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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Latest Journal Papers

• 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.
• Blanco, PJ; Bulant, CA; Müller, LO; Maso Talou, GD; Guedes Bezerra, C; Lemos, PA; Feijóo, RA. Comparison of 1D and 3D models for the estimation of fractional flow reserve. Scientific Reports, v. 8, p. 17275, 2018.
• Bezerra, CG; Lemos, PA; Pinton, FA; Müller, LO; Bulant, CA; Maso Talou, GD; Feijóo, RA; Filho, AE; Blanco, PJ. TCT-619 Comparison of one-dimensional (1D) and three-dimensional (3D) models for the estimation of coronary fractional flow reserve through cardiovascular imaging. Journal of the American College of Cardiology, v. 72, p. B248, 2018.
• Hideo-Kajita, A; Garcia-Garcia, H; Bezerra, CG; Pinton, FA; Falcão, BAA; Mariani, J; Bulant, CA; Maso Talou, GD; Filho, AE; Blanco, PJ; Lemos, PA. TCT-308 Comparison between fractional flow reserve (FFR) and Computational fractional flow reserve derived from three-dimensional intravascular ultrasound (FFR-IVUS), percentage of diameter stenosis by visual estimation and bi-dimensional quantitative coronary angiography. Journal of the American College of Cardiology, v. 72, p. B127, 2018.
• Rocha, FF; Blanco, PJ; Sánchez, PJ; Feijóo, RA. Multi-scale modelling of arterial tissue: Linking networks of fibres to continua. Computer Methods in Applied Mechanics and Engineering, v. 341, p. 740-787, 2018.
• Turello, DF; Sánchez, PJ; Blanco, PJ; Pinto, F. A variational approach to embed 1D beam models into 3D solid continua. Computers & Structures, v. 206, p. 145-168, 2018.
• Koltukluoğlu, TS; Blanco, PJ. Boundary control in computational haemodynamics. Journal of Fluid Mechanics, v. 847, p. 329-364, 2018.
• Maso Talou, GD; Blanco, PJ; Ares, GD; Guedes Bezerra, C; Lemos, PA; Feijóo, RA. Mechanical characterization of the vessel wall by data assimilation of intravascular ultrasound studies. Frontiers in Physiology, v. 9, p. 292, 2018.
• Safaei, S; Blanco, PJ; Müller, LO; Hellevik, LR; Hunter, PJ. Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations. Frontiers in Physiology, v. 9, p. 148, 2018.
• Durka, MJ; Wong, IH; Kallmes, DF; Pasalic, D; Mut, F; Jagani, M; Blanco, PJ; Cebral, JR; Robertson, AM. A data driven approach for addressing the lack of flow waveform data in studies of cerebral arterial flow in older adults. Physiological Measurement, v. 39, p. 015006, 2018.
• Bulant, CA; Blanco, PJ; Clausse, A; Guedes Bezerra, C; Lima, TP; Ávila, LFR, Lemos, PA; Feijóo, RA. Thermodynamic analogies for the characterization of 3D human coronary arteries. Biomedical Signal Processing and Control, v. 40, p. 163-170, 2018.

Articles in Press

• 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, 2018.