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

• 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