Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurology

Background:

• High-resolution magnetic resonance vessel wall imaging (HR-VWI) visualizes intracranial vessel walls but is operator-dependent.
• Deep learning (DL) excels in image analysis and is increasingly used in medical imaging.
• Challenges in HR-VWI include reliance on operator experience and potential unreliability.

Purpose of the Study:

• To review the fundamentals of DL in the context of HR-VWI.
• To explore the clinical applications of DL in assessing intracranial vascular lesions using HR-VWI.
• To highlight DL's potential as an ancillary diagnostic tool for cerebrovascular diseases.

Main Methods:

• Comprehensive literature review of DL fundamentals and applications in HR-VWI.
• Focus on DL models supporting radiologists in MR imaging analysis.
• Examination of DL's role in identifying specific intracranial vascular pathologies.

Main Results:

• DL algorithms leverage multimodal data for superior image recognition.
• DL shows promise in identifying intracranial aneurysms, arteriovenous malformations, arteriosclerosis, and moyamoya disease.
• DL-assisted HR-VWI can overcome inherent challenges and improve diagnostic confidence.

Conclusions:

• DL is a leading AI tool with significant potential in medical imaging analysis.
• DL-assisted HR-VWI offers a promising approach to enhance the diagnosis and management of cerebrovascular diseases.
• DL has the potential to become a crucial ancillary diagnostic tool for neurologists and radiologists.