@inproceedings{621c312631b74c5eb23a58beb30e42cb,
title = "Off-the-grid model based deep learning (o-modl)",
abstract = "We introduce a model based off-the grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies is the learning of non-linear annihilation relations in Fourier space. We rely on a model based framework, which allows us to use a significantly smaller deep network, compared to direct approaches that also learn how to invert the forward model. Preliminary comparisons against image domain MoDL approach demonstrates the potential of the off-the-grid formulation. The main benefit of the proposed scheme compared to structured low-rank methods is the quite significant reduction in computational complexity.",
keywords = "CNN, MRI, Off-the-grid",
author = "Aniket Pramanik and Hemant Aggarwal and Mathews Jacob",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759403",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1395--1398",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
}