A cone-beam X-ray computed tomography data collection designed for machine learning.

ABSTRACT: Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scrip

SUBMITTER: Der Sarkissian H 

PROVIDER: S-EPMC6805882 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature