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Rendering speckle statistics in scattering media and its applications in tissue imaging

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Wednesday, October 13, 2021 11am to 12:30pm

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Wednesday, October 13, 2021 11am to 12:30pm

Harvard John A. Paulson School of Engineering and Applied Sciences, SEC room 3.301 + 3.302 + 3.303
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We present a Monte Carlo rendering framework for the physically-accurate simulation of speckle patterns arising from volumetric scattering of coherent waves. These noise-like patterns are characterized by strong statistical properties, such as the so-called memory effect. These properties are at the core of imaging techniques for applications as diverse as tissue imaging, motion tracking, and non-line-of-sight imaging. Our rendering framework can replicate these properties computationally, in a way that is orders of magnitude more efficient than alternatives based on directly solving the wave equations. We use our framework to simulate memory effect observations that were previously only possible through lab measurements, and analyze their properties. We also demonstrate its applicability in the design of better computational imaging systems.

In particular, one of the important applications of speckle statistics is that their computational processing can expand our ability to see through and inside scattering layers such as biological tissues, way beyond what can be seen with conventional imaging systems. However, most experimental demonstrations of this capability focus on the far-field imaging setting, where obscured light sources are very far from the scattering layer. By contrast, realistic medical imaging applications such as fluorescent imaging operate in the near-field imaging setting, where sources are inside the scattering layer. Relying on our simulator we point out unique properties of speckle patterns formed under this setting, such as their local support. By exploiting locality we can significantly increase the signal to noise ratio of previous speckle auto-correlation algorithms. This allows us to use them in practical near field tissue imaging settings, leading to an order of magnitude increase in the range and density of recoverable targets.

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