title: MRI model-based non-invasive differential diagnosis in pulmonary hypertension.
abstract: Pulmonary hypertension(PH) is a disorder characterised by increased mean pulmonary arterial pressure. Currently, the diagnosis of PH relies upon measurements taken during invasive right heart catheterisation (RHC). This paper describes a process to derive diagnostic parameters using only non-invasive methods based upon MRI imaging alone. Simultaneous measurements of main pulmonary artery (MPA) anatomy and flow are interpreted by 0D and 1D mathematical models, in order to infer the physiological status of the pulmonary circulation. Results are reported for 35 subjects, 27 of whom were patients clinically investigated for PH and eight of whom were healthy volunteers. The patients were divided into 3 sub-groups according to the severity of the disease state, one of which represented a negative diagnosis (NoPH), depending on the results of the clinical investigation, which included RHC and complementary MR imaging. Diagnostic indices are derived from two independent mathematical models, one based on the 1D wave equation and one based on an RCR Windkessel model. Using the first model it is shown that there is an increase in the ratio of the power in the reflected wave to that in the incident wave (Wpb/Wptotal) according to the classification of the disease state. Similarly, the second model shows an increase in the distal resistance with the disease status. The results of this pilot study demonstrate that there are statistically significant differences in the parameters derived from the proposed models depending on disease status, and thus suggest the potential for development of a non-invasive, image-based diagnostic test for pulmonary hypertension.
authors.affiliation: University of Sheffield, Cardiovascular Science Department, Sheffield, South Yorkshire, UK. Electronic address: mdp11al@sheffield.ac.uk.
authors.forename: A
authors.initials: A
authors.lastname: Lungu
authors.affiliation: University of Sheffield, Cardiovascular Science Department, Sheffield, South Yorkshire, UK; INSIGNEO-Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, UK.
authors.forename: J M
authors.initials: JM
authors.lastname: Wild
authors.affiliation: University of Sheffield, Cardiovascular Science Department, Sheffield, South Yorkshire, UK.
authors.forename: D
authors.initials: D
authors.lastname: Capener
authors.affiliation: Pulmonary Vascular Disease Unit, Sheffield, South Yorkshire, UK.
authors.forename: D G
authors.initials: DG
authors.lastname: Kiely
authors.affiliation: University of Sheffield, Cardiovascular Science Department, Sheffield, South Yorkshire, UK; INSIGNEO-Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, UK.
authors.forename: A J
authors.initials: AJ
authors.lastname: Swift
authors.affiliation: University of Sheffield, Cardiovascular Science Department, Sheffield, South Yorkshire, UK; INSIGNEO-Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, UK.
authors.forename: D R
authors.initials: DR
authors.lastname: Hose
mesh_terms: D006328:Cardiac Catheterization; D003937:Diagnosis, Differential; D006801:Humans; D006976:Hypertension, Pulmonary / Q000175:diagnosis* / Q000503:physiopathology; D008279:Magnetic Resonance Imaging; D008955:Models, Cardiovascular; D010865:Pilot Projects; D011652:Pulmonary Circulation / Q000502:physiology*
keywords: Optimisation; Pulmonary hypertension; RCR; Wave reflections