Published November 2022 | Submitted
Journal Article Open

Parallel inverse-problem solver for time-domain optical tomography with perfect parallel scaling

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Abstract

This paper presents an efficient parallel radiative transfer-based inverse-problem solver for time-domain optical tomography. The radiative transfer equation provides a physically accurate model for the transport of photons in biological tissue, but the high computational cost associated with its solution has hindered its use in time-domain optical-tomography and other areas. In this paper this problem is tackled by means of a number of computational and modeling innovations, including (1) A spatial parallel-decomposition strategy with perfect parallel scaling for the forward and inverse problems of optical tomography on parallel computer systems; and, (2) A Multiple Staggered Source method (MSS) that solves the inverse transport problem at a computational cost that is independent of the number of sources employed, and which significantly accelerates the reconstruction of the optical parameters: a six-fold MSS acceleration factor is demonstrated in this paper. Finally, this contribution presents (3) An intuitive derivation of the adjoint-based formulation for evaluation of functional gradients, including the highly-relevant general Fresnel boundary conditions—thus, in particular, generalizing results previously available for vacuum boundary conditions. Solutions of large and realistic 2D inverse problems are presented in this paper, which were produced on a 256-core computer system. The combined parallel/MSS acceleration approach reduced the required computing times by several orders of magnitude, from months to a few hours.

Additional Information

© 2022 Elsevier. Received 17 February 2022, Revised 30 May 2022, Accepted 21 June 2022, Available online 24 June 2022, Version of Record 30 June 2022. This work was supported by NSF, DARPA and AFOSR through contracts DMS-2109831 and HR00111720035 and FA9550-21-1-0373, and by the NSSEFF Vannevar Bush Fellowship under contract number N00014-16-1-2808. ELG acknowledge financial support from CONICET. The codes utilized in this paper are available from the first author upon reasonable request. CRediT authorship contribution statement. E.L. Gaggioli: Conceptualization, Methodology, Software, Validation, Investigation, Visualization, Writing – original draft. Oscar P. Bruno: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – original draft, Supervision, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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August 22, 2023
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