Research
Computational Processing and Modeling of Intravascular Images Precisely Couple Arterial Morphology and Biomechanics
Cardiovascular diseases, and coronary artery disease in particular, remain a persistent devastating and prevalent menace to health and wellbeing globally despite great strides in vascular biology and medicine. While biomechanical forces are known to play a driving role in the natural history of atherosclerosis, the nuanced yet profound impact of patient- and lesion-specific biomechanics in disease presentation, course, and treatment are not fully appreciated or accounted for in clinical practice. The incredible strides in melding image processing with artificial intelligence, computational modeling, and numerical methods is increasingly filling gaps in knowledge, especially at the intersection of pathological anatomy and biomechanical structural behavior.
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We derived geometric and morphological structure, as well as constitutive material properties, from invasive intravascular image sequences to quantitatively assess and characterize the state of atherosclerotic arteries. Overcoming the challenge of limited penetration depth in the presence of signal-attenuating plaque, contextual information and spatial continuity was leveraged by a novel surface fitting method to fully delineate the mural conformation of the diseased vessel wall. Neural networks enriched with domain knowledge of vascular geometry and imaging classified pathological regions of interest within heterogeneous lesions. Construction of in silico computational models and in vitro phantom models facilitated the execution and validation of inverse methods to determine material constitutive mechanical properties non-destructively and in clinically amenable fashion. Strategic simplifying assumptions freed the approach from data acquisition limitations which inhibited previous methods of in situ mechanical characterization. Finally, to bridge the chasm between virtual and physical medicine and facilitate integration of these new capabilities into clinical practice, synthetic images were generated by an adversarial network trained in the familiar visual vernacular of vascular imaging.
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Through the insights described in this thesis, greater information can be extracted, augmented, and made accessible from clinically-available imaging data. Approaches to more quantitatively and reliably assess, model, and convey biomechanical disease states may offer mechanistic insight into disease development, progression, and treatment response, ultimately leading to improved personalized patient care in an emerging era of computational cardiology.
Journal Publications
Karmakar, A., Olender, M. L., Marlevi, D., Shlofmitz, E., Shlofmitz, R. A., Edelman, E. R., & Nezami, F. R. “Framework for Lumen Based Nonrigid Tomographic Coregistration of Intravascular Images.” Journal of Medical Imaging, vol. 9, no. 4, p. 044006, Aug. 2022. (DOI: 10.1117/1.JMI.9.4.044006)
Olender, M. L., Niu, Y., Marlevi, D., Edelman, E. R. & Nezami, F. R. “Impact and Implications of Mixed Plaque Class in Automated Characterization of Complex Atherosclerotic Lesions.” Computerized Medical Imaging and Graphics, vol. 97, p. 102051, Apr. 2022. (DOI: 10.1016/j.compmedimag.2022.102051)
Olender, M. L., Nezami, F. R., Athanasiou, L. S., de la Torre Hernández, J. M., & Edelman, E. R. “Translational Challenges for Synthetic Imaging in Cardiology.” European Heart Journal – Digital Health, vol. 2, no. 4, pp. 559-560, Dec. 2021. (DOI: 10.1093/ehjdh/ztab079)
Narayanan, B., Olender, M. L., Marlevi, D., Edelman, E. R., & Nezami, F. R. “An Inverse Method for Mechanical Characterization of Heterogeneous Diseased Arteries using Intravascular Imaging.” Scientific Reports, vol. 11, p. 22540, Nov. 2021. (DOI: 10.1038/s41598-021-01874-3)
Kadry, K., Olender, M. L., Marlevi, D., Edelman, E. R., & Nezami, F. R. “A Platform for High-Fidelity Patient-Specific Structural Modeling of Atherosclerotic Arteries: From Intravascular Imaging to Three-Dimensional Stress Distributions.” Journal of the Royal Society Interface, vol. 18, no. 182, p. 20210436, Sep. 2021. (DOI: 10.1098/rsif.2021.0436)
Olender, M. L., de la Torre Hernández, J. M., Nezami, F. R., Athanasiou, L. S., & Edelman, E. R. “Artificial Intelligence to Generate Medical Images: Augmenting the Cardiologist’s Visual Clinical Workflow.” European Heart Journal – Digital Health, vol. 2, no. 3, pp. 539-544, Sep. 2021. (DOI: 10.1093/ehjdh/ztab052)
Olender, M. L., Athanasiou, L. S., Michalis, L. K., Fotiadis, D. I., & Edelman, E. R. “A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.” IEEE Journal of Selected Topics in Signal Processing – Special Issue on Domain Enriched Learning for Medical Imaging, vol. 14, no. 6, pp. 1210-1220, Oct. 2020. (DOI: 10.1109/JSTSP.2020.3002385)
