Highlights
- •In this work, we draw an overview of several applications of Artificial Intelligence (AI) for the automation of diffusion-perfusion mismatch processing and their potential contributions in clinical practice.
- •We compare the current approaches among each others with respect to some key requirements.
- •We present future directions of research in the field of applying IA technics ti PWI-DWI data processing
Abstract
Multimodal Magnetic Resonance Imaging (MRI) techniques of Perfusion-Weighted Imaging
(PWI) and Diffusion-Weighted Imaging (DWI) data are integral parts of the diagnostic
workup in the acute stroke setting. The visual interpretation of PWI/DWI data is the
most likely procedure to triage Acute Ischemic Stroke (AIS) patients who will access
reperfusion therapy, especially in those exceeding 6 h of stroke onset. In fact, this
process defines two classes of tissue: the ischemic core, which is presumed to be
irreversibly damaged, visualized on DWI data and the penumbra which is the reversibly
injured brain tissue around the ischemic tissue, visualized on PWI data. AIS patients
with a large ischemic penumbra and limited infarction core have a high probability
of benefiting from endovascular treatment.
However, it is a tedious and time-consuming procedure. Consequently, it is subject
to high inter- and intra-observer variability. Thus, the assessment of the potential
risks and benefits of endovascular treatment is uncertain.
Fast, accurate and automatic post-processing of PWI and DWI data is important for
clinical diagnosis and is necessary to help the decision making for therapy. Therefore,
an automated procedure that identifies stroke slices, stroke hemisphere, segments
stroke regions in DWI, and measures hypoperfused tissue in PWI enhances considerably
the reproducibility and the accuracy of stroke assessment.
In this work, we draw an overview of several applications of Artificial Intelligence
(AI) for the automation processing and their potential contributions in clinical practices.
We compare the current approaches among each other's with respect to some key requirements.
Abbreviations:
MRI (Magnetic Resonance Imaging), CT (Computed Tomography), DWI (Diffusion Weighted Imaging), DSC (Dynamic Susceptibility Contrast), PWI (Perfusion Weighted Imaging), ADC (Apparent Diffusion Coefficient), Tmax (time-to-maximum), TTP (time to peak), AIF (Arterial Input Function), ROI (region of interest), TE (Echo Time), CBF (Cerebral Blood Flow), MTT (Mean Transit Time), CBV (Cerebral Blood Volume), SVD (Singular Value Decomposition), bSVD (block circulant singular value decomposition), oSVD (oscillation-index SVD), MCA (middle cerebral artery), ICA (internal carotid artery), DI (Dice Index), AI (artificial intelligence), CNN (Convolutional Neural Networks)Keywords
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Article info
Publication history
Published online: October 07, 2021
Accepted:
September 22,
2021
Received in revised form:
September 5,
2021
Received:
May 10,
2021
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