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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]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
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  Data: <searchLink fieldCode="AR" term="%22Große+Hokamp%2C+Nils%22">Große Hokamp, Nils</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Nils.Grosse-Hokamp@uk-koeln.de</i><br /><searchLink fieldCode="AR" term="%22Lennartz%2C+Simon%22">Lennartz, Simon</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Salem%2C+Johannes%22">Salem, Johannes</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pinto+dos+Santos%2C+Daniel%22">Pinto dos Santos, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Heidenreich%2C+Axel%22">Heidenreich, Axel</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Maintz%2C+David%22">Maintz, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haneder%2C+Stefan%22">Haneder, Stefan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. 2020, Vol. 30 Issue 3, p1397-1404. 8p. 2 Diagrams, 3 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Cysteine%22">Cysteine</searchLink><br /><searchLink fieldCode="DE" term="%22Equipment+%26+supplies%22">Equipment & supplies</searchLink><br /><searchLink fieldCode="DE" term="%22Kidney+stones%22">Kidney stones</searchLink><br /><searchLink fieldCode="DE" term="%22Xanthine%22">Xanthine</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Acyclic+acids%22">Acyclic acids</searchLink><br /><searchLink fieldCode="DE" term="%22Uric+acid%22">Uric acid</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+phantoms%22">Imaging phantoms</searchLink><br /><searchLink fieldCode="DE" term="%22Phosphates%22">Phosphates</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Urinary+calculi%22">Urinary calculi</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: <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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1007/s00330-019-06455-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 1397
    Subjects:
      – SubjectFull: Cysteine
        Type: general
      – SubjectFull: Equipment & supplies
        Type: general
      – SubjectFull: Kidney stones
        Type: general
      – SubjectFull: Xanthine
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      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Computed tomography
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      – SubjectFull: Acyclic acids
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      – SubjectFull: Uric acid
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      – SubjectFull: Phosphates
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      – SubjectFull: Urinary calculi
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      – TitleFull: Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
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              Text: 2020
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