A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Demographics
2.2. Data Analysis
- (1)
- For every slice Xs, s=1,…..S,
- (a)
- First is to gather the marginal empirical probability distribution Fs of gray levels.
- (b)
- Find a starting LCDG model which is nearing Fs by using the initialization algorithm to approximate the values of Cp−K, Cn, and the parameters w, Θ (weights, means, and variances) of the negative and positive discrete Gaussians (DG).
- (c)
- Fixing Cp and Cn, refine the LCDG-model with the modified EM algorithm by manipulating the other parameters.(See Appendix A for more details)
- (d)
- Separate the final LCDG model into K sub models. Each dominant mode has a corresponding sub model. This is done by minimizing the misclassification predicted errors and selecting the LCDG-sub model that has the greatest average value (corresponding to the pixels with highest brightness) to be the model of the wanted vasculature.
- (e)
- Use intensity threshold t to extract the voxels of the blood vessels in the MRA slice, which separates their LCDG-sub model from the background.
- (2)
- Remove the artifacts from the extracted voxels whole set with a connection filter which chooses the greatest connected tree system built by a 3D growing algorithm [23]. Algorithm 1 summarizes the adopted segmentation approach.
Algorithm 1. Segmentation Approach Main Steps. |
For every slice Xs, the following steps were completed:
|
2.3. Statistical Analysis
2.4. 3D Reconstruction of the Cerebral Vasculature
3. Results
4. Discussion
5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- I.
- Initialization sequentially using EM Algorithm
- II.
- Refining LCDGs using Modified EM Algorithm
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Time | Sensitivity | Specificity |
---|---|---|
Day 0 | 0.997 ± 0.006 | 0.9998 ± 0.0001 |
Day 700 | 0.996 ± 0.008 | 0.9998 ± 0.0001 |
Cumulative | 0.997 ± 0.008 | 0.9998 ± 0.0001 |
Mean Vascular Diameter below Circle of Willis | |||
Effect | χ2 | p-Value | |
Age | 3.2 μm/y | 0.356 | 0.551 |
Gender | F > M by 12.8 μm | 0.026 | 0.872 |
Mean Arterial Pressure | −5.3 μm/mmHg | 11.63 | 0.0007 |
Mean Vascular Diameter above Circle of Willis | |||
Effect | χ2 | p-Value | |
Age | −16.5 μm/y | 12.29 | 0.0005 |
Gender | F > M by 16.0 μm | 0.199 | 0.655 |
Mean Arterial Pressure | 1.6 μm/mmHg | 0.402 | 0.525 |
Patient | Day 0 | Day 700 | ||||
---|---|---|---|---|---|---|
Systolic BP | Diastolic BP | MAP | Systolic BP | Diastolic BP | MAP | |
A | 120 | 80.5 | 93.7 | 103.5 | 66.5 | 78.8 |
B | 130.5 | 83 | 98.8 | 143.5 | 94 | 110.5 |
C | 118 | 80.5 | 93 | 105.3 | 69 | 81.1 |
D | 114 | 84.5 | 94.3 | 120 | 88 | 98.7 |
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Taher, F.; Kandil, H.; Gebru, Y.; Mahmoud, A.; Shalaby, A.; El-Mashad, S.; El-Baz, A. A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure. Appl. Sci. 2021, 11, 4022. https://doi.org/10.3390/app11094022
Taher F, Kandil H, Gebru Y, Mahmoud A, Shalaby A, El-Mashad S, El-Baz A. A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure. Applied Sciences. 2021; 11(9):4022. https://doi.org/10.3390/app11094022
Chicago/Turabian StyleTaher, Fatma, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El-Mashad, and Ayman El-Baz. 2021. "A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure" Applied Sciences 11, no. 9: 4022. https://doi.org/10.3390/app11094022
APA StyleTaher, F., Kandil, H., Gebru, Y., Mahmoud, A., Shalaby, A., El-Mashad, S., & El-Baz, A. (2021). A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure. Applied Sciences, 11(9), 4022. https://doi.org/10.3390/app11094022