There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies.

Purpose: To enable rigid body motion-tolerant parallel volumetric magnetic resonance imaging by retrospective head motion correction on a variety of spatiotemporal scales and imaging sequences. Theory and methods: Tolerance against rigid body motion is based on distributed and incoherent sampling orders for boosting a joint retrospective motion estimation and reconstruction framework. Motion resilience stems from the encoding redundancy in the data, as generally provided by the coil array. Hence, it does not require external sensors, navigators or training data, so the methodology is readily applicable to sequences using 3D encodings. Results: Simulations are performed showing full inter-shot corrections for usual levels of in vivo motion, large number of shots, standard levels of noise and moderate acceleration factors. Feasibility of inter-and intra-shot corrections is shown under controlled motion in vivo. Practical efficacy is illustrated by high-quality results in most corrupted of 208 volumes from a series of 26 clinical pediatric examinations collected using standard protocols. Conclusions: The proposed framework addresses the rigid motion problem in volumetric anatomical brain scans with sufficient encoding redundancy which has enabled reliable pediatric examinations without sedation. K E Y W O R D S distributed and incoherent sampling, image reconstruction, magnetic resonance, motion correction, parallel imaging 714 | CORDERO-GRANDE Et Al.

Purpose To implement and evaluate a pseudorandom undersampling scheme for combined simultaneous multislice (SMS) balanced SSFP (bSSFP) and compressed‐sensing (CS) reconstruction to enable myocardial perfusion imaging with high spatial resolution and coverage at 1.5 T. Methods A prospective pseudorandom undersampling scheme that is compatible with SMS‐bSSFP phase‐cycling requirements and CS was developed. The SMS‐bSSFP CS with pseudorandom and linear undersampling schemes were compared in a phantom. A high‐resolution (1.4 × 1.4 mm2) six‐slice SMS‐bSSFP CS perfusion sequence was compared with a conventional (1.9 × 1.9 mm2) three‐slice sequence in 10 patients. Qualitative assessment of image quality, perceived SNR, and number of diagnostic segments and quantitative measurements of sharpness, upslope index, and contrast ratio were performed. Results In phantom experiments, pseudorandom undersampling resulted in residual artifact (RMS error) reduction by a factor of 7 compared with linear undersampling. In vivo, the proposed sequence demonstrated higher perceived SNR (2.9 ± 0.3 vs. 2.2 ± 0.6, P = .04), improved sharpness (0.35 ± 0.03 vs. 0.32 ± 0.05, P = .01), and a higher number of diagnostic segments (100% vs. 94%, P = .03) compared with the conventional sequence. There were no significant differences between the sequences in terms of image quality (2.5 ± 0.4 vs. 2.8 ± 0.2, P = .08), upslope index (0.11 ± 0.02 vs. 0.10 ± 0.01, P = .3), or contrast ratio (3.28 ± 0.35 vs. 3.36 ± 0.43, P = .7). Conclusion A pseudorandom k‐space undersampling compatible with SMS‐bSSFP and CS reconstruction has been developed and enables cardiac MR perfusion imaging with increased spatial resolution and myocardial coverage, increased number of diagnostic segments and perceived SNR, and no difference in image quality, upslope index, and contrast ratio.

PurposeTo develop a purpose‐built quiet echo planar imaging capability for fetal functional and diffusion scans, for which acoustic considerations often compromise efficiency and resolution as well as angular/temporal coverage.MethodsThe gradient waveforms in multiband‐accelerated single‐shot echo planar imaging sequences have been redesigned to minimize spectral content. This includes a sinusoidal read‐out with a single fundamental frequency, a constant phase encoding gradient, overlapping smoothed CAIPIRINHA blips, and a novel strategy to merge the crushers in diffusion MRI. These changes are then tuned in conjunction with the gradient system frequency response function.ResultsMaintained image quality, SNR, and quantitative diffusion values while reducing acoustic noise up to 12 dB (A) is illustrated in two adult experiments. Fetal experiments in 10 subjects covering a range of parameters depict the adaptability and increased efficiency of quiet echo planar imaging.ConclusionPurpose‐built for highly efficient multiband fetal echo planar imaging studies, the presented framework reduces acoustic noise for all echo planar imaging‐based sequences. Full optimization by tuning to the gradient frequency response functions allows for a maximally time‐efficient scan within safe limits. This allows ambitious in‐utero studies such as functional brain imaging with high spatial/temporal resolution and diffusion scans with high angular/spatial resolution to be run in a highly efficient manner at acceptable sound levels. Magn Reson Med 79:1447–1459, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Purpose In conventional multiband (MB) CAIPIRINHA, additional reference scans are acquired to allow the separation of the excited slices. In this study, an acquisition‐reconstruction technique that makes use of the MB data to calculate these reference data is presented. The method was integrated into a 2D time‐resolved phase‐contrast MR sequence used to assess velocities of the myocardium. Methods The RF phases of the MB pulse are cycled through time so that consecutive cardiac phases can be grouped to form reference scans at a lower temporal resolution. These reference data are subsequently used to separate the original slices at the original, high temporal resolution using slice/split‐slice GRAPPA algorithms. Slice separation performances are evaluated and compared with conventional methods at 3 T, and 3 different strategies for the calibration of the kernels are proposed and compared. Finally, 6 subjects were scanned to assess velocities of the myocardium. Results Because the acquisition of external references is not needed, no additional breath‐holds are required and the full MB acceleration could be exploited. Because the reference and MB data have the same resolution and phase structure, better slice separation was achieved when comparing the proposed technique to conventional workflows. Finally, time‐resolved velocities of the myocardial tissue were successfully quantified from MB data, showing good agreement with single‐band measurements. Conclusion Our built‐in reference method allows the full exploitation of the MB acceleration and it limits the number of breath‐holds.

