title: CT and image processing non-invasive indicators of sickle cell secondary pulmonary hypertension.
abstract: This retrospective study investigates the potential of image analysis to quantify for the presence and extent of pulmonary hypertension secondary to sickle cell disease (SCD). A combination of fast marching and geodesic active contours level sets were employed to segment the pulmonary artery from smoothed CT-Angiography images from 16 SCD patients and 16 matching controls. An algorithm based on fast marching methods was used to compute the centerline of the segmented arteries to measure automatically the diameters of the pulmonary trunk and first branches of the pulmonary arteries. Results show that the pulmonary trunk and arterial branches are significantly larger in diameter in SCD patients as compared to controls (p-values of 0.002 for trunk and 0.0003 for branches). For validation, the results were compared with manually measured values and did not demonstrate significant difference (mean p-values 0.71). CT with image processing shows great potential as a surrogate indicator of pulmonary hemodynamics or response to therapy, which could be an important tool for drug discovery and noninvasive clinical surveillance.
authors.affiliation: Diagnostic Radiology Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892 USA. lingurarum@mail.nih.gov
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
authors.forename: Babak J
authors.initials: BJ
authors.lastname: Orandi
authors.forename: Robert L
authors.initials: RL
authors.lastname: Van Uitert
authors.forename: Nisha
authors.initials: N
authors.lastname: Mukherjee
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
authors.forename: Mark T
authors.initials: MT
authors.lastname: Gladwin
authors.forename: Roberto F
authors.initials: RF
authors.lastname: Machado
authors.forename: Bradford J
authors.initials: BJ
authors.lastname: Wood
mesh_terms: D000465:Algorithms; D000755:Anemia, Sickle Cell / Q000150:complications* / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D006801:Humans; D006976:Hypertension, Pulmonary / Q000000981:diagnostic imaging* / Q000209:etiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*