  Computed Tomography (CT) is pivotal in industrial quality control and medical
diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces
challenges due to its under-sampled nature, leading to ill-posed reconstruction
problems. Recent advancements in Implicit Neural Representations (INRs) have
shown promise in addressing sparse-view CT reconstruction. Recognizing that CT
often involves scanning similar subjects, we propose a novel approach to
improve reconstruction quality through joint reconstruction of multiple objects
using INRs. This approach can potentially leverage both the strengths of INRs
and the statistical regularities across multiple objects. While current INR
joint reconstruction techniques primarily focus on accelerating convergence via
meta-initialization, they are not specifically tailored to enhance
reconstruction quality. To address this gap, we introduce a novel INR-based
Bayesian framework integrating latent variables to capture the inter-object
relationships. These variables serve as a dynamic reference throughout the
optimization, thereby enhancing individual reconstruction fidelity. Our
extensive experiments, which assess various key factors such as reconstruction
quality, resistance to overfitting, and generalizability, demonstrate
significant improvements over baselines in common numerical metrics. This
underscores a notable advancement in CT reconstruction methods.
