28 citations · 66 across the 14 of their papers we have counts for
19 papers
Projections for handling uncertainties and enabling domain truncation in diffuse optical tomography
Aada Hakula, Pauliina Hirvi, Nuutti Hyvönen +2
This paper presents a projection-based technique to mitigate the impact of modeling errors related to domain truncation, changes in the optode coupling coefficients, and misspecifi…
Sparse Dictionary-Based Solution of Dynamic Inverse Problems
Aidan Mason-Mackay, Daniela Calvetti, Erkki Somersalo +4
In ill-posed dynamic inverse problems expected spatial features and temporal correlation between frames can be leveraged to improve the quality of the computed solution, in particu…
Fast Absolute 3D CGO-Based Electrical Impedance Tomography on Experimental Tank Data
S. J. Hamilton, P. A. Muller, D. Isaacson +5
Objective: To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data. Approach: CGO-based methods for absolute EIT imaging are compared to traditio…
Embedded quantitative MRI T1rho mapping using non-linear primal-dual proximal splitting
Matti Hanhela, Antti Paajanen, Mikko J. Nissi +1
Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical MRI by providing numerical values on which diagnostic assessment or predictions of tissue properties can…
Approximation error method for imaging the human head by electrical impedance tomography
Valentina Candiani, Nuutti Hyvönen, Jari P. Kaipio +1
This work considers electrical impedance tomography imaging of the human head, with the ultimate goal of locating and classifying a stroke in emergency care. One of the main diffic…
Simultaneous reconstruction of conductivity, boundary shape and contact impedances in electrical impedance tomography
J. P. Agnelli, V. Kolehmainen, M. Lassas +2
The objective of electrical impedance tomography (EIT) is to reconstruct the internal conductivity of a physical body based on current and voltage measurements at the boundary of t…
3D EIT Reconstructions from Electrode Data using Direct Inversion D-bar and Calderon Methods
Sarah J. Hamilton, David Isaacson, Ville Kolehmainen +3
The first numerical implementation of a D-bar method in 3D using electrode data is presented. Results are compared to Calderón's method as well as more common TV and smoothness reg…
Data-driven regularization parameter selection in dynamic MRI
Matti Hanhela, Olli Gröhn, Mikko Kettunen +3
In dynamic MRI, sufficient time resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the…
Temporal Huber regularization for DCE-MRI
Matti Hanhela, Mikko Kettunen, Olli Gröhn +2
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to study microvascular structure and tissue perfusion. In DCE-MRI a bolus of gadolinium based contrast agent…
Parameter selection in dynamic contrast-enhanced magnetic resonance tomography
Kati Niinimäki, Matti Hanhela, Ville Kolehmainen
In this work we consider the image reconstruction problem of sparsely sampled dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). DCE-MRI is a technique for acquiring…
Joint Reconstruction in Low Dose Multi-Energy CT
Jussi Toivanen, Alexander Meaney, Samuli Siltanen +1
Multi-energy CT takes advantage of the non-linearly varying attenuation properties of elemental media with respect to energy, enabling more precise material identification than sin…
Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT)
S. J. Hamilton, A. Hänninen, A. Hauptmann +1
Objective: To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute Electrical Impedance Tomography (a-EIT) that pairs deep learning techniq…
Dynamic MRI Reconstruction from Undersampled Data with an Anatomical Prescan
Julian Rasch, Ville Kolehmainen, Riikka Nivajärvi +4
The goal of dynamic magnetic resonance imaging (dynamic MRI) is to visualize tissue properties and their local changes over time that are traceable in the MR signal. We propose a n…
Open 2D Electrical Impedance Tomography data archive
Andreas Hauptmann, Ville Kolehmainen, Nguyet Minh Mach +3
This document reports an Open 2D Electrical Impedance Tomography (EIT) data set. The EIT measurements were collected from a circular body (a flat tank filled with saline) with vari…
Approximate Marginalization of Absorption and Scattering in Fluorescence Diffuse Optical Tomography
Meghdoot Mozumder, Tanja Tarvainen, Simon Arridge +3
In fluorescence diffuse optical tomography (fDOT), the reconstruction of the fluorophore concentration inside the target body is usually carried out using a normalized Born approxi…
Multi-resolution parameter choice method for total variation regularized tomography
Kati Niinimäki, Matti Lassas, Keijo Hämäläinen +4
A computational method is introduced for choosing the regularization parameter for total variation (TV) regularization. The approach is based on computing reconstructions at a few…
Estimation of conductivity changes in a region of interest with electrical impedance tomography
Dong Liu, Ville Kolehmainen, Samuli Siltanen +2
This paper proposes a novel approach to reconstruct changes in a target conductivity from electrical impedance tomography measurements. As in the conventional difference imaging, t…
The inverse conductivity problem with an imperfectly known boundary in three dimensions
Ville Kolehmainen, Matti Lassas, Petri Ola
We consider the inverse conductivity problem in a strictly convex domain whose boundary is not known. Usually the numerical reconstruction from the measured current and voltage dat…
Inverse conductivity problem with an imperfectly known boundary
Ville Kolehmainen, Matti Lassas, Petri Ola
We show how to eliminate the error caused by an incorrectly modeled boundary in electrical impedance tomography (EIT). In practical measurements, one usually lacks the exact knowle…