RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate whether a computerized system developed to help delineate the upper airway and surrounding structures with magnetic resonance (MR) imaging was effective for aiding in the diagnosis of upper airway disorders in children. MATERIALS AND METHODS: The authors performed axial T2-weighted MR imaging to gather information about different aspects of the airway and its surrounding soft-tissue structures, including the adenoid and palatine tonsils, tongue, and soft palate. Images were processed and segmented to compute the architectural parameters of the airway (eg, surface description, volume, central [medial] line, and cross-sectional areas at planes perpendicular to the central line). The authors built a software package for the visualization, segmentation, registration, prefiltering, interpolation, standardization, and quantitative analysis of the airway and tonsils. RESULTS: The system was tested with 40 patient studies. For every study, the system segmented and displayed a smooth three-dimensional rendition of the airway and its central line and a plot of the cross-sectional area of the airway orthogonal to the central line as a function of the distance from one end of the central line. The precision and accuracy for segmentation was 97%. The mean time taken per study was about 4 minutes and included the operator interaction time and processing time. CONCLUSION: This method provides a robust and fast means of assessing the airway size, shape, and level of restriction, as well as a structural data set suitable for use in modeling studies of airflow and mechanics.
RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate whether a computerized system developed to help delineate the upper airway and surrounding structures with magnetic resonance (MR) imaging was effective for aiding in the diagnosis of upper airway disorders in children. MATERIALS AND METHODS: The authors performed axial T2-weighted MR imaging to gather information about different aspects of the airway and its surrounding soft-tissue structures, including the adenoid and palatine tonsils, tongue, and soft palate. Images were processed and segmented to compute the architectural parameters of the airway (eg, surface description, volume, central [medial] line, and cross-sectional areas at planes perpendicular to the central line). The authors built a software package for the visualization, segmentation, registration, prefiltering, interpolation, standardization, and quantitative analysis of the airway and tonsils. RESULTS: The system was tested with 40 patient studies. For every study, the system segmented and displayed a smooth three-dimensional rendition of the airway and its central line and a plot of the cross-sectional area of the airway orthogonal to the central line as a function of the distance from one end of the central line. The precision and accuracy for segmentation was 97%. The mean time taken per study was about 4 minutes and included the operator interaction time and processing time. CONCLUSION: This method provides a robust and fast means of assessing the airway size, shape, and level of restriction, as well as a structural data set suitable for use in modeling studies of airflow and mechanics.
Authors: Ahsan Javed; Yoon-Chul Kim; Michael C K Khoo; Sally L Davidson Ward; Krishna S Nayak Journal: IEEE Trans Biomed Eng Date: 2015-08-03 Impact factor: 4.538
Authors: Dhananjay Radhakrishnan Subramaniam; Goutham Mylavarapu; Keith McConnell; Robert J Fleck; Sally R Shott; Raouf S Amin; Ephraim J Gutmark Journal: Ann Biomed Eng Date: 2015-07-28 Impact factor: 3.934
Authors: Muhammad Laiq Ur Rahman Shahid; Junaid Mir; Furqan Shaukat; Muhammad Khurram Saleem; Muhammad Atiq Ur Rehman Tariq; Ahmed Nouman Journal: Curr Med Imaging Date: 2021
Authors: Muhammad Laiq Ur Rahman Shahid; Teodora Chitiboi; Tetyana Ivanovska; Vladimir Molchanov; Henry Völzke; Lars Linsen Journal: BMC Med Imaging Date: 2017-02-14 Impact factor: 1.930