  Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is
widely used to evaluate acute ischemic stroke to distinguish salvageable tissue
and infarct core. For this purpose, traditional methods employ deconvolution
techniques, like singular value decomposition, which are known to be vulnerable
to noise, potentially distorting the derived perfusion parameters. However,
deep learning technology could leverage it, which can accurately estimate
clinical perfusion parameters compared to traditional clinical approaches.
Therefore, this study presents a perfusion parameters estimation network that
considers spatial and temporal information, the Spatiotemporal Network
(ST-Net), for the first time. The proposed network comprises a designed
physical loss function to enhance model performance further. The results
indicate that the network can accurately estimate perfusion parameters,
including cerebral blood volume (CBV), cerebral blood flow (CBF), and time to
maximum of the residual function (Tmax). The structural similarity index (SSIM)
mean values for CBV, CBF, and Tmax parameters were 0.952, 0.943, and 0.863,
respectively. The DICE score for the hypo-perfused region reached 0.859,
demonstrating high consistency. The proposed model also maintains time
efficiency, closely approaching the performance of commercial gold-standard
software.
