Abstract #3723

Robust Reproducible Semi-Automated Perfusion-Diffusion Mismatch Assessment in Acute Ischemic Stroke Setting

Venkata Veerendranadh Chebrolu¹, Dattesh D. Shanbhag², Arun Govinda Rao³, Patrice Hervo⁴, Marc-Antoine Labeyrie⁵, Catherine Oppenheim⁵,⁶, Rakesh Mullick²

¹Medical Image Analysis Lab, GE Global Research, Bangalore , Karnataka, India; ²Medical Image Analysis Lab, GE Global Research, Bangalore, Karnataka, India; ³GE Healthcare, Bangalore, Karnataka, India; ⁴GE Healthcare, Buc, France; ⁵Departments of Radiology and Neurology, Centre Hospitalier, Sainte-Anne, Paris, France; ⁶Universit Paris Descartes , Paris, France

In semi-automated stroke lesion segmentation based on user defined seed inputs, the location and shape of the input could vary. We developed robust and reproducible semi-automated DWI and PWI lesion segmentation algorithms and evaluated their performance and reproducibility in assessing perfusion-diffusion mismatch in a cohort of acute ischemic stroke patients. Repeated measures ANOVA did not show any statistically significant differences between ground-truth lesion volumes and those obtained using the semi-automated methods (p < 0.05). Mismatch agreement was achieved in 88% of the cases with a kappa (κ) of 0.766.