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Cited by in CrossRef
Number Cited Articles
1
Pau Xiberta, Anton Bardera, Imma Boada, Marina Gispert, Albert Brun, Maria Font-i-Furnols. Evaluation of an automatic lean meat percentage quantification method based on a partial volume model from computed tomography scansComputers and Electronics in Agriculture 2018; 151: 365 doi: 10.1016/j.compag.2018.06.019
2
Debra F. McGivney, Rasim Boyacıoğlu, Yun Jiang, Megan E. Poorman, Nicole Seiberlich, Vikas Gulani, Kathryn E. Keenan, Mark A. Griswold, Dan Ma. Magnetic resonance fingerprinting review part 2: Technique and directionsJournal of Magnetic Resonance Imaging 2020; 51(4): 993 doi: 10.1002/jmri.26877
3
Jingjing Wang, Changjun Hu, Huaqiang Xu, Yan Leng, Liren Zhang, Yuefeng Zhao. A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRISignal, Image and Video Processing 2019; 13(5): 1019 doi: 10.1007/s11760-019-01440-5
4
Soonchan Park, Joon Jang, Jang-Hoon Oh, Chang-Woo Ryu, Geon-Ho Jahng. Assessment of the Cerebrospinal Fluid Effect on the Chemical Exchange Saturation Transfer Map Obtained from the Full Z-Spectrum in the Elderly Human BrainProgress in Medical Physics 2019; 30(4): 139 doi: 10.14316/pmp.2019.30.4.139
5
Buda Bajić. Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noiseJournal of Electronic Imaging 2019; 28(01): 1 doi: 10.1117/1.JEI.28.1.013046
6
Reshma Hiralal, Hema P Menon. Intelligent Systems Technologies and Applications 2016Advances in Intelligent Systems and Computing 2016; 530: 245 doi: 10.1007/978-3-319-47952-1_19
7
Roberto Duarte, Audrey Repetti, Pedro A Gómez, Mike Davies, Yves Wiaux. Greedy approximate projection for magnetic resonance fingerprinting with partial volumesInverse Problems 2020; 36(3): 035015 doi: 10.1088/1361-6420/ab356d
8
Jessica L. Panman, Yang Yang To, Emma L. van der Ende, Jackie M. Poos, Lize C. Jiskoot, Lieke H. H. Meeter, Elise G. P. Dopper, Mark J. R. J. Bouts, Matthias J. P. van Osch, Serge A. R. B. Rombouts, John C. van Swieten, Jeroen van der Grond, Janne M. Papma, Anne Hafkemeijer. Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil ComparisonFrontiers in Neuroscience 2019; 13 doi: 10.3389/fnins.2019.00729
9
Sunli Tang, Carlos Fernandez-Granda, Sylvain Lannuzel, Brett Bernstein, Riccardo Lattanzi, Martijn Cloos, Florian Knoll, Jakob Assländer. Multicompartment magnetic resonance fingerprintingInverse Problems 2018; 34(9): 094005 doi: 10.1088/1361-6420/aad1c3
10
V. C. Obmann, N. Mertineit, C. Marx, A. Berzigotti, L. Ebner, J. T. Heverhagen, A. Christe, A. T. Huber. Liver MR relaxometry at 3T – segmental normal T1 and T2* values in patients without focal or diffuse liver disease and in patients with increased liver fat and elevated liver stiffnessScientific Reports 2019; 9(1) doi: 10.1038/s41598-019-44377-y
11
Peifang Guo. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based ProceduresLecture Notes in Computer Science 2019; 11840: 85 doi: 10.1007/978-3-030-32689-0_9
12
Eileanoir B. Johnson, Sarah Gregory, Hans J. Johnson, Alexandra Durr, Blair R. Leavitt, Raymund A. Roos, Geraint Rees, Sarah J. Tabrizi, Rachael I. Scahill. Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington’s DiseaseFrontiers in Neurology 2017; 8 doi: 10.3389/fneur.2017.00519
13
Vladimir Kanchev, Roumen Kountchev. New Approaches in Intelligent Image AnalysisIntelligent Systems Reference Library 2016; 108: 183 doi: 10.1007/978-3-319-32192-9_6
14
Ali Hasan, Farid Meziane, Rob Aspin, Hamid Jalab. Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without EdgeSymmetry 2016; 8(11): 132 doi: 10.3390/sym8110132
15
Debra McGivney, Anagha Deshmane, Yun Jiang, Dan Ma, Chaitra Badve, Andrew Sloan, Vikas Gulani, Mark Griswold. Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprintingMagnetic Resonance in Medicine 2018; 80(1): 159 doi: 10.1002/mrm.27017
16
Peifang Guo. Brain tissue classification method for clinical decision-support systemsEngineering Applications of Artificial Intelligence 2017; 64: 232 doi: 10.1016/j.engappai.2017.05.015
17
