Universität zu Köln

Houbois, Christian, Haneder, Stefan, Merkt, Martin, Holz, Jasmin A., Morelli, John, Kiel, Alexandra, Doerner, Jonas, Maintz, David and Puesken, Michael (2020). Semiautomated Renal Cortex Volumetry in Multislice Computed Tomography: Effect of Slice Thickness and Iterative Reconstruction Algorithms. J. Comput. Assist. Tomogr., 44 (2). S. 236 - 242. PHILADELPHIA: LIPPINCOTT WILLIAMS & WILKINS. ISSN 1532-3145

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Abstract

Objective The aim of the study was to evaluate the effect of slice thickness, iterative reconstruction (IR) algorithm, and kernel selection on measurement accuracy and interobserver variability for semiautomated renal cortex volumetry (RCV) with multislice computed tomography (CT). Methods Ten patients (62.4 +/- 17.2 years) undergoing abdominal biphasic multislice computed tomography were enrolled in this retrospective study. Computed tomography data sets were reconstructed at 1-, 2-, and 5-mm slice thickness with 2 different IR algorithms (iDose, IMRST) and 2 different kernels (IMRS and IMRR) (Philips, the Netherlands). Two readers independently performed semiautomated RCV for each reconstructed data set to calculate left kidney volume (LKV) and split renal function (SRF). Statistics were calculated using analysis of variance with Geisser-Greenhouse correction, followed by Tukey multiple comparisons post hoc test. Statistical significance was defined as P <= 0.05. Results Semiautomated RCV of 120 data sets (240 kidneys) was successfully performed by both readers. Semiautomated RCV provides comparable results for LKV and SRF with 3 different slice thicknesses, 2 different IR algorithms, and 2 different kernels. Only the 1-mm slice thickness showed significant differences for LKV between IMRR and IMRS (P = 0.02, mean difference = 4.28 bb) and IMRST versus IMRS (P = 0.02, mean difference = 4.68 cm(3)) for reader 2. Interobserver variability was low between both readers irrespective of slice thickness and reconstruction algorithm (0.82 >= P >= 0.99). Conclusions Semiautomated RCV measurements of LKV and SRF are independent of slice thickness, IR algorithm, and kernel selection. These findings suggest that comparisons between studies using different slice thicknesses and reconstruction algorithms for RCV are valid.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Houbois, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Haneder, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Merkt, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Holz, Jasmin A. UNSPECIFIED UNSPECIFIED UNSPECIFIED Morelli, John UNSPECIFIED UNSPECIFIED UNSPECIFIED Kiel, Alexandra UNSPECIFIED UNSPECIFIED UNSPECIFIED Doerner, Jonas UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Puesken, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-342326
DOI:	10.1097/RCT.0000000000000988
Journal or Publication Title:	J. Comput. Assist. Tomogr.
Volume:	44
Number:	2
Page Range:	S. 236 - 242
Date:	2020
Publisher:	LIPPINCOTT WILLIAMS & WILKINS
Place of Publication:	PHILADELPHIA
ISSN:	1532-3145
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PULMONARY NODULES; AUTOMATED VOLUMETRY; NUCLEAR-RENOGRAPHY; CT VOLUMETRY; SEGMENTATION; PREDICTION; DONORS Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34232