Due to the respiratory function of the nasal passage, it has been suggested that abnormal nasal anatomy can worsen asthma symptoms for some individuals. This effect is highly dependent on the type of nasal abnormality and the type of symptoms experienced. Research on this topic has increasingly used medical imaging modalities requiring large sample sizes, which has proven difficult 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 to more quickly discover nasal abnormalities associated with asthma.
The method involved in aligning the cranial CT scans was a subroutine for the NIH-funded 3D Slicer program. This subroutine takes CT scans and aligns them into the Frankfort Horizontal plane so that they can 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 processed using the new python-based alignment subroutine. The subroutine resulted in a 60% reduction in alignment time and improved accuracy due to 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 intervention may result in easier clinical analysis, opening new horizons for further research, which would otherwise be too time-consuming to perform .
Source: Das, S., Maddux, S., & Kim, S. (2020, December). Nasal morphology and health disparities in asthma: A study assessing semi-automated tools for processing computed tomography scans in 3D morphometric research [Poster]. Research Appreciation Day. The University of North Texas Health Science Center. https://hdl.handle.net/20.500.12503/30382