Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
This addresses the computational bottleneck for researchers and practitioners using INRs in medical imaging, though it is incremental as it builds on existing INR methods.
The paper tackles the slow training of implicit neural representations (INRs) for CT reconstruction by proposing two acceleration strategies: a modified loss function and an algorithm based on the alternating direction method of multipliers, which significantly speed up reconstruction of a synthetic breast CT phantom in sparse-view settings.
Inspired by their success in solving challenging inverse problems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. Fitting an INR to sinogram data is similar to classical model-based iterative reconstruction methods. However, training INRs with losses and gradient-based algorithms can be prohibitively slow, taking many thousands of iterations to converge. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction. In particular, we propose two approaches: (1) using a modified loss function with improved conditioning, and (2) an algorithm based on the alternating direction method of multipliers. We illustrate that both of these approaches significantly accelerate INR-based reconstruction of a synthetic breast CT phantom in a sparse-view setting.