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Accurate Measurement of Small Features in X‐Ray CT Data Volumes, Demonstrated Using Gold Grains
Author(s) -
Ketcham R. A.,
Mote A. S.
Publication year - 2019
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2018jb017083
Subject(s) - voxel , partial volume , calipers , volume (thermodynamics) , resolution (logic) , range (aeronautics) , orientation (vector space) , image resolution , point (geometry) , data set , scanner , computer science , mathematics , algorithm , materials science , artificial intelligence , geometry , physics , quantum mechanics , composite material
We present a method for measuring small, discrete features near the resolution limit of X‐ray computed tomography (CT) data volumes with the aim of providing consistent answers across instruments and data resolutions. The appearances of small features are impacted by the partial volume effect and blurring due to the data point‐spread function, and we call our approach the partial‐volume and blurring (PVB) method. Features are segmented to encompass their total attenuation signal, which is then converted to a volume, in turn allowing a subset of voxels to be used to measure shape and orientation. We demonstrate the method on a set of gold grains, scanned with two instruments at a range of resolutions and with various surrounding media. We recover volume accurately over a factor of 27 range in grain volume and factor of 5 range in data resolution, successfully characterizing particles as small as 5.4 voxels in true volume. Shape metrics are affected variably by resolution effects and are more reliable when based on image‐based caliper measurements than perimeter length or surface area. Orientations are reproducible when maximum or minimum axis lengths are sufficiently different from the intermediate axis. Calibration requires end‐member CT numbers for the materials of interest, which we obtained empirically; we describe a first‐principles calculation and discuss its challenges. The PVB method is accurate, reproducible, resolution invariant, and objective, all important improvements over any method based on global thresholds.