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Purpose: To present a time-efficient technique for banding reduction in balanced steady-state free precession (bSSFP) imaging using phase-cycled simultaneous multislice (SMS) acquisition with CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration)., Theory: The proposed technique exploits the inherent phase modulation of SMS imaging with CAIPIRINHA to acquire multiple phase-cycled images, which can be combined for efficient banding reduction within the same scan time of a single-band bSSFP scan., Methods: Bloch equation simulation, phantom and in vivo brain, abdominal and cardiac imaging experiments were performed on healthy volunteers at 3T using multi-channel head and body array coils with SMS acceleration factors of two to four. The performance of banding reduction was quantitatively evaluated based on the percent ripple of signal distribution and signal-to-noise ratio (SNR) efficiency in both phantom and human studies., Results: The banding artifact was successfully removed or suppressed using phase-cycled SMS bSSFP imaging across SMS factors of two to four. The performance of banding reduction improved with higher SMS factors along with increased SNR efficiency., Conclusion: Phase-cycled SMS bSSFP with CAIPIRINHA is a promising technique for efficient band reduction in bSSFP without prolonged scan time. Further evaluation of this technique in clinical applications is warranted. Magn Reson Med 76:1764-1774, 2016. © 2015 International Society for Magnetic Resonance in Medicine., (© 2015 International Society for Magnetic Resonance in Medicine.)

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3 T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3 T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan., (© 2013 Elsevier Inc. All rights reserved.)

Purpose: To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation., Theory and Methods: It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest., Results: In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach., Conclusion: These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition., (Copyright © 2013 Wiley Periodicals, Inc.)

Purpose: To evaluate an alternative method for generating multibanded radiofrequency (RF) pulses for use in multiband slice-accelerated imaging with slice-GRAPPA unaliasing, substantially reducing the required peak power without bandwidth compromises. This allows much higher accelerations for spin-echo methods such as SE-fMRI and diffusion-weighted MRI where multibanded slice acceleration has been limited by available peak power., Theory and Methods: Multibanded "time-shifted" RF pulses were generated by inserting temporal shifts between the applications of RF energy for individual bands, avoiding worst-case constructive interferences. Slice profiles and images in phantoms and human subjects were acquired at 3 T., Results: For typical sinc pulses, time-shifted multibanded RF pulses were generated with little increase in required peak power compared to single-banded pulses. Slice profile quality was improved by allowing for higher pulse bandwidths, and image quality was improved by allowing for optimum flip angles to be achieved., Conclusion: A simple approach has been demonstrated that significantly alleviates the restrictions imposed on achievable slice acceleration factors in multiband spin-echo imaging due to the power requirements of multibanded RF pulses. This solution will allow for increased accelerations in diffusion-weighted MRI applications where data acquisition times are normally very long and the ability to accelerate is extremely valuable., (Copyright © 2013 Wiley Periodicals, Inc.)

A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets., (Copyright © 2011 Elsevier B.V. All rights reserved.)

q-Ball imaging is a high-angular-resolution diffusion imaging technique that has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (the probability of diffusion in a given direction) from q-ball data uses linear radial projection, neglecting the change in the volume element along each direction. This results in spherical distributions that are different from the true orientation distribution functions. For instance, they are neither normalized nor as sharp as expected and generally require postprocessing, such as artificial sharpening. In this paper, a new technique is proposed that, by considering the solid angle factor, uses the mathematically correct definition of the orientation distribution function and results in a dimensionless and normalized orientation distribution function expression. Our model is flexible enough so that orientation distribution functions can be estimated either from single q-shell datasets or by exploiting the greater information available from multiple q-shell acquisitions. We show that the latter can be achieved by using a more accurate multiexponential model for the diffusion signal. The improved performance of the proposed method is demonstrated on artificial examples and high-angular-resolution diffusion imaging data acquired on a 7-T magnet.

Parallel imaging in the form of multiband radiofrequency excitation, together with reduced k-space coverage in the phase-encode direction, was applied to human gradient echo functional MRI at 7 T for increased volumetric coverage and concurrent high spatial and temporal resolution. Echo planar imaging with simultaneous acquisition of four coronal slices separated by 44mm and simultaneous 4-fold phase-encoding undersampling, resulting in 16-fold acceleration and up to 16-fold maximal aliasing, was investigated. Task/stimulus-induced signal changes and temporal signal behavior under basal conditions were comparable for multiband and standard single-band excitation and longer pulse repetition times. Robust, whole-brain functional mapping at 7 T, with 2 x 2 x 2mm(3) (pulse repetition time 1.25 sec) and 1 x 1 x 2mm(3) (pulse repetition time 1.5 sec) resolutions, covering fields of view of 256 x 256 x 176 mm(3) and 192 x 172 x 176 mm(3), respectively, was demonstrated with current gradient performance., ((c) 2010 Wiley-Liss, Inc.)

