Tess E. Wallace^1,2, Davide Piccini^3,4,5, Tobias Kober^3,4,5, Simon K. Warfield^1,2, and Onur Afacan^1,2
1Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, ⁴Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁵LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

We propose a novel motion compensation strategy for 3D radial MRI that directly estimates rigid-body motion parameters from the central k-space signal, which acts as a self-encoded FID navigator. By modelling trajectory deviations as low-spatial-order field variations, motion parameters can be recovered using a model that predicts the impact of motion and field changes on the FID signal. The proposed method enabled robust compensation for deliberate head motion in volunteers, with position estimates and image quality equivalent to that obtained with electromagnetic tracking. Our approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large, frequent motion.

Daniel Polak^1,2,3, Stephen Cauley^2,4,5, Berkin Bilgic^2,4,5, Daniel Nicolas Splitthoff³, Peter Bachert¹, Lawrence L. Wald^2,4,5, and Kawin Setsompop^2,4,5
1Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, ²Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Siemens Healthcare GmbH, Erlangen, Germany, ⁴Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁵Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Navigation-free retrospective motion-correction typically requires estimating hundreds of coupled temporal motion parameters by solving a large non-linear inverse problem. This can be extremely demanding computationally, which has impeded implementation/adoption in clinical settings. We propose a technique that utilizes a single rapid scout scan (T_add=3sec) to drastically reduce the computation cost of this motion-estimation and create a pathway for clinical acceptance. We optimized this scout along with the sequence acquisition reordering in a 3D Turbo-Spin-Echo acquisition. Our approach was evaluated in-vivo with up to R=6-fold acceleration and robust motion-mitigation was achieved using a scout with differing contrast to the imaging sequence.

Laura Bortolotti¹, Olivier Mougin¹, and Richard Bowtell^1
1Physics, Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom

In this work, a step towards a non-contact motion correction technique has been made. Measurements of extra-cranial field perturbations made using a 16-channel magnetic field camera have been used to predict head motion parameters with good accuracy. The prediction was performed using both linear (PLS) and non-linear (NARX) methods. The number of field probes used for the prediction was reduced by performing Principal Component Analysis. Magnetic field data was also pre-processed to reduce the unwanted effect of chest movement in respiration. NARX outperformed the PLS approach producing good predictions of head position changes for a wider range of movements.

Xiaozhi Cao^1,2,3, Congyu Liao^2,3, Zijing Zhang^2,4, Mary Kate Manhard^2,3, Hongjian He¹, Jianhui Zhong¹, Berkin Bilgic^2,3,5, and Kawin Setsompop^2,3,5
1Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, ²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, charlestown, MA, United States, ³Department of Radiology, Harvard Medical School, charlestown, MA, United States, ⁴State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, ⁵Harvard-MIT Department of Health Sciences and Technology, Cambridge, MA, United States

We proposed a motion-correction method for joint reconstruction of blip-up/down EPI acquisition (BUDA-EPI) of brain diffusion MRI. Motion parameters were estimated and incorporated into the joint parallel imaging reconstruction of the blip-up/down multi-shot data, which included B0 field maps and Hankel structured low-rank constraint. The proposed motion-corrected reconstruction approach was demonstrated in vivo to provide motion-robust reconstruction of blip-up/down multi-shot EPI diffusion data. 

Jakob Slipsager^1,2,3,4, Stefan Glimberg⁴, Liselotte Højgaard², Rasmus Paulsen¹, Andre van der Kouwe^3,5, Oline Olesen^1,4, and Robert Frost^3,5
1DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark, ²Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴TracInnovations, Ballerup, Denmark, ⁵Department of Radiology, Harvard Medical School, Boston, MA, United States

This work compares prospective and retrospective motion correction based on their capabilities to remove motion artifacts from 3D-encoded MPRAGE scans. Motion artifacts in clinical and research brain MRI are a major concern and the outcome of this problem includes repeated scans and the need for patient sedation or anesthesia, causing increased study time and cost. The prospective and retrospective correction approaches substantially improve the image quality of in-vivo scans for similar motion patterns. Prospective motion correction resulted in higher image quality than retrospective correction for larger discrete movements, and for periodic motion.   

