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Luigi Perotti

Luigi Perotti

Associate Professor

EMAIL

BIOGRAPHY

Luigi Perotti received his laurea (B.S./M.S.) degree in civil engineering from Politecnico di Milano, Italy, in 2004. Subsequently, he continued his studies in mechanical engineering at the California Institute of Technology where he received his M.S. in 2006 and his Ph.D. with a minor in applied and computational mathematics in 2011. At the end of 2011, he joined Professor Klug’s group in the mechanical and aerospace engineering department at UCLA to pursue his research interests in biomechanics. Since then he has worked on several multidisciplinary projects involving collaborations across the departments of physics, radiology, and the school of medicine.

In 2014, he received the American Heart Association postdoctoral fellowship and joined Professor Ennis’ group in the bioengineering and radiological sciences departments at UCLA to develop advanced methods for evaluating cardiac mechanics of the failing heart. In 2017, he received an NIH K25 Mentored Quantitative Research Career Development Award to continue his research on combining computational models with MRI data and conduct pre-clinical studies. Perotti joined the mechanical and aerospace engineering department at UCF in 2019.

EDUCATION

  • Ph.D. in Mechanical Engineering, California Institute of Technology, 2011
  • M.S. in Mechanical Engineering, California Institute of Technology, 2006
  • B.S./M.S. in Civil Engineering from Politecnico di Milano, Italy, 2004

RESEARCH

  • Biomechanics
  • Cardiac mechanics
  • Electrophisiology
  • Virus assembly and maturation
  • Bioinspired deployable shells

