new paper

Author: mstaring

_bibliography/papers.bib

@@ -3,6 +3,22 @@
 
 @string{aps = {American Physical Society,}}
 
+@article{Chen:2025,
+  abbr =    {},
+  bibtex_show = {true},
+  author =   {Chen, Yunjie and Weber, Rianne and Neve, Olaf M. and Romeijn, Stephan R. and Hensen, Erik F. and Wolterink, Jelmer M. and Tao, Qian and Staring, Marius and Verbist, Berit M.},
+  title =    {A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern},
+  journal =  {European Radiology},
+  volume =   {},
+  pages =    {},
+  year =     {2025},
+  pdf =      {2025_j_ER.pdf},
+  html =     {},
+  arxiv =    {},
+  code =     {},
+  abstract = {<b>Objectives:</b> To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern.<br><b>Materials and methods:</b> In this multi-center retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization.<br><b>Results:</b> 203 MRI studies from 72 VS patients (mean age, 58.51 ± 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 ± 0.113 to 0.993 ± 0.009, and the peak signal-to-noise ratio increased from 21.6 ± 3.73 dB to 41.4 ± 4.84 dB. At 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent image quality (3.09 ± 0.811 and 3.23 ± 0.685), with the latter being considered more informative (3.81 ± 0.664).<br><b>Conclusion:</b> The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10% - 30% of the standard dose.<br><b>Key points</b><br><b>Question</b> Deep learning models that aid in the reduction of contrast agent dose are not extensively evaluated for MRI of the cerebellopontine angle cistern.<br><b>Finding</b> Deep learning models restored the low-dose MRI of the cerebellopontine angle cistern, yielding images sufficient for vestibular schwannoma diagnosis and management.<br><b>Clinical relevance statement</b> Deep learning models make it possible to reduce the use of gadolinium-based contrast agents for contrast-enhanced MRI of the cerebellopontine angle cistern.},
+}
+
 @article{VanDerValk:2025,
   abbr =     {},
   bibtex_show = {true},