Processing techniques for laser speckle derived from biological tissues

Laser speckle techniques are well known in the non-destructive evaluation community(1-4). One particular application is to infer strain by monitoring the motion of the speckle pattern that results from coherently illuminating the object. Typically a reference image of the speckle pattern is acquired before deformation of the object. Motion (with respect to this reference image) of subsequent speckle patterns, which occurs when the object is stressed, are used to infer the resulting strain. A problem experienced in using this technique for measurements of hydrated tissues is the rapid decorrelation of the speckle patterns'. Thus, application of speckle techniques to assessment of strain in biological tissues relies on rapid sampling of the speckle patterns and the use of processing algorithms that are aimed at inferring strain rates rather than absolute strains. We discuss a number of approaches to estimating strain rates based on sequential speckle patterns. Maximum likelihood methods are shown to be especially useful.