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01-03-2020 | Computed Tomography | Urogenital

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

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

Published in: European Radiology | Issue 3/2020

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.

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Title: Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study
Authors: Nils Große Hokamp
Simon Lennartz
Johannes Salem
Daniel Pinto dos Santos
Axel Heidenreich
David Maintz
Stefan Haneder
Publication date: 01-03-2020
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Artificial Intelligence
Xanthine Alkaloid
Published in: European Radiology / Issue 3/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06455-7

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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