Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils

Clinical phenotype modulates brain's myelin and iron content in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is associated with brain pathology extending beyond temporal lobe structures. We sought to look for informative patterns of brain tissue properties in TLE that go beyond the established morphometry differences. We hypothesised that volume differences, particularly in hippocampus, will be paralleled by changes in brain microstructure. The cross-sectional study included TLE patients (n = 25) from a primary care center and sex-/age-matched healthy controls (n = 55). We acquired quantitative relaxometry-based magnetic resonance imaging (MRI) data yielding whole-brain maps of grey matter volume, magnetization transfer (MT) saturation, and effective transverse relaxation rate R2* indicative for brain tissue myelin and iron content. For statistical analysis, we used the computational anatomy framework of voxel-based morphometry and voxel-based quantification. There was a positive correlation between seizure activity and MT saturation measures in the ipsilateral hippocampus, paralleled by volume differences bilaterally. Disease duration correlated positively with iron content in the mesial temporal lobe, while seizure freedom was associated with a decrease of iron in the very same region. Our findings demonstrate the link between TLE clinical phenotype and brain anatomy beyond morphometry differences to show the impact of disease burden on specific tissue properties. We provide direct evidence for the differential effect of clinical phenotype characteristics on processes involving tissue myelin and iron in mesial temporal lobe structures. This study offers a proof-of-concept for the investigation of novel imaging biomarkers in focal epilepsy.

Impacting the effect of fMRI noise through hardware and acquisition choices - Implications for controlling false positive rates

We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that they can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial auto-correlation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions.

Coil-to-coil physiological noise correlations and their impact on functional MRI time-series signal-to-noise ratio

PURPOSE

Physiological nuisance fluctuations ("physiological noise") are a major contribution to the time-series signal-to-noise ratio (tSNR) of functional imaging. While thermal noise correlations between array coil elements have a well-characterized effect on the image Signal to Noise Ratio (SNR₀ ), the element-to-element covariance matrix of the time-series fluctuations has not yet been analyzed. We examine this effect with a goal of ultimately improving the combination of multichannel array data.

THEORY AND METHODS

We extend the theoretical relationship between tSNR and SNR₀ to include a time-series noise covariance matrix Ψ_t , distinct from the thermal noise covariance matrix Ψ₀ , and compare its structure to Ψ₀ and the signal coupling matrix SS^H formed from the signal intensity vectors S.

RESULTS

Inclusion of the measured time-series noise covariance matrix into the model relating tSNR and SNR₀ improves the fit of experimental multichannel data and is shown to be distinct from Ψ₀ or SS^H .

CONCLUSION

Time-series noise covariances in array coils are found to differ from Ψ₀ and more surprisingly, from the signal coupling matrix SS^H . Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process. Magn Reson Med 76:1708-1719, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

POAS4SPM: a toolbox for SPM to denoise diffusion MRI data

We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multi-shell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in non-adaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experiment-dependent parameters for an optimal performance of msPOAS.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS

We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods.

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Method for structure preserving smoothing multi-shell dMRI data

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Does not rely on any dMRI diffusion model

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Outperforms naive single-shell POAS and other approaches

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Feasible for real data application

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Implemented within a freely available package dti for the R Language

Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential

Functional MRI at 3T has become a workhorse for the neurosciences, e.g., neurology, psychology, and psychiatry, enabling non-invasive investigation of brain function and connectivity. However, BOLD-based fMRI is a rather indirect measure of brain function, confounded by physiology related signals, e.g., head or brain motion, brain pulsation, blood flow, intermixed with susceptibility differences close or distant to the region of neuronal activity. Even though a plethora of preprocessing strategies have been published to address these confounds, their efficiency is still under discussion. In particular, physiological signal fluctuations closely related to brain supply may mask BOLD signal changes related to "true" neuronal activation. Here we explore recent technical and methodological advancements aimed at disentangling the various components, employing fast multiband vs. standard EPI, in combination with fast temporal ICA. Our preliminary results indicate that fast (TR <0.5 s) scanning may help to identify and eliminate physiologic components, increasing tSNR and functional contrast. In addition, biological variability can be studied and task performance better correlated to other measures. This should increase specificity and reliability in fMRI studies. Furthermore, physiological signal changes during scanning may then be recognized as a source of information rather than a nuisance. As we are currently still undersampling the complexity of the brain, even at a rather coarse macroscopic level, we should be very cautious in the interpretation of neuroscientific findings, in particular when comparing different groups (e.g., age, sex, medication, pathology, etc.). From a technical point of view our goal should be to sample brain activity at layer specific resolution with low TR, covering as much of the brain as possible without violating SAR limits. We hope to stimulate discussion toward a better understanding and a more quantitative use of fMRI.

Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation

Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD(*)), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2(*) = 1/T2(*)). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2(*) (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.

The impact of physiological noise correction on fMRI at 7 T

Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.