title: Bladder volume estimation from electrical impedance tomography.
abstract: Non-invasive estimation of bladder volume is required to progress from scheduled voiding to a demand-driven emptying scheme for patients with impaired bladder volume sensation. Electrical impedance tomography (EIT) is a promising candidate for the non-invasive monitoring of bladder volume. This article focuses on four estimation algorithms used to map recorded EIT data to a volume estimate. Two different approaches are presented: the tomographic algorithms (one based on global impedance, the other on equivalent circular diameter) rely on the reconstruction of a tomographic image and then extract a volume estimate, whereas the parametric algorithms (one based on neural networks, the other on the singular value difference method) directly map the raw data to a volume estimate. The four algorithms presented here are evaluated for volume estimation error, noise tolerance and suppression of varying urine conductivity based on finite element simulation data.
authors.affiliation: Chair for Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
authors.forename: T
authors.initials: T
authors.lastname: Schlebusch
authors.forename: S
authors.initials: S
authors.lastname: Nienke
authors.forename: S
authors.initials: S
authors.lastname: Leonhardt
authors.forename: M
authors.initials: M
authors.lastname: Walter
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017097:Electric Impedance; D020342:Finite Element Analysis; D006801:Humans; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D009929:Organ Size; D014054:Tomography / Q000379:methods*; D001743:Urinary Bladder / Q000033:anatomy & histology / Q000502:physiology*; D014556:Urine / Q000737:chemistry