Creating nasal cycle simulations by processing MRI and CT scan data with image morphing algorithms

The nasal cycle, characterized by alternating congestion and decongestion of the nasal passages, plays a vital role in nasal function. Predicting the nasal cycle using data from medical imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), can help elucidate its...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 34026 - 16
Hauptverfasser: Vithanage, Isira A. W., Ginat, Daniel Thomas, Dixon, Angela R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 30.09.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
631/114/1564
631/114/2397
639/766/930/12
Accuracy
Adult
Algorithms
Bone
Computed tomography
Female
Geometry
Humanities and Social Sciences
Humans
Image morphing
Image processing
Image processing algorithms
Image Processing, Computer-Assisted - methods
Information processing
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Morphology
multidisciplinary
Nasal airway morphology
Nasal Cavity - diagnostic imaging
Nasal Cavity - physiology
Nasal cycle
Nose
Path planning
Patients
Predictions
Respiratory tract
Review boards
Science
Science (multidisciplinary)
Temporal variations
Tomography, X-Ray Computed - methods
Turbinates - diagnostic imaging
Nasal cycle
Nasal airway morphology
Path planning
Image morphing
Image processing algorithms
ISSN:2045-2322, 2045-2322
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Beschreibung
Zusammenfassung:The nasal cycle, characterized by alternating congestion and decongestion of the nasal passages, plays a vital role in nasal function. Predicting the nasal cycle using data from medical imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), can help elucidate its impact on nasal physiology and inform surgical intervention strategies. This study introduces an image processing algorithm that predicts temporal variations in nasal airway morphology during the nasal cycle by utilizing a single MRI or CT scan from a patient. Our approach pipelines two algorithms: an active contour (snake) algorithm followed by a path planning algorithm. The active contour algorithm identifies corresponding sets of points between contours of the nasal wall and the desired turbinate geometry, while the path planning algorithm generates pathways connecting the corresponding point sets. This process enables the prediction of intermediate geometries between two different levels of nasal congestion observed at distinct time points during the nasal cycle. Prediction accuracy was assessed by comparing predicted and actual intermediate nasal turbinate geometries in scans taken from the same subject at different time points, using a total of six human patients. Two distinct path planning models, linear image morphing and A-star, were evaluated for their accuracy in predicting intermediate nasal geometries at various congestion levels. Cross-sectional area was used to characterize nasal airway geometry. Prediction accuracies for nasal geometries within respiratory regions, including middle and inferior turbinates, ranged from 72.51% − 92.17% for the linear image morphing method and from 70.73% − 90.8% for the A-star method. This algorithm-based tool offers a reliable means to estimate nasal geometries at different congestion levels throughout the nasal cycle using MRI and CT scan data. Coupling this technology with computational modeling could further aid in studying how the nasal cycle influences airflow dynamics under various breathing conditions or pathological states.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-14023-x