Purpose: 4D flow MRI permits to quantify non-invasively time-dependent velocity vector fields, but it demands long acquisition times. 2D-selective excitation allows to accelerate the acquisition by reducing the FOV in both phase encoding directions. In this study, we investigate 2D-selective excitation with reduced FOV imaging for fast 4D flow imaging while obtaining correct velocity quantification. Methods: Two different 2D-selective excitation pulses were designed using spiral kspace trajectories. Further, their isophase time point was analyzed using simulations that considered both stationary and moving spins. On this basis, the 2D-selective RF pulses were implemented into a 4D flow sequence. A flow phantom study and seven 4D flow in vivo measurements were performed to assess the accuracy of velocity quantification by comparing the proposed technique to non-selective and conventional 1D slab-selective excitation. Results: The isophase time point for spiral 2D-selective RF pulses was found to be located at the end of excitation for both stationary and moving spins. Based on that, 2D-selective excitation with reduced FOV allowed us to successfully quantify velocities both in a flow phantom and in vivo. In a flow phantom, the velocity difference Δv = (0.8 ± 5.3) cm/s between the smaller reduced FOV and the reference scan was similar to the inter-scan variability of Δv = (−1.0 ± 2.3) cm/s. In vivo, the differences in flow (P = 0.995) and flow volume (P = 0.469) between the larger reduced FOV and the reference scan were non-significant. By reducing the FOV by twothirds, acquisition time was halved. Conclusion: A reduced field-of-excitation allows to limit the FOV and therefore shorten 4D flow acquisition times while preserving successful velocity quantification. K E Y W O R D S 4D flow, fast imaging, reduced FOV, reduced FOX, spatially selective 2D RF excitation | 887 WINK et al.

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.

Conductivity tensor imaging (CTI) has been recently proposed to map the conductivity tensor in 3D using magnetic resonance imaging (MRI) at the frequency range of the brain at rest, i.e., low-frequencies. Conventional CTI mapping methods process the trans-receiver phase of the MRI signal using the MR electric properties tomography (MR-EPT) technique, which in turn involves the application of the Laplace operator. This results in CTI maps with a low signal-to-noise ratio (SNR), artifacts at tissue boundaries and a limited spatial resolution. In order to improve on these aspects, a methodology independent from the MR-EPT method is proposed. This relies on the strong assumption for which electrical conductivity is univocally pre-determined by water concentration. In particular, CTI maps are calculated by combining high-frequency conductivity derived from water maps and multi b-value diffusion tensor imaging (DTI) data. Following the implementation of a pipeline to optimize the pre-processing of diffusion data and the fitting routine of a multi-compartment diffusivity model, reconstructed conductivity images were evaluated in terms of the achieved spatial resolution in five healthy subjects scanned at rest. We found that the pre-processing of diffusion data and the optimization of the fitting procedure improve the quality of conductivity maps. We achieve reproducible measurements across healthy participants and, in particular, we report conductivity values across subjects of 0.55 ± 0.01Sm, 0.3 ± 0.01Sm and 2.15 ± 0.02Sm for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), respectively. By attaining an actual spatial resolution of the conductivity tensor close to 1 mm in-plane isotropic, partial volume effects are reduced leading to good discrimination of tissues with similar conductivity values, such as GM and WM. The application of the proposed framework may contribute to a better definition of the head tissue compartments in electroencephalograpy/magnetoencephalography (EEG/MEG) source imaging and be used as biomarker for assessing conductivity changes in pathological conditions, such as stroke and brain tumors.