Ben-Assa, E., Brown, J., Keshavarz-Motamed, Z., de la Torre Hernandez, J. M., Leiden, B., Olender, M., Kallel, F., Palacios, I. F., Inglessis, I., Passeri, J. J., Shah, P. B., Elmariah, S., Leon, M. B., & Edelman, E. R. “Ventricular Stroke Work and Vascular Impedance Refine the Characterization of Patients with Aortic Stenosis.” Science Translational Medicine, vol. 11, no. 509, p. eaaw0181, Sep. 2019. (DOI: 10.1126/scitranslmed.aaw0181)
Olender, M. L., Athanasiou, L. S., de la Torre Hernández, J. M., Ben-Assa, E., Rikhtegar Nezami, F., & Edelman, E. R. “A Mechanical Approach for Smooth Surface Fitting to Delineate Vessel Walls in Optical Coherence Tomography Images.” IEEE Transactions on Medical Imaging, vol. 38, no. 6, pp. 1384-1397, Jun. 2019. (DOI: 10.1109/TMI.2018.2884142)
Conference Papers & Abstracts
Narayanan, B.*, Olender, M. L.*, Nezami, F. R., Edelman, E. R., & Marlevi, D. (2021, July). "In Vitro Validation of a Novel Image-Based Inverse Method for Mechanical Characterization of Vessels." Paper presented at the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Athens, Greece (Online). (DOI: 10.1109/BHI50953.2021.9508547)
Niu, Y., Olender, M. L., Marlevi, D., Nezami, F. R., & Edelman, E. R. (2021, July). "Improving Automated Tissue Characterization in Optical Coherence Tomography by Melding Attenuation Compensation with Deep Learning." Paper presented at the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Athens, Greece (Online). (DOI: 10.1109/BHI50953.2021.9508521)
Olender, M. L., de la Torre Hernández, J. M., Nezami, F. R., & Edelman, E. R. (2021, May). "Artificially Synthesized Optical Coherence Tomography Images Based on Intravascular Ultrasound-Virtual Histology Examination of Diseased Coronary Artery." Image published in EuroIntervention/PCRonline (Online).
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Karmakar, A., Olender, M. L., Rikhtegar Nezami, F., Marlevi, D., Shlofmitz, E., Shlofmitz, R. A., & Edelman, E. R. (2020, December). "Detailed Investigation of Lumen-Based Tomographic Co-Registration." Paper presented at the 2020 IEEE International Conference on Bioinformatics and Biomedicine, Seoul, South Korea (Online).
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Gowrishankar, A., Athanasiou, L. S., Olender, M. L., & Edelman, E. R. (2019, October). "Neural Network Training Data Profoundly Impacts Texture-Based Intravascular Image Segmentation." Paper presented at the 2019 IEEE International Conference on Bioinformatics and Bioengineering, Athens, Greece. (DOI: 10.1109/BIBE.2019.00184)
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Olender, M. L., Athanasiou, L. S., de la Torre Hernández, J. M., Ben-Assa, E., & Edelman, E. R. (2019, May). "Simultaneous Multi-Surface Fitting for Vessel Wall Layer Delineation." Paper presented at the 2019 IEEE-EMBS International Conference on Biomedical and Health Informatics, Chicago, IL. (DOI: 10.1109/BHI.2019.8834643)
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Athanasiou, L. S., Olender, M. L., de la Torre Hernandez, J. M., Ben-Assa, E., & Edelman, E. R. (2019, February). "A Deep Learning Approach to Classify Atherosclerosis Using Intracoronary Optical Coherence Tomography." Paper presented at the 2019 SPIE Medical Imaging Conference, San Diego, CA. (DOI: 10.1117/12.2513078)
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Ben Assa, E., de la Torre Hernandez, J., Brown, J., Olender, M., Al-Bawardy, R., Motamed, Z., Shah, P., Passeri, J., Inglessis, I., Elmariah, S., Leon, M., & Edelman, E. (2018, September). "TCT-299 Pulse Wave Velocity and Aortic Distensibility in Patients with Hypertensive Response Post Transcatheter Aortic Valve Replacement." Moderated poster presented at Transcatheter Cardiovascular Therapeutics 2018, San Diego, CA. Abstract published by Journal of the American College of Cardiology, 72(13 Supplement):B123. (DOI: 10.1016/j.jacc.2018.08.1437)
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Olender, M., de la Torre Hernández, J., Cascón, J., Consuegra-Sánchez, L., Athanasiou, L., Benassa, E., de Prado, A., & Edelman, E. (2017, October). "Algoritmo Computacional para Detectar el Borde Interno de la Capa Media de Arterias Coronarias Ateromatosas Mediante Tomografía de Coherencia Óptica." Poster and abstract presented at the 2017 Spanish Society of Cardiology Congress of Cardiovascular Diseases, Madrid, Spain. Abstract published by Revista Española de Cardiología, 70(Supplement 1):929.
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Olender, M., Athanasiou, L., de la Torre Hernández, J., García Camarero, T., Cascón, J., Consuegra-Sánchez, L., & Edelman, E. (2017, February). "Estimating the Internal Elastic Membrane Cross-Sectional Area of Coronary Arteries Autonomously using Optical Coherence Tomography Images." Paper presented at the 2017 IEEE International Conference on Biomedical and Health Informatics, Orlando, FL. (DOI: 10.1109/BHI.2017.7897217)