Update README.md

README.md

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 - **SimNICT** is the first dataset for training **universal non-ideal measurement CT (NICT)** enhancement models.
 
-- The dataset comprises **over 10.9 million NICT-ICT image pairs**, including low dose CT (LDCT), sparse view CT (SVCT), and limited angle CT (LACT), under varying defect degrees across whole-body regions.
+- The dataset comprises **over 10.8 million NICT-ICT image pairs**, including low dose CT (LDCT), sparse view CT (SVCT), and limited angle CT (LACT), under varying defect degrees across whole-body regions.
 
 - We have currently uploaded part of the SimNICT dataset, [**SimNICT-AMOS-Sample**](#simnict-amos-sample), with preview images in the dataset viewer. The complete SimNICT dataset will be gradually uploaded in future releases.
 
@@ -83,18 +83,16 @@ configs:
 - [More Information Needed] -->
 
 # Ongoing
-- [ ] Release the SimNICT dataset containing 10.9 million NICT-ICT image pairs.
+- [ ] Release the SimNICT dataset containing 10.8 million NICT-ICT image pairs.
 - [x] Release the SimNICT-AMOS-Sample dataset, a subset of the SimNICT dataset.
 
 ## Citation
 ```
-@misc{liu2024imagingfoundationmodeluniversal,
-      title={Imaging foundation model for universal enhancement of non-ideal measurement CT}, 
-      author={Yuxin Liu and Rongjun Ge and Yuting He and Zhan Wu and Chenyu You and Shuo Li and Yang Chen},
-      year={2024},
-      eprint={2410.01591},
-      archivePrefix={arXiv},
-      url={https://arxiv.org/abs/2410.01591}, 
+@article{liu2024imaging,
+        title={Imaging foundation model for universal enhancement of non-ideal measurement ct},
+        author={Liu, Yuxin and Ge, Rongjun and He, Yuting and Wu, Zhan and Yang, Shangwen and Gao, Yuan and You, Chenyu and Wang, Ge and Chen, Yang and Li, Shuo},
+        journal={arXiv preprint arXiv:2410.01591},
+        year={2024}
 }
 ```
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->