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| 4984.
 | Pancreatic Fat Quantification in Human Diabetes and Its Relationship with β-Cell Reduction Na Zhang1, Dingxia Liu2, Caixia Fu3, Jiaxin Zhang1, Ruonan Zhang1, Ke Hu1, Yumei Yang1, Baomin Wang1, Jing Ma1, Yi Chen1, Runyue Yang1, Xiuzhong Yao2, and Xiaomu Li1
1Department of Endocrinology and Metabolism, Zhongshan Hospital, Shanghai, China, 2Department of Radiology, Zhongshan Hospital, Shanghai, China, 3Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China Keywords: Radiomics, Endocrine, Data acquisition, diabetes, metabolism, pancreas Motivation: Patients with type 2 diabetes have more pancreatic fat, but its impact on β-cells is unclear. Goal(s): Our goal is to explore the impact of pancreatic fat on β-cells. Approach: We employed 6-point Dixon MRI to confirm the pancreatic fat fraction (PFF) in patients with type 2 diabetes. Histologic analysis was conducted to validate our findings and immunohistochemical staining was performed to observe changes in β-cells. Results: We found a negative relationship between β-cell mass and PFF in patients with type 2 diabetes. This suggested that the increased pancreatic fat observed in type 2 diabetes might contribute to reducing the number of β-cells. Impact: This study provided evidence that increased pancreatic fat in type 2 diabetes was associated with a decreased number of β-cells. These findings suggested targeting pancreatic fat accumulation could be a potential therapeutic approach for improving glycemic control in diabetic patients. |
| 4985.
 | Validation of U-Net models for direct EPI Segmentation of brain MRI: towards faster and accurate diffusion Analysis Yu-Chen Liao1, Teng-Yi Huang1, Jia-Xiu Chen1, and Jui-Jung Yu1
1National Taiwan University of Science and Technology, Taipei City, Taiwan Keywords: Segmentation, Brain Motivation: Brain sub-region segmentation from MRI scans aids in detailed structural analysis. We attempt to directly segment EPI to simplify diffusion metric analysis, potentially allowing for swift regional analysis of diffusion metrics. Goal(s): Our primary goal is to develop deep learning U-Net models for EPI segmentation, aiming to circumvent the necessity for T1 images and to simplify the segmentation workflow. Approach: We collected 3182 datasets from public MRI databases, enhancing ground-truth labels through distortion correction methods. Results: The ASEG model achieves the highest Dice coefficient (0.709), reducing execution time significantly. Subsequent analyses show ASEG model's diffusion results correlate highly with conventional template registration. Impact: The results enhanced speed and precision in EPI segmentation, promising substantial advancements in clinical and research domains through rapid acquisition of brain structural information. The anticipated open-source availability of this methodology stands to greatly facilitate clinical research involving regional brain analysis. |
| 4986.
 | Evaluation of pituitary tumor texture using Synthetic MRI Luqiang Cao1, Weiyin Vivian Liu2, Lin Xu3, Wen Chen3, Yu Zhang4, and Zhongyan Xiao5
1Department of Radiology, Taihe Hospital, Hubei University of medicine, Shiyan, Hubei, China, 2GE Healthcare,MR Research, Beijing, China, 3Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China, 4Department of Nuclear Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China, 5Hubei University of Medicine, Shiyan, Hubei, China Keywords: fMRI Analysis, Quantitative Imaging Motivation: Surgery selection mainly relies on texture of pituitary adenomas. There is rarely a non-invasive imaging method to identify texture of pituitary adenomas that greatly determines surgical selection and outcomes. Goal(s): To explore the predictive performance of synthetic MRI in types of pituitary tumors. Approach: T1, T2 and PD values of every tumor were measured and divided into the solid and soft tumor groups according to the histology results of surgical samples. Results: Synthetic MRI-computed T2 and PD values were significantly higher in the soft pituitary tumor group than in the solid group (P<0.05) with cutoff values of 110.83 ms and 87.3 p.u., respectively. Impact: Both T2 and PD value can assist surgeons in determination of surgical treatments (e.g. transsphenoidal resection or craniotomy) and prediction of surgical outcomes, such as resection completeness, indicating synthetic MRI could a strong imaging marker. |