研究目的
To develop a non-invasive framework based on Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) for detecting cerebrovascular changes and correlating them with Mean Arterial Pressure (MAP), aiming to quantify changes in cerebral vascular diameter and perfusion pressure.
研究成果
The framework successfully detects cerebrovascular changes and demonstrates an inverse correlation between blood vessel diameters and MAP in both upper and lower brain sections. It offers a non-invasive method for early detection of hypertension-related changes, potentially aiding in clinical management. Future research should focus on automating the system for broader application.
研究不足
The study is limited by a small sample size of 15 patients, which may affect generalizability. The framework relies on MRA data, which has inherent limitations in imaging small vessels, and the segmentation algorithm may not be fully optimized for all variations in vascular anatomy. Future work could involve larger datasets and validation in diverse populations.
1:Experimental Design and Method Selection:
The framework involves adaptive segmentation of TOF-MRA images to extract cerebral vessels, estimation of the cumulative distribution function (CDF) of the 3-D distance map to represent diameter changes, and statistical analysis to correlate with MAP.
2:Sample Selection and Data Sources:
MRA data and BP measurements from 15 patients (8 males, 7 females, age
3:2±3 years) over a 700-day period, obtained with institutional review board approval. List of Experimental Equipment and Materials:
3T Trio TIM scanner with a 12-channel phased-array head coil for MRA imaging, sphygmomanometer for BP measurements.
4:Experimental Procedures and Operational Workflow:
Skull stripping and bias correction applied to MRA data, segmentation using a generalized Gauss-Markov random field model and linear combination of discrete Gaussians, followed by local adaptive segmentation and region growing. Statistical analysis using R with lme4 package for mixed-effects linear models.
5:Data Analysis Methods:
Dice similarity coefficient, sensitivity, specificity for segmentation accuracy; chi-squared tests and p-values for statistical significance of correlations.
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