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Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke

Hassan Bagher-Ebadian, Kourosh Jafari-Khouzani, Panayiotis D. Mitsias, Mei Lu, Hamid Soltanian-Zadeh, Michael Chopp, James R. Ewing, Yong He
2011 PLoS ONE  
, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI.  ...  In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome.  ...  In a recent study [26] , the final infarct volume in patients treated with IV rt-PA (recombinant Tissue Plasminogen Activator) was predicted by an artificial neural network (ANN) model that combined both  ... 
doi:10.1371/journal.pone.0022626 pmid:21853039 pmcid:PMC3154199 fatcat:cmdvcptbmfhgbfgnwpmx7gs4ei

Magnetic Resonance Characterization of Ischemic Tissue Metabolism

Jerry S Cheung, Xiaoying Wang, Phillip Zhe Sun
2011 The Open Neuroimaging Journal  
More recently, predictive algorithm based on artificial neural network has also been developed for prediction of ischemic tissue fate based on multiparametric MRI data [145] .  ...  Being highly correlated with established histological and enzymatic techniques, volume with elevated T 2 in late stages has been widely used to estimate final infarct size noninvasively [79, 80] .  ... 
doi:10.2174/1874440001105010066 pmid:22216079 pmcid:PMC3245409 fatcat:qy7gd7hp4fgedpmvm2j4of6nyy

Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal

Islem Rekik, Stéphanie Allassonnière, Trevor K. Carpenter, Joanna M. Wardlaw
2012 NeuroImage: Clinical  
Over the last 15 years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have been widely used to investigate different aspects of evolution  ...  Paving the way for potential answers to this question, both magnetic resonance (MRI) and CT (computed tomography) images have been used to visualize the lesion extent, either with or without spatial distinction  ...  ., 2010) based on artificial neural networks (ANN).  ... 
doi:10.1016/j.nicl.2012.10.003 pmid:24179749 pmcid:PMC3757728 fatcat:kztwpnmtjffklf43ulg7lbf64a

Infarct Volume Prediction by Early Magnetic Resonance Imaging in a Murine Stroke Model Depends on Ischemia Duration and Time of Imaging

Christoph Leithner, Martina Füchtemeier, Devi Jorks, Susanne Mueller, Ulrich Dirnagl, Georg Royl
2015 Stroke  
The majority of chance findings of final infarct volume differences in random group allocations of animals were associated with significant differences in early ADC lesion volumes for 90, but not for 45  ...  Early ADC imaging may be used to reduce errors in the interpretation of post MCAO treatment effects on stroke volumes. (Stroke. 2015;46:3249-3259.  ...  Sources of Funding Disclosures None.  ... 
doi:10.1161/strokeaha.114.007832 pmid:26451016 fatcat:iagzzzejfbevrnzacxppml6k4i

Abstracts

2006 Cerebrovascular Diseases  
Results: The target number of patients with a primary endpoint has been reached and SPARCL is in the final stages of closeout. Results will be available for the European Stroke Conference.  ...  Whether statins reduce the risk of recurrent stroke in patients with recent stroke and no history of CHD remain uncertain.  ...  Method: A total of 234 acute stroke patients underwent emergent multiparametric MRI and MRI <6 h.  ... 
doi:10.1159/000093822 fatcat:zlkvlediy5ea3eepiunfetasjm

Program

2009 2009 International Joint Conference on Neural Networks  
Gaweda, Room: Peachtree Ballroom E 3:20PM Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multiparametric MRI in Patients With Stroke Hassan Bagher-Ebadian  ...  Almeida P1102 Survival Prediction in Patients Undergoing Ischemic Cardiopathology Emilio Soria, Jose D.  ...  P1325 Stegnalysis of MP3stego  ... 
doi:10.1109/ijcnn.2009.5178575 fatcat:kxaceopferd23ps5uyrn3m7xjy

Poster Viewing Session II

2017 Journal of Cerebral Blood Flow and Metabolism  
The predicted time of sensorimoror recovery of 3-4 weeks concur with experimental findings based on MRI and histopathology studies (4).  ...  These results, along with the time shift analysis maps, are shown for a representative patient in the figure.  ...  Based on the use of multiparametric MRI and [ 18 F]-FMISO PET, we analyzed the ability of S t O 2 -MRI to depict the spatial heterogeneity of brain tissue in the acute phase of stroke.  ... 
doi:10.1177/0271678x17695989 pmid:28366134 pmcid:PMC5441166 fatcat:no54qir44jemni3wacaipk5wxa

Full Issue PDF

2020 JACC Cardiovascular Imaging  
Changes in LA mechanics correlated with changes in wedge pressure (r ¼ -0.61; p < 0.001).  ...  METHODS A total of 31 patients with HFrEF and immediate heart failure (age 64 AE 15 years, 74% male, left ventricular ejection fraction 20 AE 12%) underwent serial echocardiography during decongestive  ...  The authors are solely responsible for the design and conduct of this study; all study analyses; and the drafting and editing of the paper and its final contents.  ... 
doi:10.1016/s1936-878x(20)30301-6 fatcat:w765zpyw4neohdsuytjzyy4gtm

