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Dynamic ventilation imaging from four-dimensional computed tomography.

Thomas Guerrero·Kevin Sanders·Edward Castillo·Yin Zhang·Luc Bidaut·Tinsu Pan·Ritsuko Komaki
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Abstract

A novel method for dynamic ventilation imaging of the full respiratory cycle from four-dimensional computed tomography (4D CT) acquired without added contrast is presented. Three cases with 4D CT images obtained with respiratory gated acquisition for radiotherapy treatment planning were selected. Each of the 4D CT data sets was acquired during resting tidal breathing. A deformable image registration algorithm mapped each (voxel) corresponding tissue element across the 4D CT data set. From local average CT values, the change in fraction of air per voxel (i.e. local ventilation) was calculated. A 4D ventilation image set was calculated using pairs formed with the maximum expiration image volume, first the exhalation then the inhalation phases representing a complete breath cycle. A preliminary validation using manually determined lung volumes was performed. The calculated total ventilation was compared to the change in contoured lung volumes between the CT pairs (measured volume). A linear regression resulted in a slope of 1.01 and a correlation coefficient of 0.984 for the ventilation images. The spatial distribution of ventilation was found to be case specific and a 30% difference in mass-specific ventilation between the lower and upper lung halves was found. These images may be useful in radiotherapy planning.

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AlgorithmsHumansImaging, Three-DimensionalLungPulmonary VentilationRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTidal VolumeTomography, X-Ray ComputedEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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Thomas Guerrero, Kevin Sanders, Edward Castillo, Yin Zhang, Luc Bidaut, Tinsu Pan, Ritsuko Komaki. 2006-01-25. Dynamic ventilation imaging from four-dimensional computed tomography.. https://doi.org/10.1088/0031-9155%2F51%2F4%2F002

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