Hans-Aloys Wischmann

Linear and nonlinear current density reconstructions.

Minimum norm algorithms for EEG source reconstruction are studied in view of their spatial resolution, regularization, and lead-field normalization properties, and their computational efforts. Two classes of minimum norm solutions are examined: linear least squares methods and nonlinear L1-norm approaches. Two special cases of linear algorithms, the well known Minimum Norm Least Squares and an implementation with Laplacian smoothness constraints, are compared to two nonlinear algorithms comprising sparse and standard L1-norm methods. In a signal-to-noise-ratio framework, two of the methods allow automatic determination of the optimum regularization parameter. Compensation methods for the different depth dependencies of all approaches by lead-field normalization are discussed. Simulations with tangentially and radially oriented test dipoles at two different noise levels are performed to reveal and compare the properties of all approaches. Finally, cortically constrained versions of the algorithms are applied to two epileptic spike data sets and compared to results of single equivalent dipole fits and spatiotemporal source models.

An improved boundary element method for realistic volume-conductor modeling

An improved boundary element method (BEM) with a virtual triangle refinement using the vertex normals, an optimized auto solid angle approximation, and a weighted isolated problem approach is presented. The performance of this new approach is compared to analytically solvable spherical shell models and highly refined reference BEM models for tangentially and radially oriented dipoles at different eccentricities. The lead fields of several electroencephalography (EEG) and magnetoencephalography (MEG) setups are analyzed by singular-value decompositions for realistically shaped volume-conductor models. Dipole mislocalizations due to simplified volume-conductor models are investigated for EEG and MEG examinations for points on a three dimensional (3-D) grid with 10-mm spacing inside the conductor and all principal dipole orientations. The applicability of the BEM in view of the computational effort is tested with a standard workstation. Finally, an application of the new method to epileptic spike data is studied and the results are compared to the spherical-shells approximation.

Improving source reconstructions by combining bioelectric and biomagnetic data

OBJECTIVES A framework for combining bioelectric and biomagnetic data is presented. The data are transformed to signal-to-noise ratios and reconstruction algorithms utilizing a new regularization approach are introduced. METHODS Extensive simulations are carried out for 19 different EEG and MEG montages with radial and tangential test dipoles at different eccentricities and noise levels. The methods are verified by real SEP/SEF measurements. A common realistic volume conductor is used and the less well known in vivo conductivities are matched by calibration to the magnetic data. Single equivalent dipole fits as well as spatio-temporal source models are presented for single and combined modality evaluations and overlaid to anatomic MR images. RESULTS Normalized sensitivity and dipole resolution profiles of the different EEG/MEG acquisition systems are derived from the simulated data. The methods and simulations are verified by simultaneously measured somatosensory data. CONCLUSIONS Superior spatial resolution of the combined data studies is revealed, which is due to the complementary nature of both modalities and the increased number of sensors. A better understanding of the underlying neuronal processes can be achieved, since an improved differentiation between quasi-tangential and quasi-radial sources is possible.

Temporal artifacts in flat dynamic x-ray detectors

Flat X-ray detectors based on CsI:Tl scintillators and amorphous silicon photodiodes are known to exhibit temporal artefacts (ghost images) which decay over time. Previously, these temporal artefacts have been attributed mainly to residual signals from the amorphous silicon photodiodes. More detailed experiments presented here show that a second class of effects, the so-called gain effects, also contributes significantly to the observed temporal artefacts. Both the residual signals and the photodiode gain effect have been characterized under various exposure conditions in the study presented here. The results of the experiments quantitatively show the decay of the temporal artefacts. Additionally, the influence of the detectors reset light on both effects in the photodiode has been studied in detail. The data from the measurements is interpreted based on a simple trapping model which suggests a strong link between the photodiode residual signals and the photodiode gain effect. For the residual signal effect a possible correction scheme is described. Furthermore, the relevance of the remaining temporal artefacts for the applications is briefly discussed for both the photodiode residual signals and the photodiode gain effect.

Possibilities of Functional Brain Imaging Using a Combination of MEG and MRT

Neuromagnetic imaging is a relatively new diagnostic tool for the examination of electrical activities in the nervous system. It is based on the noninvasive detection of the extremely weak magnetic fields around the human body with Superconducting Quantum Interference Devices (SQUIDs) and the subsequent reconstruction of the generators.

