Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke - Wang, Kai (Stroke, 2019)
BACKGROUND AND PURPOSE: Selection of acute ischemic stroke (AIS) patients for endovascular treatment generally relies on dynamic susceptibility contrast (DSC) MRI or computed tomography perfusion (CTP). DSC MRI requires injection of contrast, while CTP requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable AIS patients for endovascular treatment based on 3D pseudo-continuous arterial spin labeling (pCASL). METHODS: A total of 167 image sets of 3D pCASL data from 137 AIS patients scanned on 1.5 and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired DSC MRI was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial. The trained DL algorithm was further applied on 12 3D pCASL data sets acquired on 1.5 and 3T GE MR systems, without fine-tuning of parameters. RESULTS: The DL algorithm can predict the DSC-defined hypoperfusion region in pCASL with a voxel-wise area under the curve (AUC) of 0.958, while the 6 ML algorithms ranged from 0.897 – 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise AUC of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. CONCLUSIONS: pCASL perfusion MRI in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in AIS patients.