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Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach
Ann Biomed Eng. 2022 Apr 1. doi: 10.1007/s10439-022-02956-7. Online ahead of print.ABSTRACTAssessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pr...
Source: Annals of Biomedical Engineering - April 2, 2022 Category: Biomedical Engineering Authors: Ivan Benemerito Ana Paula Narata Andrew Narracott Alberto Marzo Source Type: research

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients
CONCLUSION: Reliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.PMID:36682921 | DOI:10.1016/j.zemedi.2022.11.008
Source: Zeitschrift fur Medizinische Physik - January 22, 2023 Category: Radiology Authors: Artem Zatcepin Anna Kopczak Adrien Holzgreve Sandra Hein Andreas Schindler Marco Duering Lena Kaiser Simon Lindner Martin Schidlowski Peter Bartenstein Nathalie Albert Matthias Brendel Sibylle I Ziegler Source Type: research

Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI
Conclusion: The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics. Significance: Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.
Source: IEEE Transactions on Biomedical Engineering - August 21, 2018 Category: Biomedical Engineering 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

Percutaneous management of acute ischaemic stroke
Learning objectives To understand both the rationale and principles behind percutaneous management of stroke. To be aware of the evidence base for this treatment. To appreciate the current logistical challenges and how they might be overcome. Introduction In principle, the similarity between opening an occluded cerebral artery and an occluded coronary artery, when the perfusion to that organ is acutely compromised, is inescapable: to re-establish antegrade flow as quickly as possible to minimise downstream damage. There are, of course, important differences between an acute myocardial infarction (MI) and an acute ischaemic...
Source: Heart - April 25, 2023 Category: Cardiology Authors: Routledge, H., Curzen, N. Tags: Education in Heart Source Type: research

Abstract No. 720 Identification of irreversibly damaged brain tissue on computed tomography perfusion using convolutional neural network to assist selection for mechanical thrombectomy in ischemic stroke patients
Endovascular treatment of ischemic stroke has shown positive clinical outcomes. Further optimization requires identifying patients who will benefit from reperfusion. We propose using deep learning, specifically 3D convolutional neural networks (CNN), to identify infarcted tissue (core) on CT perfusion (CTP) with diffusion weighted imaging (DWI) MRI as gold standard for irreversible brain infarction and evaluate lesion size impact on the network ’s performance.
Source: Journal of Vascular and Interventional Radiology : JVIR - February 20, 2020 Category: Radiology Authors: R. Wang, K. Chang, H. Zhou, J. Wu, G. Cohan, M. Jayaraman, R. Huang, J. Boxerman, L. Yang, F. Hui, J. Woo, H. Bai Tags: Scientific Traditional Poster Source Type: research

C2MA-Net: Cross-Modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation Based on CT Perfusion Scans
Conclusion: This study demonstrates advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy, and achieves semantic decoupling by processing different parameter modalities separately. Significance: Proving the potential of cross-modal interactions in attention to assist identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies.
Source: IEEE Transactions on Biomedical Engineering - December 24, 2021 Category: Biomedical Engineering Source Type: research

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