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Erschienen in: European Radiology 3/2020

26.11.2019 | Urogenital

Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study

verfasst von: Nils Große Hokamp, Simon Lennartz, Johannes Salem, Daniel Pinto dos Santos, Axel Heidenreich, David Maintz, Stefan Haneder

Erschienen in: European Radiology | Ausgabe 3/2020

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Abstract

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 either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.

Results

Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1–90.4%.

Conclusions

Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.

Key Points

• Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition.
• Ex-vivo study demonstrates the dose independent assessment of pure and compound stones.
• Lowest accuracy is reported for compound stones with struvite as main component.
Literatur
4.
Zurück zum Zitat Pearle MS, Goldfarb DS, Assimos DG et al (2014) Medical management of kidney stones: AUA Guidelines 1–26 Pearle MS, Goldfarb DS, Assimos DG et al (2014) Medical management of kidney stones: AUA Guidelines 1–26
32.
Metadaten
Titel
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study
verfasst von
Nils Große Hokamp
Simon Lennartz
Johannes Salem
Daniel Pinto dos Santos
Axel Heidenreich
David Maintz
Stefan Haneder
Publikationsdatum
26.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 3/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06455-7

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