Affiliation:
1. Department of Veterinary Clinical Sciences, Clinic for Small Animals, Justus Liebig University, Giessen, Germany
2. Department of Diagnostic Imaging, Clinic for Small Animals Hofheim, Hofheim, Germany
Abstract
Abstract
Objective Computed tomography (CT) is used complementarily to radiography for the evaluation of medial coronoid disease (MCD). We hypothesized that a slice thickness > 2 mm would significantly affect the image quality and detection of fragmentation of the medial coronoid process. This study aimed to assess CT features indicating direct and indirect evidence of MCD in 168 CT studies with slice thicknesses of 1-, 2- and 3 mm.
Materials and Methods The CT studies were blinded in terms of CT slice thickness and patient data and randomly assessed by two independent observers. All dogs underwent arthroscopic evaluation of the elbow joints. Both observers were unaware of the arthroscopic findings.
Results Notably, blurring of the bone contour (p = 0.0001) was significantly influenced by slice thickness; here, a 1-mm thickness yielded a predominantly sharp and well-defined bone contour (observer 1, 91%; observer 2, 79%), whereas 2- (observer 1, 39.3%; observer 2, 56.3%) and especially 3-mm slice thicknesses yielded blurred margins with significantly reduced sharpness (observer 1, 0%; observer 2, 12.5%). The 1-mm slice thickness also yielded the highest fragment detection rate (observer 1, 55.4%; observer 2, 60.4%). Furthermore, the detection of fragment positions and of single fragments and fissures differed substantially with slice thickness.
Clinical Relevance The findings of this study support the hypothesis that a CT slice thickness of ≥ 2 mm significantly affects fragment detection. In conclusion, a CT slice thickness of at least 1 mm is recommended for the assessment of MCD of the canine elbow.
Subject
General Veterinary,Animal Science and Zoology
Cited by
2 articles.
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1. Orthopedic Imaging;Veterinary Clinics of North America: Small Animal Practice;2022-07
2. CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging;Mathematical Problems in Engineering;2021-05-26