Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa David M. Knigge Riccardo Valperga Nikita Moriakov Miltos Kofinas Jan-Jakob Sonke Efstratios Gavves
Conventional Computed Tomography (CT) methods require large numbers of
noise-free projections for accurate density reconstructions, limiting their
applicability to the more complex class of Cone Beam Geometry CT (CBCT)
reconstruction. Recently, deep learning methods have been proposed to overcome
these limitations, with methods based on neural fields (NF) showing strong
performance, by approximating the reconstructed density through a
continuous-in-space coordinate based neural network. Our focus is on improving
such methods, however, unlike previous work, which requires training an NF from
scratch for each new set of projections, we instead propose to leverage
anatomical consistencies over different scans by training a single conditional
NF on a dataset of projections. We propose a novel conditioning method where
local modulations are modeled per patient as a field over the input domain
through a Neural Modulation Field (NMF). The resulting Conditional Cone Beam
Neural Tomography (CondCBNT) shows improved performance for both high and low
numbers of available projections on noise-free and noisy data.