Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.

Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).

Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorders, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control based on both direct multiple shooting and direct collocation, handling musculoskeletal models. Relying on algorithmic differentiation, Bioptim is fast and it interfaces several nonlinear solvers. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control.

En el Municipio Tipitapa, Departamento de Managua, en unsistema silvopastoril, se llevó acabo un estudio con el propósitode evaluar la influencia de la estructura de las arbóreas sobrealgunas variables de la producción del pasto. Las arbóreasfueron caracterizadas a partir del cálculo de la densidad deindividuos pertenecientes a las categorías fustales y brinzales,en parcelas de 20 m x 20 m. Así mismo fueron tomadas variablesde altura, diámetro y longitud de copa. Para el componenteherbáceo (pasto) se evaluó la incidencia de las arbóreas a partirde la densidad del pasto en parcelas bajo sombra y parcelas apleno sol; además de la variable, número y longitud de brotes. Seencontraron un total de 20 especies leñosas, de las cuales cuatroson útiles como forrajeras, siendo las restantes proveedoras desombra y otras (3 especies) fijadoras de nitrógeno. Las especiesmás representativas con base en la abundancia relativa son:Tabebuia rosea (53.93%), Pithecellobium dulce (26.35%),Azadirachta indica (10.19%). Las leñosas, en la categoría defustales presentan densidades dentro del rango del número deárboles reportados en sistemas de árboles dispersos en potrerosen Centroamérica .Los valores de diámetro y altura promedioson de 1.0 cm. y 0.92m, respectivamente. El porcentaje decobertura generado por las leñosas es de 10.6. Mediante análisisestadístico se determinó que no existen diferencias entrela densidad del pasto (p > 0.05) entre parcelas bajo sombra.De la misma forma, no se encontraron diferencias (p > 0.05)entre parcelas a 5m y la densidad de parcelas a pleno sol. Seencontraron diferencias (p < 0.05) entre la densidad de pasto enparcelas bajo sombra ubicadas a 10m del centro de árboles y ladensidad de pasto en parcelas a pleno sol. De la misma forma,el análisis reveló diferencias estadísticas (p < 0.05) en cuantoal número y longitud de brotes y correlación entre las variablesárea de copa y abundancia de las arbóreas con las variablesnúmero de brotes y longitud de brotes.DOI: 10.5377/calera.v9i13.11