Deconvolution improves the accuracy and depth sensitivity of time-resolved measurements

Mamadou Diop; Keith St. Lawrence

doi:10.1117/12.2004850

Abstract

Time-resolved (TR) techniques have the potential to distinguish early- from late-arriving photons. Since light travelling through superficial tissue is detected earlier than photons that penetrate the deeper layers, time-windowing can in principle be used to improve the depth sensitivity of TR measurements. However, TR measurements also contain instrument contributions – referred to as the instrument-response-function (IRF) – which cause temporal broadening of the measured temporal-point-spread-function (TPSF). In this report, we investigate the influence of the IRF on pathlength-resolved absorption changes (Δμ_a) retrieved from TR measurements using the microscopic Beer-Lambert law (MBLL). TPSFs were acquired on homogeneous and two-layer tissue-mimicking phantoms with varying optical properties. The measured IRF and TPSFs were deconvolved to recover the distribution of time-of-flights (DTOFs) of the detected photons. The microscopic Beer-Lambert law was applied to early and late time-windows of the TPSFs and DTOFs to access the effects of the IRF on pathlength-resolved Δμ_a. The analysis showed that the late part of the TPSFs contains substantial contributions from early-arriving photons, due to the smearing effects of the IRF, which reduced its sensitivity to absorption changes occurring in deep layers. We also demonstrated that the effects of the IRF can be efficiently eliminated by applying a robust deconvolution technique, thereby improving the accuracy and sensitivity of TR measurements to deep-tissue absorption changes.

Citation Download Citation

Mamadou Diop, Mamadou Diop, Keith St. Lawrence, Keith St. Lawrence, "Deconvolution improves the accuracy and depth sensitivity of time-resolved measurements", Proc. SPIE 8578, Optical Tomography and Spectroscopy of Tissue X, 85782E (25 March 2013); doi: 10.1117/12.2004850; https://doi.org/10.1117/12.2004850

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