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Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.
Valen-Sendstad, Kristian; Bergersen, Aslak W; Shimogonya, Yuji; Goubergrits, Leonid; Bruening, Jan; Pallares, Jordi; Cito, Salvatore; Piskin, Senol; Pekkan, Kerem; Geers, Arjan J; Larrabide, Ignacio; Rapaka, Saikiran; Mihalef, Viorel; Fu, Wenyu; Qiao, Aike; Jain, Kartik; Roller, Sabine; Mardal, Kent-Andre; Kamakoti, Ramji; Spirka, Thomas; Ashton, Neil; Revell, Alistair; Aristokleous, Nicolas; Houston, J Graeme; Tsuji, Masanori; Ishida, Fujimaro; Menon, Prahlad G; Browne, Leonard D; Broderick, Stephen; Shojima, Masaaki; Koizumi, Satoshi; Barbour, Michael; Aliseda, Alberto; Morales, Hernán G; Lefèvre, Thierry; Hodis, Simona; Al-Smadi, Yahia M; Tran, Justin S; Marsden, Alison L; Vaippummadhom, Sreeja; Einstein, G Albert; Brown, Alistair G; Debus, Kristian; Niizuma, Kuniyasu; Rashad, Sherif; Sugiyama, Shin-Ichiro; Owais Khan, M; Updegrove, Adam R; Shadden, Shawn C; Cornelissen, Bart M W.
Afiliação
  • Valen-Sendstad K; Simula Research Laboratory and Center for Cardiological Innovation, Lysaker, Norway.
  • Bergersen AW; Simula Research Laboratory and Center for Cardiological Innovation, Lysaker, Norway.
  • Shimogonya Y; University of Oslo, Oslo, Norway.
  • Goubergrits L; Nihon University, Tokyo, Japan.
  • Bruening J; Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Pallares J; Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Cito S; Universitat Rovira i Virgili, Tarragona, Spain.
  • Piskin S; Universitat Rovira i Virgili, Tarragona, Spain.
  • Pekkan K; University of Texas at San Antonio, San Antonio, TX, USA.
  • Geers AJ; Koc University, Istanbul, Turkey.
  • Larrabide I; Universitat Pompeu Fabra, Barcelona, Spain.
  • Rapaka S; Universidad Nacional del Centro de la Provincia de Buenos Aires, Buenos Aires, Argentina.
  • Mihalef V; Siemens Medical Solutions USA Inc., Malvern, PA, USA.
  • Fu W; Siemens Medical Solutions USA Inc., Malvern, PA, USA.
  • Qiao A; Beijing Union University, Beijing, China.
  • Jain K; Beijing University of Technology, Beijing, China.
  • Roller S; Simula Research Laboratory and Center for Cardiological Innovation, Lysaker, Norway.
  • Mardal KA; University of Siegen, Siegen, Germany.
  • Kamakoti R; University of Zürich, Zurich, Switzerland.
  • Spirka T; University of Siegen, Siegen, Germany.
  • Ashton N; Simula Research Laboratory and Center for Cardiological Innovation, Lysaker, Norway.
  • Revell A; University of Oslo, Oslo, Norway.
  • Aristokleous N; Dassault Systemes, Paris, France.
  • Houston JG; Simpleware Software Solutions, Exeter, UK.
  • Tsuji M; University of Oxford, Oxford, UK.
  • Ishida F; University of Manchester, Manchester, UK.
  • Menon PG; University of Limerick, Limerick, Ireland.
  • Browne LD; University of Dundee, Dundee, UK.
  • Broderick S; Mie Chuo Medical Center, Tsu, Japan.
  • Shojima M; Mie Chuo Medical Center, Tsu, Japan.
  • Koizumi S; University of Pittsburgh, Pittsburgh, PA, USA.
  • Barbour M; University of Limerick, Limerick, Ireland.
  • Aliseda A; University of Limerick, Limerick, Ireland.
  • Morales HG; University of Tokyo, Tokyo, Japan.
  • Lefèvre T; University of Tokyo, Tokyo, Japan.
  • Hodis S; University of Washington, Seattle, USA.
  • Al-Smadi YM; University of Washington, Seattle, USA.
  • Tran JS; Medisys - Philips Research Paris, Paris, France.
  • Marsden AL; Medisys - Philips Research Paris, Paris, France.
  • Vaippummadhom S; Texas A&M University - Kingsville, Kingsville, TX, USA.
  • Einstein GA; Jordan University of Science and Technology, Irbid, Jordan.
  • Brown AG; Stanford University, Stanford, CA, USA.
  • Debus K; Stanford University, Stanford, CA, USA.
  • Niizuma K; EinNel Technlogies, Chennai, India.
  • Rashad S; EinNel Technlogies, Chennai, India.
  • Sugiyama SI; Siemens PLM Software, Plano, TX, USA.
  • Owais Khan M; Siemens PLM Software, Plano, TX, USA.
  • Updegrove AR; Tohoku University, Sendai, Japan.
  • Shadden SC; Tohoku University, Sendai, Japan.
  • Cornelissen BMW; Kohnan Hospital, Sendai, Japan.
Cardiovasc Eng Technol ; 9(4): 544-564, 2018 12.
Article em En | MEDLINE | ID: mdl-30203115
PURPOSE: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. METHODS: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. RESULTS: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. CONCLUSIONS: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article