IMPACT OF CONVOLUTION FILTERS ON CORONARY PLAQUE CHARACTERIZATION IN MULTISLICE COMPUTED TOMOGRAPHY
Main Article Content
Keywords
Coronary plaque attenuation, Multislice Computed Tomography (AC-CTMS), Hounsfield Units (HU), Regions of interest (ROIs), Lumen, Epicardial fat, Calcified Plaque, Noncalcified Plaque
Abstract
Objective: This study aims to evaluate the impact of different convolution filters on the variability of coronary plaque attenuation in ex vivo left coronary arteries imaged using Multislice Computed Tomography (AC-CTMS).
Methods: The research involved three post-mortem left coronary arteries, each introduced with a diluted contrast medium and imaged under standardized conditions using four convolution filters (B30f, B36f, B46f, B60f). Identification of plaque and attenuation measurements in Hounsfield Units (HU) were performed across various regions of interest (ROIs), including the Lumen, surrounding Oil (simulating epicardial fat), and calcified and noncalcified plaque components.
Results: The study found significant variability in attenuation values across the convolution filters (p<0.001). Statistical analysis revealed marked differences in the precision of structural differentiation between the plaque components based on the sharpness of the filter used. The sharper filters (B46f, B60f) provided more transparent and abrupt transitions among different plaque structures than the softer filters (B30f, B36f).
Conclusions: Convolution filter selection critically affects the attenuation values of coronary plaques in AC-CTMS and subsequently influences the accuracy and reliability of plaque characterization. The results underscore the need for careful selection of convolution filters in clinical settings to enhance the assessment of coronary artery disease and plaque vulnerability. Further research with more diverse samples is essential to confirm these findings and their applicability in clinical practice.
References
2. Guilenea, F. N., et al. (2024). "Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images." Biomedical Physics & Engineering Express 10(3): 035007.
3. Kobeisy, M. A., et al. "Incidence and Prevalence of Acute Kidney Injury In Critically Ill Patients."
4. Omori, H., et al. (2023). "Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference." Atherosclerosis 386: 117363.
5. Su, M. (2024). "Novel intravascular ultrasound transducers."
6. Tassiopoulou, S., et al. (2024). "Algorithms in Tomography and Related Inverse Problems—A Review." Algorithms 17(2): 71.
7. Ghassel, S. (2024). Artificial Count Enhancement of Lung Scintigraphic Images Using Deep Learning Techniques, Université d'Ottawa/University of Ottawa.
8. Guilenea, F. N., et al. (2024). "Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images." Biomedical Physics & Engineering Express 10(3): 035007.
9. Kobeisy, M. A., et al. "Incidence and Prevalence of Acute Kidney Injury In Critically Ill Patients."
10. Omori, H., et al. (2023). "Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference." Atherosclerosis 386: 117363.
11. Su, M. (2024). "Novel intravascular ultrasound transducers."
12. Tassiopoulou, S., et al. (2024). "Algorithms in Tomography and Related Inverse Problems—A Review." Algorithms 17(2): 71.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Dr. Ayesha Erum Hadi
Junior Resident, Dr D.Y Patil Medical College, Hospital and Research Centre, Pune, India,
Mandeep Kaur
Department of Diagnostic Radiology MD, Department of Internal Medicine, HCA Capital Regional Med Center Tallahassee Florida 32308, USA
Alvin Khangembam
MBBS, NEIGRIHMS, Department of Internal Medicine, India
Mohit Lakkimsetti
MBBS, Department of Internal Medicine, Mamata Medical College, Khammam, India
Tariq Rafique
Assistant Professor Dadabhoy Institute of Higher Education, Karachi, Pakistan
Naseem Tariq
Associate Assistant Editor, Research Journal of Innovative Ideas and Thoughts