CT perfusion (CTP) efficiently provides valuable hemodynamic information for triage of acute ischemic stroke patients at the expense of additional radiation dose from consecutive CT acquisitions. Low-dose CTP is therefore highly desirable but is often attempted by iterative or deep learning reconstructions that are computationally intensive. We aimed to demonstrate that acquiring fewer x-ray projections in a CTP scan while reconstructing with filtered back projection (FBP) can reduce radiation dose without impacting clinical utility. Six CTP studies were selected from the PRove-IT clinical database. For each axial source CTP slice, a 984-view sinogram was synthesized using a Radon Transform and uniformly under-sampled to 492, 328, 246, and 164-views. An FBP was applied on each sparse-view sinogram to reconstruct source images that were used to generate perfusion maps using a delay-insensitive deconvolution algorithm. The resulting Tmax and cerebral blood flow perfusion maps were evaluated for their ability to identify penumbra and ischemic core volumes using the Pearson correlation (R) and Bland-Altman analysis. In addition, sparse-view perfusion maps were assessed for fidelity to original full-view maps using structural similarity, peak signal-to-noise ratio, and normalized root mean squared error. Ischemic penumbra and infarct core volumes were accurately estimated by all sparse-view configurations (R<0.95, p<0.001; mean difference <3 ml) and overall perfusion map fidelity was well-maintained up to 328-views. Our preliminary analysis reveals that radiation dose can potentially be reduced by a factor of 6 with further validation that the errors in ischemic volume measurement do not impact clinical decision-making.
Stroke is a leading cause of death and disability in the western hemisphere. Acute ischemic strokes can be broadly classified based on the underlying cause into atherosclerotic strokes, cardioembolic strokes, small vessels disease, and stroke with other causes. The ability to determine the exact origin of an acute ischemic stroke is highly relevant for optimal treatment decision and preventing recurrent events. However, the differentiation of atherosclerotic and cardioembolic phenotypes can be especially challenging due to similar appearance and symptoms. The aim of this study was to develop and evaluate the feasibility of an image-based machine learning approach for discriminating between arteriosclerotic and cardioembolic acute ischemic strokes using 56 apparent diffusion coefficient (ADC) datasets from acute stroke patients. For this purpose, acute infarct lesions were semi-atomically segmented and 30,981 geometric and texture image features were extracted for each stroke volume. To improve the performance and accuracy, categorical Pearson’s χ2 test was used to select the most informative features while removing redundant attributes. As a result, only 289 features were finally included for training of a deep multilayer feed-forward neural network without bootstrapping. The proposed method was evaluated using a leave-one-out cross validation scheme. The proposed classification method achieved an average area under receiver operator characteristic curve value of 0.93 and a classification accuracy of 94.64%. These first results suggest that the proposed image-based classification framework can support neurologists in clinical routine differentiating between atherosclerotic and cardioembolic phenotypes.
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