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Changes in patients with asthma in CT imaging using with temporal and spatial correlation (302471)

Keywords: spatial heterogeneity, spatial and temporal information

In CT studies hierarchical modeling with temporal and spatial correlation are used to describe spatial heterogeneity in disease within a patient given sets of region of interests and to compare temporal changes in disease extent. It is assumed that the temporal pattern of activation is organized in a spatially coherent fashion such that clustering will extract the main temporal patterns and partition the dataset by grouping similarly behaved functions together. We propose a clustering procedure built in the hierarchical setting with prior knowledge with temporal correlation into account. In the first step, clustered maps that show the structure of disease within a lung are produced to quantitatively evaluate the heterogeneity of asthmatic symptom into 4 patterns (geographic, lobar, segmental, and mixed patterns). Second, 20 participants with asthmatic symptom patterns are analyzed by computerized density mask analysis on two occasions. We attempt to aggregate spatial and temporal information into a small number of clusters and compare the clustered maps of disease patterns for the two groups’ treatments.