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Automated MRI volumetry of the olfactory bulb
The olfactory bulb (OB) as part of the olfactory pathway plays a central role in odor perception. Several studies have already established a connection between an olfactory impairment and the occurrence of neurodegenerative diseases (Parkinson's disease, Alzheimer's disease, etc.). This impairment is often detectable years before further symptoms. Moreover, it is connected to a volume loss of the OB. Therefore, in future the volume of the OB could contribute as a marker for detection and diagnosis of such diseases. Despite this great importance, there is currently no standard procedure for the volumetric analysis of the OB and above all no objective investigator-independent measurement methods.
In the present pilot study, a method for automated MRI volumetry of the OB was developed. 10 normosmic subjects aged 21 to 37 years were included. The data sets were obtained on a 3 Tesla MRI scanner. Due to its superiority a Constructive Interference in Steady State (CISS) sequence was chosen. Subsequently, a manual and an automated segmentation of the OB was performed. The automated volumetry took place using a self-learning algorithm.
There was an average volume aberrance of the automated from the manual method of 50.32%. In this case, the average aberrance of the OB was 55.01% on the left and 45.63% on the right. The best results provided an aberrance of 2.37% on the left and 5.52% on the right in ratio of the manually measured values.
Despite the low number of cases we could show that an automated MRI volumetry of the OB is possible. However, further improvement of the method will be required in the future to minimize aberrancies. On that condition, an application in the clinical routine is already conceivable in the near future.
18 April 2018 (online)
© 2018. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Georg Thieme Verlag KG
Stuttgart · New York