Evaluation of external operculum loop tags to individually identify cage-cultured Atlantic halibut Hippoglossus hippoglossus in commercial research trials.

The growth, survival and tag retention of double-tagged [external FT4 lock-on (FT4) and internal passive integrated transponder (PIT)-tagged] Atlantic halibut Hippoglossus hippoglossus were compared to internal PIT-tagged controls in a randomized trial. The objective was to assess the suitability of these tags for monitoring the performance of individual fish in longitudinal trials under commercial cage-culture conditions in the lower Bay of Fundy, New Brunswick, Canada. The FT4 tags were chosen due to their similarity to tags used by investigators to track H. hippoglossus in the wild. A subset of the population randomly received an external FT4 tag inserted through the operculum and were monitored over a 1105 day period. The specific growth rate of FT4-tagged fish was significantly reduced in the first sea summer with no significant difference observed for the remainder of the trial. The differential growth in the first sea summer created a relative size advantage, permitting controls to increase in size significantly faster than FT4 fish in all subsequent periods. The FT4 tags did not significantly influence survival under normal commercial cage-culture conditions. Results, however, suggest that the survival of FT4-tagged H. hippoglossus may be compromised during stressful handling events. Tag retention of FT4 tags was acceptable with 76% of tags remaining at the end of the 1105 day trial. FT4 tags proved to be an effective method to identify individual H. hippoglossus, with the caveat that they seriously bias productivity measures in commercial research trials.

Creating fast finite element models from medical images.

The procedure for creating a patient-specific virtual tissue model with finite element (FE) based haptic (force) feedback varies substantially from that which is required for generating a typical volumetric model. In addition to extracting geometrical and texture map data to provide visual realism, it is necessary to obtain information for supporting a FE model. Among many differences, FE-based VR environments require a FE model with appropriate material properties assigned. The FE equation must also be processed in a manner specific to the surgical task in order to maximize deformation and haptic computation speed. We are currently developing methodologies and support software for creating patient-specific models from medical images. The steps for creating such a model are as follows: 1) obtain medical images and texture maps of tissue structures; 2) extract tissue structure contours; 3) generate a 3D mesh from the tissue structure contours; 4) alter mesh based on simulation objectives; 5) assign material properties, boundary nodes and texture maps; 6) generate a fast (or real-time) FE model; and 7) support the tissue models with task-specific tools and training aids. This paper will elaborate on the above steps with particular reference to the creation of suturing simulation software, which will also be described.