Yasser Alemán‐Gómez, Elena Najdenovska, Timo Roine, Mário João Fartaria, Erick J. Canales‐Rodríguez, Zita Rovó, Patric Hagmann, Philippe Conus, Kim Q. Do, Paul Klauser, Pascal Steullet, Philipp S. Baumann, Meritxell Bach Cuadra. Partial‐volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophreniaHuman Brain Mapping 2020;  doi: 10.1002/hbm.25108
18
Peifang Guo. A tissue-based biomarker model for predicting disease patternsKnowledge-Based Systems 2017; 131: 160 doi: 10.1016/j.knosys.2017.05.019
19
Farzad Mortazavi, Samantha E. Romano, Douglas L. Rosene, Kathleen S. Rockland. A Survey of White Matter Neurons at the Gyral Crowns and Sulcal Depths in the Rhesus MonkeyFrontiers in Neuroanatomy 2017; 11 doi: 10.3389/fnana.2017.00069
20
Martijn Nagtegaal, Peter Koken, Thomas Amthor, Mariya Doneva. Fast multi‐component analysis using a joint sparsity constraint for MR fingerprintingMagnetic Resonance in Medicine 2020; 83(2): 521 doi: 10.1002/mrm.27947
21
Peifang Guo, Alan Evans, Prabir Bhattacharya. Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology ImagesInternational Journal of Software Science and Computational Intelligence 2018; 10(2): 36 doi: 10.4018/IJSSCI.2018040103
22
Suraj D. Serai, Jonathan Dudley, James L. Leach. Comparison of whole brain segmentation and volume estimation in children and young adults using SPM and SyMRIClinical Imaging 2019; 57: 77 doi: 10.1016/j.clinimag.2019.05.008
23
Dayana Hayek, Friederike Thams, Agnes Flöel, Daria Antonenko. Dentate Gyrus Volume Mediates the Effect of Fornix Microstructure on Memory Formation in Older AdultsFrontiers in Aging Neuroscience 2020; 12 doi: 10.3389/fnagi.2020.00079
24
A.G. Beejesh, Varun P. Gopi, Jude Hemanth. Brain MR kurtosis imaging study: Contrasting gray and white matterCognitive Systems Research 2019; 55: 135 doi: 10.1016/j.cogsys.2019.01.005
25
Xiaoliang Gong, Chao Ma, Panpan Yang, Yufei Chen, Chaolin Du, Caixia Fu, Jian-Ping Lu. Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility studyActa Radiologica Open 2019; 8(3): 205846011983469 doi: 10.1177/2058460119834690
26
Richa Gandhi, Charalampos Tsoumpas. Preclinical Imaging Biomarkers for Postischaemic Neurovascular RemodellingContrast Media & Molecular Imaging 2019; 2019: 1 doi: 10.1155/2019/3128529
27
Jannike Nickander, Magnus Lundin, Goran Abdula, Peder Sörensson, Stefania Rosmini, James C. Moon, Peter Kellman, Andreas Sigfridsson, Martin Ugander. Blood correction reduces variability and gender differences in native myocardial T1 values at 1.5 T cardiovascular magnetic resonance – a derivation/validation approachJournal of Cardiovascular Magnetic Resonance 2017; 19(1) doi: 10.1186/s12968-017-0353-7
28
Juliane Dinse, Andreas Schäfer, Pierre-Louis Bazin, Nikolaus Weiskopf. Bildverarbeitung für die Medizin 2016Informatik aktuell 2016; : 14 doi: 10.1007/978-3-662-49465-3_5
29
Peifang Guo. Imaging for Patient-Customized Simulations and Systems for Point-of-Care UltrasoundLecture Notes in Computer Science 2017; 10549: 78 doi: 10.1007/978-3-319-67552-7_10
30
J. Neikter, S. Agerskov, P. Hellström, M. Tullberg, G. Starck, D. Ziegelitz, D. Farahmand. Ventricular Volume Is More Strongly Associated with Clinical Improvement Than the Evans Index after Shunting in Idiopathic Normal Pressure HydrocephalusAmerican Journal of Neuroradiology 2020; 41(7): 1187 doi: 10.3174/ajnr.A6620
31
John Au, Diana M. Perriman, Mark R. Pickering, Graham Buirski, Paul N. Smith, Alexandra L. Webb. Magnetic resonance imaging atlas of the cervical spine musculatureClinical Anatomy 2016; 29(5): 643 doi: 10.1002/ca.22731
32
Peifang Guo, Prabir Bhattacharya. A novel brain image processing method for the application of detecting the GBM disease patterns in anatomic sections of T1-weighted 3D magnetic resonance imaging2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) 2017; : 146 doi: 10.1109/ICCI-CC.2017.8109743
33
Peifang Guo, Prabir Bhattacharya. An Innovative Model for Detecting Brain Tumors and Glioblastoma Multiforme Disease PatternsInternational Journal of Software Science and Computational Intelligence 2017; 9(4): 34 doi: 10.4018/IJSSCI.2017100103
34
Hamid A. Jalab, Ali M. Hasan. Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A ReviewArchives of Neuroscience 2019; 6(Brain Mapping) doi: 10.5812/ans.84920