Three-dimensional scanner shows wound healing

Doctors have found it difficult to judge whether a wound is healing well because wounds can expand as they heal. Attempts to map wound healing using photographic techniques or caliper measurements have not yielded good results because wounds are three-dimensional (3-D) forms. Historically, researchers have filled wounds with inert, gelatinous material; once hardened, the materials` displacement in water can provide a measure of wound volume. Such methods are crude, tedious, and inaccurate.

Sep 1st, 1996

Three-dimensional scanner shows wound healing

Doctors have found it difficult to judge whether a wound is healing well because wounds can expand as they heal. Attempts to map wound healing using photographic techniques or caliper measurements have not yielded good results because wounds are three-dimensional (3-D) forms. Historically, researchers have filled wounds with inert, gelatinous material; once hardened, the materials` displacement in water can provide a measure of wound volume. Such methods are crude, tedious, and inaccurate.

Now, a wound study led by Barbara McQuiston, vice president of engineering at Sytronics (Dayton, OH), promises to change all that. Using a 3030 RGB LN 3-D scanner from Cyberware (Monterey, CA), McQuiston captures 3-D color models of wounds from patients with diabetes. As the wounds heal, specific types of tissues gradually grow and give way to other types of tissues. A color texture map shows the progress of these changes in three dimensions. Scans reveal the characteristics of the tissues and the wounds` borders.

To automatically map the progress of different healing tissues, McQuiston uses the Queen Victoria Algorithm (QVA), which combines information through rules rather than purely statistical correlation. The algorithm was originally developed to combine both pictures and separately recorded audio from a speech presented by Queen Victoria prior to the advent of motion pictures with sound.

McQuiston uses magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to evaluate the inner workings of wounds. Each of these scans is aligned by constructing a thermoplastic mold with markers placed on it to hold a patient`s wounded limb in exactly the same orientation for each scan. The markers are digitized during imaging and create a coordinate system that assists in reg- istering multiple scans.

After internal images are produced, McQuiston overlays the models from the scans with color texture maps using KB Vision from AAI (Boston, MA) or Analyze from the Mayo Clinic (Rochester, MN). By combining the Cyberware model with the MRI/ MRS models, she can see the relationships between the wounds` inner and outer features.

McQuiston hopes to collect enough data on wound healing to feed into a neural network. With enough data to generalize the healing pattern, the neural net may provide descriptions of how quickly a specific wound should heal. This information should prove invaluable in the pursuit of new wound therapies.

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