About HeMoLab

The HeMoLab (Hemodynamics Modeling Laboratory) group is, since 2006, a R&D group within the National Laboratory for Scientific Computing (LNCC/MCTI). Background areas of HeMoLab team members and collaborators are Engineering, Computer Science, Mathematics, Physiology, Cardiology, Anatomy and Physics. Core activities are related to the modeling and numerical simulation of physiological systems, more specifically the cardiovascular system. Research efforts are concentrated towards developing coupled and multiscale physical models based on variational foundations, as well as to develop and implement numerical approximations based on the Finite Element Method, the Finite Volume Method and the Lattice-Boltzmann Method. Blood flow models, fluid structure interaction, wave propagation phenomena, medical image processing, constitutive multiscale modeling and parameter identification procedures are some of the activities of the group. Software development targeting distributed computing systems is a continuous concern with the aim of popularizing modeling and simulation tools and facilitate their use in real large scale problems.


ADAN-WEB is a web application to provide users with extremely refined anatomical and functional data of the arterial network. This unprecedented dataset is based on the anatomical/medical domain knowledge, and has been developed in the HeMoLab group within the context of the INCT-MACC .

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ImageLab is a software for medical image processing designed to serve as an easy-to-use tool to aid cardiovascular research through the processing and segmentation of anatomical structures of interest from medical images. ImageLab can equally be used as a laboratory to aid the implementation of new algorithms and methods.

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New Book

Introduction to the Variational Formulation in Mechanics: Fundamentals and Applications, Published by Wiley. Available in 2020

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Latest Journal Papers

• Ziemer, PGP; Bulant, CA; Orlando, JI; Maso Talou, GD; Álvarez, LAM; Guedes Bezerra, C; Lemos, PA; García-García, HM; Blanco, PJ. Automated lumen segmentation using multi-frame convolutional neural networks in Intravascular Ultrasound datasets. European Heart Journal Digital Health, v. 1, p. 75-82, 2020.
• Su, S; Blanco, PJ; Müller, LO; Hunter, PJ; Safaei, S. Bond graph model of cerebral circulation: toward clinically feasible systemic blood flow simulations. Physiome Journal, v. 1, p. 12859424, 2020.
• Blanco, PJ; Müller, LO; Watanabe, SM; Feijóo, RA. On the anatomical definition of arterial networks in blood flow simulations: comparison of detailed and simplified models. Biomechanics and Modeling in Mechanobiology, v. 19, p. 1663-1678, 2020.
• Guzzetti, S; Mansilla Alvarez, LA; Blanco, PJ; Carlberg, KT; Veneziani, A. Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling. Computer Methods in Applied Mechanics and Engineering, v. 358, p. 112626, 2020.
• Carson, J; Pant, S; Roobottom, C; Alcock, R; Blanco, PJ; Bulant, CA; Vassilevski, Y; Simakov, S; Gamilov, T; Pryamonosov, R; Liang, F; Ge, X; Liu, Y; Nithiarasu, P. Non‐invasive coronary CT angiography‐derived fractional flow reserve (FFR): A benchmark study comparing the diagnostic performance of four different computational methodologies. International Journal for Numerical Methods in Biomedical Engineering, v. 35, p. e3235, 2019.
• Bezerra, CG; Pinton, FA; Falcão, BAA; Mariani, J; Hideo-Kajita, A; Bulant, CA; Blanco, PJ; Lemos, PA. TCT-329 Full Hemodynamic Characterization of Intracoronary Physiology: Merging Advanced Grayscale Intravascular Ultrasound With Fractional Flow Reserve. Journal of the American College of Cardiology, v. 74, p. B327, 2019.
• Pérez Zerpa, JM; Maso Talou, GD; Blanco, PJ. A new robust formulation for optical-flow/material identification problems. Computer Methods in Applied Mechanics and Engineering, v. 351, p. 766-788, 2019.
• Müller, LO; Caiazzo, A; Blanco, PJ. Reduced-order unscented Kalman filter with observations in the frequency domain: application to computational hemodynamics. IEEE Transactions on Biomedical Engineering, v. 66, p. 1269-1276, 2019.
• Bhogal, P; Yeo, LL; Müller, LO; Blanco, PJ. The effects of cerebral vasospasm on cerebral blood flow and the effects of induced hypertension: a mathematical modelling study. Interventional Neurology, v. 8, p. 152-163, 2019.
• Hideo-Kajita, A; and Bezerra, CG; Ozaki, Y; Dan, K; Melaku, GD; Pinton, FA; Falcão, BAA; Mariani, J; Bulant, CA; Maso-Talou, GD; Esteves, A; Blanco, PJ; Waksman, R; Garcia-Garcia, HM; Lemos, PA. 500.05 Comparison Between Fractional Flow Reserve (FFR) vs. Computational Fractional Flow Reserve Derived from Three-dimensional Intravascular Ultrasound (IVUSFR) and Quantitative Flow Ratio (QFR). JACC: Cardiovascular Interventions, v. 12, p. S40, 2019.
• Bezerra, CG; Hideo-Kajita, A; Bulant, CA; Maso Talou, GD; Mariani, J; Pinton, FA; Falcão, BAA; Filho, AE; Franken, M; Feijóo, RA; Kalil-Filho, R; Garcia-Garcia, HM; Blanco, PJ, Lemos, PA. Coronary fractional flow reserve derived from intravascular ultrasound imaging: Validation of a new computational method of fusion between anatomy and physiology. Catheterization and Cardiovascular Interventions, v. 93, p. 266-274, 2019.
• Mansilla Alvarez, LA; Blanco, PJ; Bulant, CA; Feijóo, RA. Towards fast hemodynamic simulations in large-scale circulatory networks. Computer Methods in Applied Mechanics and Engineering, v. 344, p. 734-765, 2019.

Articles in Press

• Fernandes, LG; Trenhago, PR; Feijóo, RA; Blanco, PJ. Integrated cardiorespiratory system model with short timescale control mechanisms. International Journal for Numerical Methods in Biomedical Engineering, 2020.