From cb919977d9495b592150ce748ae80d446f74e3da Mon Sep 17 00:00:00 2001 From: Umberto Villa Date: Thu, 18 Aug 2022 10:41:23 -0500 Subject: [PATCH] Update README.md --- README.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 882268c..73434b1 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,17 @@ # Neural Field CRT Dynamic Imaging + +Companion code of journal article + +> 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)) + + + 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 @@ -17,4 +23,4 @@ conda install pytorch torchvision torchaudio -c pytorch `scikit-image`: collection of algorithms for image processing ```bash conda install scikit-image -``` \ No newline at end of file +```