remove

Author: mstaring

_bibliography/papers_abstracts.bib

@@ -45,7 +45,7 @@ @inproceedings{Gonzalez:2025
   abstract =  {The use of Imageless MR sequences, combined with deep-learning methods, could offer a rapid, cost-effective screening technique suitable for large population-wise deployment. We showcase how this framework yields accurate detection and lesion size estimation using an MS lesions case study.},
 }
 
-@inproceedings{Ilicak:2025a,
+@inproceedings{Ilicak:2025,
   abbr =    {},
   bibtex_show = {true},
   author =    {Ilicak, Efe and Ercan, Ece and Dong, Yiming and Staring, Marius and Webb, Andrew and van Osch, Matthias JP and B{\"o}rnert, Peter and Nagtegaal, Martijn},
@@ -60,21 +60,6 @@ @inproceedings{Ilicak:2025a
   abstract =  {We investigate a prototype 0.6T MRI system for free-breathing functional lung imaging. Our findings demonstrate improved image quality compared to 1.5T, with improved tissue-background contrast and homogeneity the functional maps, underscoring the system's robustness and potential for non-invasive pulmonary imaging.},
 }
 
-@inproceedings{Ilicak:2025b,
-  abbr =    {},
-  bibtex_show = {true},
-  author =    {Ilicak, Efe and Rao, Chinmay and Najac, Chlo{\'e} and Lena, Beatrice and Webb, Andrew and Staring, Marius},
-  title =     {Simulating Very-Low-Field MRI Training Data for Learning-based Undersampled MRI Reconstruction},
-  booktitle = {International Society for Magnetic Resonance in Medicine},
-  month =     {May},
-  year =      {2025},
-  pdf =       {},
-  html =      {},
-  arxiv =     {},
-  code =      {},
-  abstract =  {We present a framework to simulate very-low-field data from high-field scans and demonstrate its use with a deep-learning network for undersampled MRI reconstruction. While further optimizations are warranted, results suggest that realistic simulations can support the development of improved networks.},
-}
-
 @inproceedings{Rao:2024,
   abbr =    {},
   bibtex_show = {true},