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Effects of Repetitive Peripheral Sensory Stimulation in the Subacute and Chronic Phases After Stroke: Study Protocol for a Pilot Randomized Trial
DiscussionThe results of this study are relevant to inform future clinical trials to tailor RPSS to patients more likely to benefit from this intervention.Trial RegistrationNCT03956407.
Source: Frontiers in Neurology - February 16, 2022 Category: Neurology Source Type: research

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis
Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.
Source: Frontiers in Neurology - May 21, 2021 Category: Neurology Source Type: research

Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients
In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were in...
Source: Frontiers in Neurology - September 9, 2021 Category: Neurology Source Type: research

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images
We present a multi-stage deep learning approach to predict collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to ...
Source: Frontiers in Neurology - February 21, 2023 Category: Neurology Source Type: research

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data
We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.MethodsWe used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images.ResultsXGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model d...
Source: Frontiers in Neurology - February 23, 2023 Category: Neurology Source Type: research

Potential Applications of Remote Limb Ischemic Conditioning for Chronic Cerebral Circulation Insufficiency
Conclusion Due to its long-term and often invisible course, CCCI has received less attention than acute cerebral ischemic stroke. However, without appropriate intervention, CCCI may lead to a variety of adverse events. Because the pathophysiological changes associated with CCCI are complex, pharmacological research in this area has been disappointing. Recent research suggests that RLIC, which is less invasive and more well-tolerated than drug treatment, can activate endogenous protective mechanisms during CCCI. In the present report, we reviewed studies related to CCCI (Table 1), as well as those related to stroke and sta...
Source: Frontiers in Neurology - May 2, 2019 Category: Neurology Source Type: research

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease
Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a tempo...
Source: Frontiers in Neurology - January 10, 2023 Category: Neurology Source Type: research

Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations
Christian Lucas, Andr é Kemmling, Nassim Bouteldja, Linda F. Aulmann, Amir Madany Mamlouk, Mattias P. Heinrich
Source: Frontiers in Neurology - November 26, 2018 Category: Neurology Source Type: research