Robert Frost^1,2, M. Dylan Tisdall³, Malte Hoffmann^1,2, Bruce Fischl^1,2,4, David H. Salat^1,2, and André J. W. van der Kouwe^1,2
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, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, ⁴Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

In studies that acquire a single prospectively-corrected scan it is unclear whether motion correction was beneficial when inspecting residual artifacts and the motion profiles. Here we used reverse motion correction to estimate images that would have resulted without vNav prospective motion correction (PMC). Matched motion tests were used to assess whether the reverse correction step was an accurate representation of images acquired during similar motion but without PMC. Using reverse motion correction on a subset of scans from the Human Connectome Project Aging study suggests that vNav PMC and selective reacquisition substantially improved image quality when there was motion.

Lucilio Cordero-Grande¹, Raphael Tomi-Tricot², Giulio Ferrazzi³, Jan Sedlacik¹, Shaihan Malik¹, and Joseph V Hajnal^1
1Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences / MR Research Collaborations, King's College London / Siemens Healthcare Limited, London / Frimley, United Kingdom, ³Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Retrospective motion correction is applied for preserved image resolution on ultra-high field volumetric in-vivo brain MRI. Correction is based on the synergistic combination of appropriate view reorderings for increasing the sensitivity to motion and aligned reconstructions for deconvolving the effect of motion. Resolution loss introduced by motion is reverted without resorting to external motion tracking systems, navigators or training data. Contrast and sharpness improvements are shown on high resolution flow and susceptibility sensitive T1- and T2*-weighted spoiled gradient echo sequences acquired on cooperative volunteers.

Zhe Wu¹ and Kâmil Uludağ^1,2,3
1Techna Institute, University Health Network, Toronto, ON, Canada, ²Koerner Scientist in MR Imaging, University Health Network, Toronto, ON, Canada, ³Center for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

Wave-CAIPI is a recently introduced parallel imaging method with high reduction factor and low g-factor penalty, thus is less prone to motion for the patients who cannot hold steady for long time. This study revealed that wave-CAIPI is still sensitive to during-scan motion, and proposed a joint optimization method to estimate motion and mitigate the introduced artifacts in wave-CAIPI images.

Adam M. J. van Niekerk¹, Tim Sprenger^2,3, Henric Rydén^1,2, Enrico Avventi^1,2, Ola Norbeck^1,2, and Stefan Skare^1,2
1Clinical Neuroscience, Karolinska Intitutet, Stockholm, Sweden, ²Neuroradiology, Karolinska University Hospital, Stockholm, Sweden, ³MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden

We explore a real-time method of controlling a wireless device using the pulse sequence - with a series of short RF pulses. We show that it is possible to encode and detect eight unique identifiers with a high reliability in 52 μs. Some identifiers are followed by short (< 1 ms) navigators that encode the pose of the device in the imaging volume. Other identifiers are followed by more RF pulses that encode information used for device configuration.  These tools minimise the impact on the main pulse sequence and allow the device to tailor feedback precision to the pulse sequence requirements.

Ryan Beckerleg¹, Joseph Whittaker¹, Daniel Gallichan², and Kevin Murphy^1
1CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom, ²CUBRIC, School of Engineering, Cardiff University, Cardiff, United Kingdom

Motion correction is an important preprocessing step in fMRI research¹. Motion artefacts not only affect image quality but can lead to erroneous results which are normally corrected using a volume registration algorithm (VRA). Here we demonstrate that when global intensity changes are present in the data (e.g., caused by a CO₂ challenge during measurement of cerebrovascular reactivity (CVR) or by ASL tagging), the VRA misinterprets such intensity changes as motion. We compare the motion derived from the VRA with motion parameters derived from an external optical tracking system to determine the extent of the problem.