Biomedical subjects

V Kolehmainen

Publications and source records attributed to V Kolehmainen.

6 recordsLinked to original sources

PubMed2006

Reconstructing absorption and diffusion shape profiles in optical tomography by a level set technique.

A shape reconstruction algorithm for optical tomography is introduced that uses a level-set formulation for the shapes. Evolution laws based on gradient directions for a cost functional are derived for two different level-set functions, one describing the absorption and one the diffusion parameter, as well as for the parameter values inside these shapes. Numerical experiments are presented in 2D that show that the new method is able to simultaneously recover shapes and contrast values of absorbing and scattering objects embedded in a moderately heterogeneous background medium from simulated noisy data.

Absorption↗

PubMed2003

Statistical inversion for medical x-ray tomography with few radiographs: I. General theory.

In x-ray tomography, the structure of a three-dimensional body is reconstructed from a collection of projection images of the body. Medical CT imaging does this using an extensive set of projections from all around the body. However, in many practical imaging situations only a small number of truncated projections are available from a limited angle of view. Three-dimensional imaging using such data is complicated for two reasons: (i) typically, sparse projection data do not contain sufficient information to completely describe the 3D body, and (ii) traditional CT reconstruction algorithms, such as filtered backprojection, do not work well when applied to few irregularly spaced projections. Concerning (i), existing results about the information content of sparse projection data are reviewed and discussed. Concerning (ii), it is shown how Bayesian inversion methods can be used to incorporate a priori information into the reconstruction method, leading to improved image quality over traditional methods. Based on the discussion, a low-dose three-dimensional x-ray imaging modality is described.

Algorithms↗

PubMed2003

Statistical inversion for medical x-ray tomography with few radiographs: II. Application to dental radiology.

Diagnostic and operational tasks in dental radiology often require three-dimensional information that is difficult or impossible to see in a projection image. A CT-scan provides the dentist with comprehensive three-dimensional data. However, often CT-scan is impractical and, instead, only a few projection radiographs with sparsely distributed projection directions are available. Statistical (Bayesian) inversion is well-suited approach for reconstruction from such incomplete data. In statistical inversion, a priori information is used to compensate for the incomplete information of the data. The inverse problem is recast in the form of statistical inference from the posterior probability distribution that is based on statistical models of the projection data and the a priori information of the tissue. In this paper, a statistical model for three-dimensional imaging of dentomaxillofacial structures is proposed. Optimization and MCMC algorithms are implemented for the computation of posterior statistics. Results are given with in vitro projection data that were taken with a commercial intraoral x-ray sensor. Examples include limited-angle tomography and full-angle tomography with sparse projection data. Reconstructions with traditional tomographic reconstruction methods are given as reference for the assessment of the estimates that are based on the statistical model.

Algorithms↗

PubMed2000

Simultaneous reconstruction of internal tissue region boundaries and coefficients in optical diffusion tomography.

In this paper we propose a new numerical method to the inverse problem in optical diffusion tomography. We consider the reconstruction of the diffusion and absorption coefficients (kappa, mu(a)) within a domain omega which is known to consist of a set of disjoint regions of distinct tissue types. The assumption is that the regions of different tissues are bounded by smooth boundary curves and have constant absorption and diffusion coefficients. The goal in the proposed method is to reconstruct simultaneously the boundaries of the tissue regions together with the absorption and diffusion coefficients within these regions. The solution of the problem is based on the finite element method and subdivision of the elements. The performance of the proposed method is evaluated by simulations in which the optical parameters (kappa, mu(a)) are relevant in medical applications of optical tomography. It is shown that the proposed method is able to recover both the boundaries and the coefficients with good accuracy.

Image Processing, Computer-Assisted↗

PubMed1998

Pharmacokinetics and dosimetry of iodine-123 labelled PE2I in humans, a radioligand for dopamine transporter imaging.

The iodine-123 labelled selective ligand N-(3-iodoprop-2E-enyl)-2-beta-carbomethoxy-3beta-(4-methylphenyl) nortropane ([123I]PE2I) was evaluated as a probe for in vivo dopamine transporter imaging in the human brain. Six healthy subjects were imaged with a high-resolution single-photon emission tomography scanner. Striatal radioactivity peaked at 1 h after injection. The background radioactivity was low. The volume of distribution in the striatum was 94+/-24 ml/ml. The results were compared with those of [123I]beta-CIT imaging. There was no significant uptake of [123I]PE2I in serotonin-rich regions such as the midbrain, hypothalamus and anterior gingulus, suggesting that in vivo binding is specific for the dopamine transporter. One main polar metabolite of [123I]PE2I was found in plasma, and the parent plasma concentration decayed rapidly. Radiation exposure to the study subject is 0.022+/-0.004 mSv/MBq (effective dose). The preliminary results suggest that [123I]PE2I is a selective SPET ligand for imaging striatal dopamine transporter density.

Adult↗

PubMed1997

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns.

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Computer Simulation↗