Memory-efficient optimization of implicit neural representations for CT reconstruction

Implicit neural representations (INRs) provide a parameter-efficient and fully differentiable image model for CT reconstruction. However, optimizing INRs for CT reconstruction using standard auto-differentiation techniques can be prohibitively GPU memory-intensive, especially in 3D imaging, due to the large number of INR evaluations needed to simulate ray projections. To address this issue, we propose a memory-efficient stochastic gradient approximation based on decomposing the gradient into a Jacobian-vector product that is amenable to stochastic subsampling. This approximation allows the user to trade-off between GPU memory usage and gradient approximation accuracy. Our experiments on synthetic 2D data demonstrate that gradient approximation uses far less GPU memory than standard INR training, while yielding reconstructions that are comparable in convergence behavior and mean squared error. Finally, we demonstrate that the proposed approach allows for memory-efficient 3D cone beam CT reconstruction in a sparse-view setting.