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Accurate reconstruction of 2D and 3D isotope densities is a desiredcapability with great potential impact in applications such as evaluation anddevelopment of next-generation nuclear fuels. Neutron time-of-flight (TOF)resonance imaging offers a potential approach by exploiting the characteristicneutron absorption spectra of each isotope. However, it is a major challenge tocompute quantitatively accurate images due to a variety of confounding effectssuch as severe Poisson noise, background scatter, beam non-uniformity,absorption non-linearity, and extended source pulse duration.  We present the TRINIDI algorithm which is based on a two-step process inwhich we first estimate the neutron flux and background counts, and thenreconstruct the areal densities of each isotope and pixel. Both components arebased on the inversion of a forward model that accounts for the highlynon-linear absorption, energy-dependent emission profile, and Poisson noise,while also modeling the substantial spatio-temporal variation of the backgroundand flux. To do this, we formulate the non-linear inverse problem as twooptimization problems that are solved in sequence. We demonstrate on bothsynthetic and measured data that TRINIDI can reconstruct quantitativelyaccurate 2D views of isotopic areal density that can then be reconstructed intoquantitatively accurate 3D volumes of isotopic volumetric density.

TRINIDI: Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inference