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.
@article{arxiv.2312.05279,
title = {Quantitative perfusion maps using a novelty spatiotemporal convolutional neural network},
author = {Anbo Cao and Pin-Yu Le and Zhonghui Qie and Haseeb Hassan and Yingwei Guo and Asim Zaman and Jiaxi Lu and Xueqiang Zeng and Huihui Yang and Xiaoqiang Miao and Taiyu Han and Guangtao Huang and Yan Kang and Yu Luo and Jia Guo},
journal= {arXiv preprint arXiv:2312.05279},
year = {2023}
}