PERFUSION MAPS QUANTIFICATION USING A NOVELTY SPATIOTEMPORAL CONVOLUTIONAL NEURAL NETWORK

Motivation: Traditional methods employing deconvolution techniques to estimate perfusion parameters, like singular value decomposition, are known to be vulnerable to noise, potentially distorting the derived perfusion parameters. Goal(s): We try to use deep learning methods to achieve accurate perfusion parameter estimation and we also identified the clinical utility of these parameters. Approach: Data and preprocessing: The gold standard perfusion parameter maps and hypo-perfused masks were generated using commercial software RAPID. 52/86 for the training and validation/testing. Network architecture: Spatio network and Temporal network. Loss function: the supervised and unsupervised loss function. Results: All metrics showed a high degree of consistency with the ground truth. Impact: Based on this study, we can achieve AI-based automation of imaging, quantification, and analysis in the future, which will significantly change the current landscape of clinical treatment, reducing costs while minimizing harm to the human body.