ABSTRACT
Anatomically based finite element geometries are becoming increasingly popular in physiological modelling, owing to the demand for modelling that links organ function to spatially distributed properties at the protein, cell and tissue level. We present a collection of anatomically based finite element geometries of the musculo-skeletal system and other organs suitable for use in continuum analysis. These meshes are derived from the widely used Visible Human (VH) dataset and constitute a contribution to the world wide International Union of Physiological Sciences (IUPS) Physiome Project (www.physiome.org.nz). The method of mesh generation and fitting of tricubic Hermite volume meshes to a given dataset is illustrated using a least-squares algorithm that is modified with smoothing (Sobolev) constraints via the penalty method to account for sparse and scattered data. A technique ("host mesh" fitting) based on "free-form" deformation (FFD) is used to customise the fitted (generic) geometry. Lung lobes, the rectus femoris muscle and the lower limb bones are used as examples to illustrate these methods. Geometries of the lower limb, knee joint, forearm and neck are also presented. Finally, the issues and limitations of the methods are discussed.
Subject(s)
Musculoskeletal System/anatomy & histology , Algorithms , Biomechanical Phenomena , Computer Simulation , Databases as Topic , Female , Human Body , Humans , Internet , Male , Models, Anatomic , Models, Statistical , Models, Theoretical , SoftwareABSTRACT
The authors describe a fast method for calculating left ventricle (LV) mass and volumes from multiplanar magnetic resonance (MR) images. Mathematic models were fitted to a small number of user-selected guide points in 15 healthy volunteers, 13 patients after myocardial infarction, and a canine model of mitral regurgitation in eight dogs. Errors between model and manual contours were small (LV mass, 1.8 g +/- 4.9 [mean +/- SD]; end-diastolic volume, 2.2 mL +/- 4.6; end-systolic volume, 2.3 mL +/- 3.8). Estimates of global function could be obtained in 6 minutes, a time saving of 5-10 times over estimates with manual contouring.
