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

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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: Große Hokamp, Nils1 (AUTHOR) Nils.Grosse-Hokamp@uk-koeln.de, Lennartz, Simon1,2 (AUTHOR), Salem, Johannes3 (AUTHOR), Pinto dos Santos, Daniel1 (AUTHOR), Heidenreich, Axel3 (AUTHOR), Maintz, David1 (AUTHOR), Haneder, Stefan1 (AUTHOR)
Source: European Radiology. 2020, Vol. 30 Issue 3, p1397-1404. 8p. 2 Diagrams, 3 Charts.
Subjects: Cysteine, Equipment & supplies, Kidney stones, Xanthine, Research funding, Computed tomography, Acyclic acids, Uric acid, Imaging phantoms, Phosphates, Algorithms, Urinary calculi
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. [ABSTRACT FROM AUTHOR]
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Abstract:<bold>Objectives: </bold>To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.<bold>Methods: </bold>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.<bold>Results: </bold>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%.<bold>Conclusions: </bold>Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.<bold>Key Points: </bold>• 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. [ABSTRACT FROM AUTHOR]
ISSN:09387994
DOI:10.1007/s00330-019-06455-7