Functional magnetic resonance imaging and transcranial magnetic stimulation: effects of motor imagery, movement and coil orientation.

OBJECTIVE: To compare fMRI activations during movement and motor imagery to corresponding motor evoked potential (MEP) maps obtained with the TMS coil in three different orientations. METHODS: fMRI activations during executed (EM) and imagined (IM) movements of the index finger were compared to MEP maps of the first dorsal interosseus (FDI) muscle obtained with the TMS coil in anterior, posterior and lateral handle positions. To ensure spatial registration of fMRI and MEP maps, a special grid was used in both experiments. RESULTS: No statistically significant difference was found between the TMS centers of gravity (TMS CoG) obtained with the three coil orientations. There was a significant difference between fMRI centers of gravity during IMs (IM CoG) and EMs (EM CoG), with IM CoGs localized on average 10.3mm anterior to those of EMs in the precentral gyrus. Most importantly, the IM CoGs closely matched cortical projections of the TMS CoGs while the EM CoGs were on average 9.5mm posterior to the projected TMS CoGs. CONCLUSIONS: TMS motor maps are more congruent with fMRI activations during motor imagery than those during EMs. These findings are not significantly affected by changing orientation of the TMS coil. SIGNIFICANCE: Our results suggest that the discrepancy between fMRI and TMS motor maps may be largely due to involvement of the somatosensory component in the EM task.

Characterisation of electrocardiogram signals based on blind source separation.

Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3 s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.

Algebraic reconstruction for magnetic resonance imaging under B0 inhomogeneity.

In magnetic resonance imaging, spatial localization is usually achieved using Fourier encoding which is realized by applying a magnetic field gradient along the dimension of interest to create a linear correspondence between the resonance frequency and spatial location following the Larmor equation. In the presence of B0 inhomogeneities along this dimension, the linear mapping does not hold and spatial distortions arise in the acquired images. In this paper, the problem of image reconstruction under an inhomogeneous field is formulated as an inverse problem of a linear Fredholm equation of the first kind. The operators in these problems are estimated using field mapping and the k-space trajectory of the imaging sequence. Since such inverse problems are known to be ill-posed in general, robust solvers, singular value decomposition and conjugate gradient method, are employed to obtain corrected images that are optimal in the Frobenius norm sense. Based on this formulation, the choice of the imaging sequence for well-conditioned matrix operators is discussed, and it is shown that nonlinear k-space trajectories provide better results. The reconstruction technique is applied to sequences where the distortion is more severe along one of the image dimensions and the two-dimensional reconstruction problem becomes equivalent to a set of independent one-dimensional problems. Experimental results demonstrate the performance and stability of the algebraic reconstruction methods.

Simulated phase evolution rewinding (SPHERE): a technique for reducing B0 inhomogeneity effects in MR images.

A novel method for reducing field inhomogeneity effects in magnetic resonance images is described in this paper. Observing that image degradation arises from B0 inhomogeneity-induced phase accrual during data acquisition, the present method numerically rewinds the accumulated phase in the k-space data based on an initial estimate of the image and a corresponding field map. The rewinding process generates a corrected k-space data set that is subsequently Fourier transformed to produce the final image. In this paper, a theoretical analysis of the method and applications of the technique to magnetic resonance imaging data are presented. The theoretical analysis of the method indicates that it is a general approach applicable to a variety of sequences. Results obtained by applying the method to experimental data acquired with single-shot echo-planar imaging, segmented echo-planar imaging with centric reordering, and spiral sequences demonstrate that it is robust in reducing image degradation induced by B0 inhomogeneity.

Pseudo-Fourier imaging (PFI): a technique for spatial encoding in MRI.

In magnetic resonance imaging (MRI), spatial discrimination is usually achieved with selective excitation and/or Fourier encoding. While these approaches are favorable in most situations, it is sometimes desirable to have an approach that takes advantage of both selective excitation and Fourier encoding. In this paper, we describe the theory and experimental results of a new technique, which we will call pseudo-Fourier imaging (PFI), that provides a flexible combination of both approaches. The technique is based on a windowed Fourier transform that expands the continuous object spatial distribution in terms of coherent states. A detailed description of the proposed technique is presented in this paper. The practical implementation of this technique is described and shown to be achieveable using a set of selective excitations combined with a number of Fourier encoding steps. Then, the signal-to-noise ratio of the new technique is derived to show that it can be varied at will anywhere in the range between the ratios for selective excitation and Fourier encoding. Finally, the experimental results of implementing the technique are presented and some potential applications of the technique such as volume imaging, dynamic imaging and magnetic resonance angiography are discussed.

Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images.

Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.