Universität zu Köln

Fervers, Philipp, Fervers, Florian, Weisthoff, Mathilda, Rinneburger, Miriam, Zopfs, David, Reimer, Robert Peter, Pahn, Gregor, Kottlors, Jonathan ORCID: 0000-0001-5021-6895, Maintz, David, Lennartz, Simon, Persigehl, Thorsten and Hokamp, Nils Grosse (2022). Dual-Energy CT, Virtual Non-Calcium Bone Marrow Imaging of the Spine: An AI-Assisted, Volumetric Evaluation of a Reference Cohort with 500 CT Scans. Diagnostics, 12 (3). BASEL: MDPI. ISSN 2075-4418

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Abstract

Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa bone marrow images of the spine in a large-scale cohort of healthy individuals. DECT was performed after resection of a malignant skin tumor without evidence of metastatic disease. Image analysis was fully automated and did not require specific user interaction. The thoracolumbar spine was segmented by a pretrained convolutional neuronal network. Volumetric VNCa data of the spine's bone marrow space were processed using the maximum, medium, and low calcium suppression indices. Histograms of VNCa attenuation were created for each exam and suppression setting. We included 500 exams of 168 individuals (88 female, patient age 61.0 +/- 15.9). A total of 8298 vertebrae were segmented. The attenuation histograms' overlap of two consecutive exams, as a measure for intraindividual consistency, yielded a median of 0.93 (IQR: 0.88-0.96). As our main result, we provide the age- and sex-specific bone marrow attenuation profiles of a large-scale cohort of individuals with healthy trabecular bone structure as a reference for future studies. We conclude that artificial-intelligence-supported, fully automated volumetric assessment is an intraindividually robust method to image the spine's bone marrow using VNCa data from DECT.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Fervers, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Fervers, Florian UNSPECIFIED UNSPECIFIED UNSPECIFIED Weisthoff, Mathilda UNSPECIFIED UNSPECIFIED UNSPECIFIED Rinneburger, Miriam UNSPECIFIED UNSPECIFIED UNSPECIFIED Zopfs, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Reimer, Robert Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Pahn, Gregor UNSPECIFIED UNSPECIFIED UNSPECIFIED Kottlors, Jonathan UNSPECIFIED orcid.org/0000-0001-5021-6895 UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Lennartz, Simon UNSPECIFIED UNSPECIFIED UNSPECIFIED Persigehl, Thorsten UNSPECIFIED UNSPECIFIED UNSPECIFIED Hokamp, Nils Grosse UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-668960
DOI:	10.3390/diagnostics12030671
Journal or Publication Title:	Diagnostics
Volume:	12
Number:	3
Date:	2022
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2075-4418
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SPECTRAL COMPUTED-TOMOGRAPHY; PERCENTILES; METASTASES; SEPARATION; ACCURACY; CHILDREN; OBESITY; RISK Multiple languages Medicine, General & Internal Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66896