The Simulated Detection of the Objects at Variant Depth Using Depth-Adaptive Voxel Size Scheme in Diffuse Reflective Optical Tomography (DROT)

Abstract

We study diffuse reflective optical tomography (DROT), aiming at the real-time imaging of the redox state change of the human cerebral cortex with the brain activity. In the uniform voxel size and simple regularization scheme we studied so far, the spatial resolution in the shallow region is unnecessarily good, whereas the noise and the sensitivity in the shallow region are too large to recover the signal in the depth. In this paper, we propose depth-adaptive voxel size reconstruction. By assigning wider lateral voxel size in the deep range, the relative sensitivity in the deep range is increased so that the signal in the deep range would be less masked by the presence of the unwanted target in the shallow range. We demonstrate that we can detect the deep objects without being masked by the shallow object using the proposed method.