BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:SCS: Samuli Siltanen (University of Helsinki)
DTSTART:20240924T140000Z
DTEND:20240924T150000Z
DTSTAMP:20241103T224500Z
UID:indico-event-3997@indico.flatironinstitute.org
DESCRIPTION:Speakers: Cindy Rampersad (Simons Foundation/Flatiron Institut
e)\n\nPresenter: Samuli Siltanen (University of Helsinki)\nElectrical im
pedance tomography and virtual X-rays\nElectrical Impedance Tomography (EI
T) is a nonlinear PDE-based imaging modality where a patient is probed wit
h harmless electric currents\, and the resulting surface voltages are meas
ured. EIT image reconstruction is an ill-posed inverse problem\, meaning v
ery sensitive to noise in the data and modelling errors. However\, one can
use complex geometric optics (CGO) solutions and a nonlinear Fourier tran
sform to do robust medical imaging\; this is the so-called regularized D-b
ar method. A connection between EIT and X-ray tomography was found in [Gre
enleaf et al. 2018] using microlocal analysis. Fourier transform applied t
o the spectral parameter of CGO solutions produces virtual X-ray projectio
ns\, enabling a novel filtered back-projection type nonlinear reconstructi
on algorithm for EIT. It is remarkable how this new approach decomposes th
e EIT image reconstruction process in several steps\, where all ill-posedn
ess is confined in two linear steps. Therefore\, we can separate the nonli
nearity and ill-posedness of the fundamental EIT problem. Furthermore\, th
e new decomposition enables targeted machine learning approaches as only o
ne or two (mathematically well-structured) steps in the imaging chain are
solved using neural networks.\n\nhttps://indico.flatironinstitute.org/even
t/3997/
LOCATION:3rd floor classroom (162)
URL:https://indico.flatironinstitute.org/event/3997/
END:VEVENT
END:VCALENDAR