Nasal morphology and health disparities in asthma: A study assessing semi-automated tools for processing computed tomography scans in 3D morphometric research

Date

2020

Authors

Maddux, Scott D.
Das, Siddharth
Kim, Suhhyun

ORCID

0000-0003-0390-7751 (Das, Siddharth)

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Abstract

Purpose: As the nasal complex is predominantly responsible for respiratory heat and moisture exchange, it has been suggested that abnormal nasal anatomy may increase asthmatic symptoms. Recent research has increasingly turned to medical imaging modalities, requiring processing of large samples (n>10,000). The lack of automated procedures for scan processing has represented a significant obstacle for such studies. Accordingly, the purpose of this project was to evaluate the applicability of a newly developed subroutine for the NIH-funded 3D Slicer program to semi-automate the alignment of cranial CT-scans into the Frankfort Horizontal plane for subsequent morphometric assessment as a part of a larger asthma-related study. Methods: Selected CT scans were drawn from 5,221 asthma and control cohorts from JPS hospital in Fort Worth, TX. Each scan was first processed using traditional methods for aligning the cranium into the Frankfort Horizontal plane, followed by a trial employing a new python-based alignment subroutine for "SlicerMorph" extension on 3D Slicer for comparison. Results: Overall, the subroutine showed a significant improvement in image processing times, reducing alignment time for a single scan by approximately 60%. The accuracy of alignment was found to be substantially improved due to the relative ease of locating three fiducial landmarks (left orbitale, left porion, right porion) for alignment compared to the traditional method. Conclusion: This novel subroutine allows for efficient processing of CT scans. Furthermore, we expect use of this subroutine will significantly decrease intra- and inter-observer error, increasing the accuracy of obtained morphometric data.

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