Ulas, CagdasDas, DhritimanThrippleton, Michael JValdes Hernandez, Maria del C.Armitage, Paul AMakin, Stephen DWardlaw, Joanna MMenze, Bjoern H2019-09-052019-09-052019-01-08Ulas, C, Das, D, Thrippleton, M J, Valdes Hernandez, M D C, Armitage, P A, Makin, S D, Wardlaw, J M & Menze, B H 2019, 'Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters : Application to Stroke Dynamic Contrast-Enhanced MRI', Frontiers in Neurology, vol. 9, 1147. https://doi.org/10.3389/fneur.2018.011471664-2295RIS: urn:E4A800EBF833C192FD9FED40C4E1FFECRIS: 52980PubMedCentral: PMC6331464Mendeley: b2d28a64-9523-3974-a73a-eb38241f6262ORCID: /0000-0001-8701-9043/work/76976267http://hdl.handle.net/2164/12854Ulas, Cagdas Das, Dhritiman Thrippleton, Michael J Valdes Hernandez, Maria Del C Armitage, Paul A Makin, Stephen D Wardlaw, Joanna M Menze, Bjoern H eng Switzerland Front Neurol. 2019 Jan 8;9:1147. doi: 10.3389/fneur.2018.01147. eCollection 2018.3837501engcontrast agent concentration convolutional neural networks dynamic contrast enhanced MRI ischaemic stroke loss function pharmacokinetic parameter inference tracer kinetic modelingLoss functionDynamic contrast enhanced MRITracer kinetic modelingPharmacokinetic parameter inferenceConvolutional neural networksContrast agent concentrationIschaemic strokeSMALL VESSEL DISEASEdynamic contrast enhanced MRIcontrast agent concentrationloss functionischaemic strokepharmacokinetic parameter inferenceMODELSTRACERconvolutional neural networksBLOODtracer kinetic modelingR MedicineClinical NeurologyNeurologyRJournal article10.3389/fneur.2018.01147http://www.scopus.com/inward/record.url?scp=85065397393&partnerID=8YFLogxKhttp://www.mendeley.com/research/parameters-application-stroke-dynamic-contrastenhanced-mrihttps://www.ncbi.nlm.nih.gov/pubmed/306710159