Research Article - Biomedical Research (2017) Volume 28, Issue 17
Fusion images of perfusion- and diffusion-weighted MR imaging: A simple diagnostic approach of ischemic penumbra selection in patients with acute ischemic stroke
Objective: This study aimed to assess the diagnostic value of ischemic penumbra (IP) selection using fusion images of Perfusion-Weighted Imaging (PWI) and Diffusion-Weighted Imaging (DWI), compared with volumetric-based Perfusion-Diffusion Mismatch (PDM) in patients with Acute Ischemic Stroke (AIS).
Materials and Methods: A total of 42 patients with a confirmed diagnosis of AIS were included in this retrospective study. DWI images were overlaid onto PWI images to obtain fused images using 3D fusion software. Two experienced neuroradiologists estimated the qualitative PDM using fused and paired images, respectively. The results were comparable to the results of volumetric-based PDM, which were obtained using semi-automated quantitative software.
Results: The time consumed for qualitative measurement was significantly different among different assessment methods, but no significant differences were found in time consumed for qualitative PDM assessment using fused and paired images. The qualitative PDM was significantly lower than quantitative PDM. The two qualitative PDM subgroups related to quantitative PDM had a sensitivity of 84.21% and 77.78%, specificity of 78.26% and 60.86%, positive predictive rate of 94.12% and 87.50%, and negative predictive rate of 90.00% and 82.35%, respectively. A high consistency in qualitative PDM assessment using fused images (Kappa=0.762) but a low consistency in qualitative PDM assessment using paired images between two observers were observed (Kappa=0.561).
Conclusions: The fusion images of DWI and PWI provided information comparable to the information provided by volumetric-based PDM. It took less time and hence might be an effective approach for IP selection in patients with AIS.Author(s): Tian-Le Wang, Li Zhu, Chun-Hong Hu, Shen-Chu Gong, Hong-Biao Jiang, Hai-Tao Chen, Jia Li