Due to the function of the nasal passage, it has been suggested that abnormal nasal anatomy can worsen asthma symptoms for some patients. This is highly dependent on the type of nasal abnormality, as well as the type of symptoms being experienced. Research on this topic has increasingly used medical imaging modalities requiring large sample sizes, which has proven strenuous and slow to yield results due to the time and effort involved. This study evaluates a novel method for automating the alignment of cranial CT scans in an effort to more quickly discover nasal abnormalities related to the health disparities they can bring about.
The method involved to align the cranial CT scans is a subroutine for the NIH-funded 3D Slicer program. This subroutine would take CT scans and align them into the Frankfort Horizontal plane so that they could be morphometrically assessed in further detail.
CT scans were drawn from 5,221 asthma and control cohorts from Fort Worth, Texas. Each scan was manually aligned to the Frankfort Horizontal plane, and also processed using the new python-based alignment subroutine. This resulted in a 60% reduction in alignment time, as well as improved accuracy as a result of the ease in locating three key fiducial landmarks.
This significant improvement in time spent aligning CT scans is a great leap forward in our ability to analyze CT scans for morphometric anomalies. This can not only result in easier clinical analysis, but also opens up new horizons for further research to be done, which has so far been too time consuming to commit to in many cases.
Adejare, A. A., Gautam, Y., Madzia, J., & Mersha, T. B. (2020). Unraveling racial disparities in asthma emergency department visits using electronic healthcare records and machine learning. Journal of Asthma, 1–15. https://doi.org/10.1080/02770903.2020.1838539