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August 27, 2008
From law enforcement to cancer detection, the uncanny ability of dogs to sniff out the earliest signs of danger and disease is receiving ever-increasing attention by researchers -- with good reason.
Despite tremendous advances in law enforcement technology, there is still no man-made tool that can detect the presence of explosives quite like a canine's sophisticated sniffing system. Some experts believe it exceeds the human sense of smell by a factor of at least 10,000 and possibly as much as 100,000. Even in today's high-tech world, trained dogs are still the best bomb detectors.
In the medical arena, dogs have shown an ability to detect some cancers at their earliest stages, when the possibility for cure is highest. Some studies have documented the ability of dogs to distinguish people with cancers from healthy controls through sniffing their breath or skin with an accuracy between 88 percent and 97 percent.
"The dog's nose is the gold standard for chemical trace detection," explains Brent Craven, a researcher with the Gas Dynamics Lab and the Applied Research Lab at Penn State University. Craven and his team, which includes Drs. Gary Settles and Eric Paterson of the Mechanical Engineering Department, are using computer models to study the canine sense of smell to help develop 'artificial sniffer' technologies.
"We are trying to figure out what specifically makes dogs so efficient in this area," says Craven. He has been analyzing the internal aerodynamics and transport phenomena in the canine nose using computational fluid dynamics (CFD). Large parallel simulations on high-performance computers at Penn State with AcuSolve (ACUSIM Software, Inc.) CFD software yield terabytes of data. One challenge has been the inability of visualization software to handle the very large models required for this study.
"The complexity of the dog's nose rivals the human lung with its many different branching airways, and it's essential that we understand exactly how it functions," says Craven. "Constructing a 3D virtual computer model and running simulations with such a complicated model can require nearly a 100 million cells -- not all CFD visualization software can handle such a large data set."
To meet that challenge, Craven and other researchers at Penn State turned to EnSight Gold scientific visualization software from Computational Engineering International, Inc. The software supports very large models that contain millions or billions of nodes, providing intensive parallel processing and rendering capabilities for applications such as airflow animation. Researchers also used Python scripts to automate the data analysis and visualization.
His artificial sniffer project began three years ago with high-resolution MRI scans of a cadaver dog. Craven then reconstructed those into a virtual computer model of a canine nose and, using experimental sniffing data from live dogs, was able to simulate the airflow through it. The model can steadily inspire, expire, or sniff, so Craven can analyze the fluid dynamics of trace detection in nature's best sniffer.
"One of the questions we are trying to answer is why dogs sniff so fast," says Craven. Using the visualization software, they are able to analyze simulation results of their model of a Labrador retriever sniffing at 5 Hz (5 sniffs per second), the nominal sniff frequency of a dog that size. The eventual goal is to create a complete virtual dog's nose, complete with virtual scent receptors that are located in the back of the nose.
The computer model is able to show the role of the various anatomical structures of the canine nose in respiration and olfaction. These include the nasal vestibule, which filters and distributes inspired air within the nasal cavity, and directs an expired air jet; the respiratory airways, which filter, warm/cool, humidify inspired air, and dehumidify expired air; and the olfactory airways, where chemical trace detection occurs.
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