Correction of amplifier nonlinearity, offset, gain, temporal artifacts, and defects for flat-panel digital imaging devices

Flat X-ray detectors require a systematic calibration and correction of image artifacts. Based on an analysis of the physics of the image generation chain, this work presents a unified framework for the correction of these artifacts. Algorithms for the correction steps are presented, including a new method for the calibration and correction of the intertwined offset, gain, and non-linearity as well as an improved method for the interpolation of defects, where the interpolation direction is chosen based on a novel method. Experiments using a hand phantom without and with a wire, imaged on a flat detector, demonstrate that line artifacts in Digital Subtraction Angiography (DSA) applications due to differences in non-linearity between adjacent amplifiers are significantly reduced by applying the non-linearity, offset, and gain correction in the correct order, as proposed in this work. For the defect interpolation investigations, we used medical images of angiographic image subtraction sequences, containing small vessels. Artificial clusters of pixel defects were added to these images and subsequently corrected. The experimental verification clearly demonstrates the robustness and superior performance of the new interpolation scheme, especially for clusters of defects.

Effect of the signal-to-noise ratio on the quality of linear estimation reconstructions of distributed current sources

SummaryCurrently, linear estimation reconstruction is the only feasible method for extracting information about spatially distributed current sources from measurements of neural magnetic fields. We present the results of a systematic study of the effect of the signal-to-noise ratio on the imaging quality of one such algorithm in over- as well as underdetermined circumstances. In particular, we will discuss the necessary trade-off between the contradictory goals of a minimum norm of the reconstructed current density distribution and of a minimal deviation of the reconstructed fields from the measured fields. As an example, we show the reconstruction of a simple arrangement of two nearly parallel dipoles in two different depths inside a spherical volume conductor, discussing the differences between the computer simulation without noise and simulation with a realistic noise level.

Methodology to measure fundamental performance parameters of x-ray detectors

To judge the potential benefit of a new x-ray detector technology and to be able to compare different technologies, some standard performance measurements must be defined. In addition to technology-related parameters which may influence weight, shape, image distortions and readout speed, there are fundamental performance parameters which directly influence the achievable image quality and dose efficiency of x-ray detectors. A standardization activity for detective quantum efficiency (DQE) for static detectors is already in progress. In this paper we present a methodology for noise power spectrum (NPS), low frequency drop (LFD) and signal to electronic noise ratio (SENR), and the influence of these parameters on DQE. The individual measurement methods are described in detail with their theoretical background and experimental procedure. Corresponding technical phantoms have been developed. The design of the measurement methods and technical phantoms is tuned so that only minimum requirements are placed on the detector properties. The measurement methods can therefore be applied to both static and dynamic x-ray systems. Measurement results from flat panel imagers and II/TV systems are presented.

Validation of elastic registration algorithms based on adaptive irregular grids for medical applications

Elastic registration of medical images is an active field of current research. Registration algorithms have to be validated in order to show that they fulfill the requirements of a particular clinical application. Furthermore, validation strategies compare the performance of different registration algorithms and can hence judge which algorithm is best suited for a target application. In the literature, validation strategies for rigid registration algorithms have been analyzed. For a known ground truth they assess the displacement error at a few landmarks, which is not sufficient for elastic transformations described by a huge number of parameters. Hence we consider the displacement error averaged over all pixels in the whole image or in a region-of-interest of clinical relevance. Using artificially, but realistically deformed images of the application domain, we use this quality measure to analyze an elastic registration based on transformations defined on adaptive irregular grids for the following clinical applications: Magnetic Resonance (MR) images of freely moving joints for orthopedic investigations, thoracic Computed Tomography (CT) images for the detection of pulmonary embolisms, and transmission images as used for the attenuation correction and registration of independently acquired Positron Emission Tomography (PET) and CT images. The definition of a region-of-interest allows to restrict the analysis of the registration accuracy to clinically relevant image areas. The behaviour of the displacement error as a function of the number of transformation control points and their placement can be used for identifying the best strategy for the initial placement of the control points.

Configurable real-time motion estimation for medical imaging: application to X-ray and ultrasound

AbstractMotion estimation is a key building block of image processing pipelines in many different contexts, ranging from efficient coding of video sequences in the consumer electronics domain (TV, DVD, BD) to professional medical applications. Many block-matching approaches have been proposed in the literature for motion detection and compensation in general, including both lossless and lossy algorithms. However, in real-time medical imaging applications, characterized by high frame rates, the needs for low latency and jitter, accuracy and robustness against noise are quite difficult to achieve with standard block-matching methods. We introduce a new hybrid image processing approach to block-matching that takes advantage of both types of algorithms (lossless and lossy), adapts to the image content and noise, and provides high flexibility for the speed/accuracy tradeoff. The presented approach has been successfully tested on interventional X-ray fluoroscopy and cardiac ultrasound images sequences.