $$y2018
000145540 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-28$$wger
000145540 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-28
000145540 9201_ $$0I:(DE-2719)1040310$$kAG Reuter$$lArtificial Intelligence in Medicine$$x0
000145540 9201_ $$0I:(DE-2719)1012001$$kAG Breteler$$lPopulation Health Sciences$$x1
000145540 9201_ $$0I:(DE-2719)1013026$$kAG Stöcker$$lMR Physics$$x2
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