Nadine N Graedel¹, Oliver Warrington¹, Barbara Dymerska¹, Vahid Malekian¹, Oliver Josephs¹, Peter Kok¹, and Martina F Callaghan^1
1Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

Functional MRI in the entorhinal cortex is very challenging due to large and geometrically complex B₀ field inhomogeneity. As a result, a quarter of voxels are lost due to T₂^*-driven dephasing and/or because the echo is pushed outside of the acquisition window. Additionally, off-resonant voxels suffer from poor excitation efficiency with frequency-selective binomial pulse excitation, which are preferred for fat suppression for SAR and time efficiency. We empirically demonstrate substantial signal, and consequently tSNR, gains in off-resonance regions by reducing binomial pulse order (from 1-3-3-1 to 1-2-1 or 1-1) that match numerical predictions.

Kimberly J. Hemmerling^1,2, Mark A. Hoggarth², Todd Parrish³, Robert L. Barry^4,5,6, and Molly G. Bright^1,2
1Biomedical Engineering, Northwestern University, Evanston, IL, United States, ²Physical Therapy & Human Movement Sciences, Northwestern University, Chicago, IL, United States, ³Department of Radiology, Northwestern University, Chicago, IL, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁶Harvard–Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, United States

SpinalCompCor is a denoising technique in which principal component (PC) analysis is performed in a region outside of the spinal cord to define nuisance regressors. Temporal SNR was greater when PC regressors were included in the general linear model, compared to when they were regressed out of the data prior to motion correction. In comparing models that vary the number of PC regressors, models with multiple PCs generally performed better than those with single PCs. SpinalCompCor is a promising technique for spinal cord fMRI denoising.

Igor Fabian Tellez Ceja¹, Thomas Gladytz¹, Ludger Benedikt Starke¹, Karsten Tabelow², Thoralf Niendorf^1,3, and Henning Matthias Reimann^1
1Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Berlin, Germany, ²Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ³Experimental and Clinical Research Center (ECRC), a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

Superior spatial fidelity of fMRI at ultrahigh magnetic field strengths (≥7T) in principle allows for resolving fine grained task-specific fMRI neurosignatures. Yet, spatial details of BOLD clusters are conventionally blurred by Gaussian smoothing. To preserve spatial details, spatial-adaptive non-local means (SANLM) denoising has been introduced in fMRI as an alternative to Gaussian smoothing. Here, we evaluate SANLM denoising at 7T. SANLM removes noise in homogeneous areas while maintaining edges. It prevents the spread of BOLD patterns between adjacent brain regions, but should be employed with caution, as its spatial detail is partially driven by the underlying anatomy. 

Didi Chi¹, Yasmin Blunck^1,2, Rebecca Glarin², Catherine E. Davey^1,2, Daniel Staeb³, and Leigh A. Johnston^1,2
1Department of Biomedical Engineering, University of Melbourne, Parkville, Australia, ²Melbourne Brain Centre Imaging Unit, University of Melbourne, Parkville, Australia, ³MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia

A Field-Mapping-Embedded (FME) EPI sequence is proposed in which the map is acquired during EPI acquisition without increasing scan time, using phase-encoded phase correction navigators. Results from in vivo experiment demonstrate accurate  measurement and robust geometric distortion that performs favourably in comparison with existing techniques that require additional scans.

Mo Shahdloo¹ and Mark Chiew^1
1Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB, University of Oxford, Oxford, United Kingdom

Singular-value truncation techniques have shown promise for reducing thermal noise in fMRI, where singular-values below a certain threshold are assumed to be noise and are discarded. However, this approach could lead to suboptimal signal recovery, since the remaining singular-values could still have variance contributed by noise. Here instead we propose to use a theoretically MSE-optimal function to shrink the remaining singular-values. The proposed method is evaluated using simulations and high-resolution in-vivo human brain data, and is shown to improve signal-to-noise ratio and functional statistics while leaving the spatial precision intact.

