RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging

• URL: http://arxiv.org/abs/2503.10015v1
• Date: Thu, 13 Mar 2025 03:50:47 GMT
• Title: RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
• Authors: Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar, Marc L. Klasky, Yoram Bresler,
• Abstract summary: In inverse problem of dynamic computed tomography (dCT), only a single projection at one view angle is available at a time.<n>Ground-truth dynamic data is usually either unavailable or too scarce to be used for supervised learning techniques.<n>We use an ADMM-based algorithm with variable splitting to efficiently optimize the variational objective.
• Score: 5.569092860148177
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only a single projection at one view angle is available at a time, making the inverse problem very challenging. Moreover, ground-truth dynamic data is usually either unavailable or too scarce to be used for supervised learning techniques. To tackle this problem, we propose RSR-NF, which uses a neural field (NF) to represent the dynamic object and, using the Regularization-by-Denoising (RED) framework, incorporates an additional static deep spatial prior into a variational formulation via a learned restoration operator. We use an ADMM-based algorithm with variable splitting to efficiently optimize the variational objective. We compare RSR-NF to three alternatives: NF with only temporal regularization; a recent method combining a partially-separable low-rank representation with RED using a denoiser pretrained on static data; and a deep-image prior-based model. The first comparison demonstrates the reconstruction improvements achieved by combining the NF representation with static restoration priors, whereas the other two demonstrate the improvement over state-of-the art techniques for dCT.

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