PUBLICATIONS

  • Von Zuben, A., Whitt, E., Viana, F.A.C., Perotti, L.E. (2023). Long Axis Cardiac MRI Segmentation Using Anatomically-Guided UNets and Transfer Learning. Functional Imaging and Modeling of the Heart, 274-282, Lyon, France.
  • Ogiermann, D., Balzani, D., Perotti, L.E. (2023). An Extended Generalized Hill Model for Cardiac Tissue: Comparison with Different Approaches Based on Experimental Data. Functional Imaging and Modeling of the Heart, 555-564, Lyon, France.
  • Wilson, A.J., Han, J.Q., Perotti, L.E., Ennis, D.B. (2023). Ventricular Helix Angle Trends and Long-Range Connectivity. Functional Imaging and Modeling of the Heart, 64-73, Lyon, France.
  • Von Zuben, A., Perotti, L.E., Viana, F.A.C. (2023). Anatomically-guided deep learning for left ventricle geometry generation with uncertainty quantification based on short-axis MR images. Engineering Applications of Artificial Intelligence, 121, 106012.
  • Ogiermann, D., Perotti, L.E., Balzani D. (2023). A Simple and Efficient Adaptive Time Stepping Technique for Low-Order Operator Splitting Schemes Applied to Cardiac Electrophysiology.  International Journal for Numerical Methods in Biomedical Engineering, 39 (2), e3670.
  • Wei, F., Flowerdew, K., Kinzel, M., Perotti, L.E., Asiatico, J., Omer, M., Hovell, C., Reumers, V., Coathup, M.J. (2022). Changes in interstitial fluid flow, mass transport and the bone cell response in microgravity and normogravity. Bone Research, 10(1), 1-19.
  • Dharmavaram, S., Wan, X., Perotti, L.E. (2022) A Lagrangian Thin-Shell Finite Element Method for Interacting Particles on Fluid Membranes. Membranes, 12 (10), 960.
  • Rahman, T., Moulin, K., Perotti, L.E. (2022) Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data. Applied Sciences, 12 (7), 3512.
  • Loecher, M., Perotti, L.E., Ennis, D.B. (2021). Using synthetic data generation to train a cardiac motion tag tracking neural network. Medical Image Analysis, 74, 102223.
  • Rahman, T., Moulin, K., Ennis, D.B., & Perotti, L.E. (2021, June). Diffusion Biomarkers in Chronic Myocardial Infarction. Functional Imaging and Modeling of the Heart, 137-147, Virtual.
  • Ogiermann, D., Balzani, D., & Perotti, L.E. (2021, June). The Effect of Modeling Assumptions on the ECG in Monodomain and Bidomain Simulations. Functional Imaging and Modeling of the Heart, 503-514, Stanford, Virtual.
  • Loecher, M., Hannum, A.J., Perotti, L.E., Ennis D.B. (2021, June). Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI. Functional Imaging and Modeling of the Heart, 213-222, Virtual.
  • Von Zuben, A., Heckman, K., Viana, F.A.,  Perotti, L.E. (2021, June). A Multi-step Machine Learning Approach for Short Axis MR Images Segmentation. Functional Imaging and Modeling of the Heart, 122-133, Virtual.
  • Moulin, K., Croisille, P., Viallon, M., Verzhbinsky, I. A., Perotti, L. E., & Ennis, D. B. (2021). Myofiber strain in healthy human using DENSE and cDTI. Magnetic Resonance in Medicine, 86 (1), 277-292.
  • Perotti, L. E., Verzhbinsky, I. A., Moulin, K., Cork, T. E., Loecher, M., Balzani, D., & Ennis, D. B. (2021). Estimating cardiomyofiber strain in vivo by solving a computational model. Medical Image Analysis, 68, 101932.
  • Moulin, K., Verzhbinsky, I. A., Maforo, N. G., Perotti, L. E., & Ennis, D.B. (2020). Probing cardiomyocyte mobility with multi-phase cardiac diffusion tensor MRI. PloS one, 15 (11), e0241996.
  • Dharmavaram, S., & Perotti, L. E. (2020). A Lagrangian formulation for interacting particles on a deformable medium. Computer Methods in Applied Mechanics and Engineering, 364, 112949.
  • Li, X., Perotti, L. E., Martinez, J. A., Duarte-Vogel, S., Ennis, D. B., & Wu, H. H. (2020). Real-time 3T MRI-guided cardiovascular catheterization in a porcine model using a glass-fiber epoxy-based guidewire. PLOS One, 15 (2), e0229711.
  • Verzhbinsky, I. A., Perotti, L. E., Moulin, K., Cork, T. E., Loecher, M. & Ennis, D. B. (2020). Estimating aggregate cardiomyocyte strain using in vivo diffusion and displacement encoded MRI. IEEE Transactions on Medical Imaging, 39(3), 656-667.
  • Ponnaluri, V. S., Verzhbinsky, I. A., Eldredge, J., Garfinkel, A., Ennis, D. B., & Perotti, L. E. (2019, June). Model of left ventricular contraction: Validation criteria and boundary conditions. International Conference on Imaging and Modeling of the Heart. Bordeaux, France.
  • T. E., Cork, Perotti, L. E., Verzhbinsky, I. A., Loecher, M., & Ennis, D. B. (2019, June). High-resolution ex vivo microstructural MRI after restoring ventricular geometry via 3D printing. Functional Imaging and Modeling of the Heart. Bordeaux, France.
  • Perotti, L. E., Zhang, K., Bruinsma, R. F. & Rudnick, J. (2019). Kirigami and the Caspar-Klug construction for viral shells with negative Gauss curvature. Physical Review E, 99(2), 022413.
  • Verzhbinsky, I. A., Magrath, P., Aliotta, E., Ennis, D. B., & Perotti, L. E. (2018). Time resolved displacement-based registration of in vivo cDTI cardiomyocyte orientations. Proceedings of the IEEE 15th International Symposium on Biomedical Engineering, 474-478.
  • Singh A. R., Perotti, L. E., Bruinsma, R. F., Rudnick, J., & Klug, W. S. (2017). Ground state instabilities of protein shells are eliminated by buckling. Soft Matter, 13 (44), 8300-8308.
  • Perotti, L. E., Ponnaluri, A. V., Krishnamoorthi, S., Balzani, D., Ennis, D. B. & Klug, W. S. (2017). Method for the unique identification of hyperelastic material properties using full‐field measures: Application to the passive myocardium material response. International Journal for Numerical Methods in Biomedical Engineering, 33(11), e2866.
  • Perotti, L. E., Magrath, P., Verzhbinsky, I. A., Aliotta, E., Moulin, K. & Ennis, D. B. (2017). Microstructurally anchored cardiac kinematics by combining in vivo DENSE MRI and cDTI. Functional Imaging and Modeling of the Heart, 381-391.
  • Ponnaluri, V., Perotti, L. E., Ennis, D. B., & Klug, W. S. (2017). A viscoactive constitutive modeling framework with variational updates for the myocardium. Computer Methods in Applied Mechanics and Engineering, 314, 85-101.
  • Perotti, L. E., Dharmavaram, S., Klug, W. S., Marian, J., Rudnick, J. & Bruinsma, R. F. (2016). Useful scars: Physics of the capsids of archaeal viruses. Physical Review E, 94(1), 012404.
  • Ponnaluri, A. V., Perotti, L. E., Liu, M., Qu, Z., Weiss, J. N., Ennis, D. B., Klug, W. S. & Garfinkel, A. (2016). Electrophysiology of heart failure using a rabbit model: From the failing myocyte to ventricular fibrillation. PLOS Computational Biology, 12(6), e1004968.

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