Samuel Bianchi¹, Maria Engel¹, Jakob Heinzle², Stefan Frässle², Klaas E. Stephan^2,3, and Klaas P. Pruessmann^1
1Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zürich, Switzerland, ²Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zürich, Switzerland, ³Max Planck Institute for Metabolism Research, Cologne, Germany

fMRI is one of the driving technologies of neuroscience. During the past years, acquisition speeds below one second per whole-brain volume have been achieved. The recently proposed 3D T-Hex Spiral trajectories provide a powerful approach to enable even faster fMRI. Here, we demonstrate the feasibility of fMRI at a sampling rate of 5Hz with these trajectories at 7T. We acquired whole-brain images of one subject performing a visuo-motor task in the scanner. Our results indicate high BOLD sensitivity and fidelity of voxel timeseries. In total, we analyzed 9000 volumes obtained during 30min of scanning.

Praveen Iyyappan Valsala¹, Philipp Ehses², Marten Veldmann², and Klaus Scheffler^1,3
1Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, ²German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ³Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany

We investigate the possibility of using longer multi-shot spiral readouts to accelerate balanced steady-state free precession (bSSFP) at ultra-high field (UHF) for functional imaging. The reconstruction is performed with the full signal model accounting for spatial B₀ inhomogeneity and gradient imperfections. We present the activation maps from flickering checkerboard stimulus experiment and discuss challenges of spiral bSSFP at UHF.

Antonia Barghoorn¹, Bruno Riemenschneider², Wenchao Yang¹, and Jürgen Hennig^1
1Department of Radiology, Medical Physics, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ²Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

MREG allows very fast measurement of physiological signal changes at 100 ms acquisition time per volume. In order to investigate BOLD and non-BOLD contributions to the observed signal variations we have implemented an interleaved measurement scheme based on signal readout with two different echo times (15 and 36 ms). The interleaved implementation with alternating echo times increases the acquisition time to 200 ms, which still allows to investigate BOLD vs. non-BOLD signal changes at frequencies up to ±2.5 Hz.  Preliminary measurements demonstrate the feasibility of ME-MREG in detecting both neuronal activations and resting-state functional connectivities.

Denizhan Kurban¹, Renzo Huber¹, Gilad Liberman², Dimo Ivanov¹, and Benedikt A Poser^1
1Maastricht University, Maastricht, Netherlands, ²Athinoula A. Martinos Center for Biomedical Imaging, MGH, Boston, MA, United States

High-resolution blood volume-sensitive fMRI VASO can capture functional signal changes with high localization specivity compared to conventional BOLD sequences. However, it suffers from reduced sampling efficiencies and lower detection sensitivity. Here, we overcome these limitations by combining VASO contrasts with the high efficiency of multi-echo enabled spiral k-space sampling and CAIPI-optimized SMS acceleration. We find that spiral sampling enables faster VASO acquisitions than possible with Cartesian EPI sampling.  For high spatial resolutions (1.25mm), we confirm that the proposed method is insensitive to large draining veins while having more stable fMRI signals (higher tSNR).

Sangcheon Choi^1,2, Xin Yu³, Klaus Scheffler^1,4, and Kai Herz^1,4
1High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, ²Graduate Training Centre of Neuroscience, University of Tuebingen, Tuebingen, Germany, ³Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany

GRE- or SE-based line-scanning fMRI methods can characterize dynamic fMRI signals with high spatial and temporal resolution. Here, we present a combination of both using a 1D GRASE sequence with perpendicular excitation and refocusing slices. We applied the sequence in-vivo at 3T and analyzed slice pofiles and tSNR.

Avery JL Berman^1,2, Kawin Setsompop^3,4, Thomas Witzel⁵, William A Grissom^6,7, and Jonathan R Polimeni^1,2,8
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Department of Radiology, Stanford University, Palo Alto, CA, United States, ⁴Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States, ⁵Q Bio, Inc., San Carlos, CA, United States, ⁶Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ⁷Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ⁸Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

We propose a novel slice-GRAPPA reconstruction algorithm, termed multi-kernel slice-GRAPPA (mks-GRAPPA), to tackle the challenge of reconstructing high spatial resolution segmented multi-shot EPI data for fMRI. This is particularly relevant for the recently proposed Variable-Flip-Angle “FLEET” pulse sequence. For a segmentation factor, S, by training 2×S slice-GRAPPA kernels, rather than one, we demonstrate significant improvements in image quality metrics under a wide range of protocols. The multitude of kernels account for static signal discontinuities within and across segments in multi-shot EPI. In the SNR starved regime of high-res fMRI, mks-GRAPPA allows us to recover a significant portion of lost tSNR.