There currently is a dearth of phantom scans on large samples. This data collection contains one physical phantom, imaged across three protocols, on 100 scanners. This provides population data that can be used to quantify inter-scanner variability. This data can be used to determine how robust specific radiomics or other quantitative imaging signatures are.
Computed tomography scans were acquired...
These retrospective NIfTI image datasets consists of unenhanced chest CTs:
The initial images for both datasets were acquired at the point of care in an outbreak setting from patients...
The National CT Colonography Trial (ACRIN 6664) collection contains 825 cases of CT colonography imaging with accompanying spreadsheets that provide polyp descriptions and their location within the colon segments. Additional information about the trial is available in the
This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed...
This collection consists of 17 CT scans of the Credence Cartridge Radiomics (CCR) phantom, which was designed for use in studies of texture feature robustness. The scans were acquired at four medical centers using each center’s chest protocol and were taken using GE (7 scans), Philips (5 scans), Siemens (2 scans), and Toshiba (3 scans) scanners. The CCR phantom has 10 cartridges, each with a unique texture, Fig 1....
Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.
Train set composition:
Total subjects:...
Radiology imaging is playing an increasingly vital role in the diagnosis of COVID-19 patients and determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses. All too frequently rural populations are underrepresented in such public...
