Yasser M. Kadah

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

OBJECTIVEnTo compare fMRI activations during movement and motor imagery to corresponding motor evoked potential (MEP) maps obtained with the TMS coil in three different orientations.nnnMETHODSnfMRI 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.nnnRESULTSnNo 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.nnnCONCLUSIONSnTMS 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.nnnSIGNIFICANCEnOur 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.

Progressive magnetic resonance image reconstruction based on iterative solution of a sparse linear system

Image reconstruction from nonuniformly sampled spatial frequency domain data is an important problem that arises in computed imaging. Current reconstruction techniques suffer from limitations in their model and implementation. In this paper, we present a new reconstruction method that is based on solving a system of linear equations using an efficient iterative approach. Image pixel intensities are related to the measured frequency domain data through a set of linear equations. Although the system matrix is too dense and large to solve by direct inversion in practice, a simple orthogonal transformation to the rows of this matrix is applied to convert the matrix into a sparse one up to a certain chosen level of energy preservation. The transformed system is subsequently solved using the conjugate gradient method. This method is applied to reconstruct images of a numerical phantom as well as magnetic resonance images from experimental spiral imaging data. The results support the theory and demonstrate that the computational load of this method is similar to that of standard gridding, illustrating its practical utility.

Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis

The application of independent components analysis (ICA) to functional magnetic resonance imaging data has been proven useful to decompose the signal in terms of its basic sources. The main advantage is that ICA requires no prior assumption about the neuronal activity or the noise structure, which are usually unknown in fMRI. This enables the detection of true activation components free of random and physiological noise. Hence, this technique is superior to other techniques such as subspace modeling or canonical correlation analysis, which have underlying assumptions about the signal components. Nevertheless, this technique suffers from a fundamental limitation of not providing a consistent ordering of the signal components as a result of the whitening step involved in ICA. This mandates human intervention to pick out the relevant activation components from the outcome of ICA, which poses a significant obstacle to the practicality of this technique. In this work, a simple yet robust technique is proposed for ranking the resultant independent components. This technique adds a second step to ICA based on canonical correlation analysis and the prior information about the activation paradigm. This enables the proposed technique to provide a consistent and reproducible ordering of independent components. The proposed technique was applied to real event-related functional magnetic resonance imaging data and the results confirm the practicality and robustness of the proposed method.

Combined intra- and inter-slice motion artifact suppression in magnetic resonance imaging

We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.

Nonparametric suppression of random and physiological noise components in functional magnetic resonance imaging using cross-correlation spectrum subtraction

The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a new non-parametric technique for noise suppression in event related fMRI data is proposed based on spectrum subtraction. The new technique is based on generalized spectral subtraction that allows correlated noise components to be treated robustly. Moreover, it adaptively estimates a nonparametric model for random and physiological components of noise from the acquired data in a simple and computationally efficient manner. This allows the new method to overcome the limitations of previous methods while maintaining a robust performance given its fewer assumptions and suggests its value as a useful preprocessing step for fMRI data analysis.

Computer assisted radiographic characterization of alloimplant materials used as bone substitutes in dentistry

We develop a computerized system for evaluation of alloimplant procedures in dentistry from x-ray images. The goal of this system is to help clinicians make more accurate evaluation of their surgical procedures as well as to guide them in selecting the most appropriate alloimplant material in an objective manner. A study was conducted whereby three types of alloimplant materials were inserted in surgical defects in the tibia of dogs. Each animal had four such defects for the three different materials in addition to a control defect that was intentionally left empty. The defect locations were imaged using x-rays at periodic intervals starting immediately after the operation. The animals were sacrificed at different times after the surgical operation. The acquired images were paired with their correct diagnosis and split into two sets representing the learning and testing data for our computerized system. The plain x-ray films were scanned using a standard film digitizer and standardized in size and intensity using a step wedge that was imaged beside the region of interest. A set of first and second order textural and radiometric parameters were extracted from each alloimplant location outlined by the radiographer to describe its clinical status in a quantitative manner.