Brain PET Poster Sessions PP01-M01 to PP02-N07

2019 Journal of Cerebral Blood Flow and Metabolism  
Highthroughput rat brain PET imaging and automatic spatial normalization of the dopamine D2/3 receptor ligand [F-18]fallypride.  ...  Salinas for technical support in radiosynthesis and technical assistance in the PET studies. This study was funded by MINECO SAF2014-54070-JIN.  ...  For predicting final infarct, the highest AUC was also found for TTP with an optimal threshold of 1.2 sec.  ... 
doi:10.1177/0271678x19851018 pmid:31265793 pmcid:PMC6610575 fatcat:auejdo6e3rb2pjtxgqq6rkm7ie

Subject index

2007 Journal of the American College of Cardiology  
Neural Networks Are a Potential Tool for Estimating Prognosis in Coronary Artery Disease: Are Standard Algorithms Sufficient?  ...  ##909-228·146A Doppler Restrictive Filling Pattern Is an Independent Predictor of Survival in Patients With Heart Failure of Both Ischemic and Nonischemic Etiology: Results From an Individual Patient Meta-Analysis  ... 
doi:10.1016/j.jacc.2007.01.047 fatcat:w6ingwinqrckdghdkhndushcfq

ESGAR 2020 Book of Abstracts

2020 Insights into Imaging  
These were used to train, validate and test 5 convolutional neural networks. These were combined into a predictive ensemble model.  ...  The contoured volume of interest (VOI) and the PET images were input into a fully convolutional neural network (CNN) for volumetric medical imaging (VNet) for the prediction of pCR.  ... 
doi:10.1186/s13244-020-00873-8 pmid:32419107 fatcat:jnt6ow5qljejvcyo257fgdwpbe

Full Issue PDF

2020 JACC Cardiovascular Imaging  
CONCLUSIONS US of microbubbles when using optimized acoustic environments can increase perfusion in limb skeletal muscle, raising the possibility of a therapy for patients with PAD.  ...  Application of these optimized conditions in humans together with intravenous contrast increased calf muscle blood flow by >2-fold in both healthy subjects and patients with PAD, whereas US alone had no  ...  Convolutional neural networks (CNNs), have achieved previously unmatched accuracy in many image analysis challenges (8).  ... 
doi:10.1016/s1936-878x(20)30146-7 fatcat:4lngerhk4ngkfpdi6ddinbl4te

Athletic injuries of the extensor carpi ulnaris subsheath: MRI findings and utility of gadolinium-enhanced fat-saturated T1-weighted sequences with wrist pronation and supination

Jeremy Jeantroux, Fabio Becce, Henri Guerini, Bernard Montalvan, Dominique Le Viet, Jean-Luc Drapé
2010 European Radiology  
However, the final volumes of infarct were approximately 20% bigger. Conclusion: Low MTT in the region of ischaemia is an interesting finding that accurately predicts the infarct core.  ...  B-294 14:27 Evaluation of a novel post-processing filter of native CT images for detection of early ischemic stroke: improved differentiation of early infarct core A.  ...  MRI was obtained on a 1.5 T imager, with the patient in the supine position using a flexible body surface coil.  ... 
doi:10.1007/s00330-010-1887-3 pmid:20680292 fatcat:c73xl4gmnjbp5hahuek6dg2pcu

Guidelines os the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019

Marcelo Antônio Cartaxo Queiroga Lopes, Glaucia Maria Moraes de Oliveira, Antônio Luiz Pinho Ribeiro, Fausto Pinto, Helena Cramer Veiga Rey, Andre´a Arau´jo Branda~o, Leandro Ioschpe Zimerman, Ludhmila Abraha~o Hajjar, Carlos Eduardo Rochitte, Fernando Bacal, Carisi Anne Polanczyk, Cidio Halperin (+46 others)
2019 Arquivos Brasileiros de Cardiologia  
Some examples include artificial neural networks (particularly deep learning models and convolutional networks), support vector machines, evolutionary algorithms, and natural language processing.  ...  patients with acute stroke. 135 The use of DSSs could increase the adherence to guideline recommendations in the management of patients with ACS, but evidence on its impact on clinical outcomes in this  ...  This is an open-access article distributed under the terms of the Creative Commons Attribution License  ... 
doi:10.5935/abc.20190205 pmid:31800728 pmcid:PMC7020958 fatcat:rcwoe3zyejford4yyjfbqmd4sa

ECR 2016 Book of Abstracts - A - Postgraduate Educational Programme

2016 Insights into Imaging  
, there is a growing interest for multiparametric MRI as a diagnostic tool that improves the prediction of tumour grade, tumour extent and help in better selecting patients for AS or other management modality  ...  CAA is also associated with cSS. CMBs occur in the normal ageing population and are seen with increasing frequency in patients with Alzheimer's disease, ischemic stroke and intracerebral haemorrhage.  ...  In the neoadjuvant setting, MRI with advanced techniques such as DWI and MRS may be used to predict the likelihood of eventual response and thus the usefulness of the therapy.  ... 
doi:10.1007/s13244-016-0474-9 pmid:26873353 pmcid:PMC4762839 fatcat:itxslbcacjhh3kixfkcwmdbt44
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