Low-Dose CT Images of Healthy Cohort (Healthy-Total-Body-CTs)

This data set includes low-dose whole body CT images and tissue segmentations of thirty healthy adult research participants who underwent PET/CT imaging on the uEXPLORER total-body PET/CT system at UC Davis. Participants included in this study were healthy adults, 18 years of age or older, who were able to provide informed written consent. The participants' age, gender, weight, height, and body mass index are also provided.
Fifteen participants underwent PET/CT imaging at three timepoints during a 3-hour period (0 minutes, 90 minutes, and 180 minutes) after PET radiotracer injection, while the remaining 15 participants were imaged at six timepoints during a 12-hour period (additionally at 360 minutes, 540 minutes, and 720 minutes). The imaging timepoint is indicated in the Series Description DICOM tag, with a value of either 'dyn', '90min', '3hr', '6hr', '9hr', or '12hr', corresponding to the delay after PET tracer injection. CT images were acquired immediately before PET image acquisition. Currently, only CT images are included in the data set from either three or six timepoints. The tissue segmentations include 37 tissues consisting of 13 abdominal organs, 20 different bones, subcutaneous and visceral fat, skeletal and psoas muscle. Segmentations were automatically generated using MOOSE, an AI segmentation tool for whole body data. The segmentations are provided in NIFTI format and may need to be re-oriented to correctly match the CT image data in DICOM format.

本数据集包含30名健康成年研究受试者的低剂量全身CT图像与组织分割结果，所有受试者均在加州大学戴维斯分校（UC Davis）的uEXPLORER全身正电子发射断层扫描/计算机断层扫描（PET/CT）系统上完成了PET/CT成像。本研究纳入的受试者为年满18周岁且能够签署书面知情同意书的健康成年人，同时提供了受试者的年龄、性别、体重、身高及体质量指数信息。

其中15名受试者在PET放射性示踪剂注射后的3小时周期内（0分钟、90分钟、180分钟）的三个时间点接受PET/CT成像，剩余15名受试者则在12小时周期内的六个时间点接受成像（额外包含360分钟、540分钟及720分钟三个时间点）。成像时间点通过DICOM标签中的序列描述（Series Description）字段标识，其取值为'dyn'、'90min'、'3hr'、'6hr'、'9hr'或'12hr'，分别对应PET示踪剂注射后的延迟时长。CT图像均在PET图像采集前即刻获取。目前本数据集仅收录了三个或六个时间点的CT图像。

本次组织分割涵盖37种组织类型，包括13个腹部器官、20种不同骨骼、皮下与内脏脂肪、骨骼肌及腰大肌。分割结果由MOOSE——一款专为全身数据开发的AI分割工具——自动生成，以NIFTI格式提供，可能需要重新调整方位以与DICOM格式的CT图像实现精准匹配。