Recent studies have demonstrated that multivariate machine learning algorithms applied to human functional MRI data can decode information segregated in cortical columns, despite the voxel size being large relative to the width of columns. The mechanism by which low spatial resolution imaging decodes information represented in a fine-scale organization is not clear. To investigate mechanisms underlying decoding signals we employed high-resolution gradient-echo BOLD functional MRI of visual area V1. We show that in addition to the fine-scale ocular dominance columns, coarse-scale structures extending over several millimeters also convey discriminative power for decoding the stimulated eye. Discriminative power is conveyed by both macroscopic blood vessels and gray matter regions. We hypothesize that gray-matter regions which drain into specific vessels may preferentially contain ocular-dominance columns biased towards one eye; the bias of a specific region thereby causing a functionally selective ocular-dominance response in the associated vessel. Our findings indicate that coarse-scale structures and macroscopic blood vessels contribute to decoding of the stimulated eye based on low-resolution multivariate data., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

At high magnetic field, MR images exhibit large, undesirable signal intensity variations commonly referred to as "intensity field bias". Such inhomogeneities mostly originate from heterogeneous RF coil B(1) profiles and, with no appropriate correction, are further pronounced when utilizing rooted sum of square reconstruction with receive coil arrays. These artifacts can significantly alter whole brain high resolution T(1)-weighted (T(1)w) images that are extensively utilized for clinical diagnosis, for gray/white matter segmentation as well as for coregistration with functional time series. In T(1) weighted 3D-MPRAGE sequences, it is possible to preserve a bulk amount of T(1) contrast through space by using adiabatic inversion RF pulses that are insensitive to transmit B(1) variations above a minimum threshold. However, large intensity variations persist in the images, which are significantly more difficult to address at very high field where RF coil B(1) profiles become more heterogeneous. Another characteristic of T(1)w MPRAGE sequences is their intrinsic sensitivity to Proton Density and T(2)(*) contrast, which cannot be removed with post-processing algorithms utilized to correct for receive coil sensitivity. In this paper, we demonstrate a simple technique capable of producing normalized, high resolution T(1)w 3D-MPRAGE images that are devoid of receive coil sensitivity, Proton Density and T(2)(*) contrast. These images, which are suitable for routinely obtaining whole brain tissue segmentation at 7 T, provide higher T(1) contrast specificity than standard MPRAGE acquisitions. Our results show that removing the Proton Density component can help in identifying small brain structures and that T(2)(*) induced artifacts can be removed from the images. The resulting unbiased T(1)w images can also be used to generate Maximum Intensity Projection angiograms, without additional data acquisition, that are inherently registered with T(1)w structural images. In addition, we introduce a simple technique to reduce residual signal intensity variations induced by transmit B(1) heterogeneity. Because this approach requires two 3D images, one divided with the other, head motion could create serious problems, especially at high spatial resolution. To alleviate such inter-scan motion problems, we developed a new sequence where the two contrast acquisitions are interleaved within a single scan. This interleaved approach however comes with greater risk of intra-scan motion issues because of a longer single scan time. Users can choose between these two trade offs depending on specific protocols and patient populations. We believe that the simplicity and the robustness of this double contrast based approach to address intensity field bias at high field and improve T(1) contrast specificity, together with the capability of simultaneously obtaining angiography maps, advantageously counter balance the potential drawbacks of the technique, mainly a longer acquisition time and a moderate reduction in signal to noise ratio.

Q-ball imaging (QBI) is a high angular resolution diffusion imaging (HARDI) technique which has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (ODF, the probability of diffusion in a given direction) from q-ball uses linear radial projection, neglecting the change in the volume element along the ray, thereby resulting in distributions different from the true ODFs. A new technique has been recently proposed that, by considering the solid angle factor, uses the mathematically correct definition of the ODF and results in a dimensionless and normalized ODF expression from a single q-shell. In this paper, we extend this technique in order to exploit HARDI data from multiple q-shells. We consider the more flexible multi-exponential model for the diffusion signal, and show how to efficiently compute the ODFs in constant solid angle. We describe our method and demonstrate its improved performance on both artificial and real HARDI data.

This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing.