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.