Summary of Implicit Neural Representations For Robust Joint Sparse-view Ct Reconstruction, by Jiayang Shi et al.
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
by Jiayang Shi, Junyi Zhu, Daniel M. Pelt, K. Joost Batenburg, Matthew B. Blaschko
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel approach improves reconstruction quality through joint reconstruction of multiple objects using Implicit Neural Representations (INRs) for sparse-view Computed Tomography (CT). By recognizing common patterns across different objects, this method can enhance the final reconstruction quality while reducing ionizing radiation. The Bayesian framework integrates latent variables to capture these patterns, which then assist in reconstructing each object individually. Experimental results demonstrate higher reconstruction quality with sparse views and robustness to noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new way to improve Computed Tomography (CT) scans by using computers to find patterns in similar objects. This helps make the scan better and reduces the amount of radiation used. It’s like finding a template that can be used for many different things, which makes it easier and faster to get good results. |
