try10

Author: mstaring

_bibliography/papers.bib

@@ -38,7 +38,24 @@ @article{Malimban2025
   abstract = {<b>Objective:</b> The integration of proton beamlines with X-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and  tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.<br><b>Approach:</b> The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.<br><b>Main Results:</b> The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90kV/Cu) and DECT (50kV/Al-90kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.<br><b>Significance:</b> The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.},
 }
 
-@article{Jia2024,
+@article{Jia2024b,
+  abbr =    {},
+  bibtex_show = {true},
+  title =   {Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts},
+  author =  {Jia, Jingnan },
+  journal = {Scientific Reports},
+  volume =  {14},
+  pages =   {26666},
+  month =   {},
+  year =    {2024},
+  pdf =     {2024_j_SR.pdf},
+  html =    {https://doi.org/10.1038/s41598-024-78393-4},
+  arxiv =   {},
+  code =    {},
+  abstract = {},
+}
+
+@article{Jia2024a,
   abbr =     {},
   bibtex_show = {true},
   title =   {Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT},