Low-dose CT image and projection dataset

Taylor R. Moen, Baiyu Chen, David R. Holmes, Xinhui Duan, Zhicong Yu, Lifeng Yu, Shuai Leng, Joel G. Fletcher, Cynthia H. McCollough

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods: The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes: Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). Potential Applications: This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.

Original languageEnglish (US)
Pages (from-to)902-911
Number of pages10
JournalMedical physics
Volume48
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • CT projection data
  • iterative reconstruction
  • low-dose CT
  • machine learning
  • patient data

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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