Abstract
We present 3D linear reconstructions of time-domain (TD) diffuse optical imaging differential data. We first compute the sensitivity matrix at different delay gates within the diffusion approximation for a homogeneous semi-infinite medium. The matrix is then inverted using spatially varying regularization. The performances of the method and the influence of a number of parameters are evaluated with simulated data and compared to continuous-wave (CW) imaging. In addition to the expected depth resolution provided by TD, we show improved lateral resolution and localization. The method is then applied to reconstructing phantom data consisting of an absorbing inclusion located at different depths within a scattering medium.
Highlights
In complement of traditional techniques such as functional Magnetic Resonance Imaging, Diffuse Optical Tomography (DOT) is emerging as a low-cost and portable method for noninvasive cerebral imaging [1,2,3]
Most DOT instruments used in neuroscience are continuous wave (CW) systems, but time-domain (TD) technology is a recent promising alternative with advantages compensating for its increased cost and difficulty of implementation
We describe an actual 3D linear reconstruction for differential imaging, based on inversion of the forward sensitivity matrix calculated in different delay gates, in a similar way that is sometimes implemented in CW DOT imaging [29]
Summary
In complement of traditional techniques such as functional Magnetic Resonance Imaging, Diffuse Optical Tomography (DOT) is emerging as a low-cost and portable method for noninvasive cerebral imaging [1,2,3]. Most DOT instruments used in neuroscience are continuous wave (CW) systems, but time-domain (TD) technology is a recent promising alternative with advantages compensating for its increased cost and difficulty of implementation These advantages include absolute characterization of tissue optical properties [4,5] (both absorption coefficient μa and reduced scattering coefficient μs’), depth resolution with single source-detector separation [6,7], and better sensitivity to cortical activation [8,9]. We used a simplified 3layer model – scalp, skull, and brain – and showed that we could experimentally distinguish between superficial systemic signals and cerebral activation signals during a motor stimulus on a single source-detector pair [8] In all these studies, depth resolution has been shown, but no 3D imaging was implemented, since a single source-detector pair was used. We apply the reconstruction technique to phantom data obtained with our time-gated system [30]
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