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19 February 2018
Echo planar imaging (EPI) is an MRI technique of particular value to neuroscience, with its use for virtually all functional MRI (fMRI) and diffusion imaging of fiber connections in the human brain. EPI generates a single 2D image in a fraction of a second; however, it requires 2-3 seconds to acquire multi-slice whole brain coverage for fMRI and even longer for diffusion imaging. Here we report on a large reduction in EPI whole brain scan time at 3 and 7 Tesla, without significantly sacrificing spatial resolution, and while gaining functional sensitivity. The multiplexed-EPI (M-EPI) pulse sequence combines two forms of multiplexing: temporal multiplexing (m) utilizing simultaneous echo refocused (SIR) EPI and spatial multiplexing (n) with multibanded RF pulses (MB) to achieve m×n images in an EPI echo train instead of the normal single image. This resulted in an unprecedented reduction in EPI scan time for whole brain fMRI performed at 3 Tesla, permitting TRs of 400 ms and 800 ms compared to a more conventional 2.5 sec TR, and 2-4 times reductions in scan time for HARDI imaging of neuronal fibertracks. The simultaneous SE refocusing of SIR imaging at 7 Tesla advantageously reduced SAR by using fewer RF refocusing pulses and by shifting fat signal out of the image plane so that fat suppression pulses were not required. In preliminary studies of resting state functional networks identified through independent component analysis, the 6-fold higher sampling rate increased the peak functional sensitivity by 60%. The novel M-EPI pulse sequence resulted in a significantly increased temporal resolution for whole brain fMRI, and as such, this new methodology can be used for studying non-stationarity in networks and generally for expanding and enriching the functional information.
12 February 2018
In neuroimaging cluster-based inference has generally been found to be more powerful than voxel-wise inference. However standard cluster-based methods assume stationarity (constant smoothness), while under nonstationarity clusters are larger in smooth regions just by chance, making false positive risk spatially variant. Hayasaka et al. proposed a Random Field Theory (RFT) based nonstationarity adjustment for cluster inference and validated the method in terms of controlling the overall family-wise false positive rate. The RFT-based methods, however, have never been directly assessed in terms of homogeneity of local false positive risk. In this work we propose a new cluster size adjustment that accounts for local smoothness, based on local empirical cluster size distributions and a two-pass permutation method. We also propose a new approach to measure homogeneity of local false positive risk, and use this method to compare the RFT-based and our new empirical adjustment methods. We apply these techniques to both cluster-based and a related inference, threshold-free cluster enhancement (TFCE). Using simulated and real data we confirm the expected heterogeneity in false positive risk with unadjusted cluster inference but find that RFT-based adjustment does not fully eliminate heterogeneity; we also observe that our proposed empirical adjustment dramatically increases the homogeneity and TFCE inference is generally quite robust to nonstationarity.
18 December 2017
fMRI is a powerful tool used in the study of brain function. It can non-invasively detect signal changes in areas of the brain where neuronal activity is varying. This chapter is a comprehensive description of the various steps in the statistical analysis of fMRI data. This will cover topics such as the general linear model (including orthogonality, haemodynamic variability, noise modelling, and the use of contrasts), multi-subject statistics, and statistical thresholding (including random field theory and permutation methods). © 2009 Humana Press.
Independent component analysis of functional magnetic resonance imaging data using wavelet dictionaries
29 December 2017
Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable. © Springer-Verlag Berlin Heidelberg 2007.
Factors associated with progression of brain atrophy during ageing: 6 year follow-up from the Austrian stroke prevention study
27 December 2017
Neuroimaging techniques are increasingly used to study mechanisms leading to cognitive impairment. In particular, brain atrophy has been proposed as a surrogate marker of dementia. However, little is known regarding confounding factors which might modulate the evolution of brain atrophy during ageing. We therefore determined the rate of atrophy over 6 years for 201 participants (F/M=96/105; 59.8±5.9 yrs) in the Austrian Stroke Prevention Study and probed the impact of baseline variables on its progression. The mean annual brain volume change was -0.40±0.29%. The rate of brain atrophy was significantly higher in subjects of greater age and those with higher HbA 1c , higher body-mass-index, high alcohol intake, severe white matter hyperintensities, and in APOEε4-carriers. Multivariate analysis suggested that baseline brain volume, HbA 1c and the extent of white matter hyperintensities explain a major proportion of variance in the rates of brain atrophy. These results indicate that neurologically asymptomatic elderly experience continuing brain volume loss, which appears to accelerate with age. HbA 1c was identified as a risk factor for a greater rate of brain atrophy. Clustering of factors associated with the so-called "metabolic syndrome" in subjects with high HbA 1c suggests a link between this syndrome and late-life brain tissue loss. Together, this underscores the need to control for confounding factors in future Clinical trials and indicates possible new directions for intervention.
A comparison of the tissue classification and the segmentation propagation techniques in MRI brain image segmentation
27 October 2017
Tissue classifications of the MRI brain images can either be obtained by segmenting the images or propagating the segmentations of the atlas to the target image. This paper compares the classification results of the direct segmentation method using FAST with those of the segmentation propagation method using nreg and the MNI Brainweb phantom images. The direct segmentation is carried out by extracting the brain and classifying the tissues by FAST. The segmentation propagation is carried out by registering the Brainweb atlas image to the target images by affine registration, followed by non-rigid registration at different control spacing, then transforming the PVE (partial volume effect) fuzzy membership images of cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) of the atlas image into the target space respectively. We have compared the running time, reproducibility, global and local differences between the two methods. Direct segmentation is much faster. There is no significant difference in reproducibility between the two techniques. There are significant global volume differences on some tissue types between them. Visual inspection was used to localize these differences. This study had no gold standard segmentations with which to compare the automatic segmentation solutions, but the global and local volume differences suggest that the most appropriate algorithm is likely to be application dependent.
17 November 2017
Computed tomography has played a key role in bone structure imaging for over two decades. However, when a metal implant is present in the sample, the reconstructions are seriously distorted by artifact, and no method has successfully met the clinical demands. This paper presents a new method for metal artifact reduction in Computed Tomography based on sinusoidal description with the concentration of clinical applications. A piece of pig's leg with a lead nail placed inside the bone was scanned, generating 224 slices, in 177 of which the metal implant was present. The method includes detection of the correspondence of metal implants, fitting, amendment, and reconstruction based on sinusoidal description. Simulation and statistical error analysis show that the method improves PSNR (Peak Signal-to-Noise Ratio). A 3D modeling based on the reconstruction using the sinusoidal amendment method for a real case demonstrates that most of the metal artifact has been removed, which is compared with that based on the default output of the scanner. Metal artifacts in CT can be reduced effectively by the method based on the sinusoidal description, which isolates the correspondence of a metal implant from the original projection, so that a high quality reconstruction can be obtained.
8 December 2017
Computed Tomograghy has played a key role in bone structure imaging for over two decades. However, when a metal implant present in the sample, the reconstructions are seriously distorted by artefacts, and no method has successfully met the clinical demands. This paper presents a new method for partial reconstruction in Computed Tomography. The metal implant is reconstructed separately and the correspondence is isolated from the projection. The boundary between the metal implants and other tissues are clearer. The method is demonstrated by experiments.