Daniel Gembris

Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging.

Improved data acquisition and processing strategies for blood oxygenation level‐dependent (BOLD)‐contrast functional magnetic resonance imaging (fMRI), which enhance the functional contrast‐to‐noise ratio (CNR) by sampling multiple echo times in a single shot, are described. The dependence of the CNR on T2*, the image encoding time, and the number of sampled echo times are investigated for exponential fitting, echo summation, weighted echo summation, and averaging of correlation maps obtained at different echo times. The method is validated in vivo using visual stimulation and turbo proton echoplanar spectroscopic imaging (turbo‐PEPSI), a new single‐shot multi‐slice MR spectroscopic imaging technique, which acquires up to 12 consecutive echoplanar images with echo times ranging from 12 to 213 msec. Quantitative T2*‐mapping significantly increases the measured extent of activation and the mean correlation coefficient compared with conventional echoplanar imaging. The sensitivity gain with echo summation, which is computationally efficient provides similar sensitivity as fitting. For all data processing methods sensitivity is optimum when echo times up to 3.2 T2* are sampled. This methodology has implications for comparing functional sensitivity at different magnetic field strengths and between brain regions with different magnetic field inhomogeneities. Magn Reson Med 42:87–97, 1999.

Functional magnetic resonance imaging in real time (FIRE): Sliding-window correlation analysis and reference-vector optimization

New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time‐series data and a reference vector with “detrending,” a technique for the suppression of non‐stimulus‐related signal components, and the “sliding‐window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the reference vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized reference vector to the measured data. Based on the described correlation method, real‐time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259–268, 2000.

A new approach to measure single-event related brain activity using real-time fMRI: Feasibility of sensory, motor, and higher cognitive tasks

Real‐time fMRI is a rapidly emerging methodology that enables monitoring changes in brain activity during an ongoing experiment. In this article we demonstrate the feasibility of performing single‐event sensory, motor, and higher cognitive tasks in real‐time on a clinical whole‐body scanner. This approach requires sensitivity optimized fMRI methods: Using statistical parametric mapping we quantified the spatial extent of BOLD contrast signal changes as a function of voxel size and demonstrate that sacrificing spatial resolution and readout bandwidth improves the detection of signal changes in real time. Further increases in BOLD contrast sensitivity were obtained by using real‐time multi‐echo EPI. Real‐time image analysis was performed using our previously described Functional Imaging in REal time (FIRE) software package, which features real‐time motion compensation, sliding window correlation analysis, and automatic reference vector optimization. This new fMRI methodology was validated using single‐block design paradigms of standard visual, motor, and auditory tasks. Further, we demonstrate the sensitivity of this method for online detection of higher cognitive functions during a language task using single‐block design paradigms. Finally, we used single‐event fMRI to characterize the variability of the hemodynamic impulse response in primary and supplementary motor cortex in consecutive trials using single movements. Real‐time fMRI can improve reliability of clinical and research studies and offers new opportunities for studying higher cognitive functions. Hum. Brain Mapping 12:25–41, 2001.

Correlation analysis on GPU systems using NVIDIA's CUDA

Functional magnetic resonance imaging allows non-invasive measurements of brain dynamics and has already been used for neurofeedback experiments, which relies on real time data processing. The limited computational resources that are typically available for this have hindered the use of connectivity analysis in this context. A basic, but already computationally demanding analysis method of neural connectivity is correlation analysis that computes all pairwise correlations coefficients between the measured time series. The parallel nature of the problem predestines it for an implementation on massive parallel architectures as realized by GPUs and FPGAs. We show what performance benefits can be achieved when compared with current desktop CPUs. The use of correlation analysis is not limited to brain research, but is also relevant in other fields of image processing, e.g. for the analysis of video streams.

Comparison of gradient encoding directions for higher order tensor diffusion data

Recently, higher order tensors were proposed for a more advanced representation of non‐Gaussian diffusion. These advanced diffusion models have new requirements for the gradient encoding schemes used in the diffusion weighted image acquisition. The influence of the gradient encoding schemes on the estimated standard second order diffusion tensor was previously investigated. Here, we focus on the suitability of different encoding scheme types for higher order tensor models. Two quality measures for the gradient encoding schemes, the condition number of the estimation matrix and a new measure that evaluates the signal deviation on simulated data, are used to determine which gradient encoding is suited best for higher order tensor estimations. Six different gradient encoding scheme types were investigated. A certain force‐minimizing scheme type gave the best results in the evaluations presented here. Magn Reson Med 61:335–343, 2009.

Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi‐institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application‐specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48–61, 2018.

Diffusion simulation-based fiber tracking using time-of-arrival maps: a comparison with standard methods

ObjectWe propose a new tracking method based on time-of-arrival (TOA) maps derived from simulated diffusion processes.Materials and methodsThe proposed diffusion simulation-based tracking consists of three steps that are successively evaluated on small overlapping sub-regions in a diffusion tensor field. First, the diffusion process is simulated for several time steps. Second, a TOA map is created to store simulation results for the individual time steps that are required for the tract reconstruction. Third, the fiber pathway is reconstructed on the TOA map and concatenated between neighboring sub-regions. This new approach is compared with probabilistic and streamline tracking. All methods are applied to synthetic phantom data for an easier evaluation of their fiber reconstruction quality.ResultsThe comparison of the tracking results did show severe problems for the streamline approach in the reconstruction of crossing fibers, for example. The probabilistic method was able to resolve the crossing, but could not handle strong curvature. The new diffusion simulation-based tracking could reconstruct all problematic fiber constellations.ConclusionThe proposed diffusion simulation-based tracking method used the whole tensor information of a neighborhood of voxels and is, therefore, able to handle problematic tracking situations better than established methods.

A First-Principles Approach for Diffusion Tensor based Fiber Tracking

MR diffusion imaging provides information about brain structure and function on a microscopic level. Diffusion-tensor imaging in particular allows to determine the anisotropy of water diffusion and hence the local orientation of nerve fiber bundles. To study of neuronal connectivity it is desirable to derive the (continuous) course of these fiber bundles from the (discrete) diffusion tensor data. For this purpose several algorithms have been developed recently (l-9). We describe here a novel approach based on a physical simulation which combines advantages of the existing techniques while avoiding some of their problems (e.g. the choice of arbitrary parameters).

Rekonstruktion von neuronalen Trajektorien mittels Time-of-Arrival-Maps

Die Forschung im Bereich der Hirn-Konnektivitat befasst sich damit, wie die einzelnen Regionen im Gehirn miteinander und mit peripheren Nerven verbunden sind. Mit der Diffusionstensor-Magnetresonanztomographie (DT-MRT) ist es moglich, diese Fragestellung nicht-invasiv am Menschen invivo zu untersuchen. Im Folgenden wird eine neue Methode zur Rekonstruktion des Verlaufs von Nervenfaserbahnen auf Basis der mit DT-MRT gewonnenen Daten („Fiber-Tracking“) vorgestellt. Zur Rekonstruktion der Trajektorien wird zunachst ein Diffusionsprozess vom Ursprung der Trajektorien aus simuliert. Auf einer aus den Ergebnissen der Simulation fur diesen Ursprung generierten Time-of-Arrival-Map, die jedem Punkt im Datensatz den Zeitpunkt an dem ihn die Diffusionsfront erreicht zuordnet, wird ein Gradientenabstieg durchgefuhrt um die Trajektorien zu rekonstruieren. Diese Methode ist stabiler gegenuber Rauschen und komplexen Subvoxelstrukturen als die verbreiteten Streamline-Methoden.