Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA.
Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA.
Med Phys. 2019 Apr;46(4):1931-1937. doi: 10.1002/mp.13412. Epub 2019 Feb 19.
To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies [e.g., computer aided detection (CAD) and segmentation algorithms].
This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams. One image series per patient exam was utilized based on coverage of the anatomical region of interest (either the thorax or abdomen). All image series were de-identified. Simulated lesions were derived from a library of anatomically informed digital lesions (93 lung and 50 liver lesions) where six and four digital lesions with nominal diameters ranging from 4 to 20 mm were inserted into lung and liver image series, respectively. Locations for lesion insertion were randomly chosen. A previously validated lesion simulation and virtual insertion technique were utilized. The resulting hybrid images were reviewed by three experienced radiologists to assure similarity with routine clinical imaging in a diverse adult population.
The database is composed of four datasets that contain 100 patient cases each, for a total of 400 image series accompanied by Matlab.mat tables that provide descriptive information about the virtually inserted lesions (i.e., size, shape, opacity, and insertion location in physical (world) coordinates and voxel indices). All image and metadata are stored in DICOM format on the Quantitative Imaging Data Warehouse (https://qidw.rsna.org/#collection/57d463471cac0a4ec8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login), in two sets: (a) QIBA CT Hybrid Dataset I which contains Lung I and Liver I datasets, and (b) QIBA CT Hybrid Dataset II which contains Lung II and Liver II datasets. The QIDW is supported by the Radiological Society of North America (RSNA). Registration is required upon initial log in.
By simulating lesion opacity (full solid, part solid and ground glass), size, and texture, the relationship between lesion morphology and segmentation or CAD algorithm performance can be investigated without the need for repetitive patient exams. This database can also serve as a reference standard for device and reader performance studies.
为医学成像社区提供一个由混合数据集（带有数字插入的人体病变的患者 CT 图像）组成的计算机断层扫描（CT）图像数据库，其中事先知道病变的真实情况。可以设想，这样的数据集可以成为评估 CT 图像质量、机器学习和成像技术（例如计算机辅助检测（CAD）和分割算法）的资源。
这项符合 HIPAA 规定且获得 IRB 豁免批准的研究包括使用 120 例胸部和 100 例腹部临床采集的成人 CT 检查。基于感兴趣的解剖区域（胸部或腹部）的覆盖范围，每位患者检查使用一个图像系列。所有图像系列均进行去识别处理。模拟病变源自具有解剖学信息的数字病变库（93 个肺病变和 50 个肝病变），其中六个和四个具有标称直径范围为 4 至 20 毫米的数字病变分别插入到肺和肝图像系列中。病变插入的位置是随机选择的。使用了经过验证的病变模拟和虚拟插入技术。三位有经验的放射科医生对生成的混合图像进行了审查，以确保其在不同的成年人群体中的常规临床成像中具有相似性。
该数据库由包含 100 例患者病例的四个数据集组成，总共有 400 个图像系列，并附有 Matlab.mat 表，其中提供了有关虚拟插入病变的描述性信息（即大小、形状、不透明度以及在物理（世界）坐标和体素索引中的插入位置）。所有图像和元数据均以 DICOM 格式存储在定量成像数据仓库（https://qidw.rsna.org/#collection/57d463471cac0a4ec8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login）中，分为两组：（a）QIBA CT 混合数据集 I，其中包含 Lung I 和 Liver I 数据集，以及（b）QIBA CT 混合数据集 II，其中包含 Lung II 和 Liver II 数据集。QIDW 得到了北美放射学会（RSNA）的支持。初始登录时需要注册。
通过模拟病变的不透明度（全实性、部分实性和磨玻璃状）、大小和纹理，可以在不需要重复患者检查的情况下研究病变形态与分割或 CAD 算法性能之间的关系。该数据库还可以作为设备和阅读器性能研究的参考标准。
