Universität zu Köln

Hokamp, Nils Grosse, Lennartz, Simon, Salem, Johannes, dos Santos, Daniel Pinto, Heidenreich, Axel, Maintz, David and Haneder, Stefan (2020). Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study. Eur. Radiol., 30 (3). S. 1397 - 1405. NEW YORK: SPRINGER. ISSN 1432-1084

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Hokamp, Nils Grosse UNSPECIFIED UNSPECIFIED UNSPECIFIED Lennartz, Simon UNSPECIFIED UNSPECIFIED UNSPECIFIED Salem, Johannes UNSPECIFIED UNSPECIFIED UNSPECIFIED dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Heidenreich, Axel UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Haneder, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-343137
DOI:	10.1007/s00330-019-06455-7
Journal or Publication Title:	Eur. Radiol.
Volume:	30
Number:	3
Page Range:	S. 1397 - 1405
Date:	2020
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-1084
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language URIC-ACID; URINARY CALCULI; CT; DIFFERENTIATION; FEASIBILITY; UROLITHIASIS; ACCURACY Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34313