papers donghang

Author: mstaring

_bibliography/papers.bib

@@ -96,9 +96,9 @@ @article{ChavezDePlaza2025
   title =   {Implementation of Delineation Error Detection Systems in Time-Critical Radiotherapy: Do AI-Supported Optimization and Human Preferences Meet?},
   author =  {Chaves-de-Plaza, Nicolas F. and Mody, Prerak and Hildebrandt, Klaus  and Staring, Marius and Astreinidou, Eleftheria and de Ridder, Mischa and de Ridder, Huib and Vilanova, Anna and van Egmond, Rene},
   journal = {Cognition, Technology & Work},
-  volume =  {},
-  pages =   {},
-  month =   {},
+  volume =  {27},
+  pages =   {41 -- 57},
+  month =   {June},
   year =    {2025},
   pdf =     {2025_j_CTW.pdf},
   html =    {https://doi.org/10.1007/s10111-024-00784-4},

_bibliography/papers_conf.bib

@@ -3,6 +3,44 @@
 
 @string{aps = {American Physical Society,}}
 
+@inproceedings{Lyu:2025b,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Lyu, Donghang and Staring, Marius and Lamb, Hildo and Doneva, Mariya},
+  title =     {CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction},
+  booktitle = {CMRxRecon2025 challenge at STACOM, MICCAI},
+  address =   {Daejeon, South Korea},
+  series =    {Lecture Notes in Computer Science},
+  volume =    {},
+  pages =     {},
+  month =     {September},
+  year =      {2025},
+  pdf =       {2025_c_STACOMb.pdf},
+  html =      {},
+  arxiv =     {},
+  code =      {},
+  abstract =  {In recent years, deep learning has attracted increasing attention in the field of Cardiac MRI (CMR) reconstruction due to its superior performance over traditional methods, particularly in handling higher acceleration factors, highlighting its potential for real-world clinical applications. However, current deep learning methods remain limited in generalizability. CMR scans exhibit wide variability in image contrast, sampling patterns, scanner vendors, anatomical structures, and disease types. Most existing models are designed to handle only a single or narrow subset of these variations, leading to performance degradation when faced with distribution shifts. Therefore, it is beneficial to develop a unified model capable of generalizing across diverse CMR scenarios. To this end, we propose CRUNet-MR-Univ, a foundation model that leverages spatio-temporal correlations and prompt-based priors to effectively handle the full diversity of CMR scans. Our approach consistently outperforms baseline methods across a wide range of settings, highlighting its effectiveness and promise.},
+}
+
+@inproceedings{Lyu:2025a,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Lyu, Donghang and Staring, Marius and Doneva, Mariya and Lamb, Hildo and Pezzotti, Nicola},
+  title =     {KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction},
+  booktitle = {Statistical Atlases and Computational Modeling of the Heart (STACOM) at MICCAI},
+  address =   {Daejeon, South Korea},
+  series =    {Lecture Notes in Computer Science},
+  volume =    {},
+  pages =     {},
+  month =     {September},
+  year =      {2025},
+  pdf =       {2025_c_STACOMa.pdf},
+  html =      {},
+  arxiv =     {2508.12147},
+  code =      {},
+  abstract =  {Cardiac Magnetic Resonance (CMR) imaging is a non-invasive method for assessing cardiac structure, function, and blood flow. Cine MRI extends this by capturing heart motion, providing detailed insights into cardiac mechanics. To reduce scan time and breath-hold discomfort, fast acquisition techniques have been utilized at the cost of lowering image quality. Recently, Implicit Neural Representation (INR) methods have shown promise in unsupervised reconstruction by learning coordinate-to-value mappings from undersampled data, enabling high quality image recovery. However, current existing INR methods primarily focus on using coordinate-based positional embeddings to learn the mapping, while overlooking the feature representations of the target point and its neighboring context. In this work, we propose KP-INR, a dual-branch INR method operating in k-space for cardiac cine MRI reconstruction: one branch processes the positional embedding of k-space coordinates, while the other learns from local multi-scale k-space feature representations at those coordinates. By enabling cross-branch interaction and approximating the target k-space values from both branches, KP-INR can achieve strong performance on challenging Cartesian k-space data. Experiments on the CMRxRecon2024 dataset confirms its improved performance over baseline models and highlights its potential in this field.},
+}
+
 @inproceedings{Gao:2025,
   abbr =    {},
   bibtex_show = {true},