Accurate image-based CSF volume calculation of the lateral ventricles

Area of Science:

• Neuroimaging
• Medical device technology
• Artificial intelligence in medicine

Background:

• Accurate measurement of brain ventricle volume is critical for diagnosing and monitoring neurological disorders, particularly hydrocephalus.
• Current methods relying on manual segmentation for volume estimation have limitations in precision, especially for subtle changes.
• Artificial intelligence (AI) has shown promise in automating ventricular volume calculation, but requires robust validation.

Purpose of the Study:

• To introduce and validate a novel strategy for measuring brain ventricular volumes without manual segmentation.
• To establish a reliable method for certifying AI-derived ventricular volume estimations.
• To provide validated ventricular volume data for pediatric subjects and hydrocephalus patients.

Main Methods:

• Creation of 3D printed models mimicking lateral ventricles.
• Volume measurement of 3D models using a calibrated water displacement device.
• Acquisition of MRI scans of the 3D models within a gel phantom.
• Validation of an AI algorithm by comparing its volume estimations against water displacement measurements.
• Application of the validated AI algorithm to determine ventricular volumes in human subjects.

Main Results:

• The AI-based volume estimations were validated against precise water displacement measurements from 3D printed ventricular models.
• The study provides certified ventricular volume data for subjects aged 1-114 months.
• Validated ventricular volumes were also determined for two hydrocephalus patients.

Conclusions:

• The proposed method offers a reliable, AI-driven approach to accurately measure brain ventricle volumes.
• This technique overcomes the limitations of manual segmentation, improving diagnostic accuracy for neurological conditions.
• The validated AI tool can aid in the precise monitoring of hydrocephalus and other ventricular abnormalities across a wide age range.