five

The added value of non-contrast 3-Tesla MRI for the pre-operative localization of hyperparathyroidism|医学影像数据集|甲状旁腺疾病数据集

收藏
DataCite Commons2023-01-21 更新2024-08-18 收录
医学影像
甲状旁腺疾病
下载链接:
https://scielo.figshare.com/articles/dataset/The_added_value_of_non-contrast_3-Tesla_MRI_for_the_pre-operative_localization_of_hyperparathyroidism/21936684
下载链接
链接失效反馈
资源简介:
Abstract Objective: We investigated the efficacy of non-contrast 3-Tesla MR imaging added to the combination of sestamibi with99mTc (MIBI) scintigraphy and Ultrasonography (US) for the pre-operative localization of Primary Hyperparathyroidism (PHPT) lesions. Methods: A total of 34 parathyroid glands, including nine normal glands, were examined with MIBI, US, and non-contrast 3-Tesla MRI. MRI was performed with the acquisition of T1- and T2-weighted images and fat-suppressed T2-weighted images. We calculated the sensitivities of MIBI, US, and the ‛additional’ MRI, with knowledge of the former two modalities’ results. Results: For the diagnosis of PHPT lesions, the sensitivity values of MIBI, US, and additional MRI were 88.0% (22/25), 84.0% (21/25), and 92.0% (23/25), respectively. Normal glands were not visualized with any modality (0/9). One lesion was detected neither with US nor MRI, but only with MIBI, with the limitation that MIBI represented no more than laterality. The two glands not identified in MRI were 4 mm and 6 mm in their size, which are within the range of normal gland’s size. Two lesions were not detected with US or MIBI but were visualized with the additional MRI, which indicated that the MRI contributed an 8.0% (2/25) improvement of sensitivity, compared from that of US. Fat-suppressed T2-weighted images were useful in the identification of parathyroid lesions, as these images helped to differentiate between the lesion and the adjacent tissue. Conclusion: Additional non-contrast 3-Tesla MRI was a useful adjunctive tool for localization of PHPT, which improved the sensitivity of the pre-operative localization of PHPT lesions. Fatsuppressed T2-weighted images contributed to their identification. Level VI: Evidence from a single descriptive or qualitative study.
提供机构:
SciELO journals
创建时间:
2023-01-21
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

rag-datasets/rag-mini-bioasq

该数据集主要用于问答和句子相似性任务,涉及生物医学领域。数据集包含两个配置:text-corpus和question-answer-passages,分别对应不同的数据文件路径。数据集来源于BioASQ任务11b的训练数据集,并通过`generate.py`脚本生成了子集。

hugging_face 收录

Natural Scene Braille Character Recognition Dataset

There are a total of 1157 Braille segment images in this dataset, including 925 in the training set and 232 in the testing set. There are two folders in the directory of this dataset: character_label and segment_label. The character_rabel file contains three formats of Braille segment images: (1) Braille segment images and label files stored in ICDAR-2015 format, each. jpg file corresponds to a. txt file, where each line stores the position and recognition label of a braille character rectangle box. The data corresponds to the coordinates of the four points in the rectangle box and the recognized numerical label; (2) The original format of the data is stored in the folder org. Each .jpg file in this folder corresponds to a .json file which marked by labelme software; (3) VOC format, stored in voc-data folder. This folder stores images and corresponding .xml files in VOC format, and marks the position of each braille character rectangle box and its corresponding numerical label information in the .xml file. In addition, the original Braille images of natural scenes and the corresponding Braille segment markings .json files are stored in the folder segment_label.

DataCite Commons 收录

Breast Cancer Dataset

该项目专注于清理和转换一个乳腺癌数据集,该数据集最初由卢布尔雅那大学医学中心肿瘤研究所获得。目标是通过应用各种数据转换技术(如分类、编码和二值化)来创建一个可以由数据科学团队用于未来分析的精炼数据集。

github 收录

Beijing Traffic

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

Papers with Code 收录

长江干流实时水位观测数据集(2024年)

该数据集为长江干流主要水文站实时水位观测数据集,包含了汉口、户口、九江、宜昌等16个水文站点的逐小时或逐日水位观测数据。 该数据集包含3个excel表格文件,长江干流站点.xls,逐日水位.xlsx,逐小时水位.xlsx。

国家地球系统科学数据中心 收录