Real-time identification of tissue would improve procedures such as stereotactic brain biopsy (SBX), functional and implantation neurosurgery, and brain tumor excision. To standard SBX equipment has been added: (1) computer-controlled stepper motors to drive the biopsy needle/probe precisely; (2) multiple microprobes to track tissue density, detect blood vessels and changes in blood flow, and distinguish the various tissues being penetrated; (3) neural net learning programs to allow real-time comparisons of current data with a normative data bank; (4) three-dimensional graphic displays to follow the probe as it traverses brain tissue. The probe can differentiate substances such as pig brain, differing consistencies of the ‘brain-like’ foodstuff tofu, and gels made to simulate brain, as well as detect blood vessels imbedded in these substances. Multimodality probes should improve the safety, efficacy, and diagnostic accuracy of SBX and other neurosurgical procedures.

Real-time tissue identification can benefit procedures such as stereotactic brain biopsy, functional neurosurgery and brain tumor excision. Optical scattering spectroscopy has been shown to be effective at discriminating cancer from noncancerous conditions in the colon, bladder and breast. The NASA Smart Probe extends the concept of ‘optical biopsy’ by using neural network techniques to combine the output from 3 microsensors contained within a cannula 2.7 mm in diameter (i.e. the diameter of a stereotactic brain biopsy needle). Experimental data from 5 rats show the clear differentiation between tissues such as brain, nerve, fat, artery and muscle that can be achieved with optical scattering spectroscopy alone. These data and previous findings with other modalities such as (1) analysis of the image from a fiberoptic neuroendoscope and (2) the output from a microstrain gauge suggest the Smart Probe multiple microsensor technique shows promise for real-time tissue identification in neurosurgical procedures.

Realistic simulations of the movement of infantry in urban environments with 3D models and at interactive rates is of wide and enduring interest. Many video games have attempted it, and military simulations are increasingly doing the same. Previous attempts have fallen short in two areas: properly coordinating movement, and adequate modeling of the detection of hostile targets. Novel algorithms to simulate fireteam movement and visual scanning appropriate to urban environments are described. Measurements of the computational performance of the most expensive components of the approach are provided.

No abstract

No abstract