Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases.

Published

Journal Article

PURPOSE: 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]. ACQUISITION AND VALIDATION METHODS: 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. DATA FORMAT AND USAGE NOTES: 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. POTENTIAL APPLICATIONS: 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.

Full Text

Duke Authors

Cited Authors

  • Robins, M; Solomon, J; Koweek, LMH; Christensen, J; Samei, E

Published Date

  • April 2019

Published In

Volume / Issue

  • 46 / 4

Start / End Page

  • 1931 - 1937

PubMed ID

  • 30703259

Pubmed Central ID

  • 30703259

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1002/mp.13412

Language

  • eng

Conference Location

  • United States