Dose independent characterization of renal stones by means of dual energy computed tomography and ma...
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objectives
To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.
Methods
200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of e...
Alternative Titles
Full title
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2359145437
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2359145437
Other Identifiers
ISSN
0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-019-06455-7
