Biomedical subjects

Janne Heikkilä

Publications and source records attributed to Janne Heikkilä.

4 recordsLinked to original sources

PubMed2006

Simulations of localized harmonic motions on a blood vessel wall induced by an acoustic radiation force used in ultrasound elastography.

Many noninvasive techniques have been developed recently to explore the mechanical properties of soft tissue. In this paper, dynamic acoustic radiation force induced vibrations on a blood vessel wall were simulated using different stimulation frequencies and stiffness parameters for the vessel wall. The stimulation frequency was varied between 20 Hz and 20 kHz and the stiffness parameter (Young's modulus) was varied between 60 kPa and 360 kPa. The vibration simulations were computed using a finite-element method in a 3D geometry that contained a vessel wall surrounded by soft tissue. The results indicate that vibrations caused by acoustic stimulation are sensitive to the changes in mechanical properties of the vessel wall and that the vibrations are highly dependent on the stimulation frequency and target structure. Therefore, measurements of absolute stiffness parameters may not be accurately achieved because this method is so dependent on the whole target structure, whereas the monitoring of changes during some process may be feasible.

Animals↗

PubMed2006

A new convexity measure based on a probabilistic interpretation of images.

In this paper, we present a novel convexity measure for object shape analysis. The proposed method is based on the idea of generating pairs of points from a set and measuring the probability that a point dividing the corresponding line segments belongs to the same set. The measure is directly applicable to image functions representing shapes and also to gray-scale images which approximate image binarizations. The approach introduced gives rise to a variety of convexity measures which make it possible to obtain more information about the object shape. The proposed measure turns out to be easy to implement using the Fast Fourier Transform and we will consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones.

Algorithms↗

PubMed2006

Investigation of optimal method for inducing harmonic motion in tissue using a linear ultrasound phased array--a simulation study.

Many noninvasive ultrasound techniques have been developed to explore mechanical properties of soft tissues. One of these methods, Localized Harmonic Motion Imaging (LHMI), has been proposed to be used for ultrasound surgery monitoring. In LHMI, dynamic ultrasound radiation-force stimulation induces displacements in a target that can be measured using pulse-echo imaging and used to estimate the elastic properties of the target. In this initial, simulation study, the use of a one-dimensional phased array is explored for the induction of the tissue motion. The study compares three different dual-frequency and amplitude-modulated single-frequency methods for the inducing tissue motion. Simulations were computed in a homogeneous soft-tissue volume. The Rayleigh integral was used in the simulations of the ultrasound fields and the tissue displacements were computed using a finite-element method (FEM). The simulations showed that amplitude-modulated sonication using a single frequency produced the largest vibration amplitude of the target tissue. These simulations demonstrate that the properties of the tissue motion are highly dependent on the sonication method and that it is important to consider the full three-dimensional distribution of the ultrasound field for controlling the induction of tissue motion.

Acoustics↗

PubMed2005

Affine invariant pattern recognition using Multiscale Autoconvolution.

This paper presents a new affine invariant image transform called Multiscale Autoconvolution (MSA). The proposed transform is based on a probabilistic interpretation of the image function. The method is directly applicable to isolated objects and does not require extraction of boundaries or interest points, and the computational load is significantly reduced using the Fast Fourier Transform. The transform values can be used as descriptors for affine invariant pattern classification and, in this article, we illustrate their performance in various object classification tasks. As shown by a comparison with other affine invariant techniques, the new method appears to be suitable for problems where image distortions can be approximated with affine transformations.

Algorithms↗