fMRI Pattern Classification using Neuroanatomically Constrained Boosting

Pattern classification in functional MRI (fMRI) is a novel methodology to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. Thus, most previous fMRI classification methods were applied in individual subjects. In this study, we developed a novel approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. Spatially normalized activation maps were segmented into functional areas using a neuroanatomical atlas and each map was classified separately using local classifiers. A single multiclass output was applied using a weighted aggregation of the classifier’s outputs. An Adaboost technique was applied, modified to find the optimal aggregation of a set of spatially distributed classifiers. This Adaboost combined the regionspecific classifiers to achieve improved classification accuracy with respect to conventional techniques. Multiclass classification accuracy was assessed in an fMRI group study with interleaved motor, visual, auditory, and cognitive task design. Data were acquired across 18 subjects at different field strengths (1.5 T, 4 T), with different pulse sequence parameters (voxel size and readout bandwidth). Misclassification rates of the boosted classifier were between 3.5% and 10%, whereas for the single classifier, these were between 15% and 23%, suggesting that the boosted classifier provides a better generalization ability together with better robustness. The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patternsPublicad

Automatic Placement of Outer Volume Suppression Slices in MR Spectroscopic Imaging of the Human Brain

Spatial suppression of peripheral regions (outer volume suppression) is used in MR spectroscopic imaging to reduce contamination from strong lipid and water signals. The manual placement of outer volume suppression slices requires significant operator interaction, which is time consuming and introduces variability in volume coverage. Placing a large number of outer volume saturation bands for volumetric MR spectroscopic imaging studies is particularly challenging and time consuming and becomes unmanageable as the number of suppression bands increases. In this study, a method is presented that automatically segments a high-resolution MR image in order to identify the peripheral lipid-containing regions. This method computes an optimized placement of suppression bands in three dimensions and is based on the maximization of a criterion function. This criterion function maximizes coverage of peripheral lipid-containing areas and minimizes suppression of cortical brain regions and regions outside of the head. Computer simulation demonstrates automatic placement of 16 suppression slices to form a convex hull that covers peripheral lipid-containing regions above the base of the brain. In vivo metabolite mapping obtained with short echo time proton-echo-planar spectroscopic imaging shows that the automatic method yields a placement of suppression slices that is very similar to that of a skilled human operator in terms of lipid suppression and usable brain voxels.Publicad

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality.The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients).RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality.This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI

Development of a Symmetric Echo-Planar Spectroscopy Imaging Framework for Hyperpolarized 13C Imaging in a Clinical PET/MR Scanner

Here, we developed a symmetric echo-planar spectroscopic imaging (EPSI) sequence for hyperpolarized 13C imaging on a clinical hybrid positron emission tomography/magnetic resonance imaging system. The pulse sequence uses parallel reconstruction pipelines to separately reconstruct data from odd-and-even gradient echoes to reduce artifacts from gradient imbalances. The ramp-sampled data in the spatiotemporal frequency space are regridded to compensate for the chemical-shift displacements. Unaliasing of nonoverlapping peaks outside of the sampled spectral width was performed to double the effective spectral width. The sequence was compared with conventional phase-encoded chemical-shift imaging (CSI) in phantoms, and it was evaluated in a canine cancer patient with ameloblastoma after injection of hyperpolarized [1-13C]pyruvate. The relative signal-to-noise ratio of EPSI with respect to CSI was 0.88, which is consistent with the decrease in sampling efficiency due to ramp sampling. Data regridding in the spatiotemporal frequency space significantly reduced spatial blurring compared with direct fast Fourier transform. EPSI captured the spatial distributions of both metabolites and their temporal dynamics in vivo with an in-plane spatial resolution of 5 × 9 mm2 and a temporal resolution of 3 seconds. Significantly higher spatial and temporal resolution for delineating anatomical structures in vivo was achieved for EPSI metabolic maps than for CSI maps, which suffered spatiotemporal blurring. The EPSI sequence showed promising results in terms of short acquisition time and sufficient spectral bandwidth of 500 Hz, allowing to adjust the trade-off between signal-to-noise ratio and encoding speed

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