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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Ischemic Stroke l: Introduction

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

Ischemic Stroke ll: Pathophysiology

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...

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A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

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Artificial intelligence in ischemic stroke images: current applications and future directions.

Ying Liu^1,2, Zhongjian Wen^1,3, Yiren Wang^1,3

¹School of Nursing, Southwest Medical University, Luzhou, China.

Frontiers in Neurology

|July 25, 2024

Summary

Artificial Intelligence (AI) shows great promise for ischemic stroke imaging, improving diagnosis and prediction. Overcoming challenges in data, interpretability, and real-time updates is key for clinical use.

Keywords:

artificial intelligence deep learning ischemic stroke machine learning medical imaging prediction model

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Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

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Published on: June 2, 2023

Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Ischemic stroke imaging analysis is crucial for timely diagnosis and treatment.
Current diagnostic methods can be time-consuming and require expert interpretation.
Artificial Intelligence (AI) offers potential solutions to enhance stroke imaging analysis.

Purpose of the Study:

To review AI applications in ischemic stroke imaging.
To analyze current challenges and explore future research directions.
To highlight the potential of Machine Learning (ML) and Deep Learning (DL) in stroke management.

Main Methods:

Review of current research on AI in ischemic stroke imaging.
Analysis of AI applications including infarct segmentation, large vessel occlusion detection, outcome prediction, and collateral circulation assessment.
Discussion of challenges like data limitations and model interpretability.

Main Results:

AI, particularly ML and DL, demonstrates significant potential in improving diagnostic accuracy and predicting stroke progression.
AI applications are being developed for infarct segmentation, large vessel occlusion detection, outcome prediction, and collateral circulation grading.
Challenges include data volume limitations, model interpretability, and the need for real-time monitoring.

Conclusions:

AI holds substantial value in managing ischemic stroke through advanced imaging analysis.
Further research is needed to address technological and practical challenges for widespread clinical adoption.
Developing large public databases and focusing on algorithm interpretability are crucial for future progress.