Author ORCID Identifier

0000-0002-8784-2109

Document Type

Book Chapter

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

3. MEDICAL AND HEALTH SCIENCES

Publication Details

https://pubmed.ncbi.nlm.nih.gov/34279887/

https://doi.org/10.36255/exonpublications.stroke.timingischemicstroke.2021

Abstract

The advent of recanalization therapies has transformed the management of acute ischemic stroke patients. The timing of symptom onset is one of the key criteria for selecting the recanalization method as pharmacological and non- pharmacological recanalization therapies are only safe when administered within strict, but evolving, time windows. Magnetic resonance imaging (MRI) reveals ischemia within minutes and estimates ischemia duration in brain parenchyma. Preclinical studies have shown that by combining diffusion and relaxometric MRI, timing ischemic strokes is possible with clinically acceptable accuracy. MRI-based stroke timing techniques have been adopted in stroke clinics to stratify patients with unknown onset time for intravenous thrombolysis, resulting in improved outcomes in clinical trials. More recent MRI approaches use absolute apparent diffusion coefficient (ADC) and T2 relaxation time data in a user-independent manner to estimate the stroke onset time in absolute terms. The introduction of expedited MRI acquisition protocols has made MRI a fast neurodiagnosis modality. Exploiting advanced technologies such as Magnetic Resonance Fingerprinting (MRF), artificial intelligence (AI), and machine learning (ML) for the post-processing of MRI data, combined with fast MRI techniques, is expected to speed up the translation of objective stroke timing procedures into patient management.

DOI

https://doi.org/10.36255/exonpublications.stroke.timingischemicstroke.2021

Funder

No funding was received for this work.

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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