Implicit Neural Representations for Joint Sparse-View CT Reconstruction
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Keywords: implicit neural representations, CT reconstruction, representation learning, bayesian framework, variational inference
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Abstract: Sparse-view Computed Tomography (CT) is favored over standard CT for its reduced ionizing radiation but poses challenges due to its inherently ill-posed nature arising from undersampled measurement data. Implicit Neural Representations (INRs) have emerged as a promising solution, demonstrating effectiveness in sparse-view CT reconstruction. Given that modern CT often scans similar subjects, we propose to improve reconstruction quality via 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 existing techniques of INR joint reconstruction focus on enhancing convergence rates through meta-initialization, they do not optimize for final reconstruction quality. To fill this gap, we introduce a novel INR-based Bayesian framework that incorporates latent variables to capture inter-object relationships. These latent variables act as a continuously updated reference during the optimization process, thereby enhancing the quality of individual reconstructions. We conduct extensive experiments to evaluate various aspects such as reconstruction quality, susceptibility to overfitting, and generalizability. Results demonstrate that our method sets a new standard in CT reconstruction performance. Our code will be released.
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Submission Number: 5462
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