  The non-interference three-dimensional refractive index(RI) tomography has
attracted extensive attention in the life science field for its simple system
implementation and robust imaging performance. However, the complexity inherent
in the physical propagation process poses significant challenges when the
sample under study deviates from the weak scattering approximation. Such
conditions complicate the task of achieving global optimization with
conventional algorithms, rendering the reconstruction process both
time-consuming and potentially ineffective. To address such limitations, this
paper proposes an untrained multi-slice neural network(MSNN) with an optical
structure, in which each layer has a clear corresponding physical meaning
according to the beam propagation model. The network does not require
pre-training and performs good generalization and can be recovered through the
optimization of a set of intensity images. Concurrently, MSNN can calibrate the
intensity of different illumination by learnable parameters, and the multiple
backscattering effects have also been taken into consideration by integrating a
"scattering attenuation layer" between adjacent "refractive index" layers in
the MSNN. Both simulations and experiments have been conducted carefully to
demonstrate the effectiveness and feasibility of the proposed method.
Experimental results reveal that MSNN can enhance clarity with increased
efficiency in RI tomography. The implementation of MSNN introduces a novel
paradigm for RI tomography.
