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README.md

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 # Neural Field CRT Dynamic Imaging
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+Companion code of journal article
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+> L. Lozenski, M. Anastasio, U. Villa. _A Memory-Efficient Self-Supervised Dynamic Image Reconstruction Method using Neural Fields_, submitted to IEEE Transactions on Computational Imaging, 2022 ([preprint](https://arxiv.org/abs/2205.05585?context=eess))
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 Neural Fields for solving dynamic CRT imaging problems
 
 Neural fields are a particular class of neural networks that represent the dynamic object as a continuous function of space and time. Neural field representation reduces image reconstruction to estimating the network parameters via a nonlinear optimization problem (training). Once trained, the neural field can be evaluated at arbitrary locations in space and time, allowing for high-resolution rendering of the object. Key advantages of the proposed approach are that neural fields automatically learn and exploit redundancies in the sought-after object to both regularize the reconstruction and significantly reduce memory storage requirements. 
 
 In this repository we display this proposed neural field framework with a supervised learning example and two unsupervised image reconstruction examples using the dynamic circular radon transform (CRT).
 
-Please cite our upcoming journal article “A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields”.
 
 # Dependencies 
 
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 `scikit-image`: collection of algorithms for image processing
 ```bash
 conda install scikit-image
-```
+```