trying to render Rob's last name well

Author: mstaring

_bibliography/papers.bib

@@ -3,11 +3,11 @@
 
 @string{aps = {American Physical Society,}}
 
-@article{Elmahdyu2025,
+@article{Elmahdy2025,
   abbr =     {},
   bibtex_show = {true},
   title =   {CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI},
-  author =  {Elmahdy, Mohamed S. and Staring, Marius and de Koning, Patrick J. H. and Alabed, Samer and Salehi, Mahan and Alandejani, Faisal and Sharkey, Michael and Aldabbagh, Ziad and Swift, Andrew J. and van der Geest, Rob J.},
+  author =  {Elmahdy, Mohamed S. and Staring, Marius and de Koning, Patrick J. H. and Alabed, Samer and Salehi, Mahan and Alandejani, Faisal and Sharkey, Michael and Aldabbagh, Ziad and Swift, Andrew J. and "van der Geest", Rob J.},
   journal = {arXiv},
   volume =  {},
   pages =   {},

_bibliography/papers_abstracts.bib

@@ -7,8 +7,10 @@ @inproceedings{Gao:2025
   booktitle = {Radiotherapy and Oncology (ESTRO)},
   month =     {May},
   year =      {2025},
+  volume =    {206},
+  pages =     {S2792 -- S2794},
   pdf =       {},
-  html =      {},
+  html =      {https://doi.org/10.1016/S0167-8140(25)00691-7},
   arxiv =     {},
   code =      {},
   abstract =  {In recent years, deep learning-based approaches for dose prediction in head and neck cancer treatments have made significant progress. However, the effects of key factors, such as the choice of loss function and model architecture, on the accuracy of dose predictions - evaluated using clinically relevant dosimetric parameters - remain underexplored. This study aims to examine how these factors influence the performance of deep learning dose prediction models, focusing on clinically relevant dosimetric parameters (e.g., V95% and mean dose).},