Biomedical subjects

Ville Kolehmainen

Publications and source records attributed to Ville Kolehmainen.

8 recordsLinked to original sources

PubMed2006

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.

Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.

Algorithms↗

PubMed2005

Dynamic physiological modeling for functional diffuse optical tomography.

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.

Blood Pressure↗

PubMed2005

Coupled radiative transfer equation and diffusion approximation model for photon migration in turbid medium with low-scattering and non-scattering regions.

In this paper, a coupled radiative transfer equation and diffusion approximation model is extended for light propagation in turbid medium with low-scattering and non-scattering regions. The light propagation is modelled with the radiative transfer equation in sub-domains in which the assumptions of the diffusion approximation are not valid. The diffusion approximation is used elsewhere in the domain. The two equations are coupled through their boundary conditions and they are solved simultaneously using the finite element method. The streamline diffusion modification is used to avoid the ray-effect problem in the finite element solution of the radiative transfer equation. The proposed method is tested with simulations. The results of the coupled model are compared with the finite element solutions of the radiative transfer equation and the diffusion approximation and with results of Monte Carlo simulation. The results show that the coupled model can be used to describe photon migration in turbid medium with low-scattering and non-scattering regions more accurately than the conventional diffusion model.

Animals↗

PubMed2005

Computational calibration method for optical tomography.

We propose a computational calibration method for optical tomography. The model of the calibration scheme is based on the rotation symmetry of source and detector positions in the measurement setup. The relative amplitude losses and phase shifts at the optic fibers are modeled by complex-valued coupling coefficients. The coupling coefficients can be estimated when optical tomography data from a homogeneous and isotropic object are given. Once these coupling coefficients have been estimated, any data measured with the same measurement setup can be corrected for the relative variation in the data due to source and detector losses. The final calibration of the data for the source and detector losses and the source calibration between the data and the forward model are obtained as part of the initial estimation for reconstruction. The calibration method was tested with simulations and measurements. The results show that the coupling coefficients of the sources and detectors can be estimated with good accuracy. Furthermore, the results show that the method can significantly improve the quality of reconstructed images.

Algorithms↗

PubMed2005

Hybrid radiative-transfer-diffusion model for optical tomography.

A hybrid radiative-transfer-diffusion model for optical tomography is proposed. The light propagation is modeled with the radiative-transfer equation in the vicinity of the laser sources, and the diffusion approximation is used elsewhere in the domain. The solution of the radiative-transfer equation is used to construct a Dirichlet boundary condition for the diffusion approximation on a fictitious interface within the object. This boundary condition constitutes an approximative distributed source model for the diffusion approximation in the remaining area. The results from the proposed approach are compared with finite-element solutions of the radiative-transfer equation and the diffusion approximation and Monte Carlo simulation. The results show that the method improves the accuracy of the forward model compared with the conventional diffusion model.

Computer Simulation↗

PubMed2005

Physiological system identification with the Kalman filter in diffuse optical tomography.

Algorithms↗

PubMed2003

Time-series estimation of biological factors in optical diffusion tomography.

We apply state space estimation techniques to the time-varying reconstruction problem in optical tomography. We develop a stochastic model for describing the evolution of quasi-sinusoidal medical signals such as the heartbeat, assuming these are represented as a known frequency with randomly varying amplitude and phase. We use the extended Kalman filter in combination with spatial regularization techniques to reconstruct images from highly under-determined time-series data. This system also naturally segments activity belonging to different biological processes. We present reconstructions of simulated data and of real data recorded from the human motor cortex (Franceschini et al 2000 Optics Express 6 49-57). It is argued that the application of these time-series techniques improves both the fidelity and temporal resolution of reconstruction in optical tomography.

Algorithms↗

PubMed2003

State-estimation approach to the nonstationary optical tomography problem.

We propose a new numerical approach to the nonstationary optical (diffusion) tomography (OT) problem. The assumption in the method is that the absorption and/or diffusion coefficients are nonstationary in the sense that they may exhibit significant changes during the time that is needed to measure data for one traditional image frame. In the proposed method, the OT problem is formulated as a state-estimation problem. Within the state-estimation formulation, the absorption and/or diffusion coefficients are considered a stochastic process. The objective is to estimate a sequence of states for the process when the state evolution model for the process, the observation model for OT experiments, and data on the exterior boundary are given. In the proposed method, the state estimates are computed by using Kalman filtering techniques. The performance of the proposed method is evaluated on the basis of synthetic data. The simulations also illustrate that further improvements to the results in nonstationary applications can be obtained by adjustment of the measurement protocol.

Models, Theoretical↗