added some papers

Author: mstaring

_bibliography/papers.bib

@@ -3,6 +3,23 @@
 
 @string{aps = {American Physical Society,}}
 
+@article{Verheijen2025,
+  abbr =     {},
+  bibtex_show = {true},
+  title =   {Artificial Intelligence for Segmentation and Classification in Lumbar Spinal Stenosis: an overview of current methods},
+  author =  {Verheijen, E.J.A. and Kapogiannis, T. and Munteh, D. and Chabros, J. and Staring, M. and Smith, T.R. and Vleggeert-Lankamp, C.L.A.},
+  journal = {European Spine Journal},
+  volume =  {},
+  pages =   {},
+  month =   {},
+  year =    {2025},
+  pdf =     {2025_j_ESJ.pdf},
+  html =    {https://doi.org/10.1007/s00586-025-08672-9},
+  arxiv =   {},
+  code =    {},
+  abstract = {<b>Purpose:</b> Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking. Machine Learning (ML) has the potential to aid physicians in this process by automating segmentation and classification of LSS. However, it is unclear what models currently exist to perform these tasks.<br><b>Methods:</b> A systematic review of literature was performed by searching the Cochrane Library, Embase, Emcare, PubMed, and Web of Science databases for studies describing an ML-based algorithm to perform segmentation or classification of the lumbar spine for LSS. Risk of bias was assessed through an adjusted version of the Newcastle-Ottawa Quality Assessment Scale that was more applicable to ML studies. Qualitative analyses were performed based on type of algorithm (conventional ML or Deep Learning (DL)) and task (segmentation or classification).<br><b>Results:</b> A total of 27 articles were included of which nine on segmentation, 16 on classification and 2 on both tasks. The majority of studies focused on algorithms for MRI analysis. There was wide variety among the outcome measures used to express model performance. Overall, ML algorithms are able to perform segmentation and classification tasks excellently. DL methods tend to demonstrate better performance than conventional ML models. For segmentation the best performing DL models were U-Net based. For classification U-Net and unspecified CNNs powered the models that performed the best for the majority of outcome metrics. The number of models with external validation was limited.<br><b>Conclusion:</b> DL models achieve excellent performance for segmentation and classification tasks for LSS, outperforming conventional ML algorithms. However, comparisons between studies are challenging due to the variety in outcome measures and test datasets. Future studies should focus on the segmentation task using DL models and utilize a standardized set of outcome measures and publicly available test dataset to express model performance. In addition, these models need to be externally validated to assess generalizability.},
+}
+
 @article{Rezaei2025,
   abbr =     {},
   bibtex_show = {true},

_bibliography/papers_abstracts.bib

@@ -1,4 +1,79 @@
 
+@inproceedings{Jabarimani:2025a,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Jabarimani, Navid and Ercan, Ece and Dong, Yiming and Pezzotti, Nicola and Webb, Andrew and B{\"o}rnert, Peter and Staring, Marius and van Osch, Matthias J.P. and Nagtegaal, Martijn},
+  title =     {Characterizing differences between white and gray matter T1W-based segmentations at 0.6T and 1.5T},
+  booktitle = {International Society for Magnetic Resonance in Medicine},
+  month =     {May},
+  year =      {2025},
+  pdf =       {},
+  html =      {},
+  arxiv =     {},
+  code =      {},
+  abstract =  {Mid field MR scanners (0.1T-1T) are gaining increasing attention in the last few years (Lavrova et al. (2024), (Campbell Washburn et al., 2019) due to increased safety and lower manufacturing and maintenance cost and consequently improving the accessibility of MRI for clinical purposes (Arnold et al.,
+2023). The reduced field strength leads to a reduced signal and change in T1 relaxation times and therefore different contrast and signal to noise as 1.5T and 3T systems.<br>MRI is often used for diagnosing and monitoring brain tumors, lesions, and disorders such as neurodegenerative diseases. Especially for the last afflictions gray matter (GM) and white matter (WM) volume are interesting markers due to the atrophy associated with these diseases. Automatic segmentation models including Adaptive Maximum A Posteriori (MAP) segmentation (Rajapakse et al., 1997) and partial volume estimation (PVE) (Tohka et al., 2004) are well established models that are used by applications such as CAT12 to estimate GM and WM volumes.<br>In this work we study the quality of T1 weighted based brain segmentations at 0.6T in the context of brain volume measurements in healthy volunteers. We compare segmentations from 0.6T and 1.5T and study how noise suppression by deep learning based reconstruction as provided by the vendor might affect these.},
+}
+
+@inproceedings{Jabarimani:2025b,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Jabarimani, Navid and Rao, Chinmay and Ercan, Ece and Dong, Yiming and Pezzotti, Nicola and Doneva, Mariya and de Weerdt, Elwin and van Osch, Matthias J.P. and Staring, Marius and Nagtegaal, Martijn},
+  title =     {Accelerated FLAIR imaging at 0.6T using T2w-guided multi-contrast deep learning-based reconstruction using a Zero-Shot approach},
+  booktitle = {International Society for Magnetic Resonance in Medicine},
+  month =     {May},
+  year =      {2025},
+  pdf =       {},
+  html =      {},
+  arxiv =     {},
+  code =      {},
+  abstract =  {Fluid-Attenuated Inversion Recovery (FLAIR) images are an important part of clinical brain protocols, especially due to the excellent contrast for diagnosing lesions, edema, etc. (Campbell-Washburn et al., 2019). Mid field MR scanners (0.1T-1T) provide a more accessible and affordable option for clinical use compared to high-field scanners (Arnold et al., 2023). However, lower field strength often produces lower-quality FLAIR images due to longer T1 relaxation time and lower signal to noise ratio (Lavrova et al., 2024). Moreover, from our initial tests with a 0.6T MRI-scanner, we noticed that the relative drop in quality of these images compared to 1.5T was much higher for FLAIR than for example T2W images, which could limit their diagnostic value.<br>This project aims to address this issue by applying a content/style-based plug-and-play reconstruction framework PnP-MUNIT (Rao et al., 2024) to guide the reconstruction of FLAIR images using information from T2-weighted scans, with the goal of improving image quality and reducing the scan time. In this work we assess this concept using data from a 3T scanner and adapt it for 0.6T data, applying the content/style model to the lower field strength in a zero-shot manner without any fine-tuning.},
+}
+
+@inproceedings{Gonzalez:2025,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Gonz{\'a}lez-Cebri{\'a}n, Alba and Garc{\'i}a-Crist{\'o}bal, Pablo and Galve, Fernando and Van Der Valk, Viktor and Ilicak, Efe and Staring, Marius and Webb, Andrew and Alonso, Joseba},
+  title =     {An Imageless Magnetic Resonance Diagnosis procedure for fast and affordable screening and follow-up},
+  booktitle = {International Society for Magnetic Resonance in Medicine},
+  month =     {May},
+  year =      {2025},
+  pdf =       {},
+  html =      {},
+  arxiv =     {},
+  code =      {},
+  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,
+  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},
+  title =     {Free-Breathing Functional Lung Imaging at 0.6T compared to 1.5T},
+  booktitle = {International Society for Magnetic Resonance in Medicine},
+  month =     {May},
+  year =      {2025},
+  pdf =       {},
+  html =      {},
+  arxiv =     {},
+  code =      {},
+  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 =    {},