Christoph Michael Schildknecht¹ and Klaas Paul Prüssmann^1
1Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zürich, Switzerland

Precisely characterizing motion tracking modalities in the MRI bore is quite a challenge. Especially when not only static positions ought to be evaluated, but also dynamic characterization is desired. In this work, we show how a compliant mechanism in the form of a folded-beam suspension can be utilized for accurate dynamic positioning. A piezoelectric actuator drives the system, and position feedback is acquired with a laser interferometer. Testing the system with short-wave motion tracking revealed a sub-micrometre agreement.

Zakaria Zariry^1,2, Robert Frost^3,4, Franck Lamberton^2,5, Thomas Troalen⁶, Nathalie Richard^1,2, Andre van der Kouwe^3,4, and Bassem Hiba^1,2
1Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, BRON, France, Metropolitan, ²Université Claude Bernard - Lyon 1, Villeurbanne, France, Metropolitan, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁵Cermep, CNRS / UAR 3453, Lyon, France, Metropolitan, ⁶Siemens Healthcare SAS, Saint-Denis, France, Metropolitan

Head-motion is a main cause of artifacts in MRI. The performance of a markerless optical system, which records the subject's face, estimates head-motion, and allows real-time repositioning of the FOV, is evaluated for neuroanatomical MRI. Sets of T1W/T2W-images were collected from 2 subjects instructed to perform different head-motion protocols during the acquisitions. The System performance is evaluated by comparing images with/without motion-correction. The optical system ensures good corrections of head-motions. However, the correction quality depends on the amplitude of the movement, its location in k-space and its nature. Residual effects of large amplitudes/amounts movements may persist on corrected images.

Julian Hossbach^1,2, Daniel Nicolas Splitthoff³, Bryan Clifford⁴, Daniel Polak^3,5, Wei-Ching Lo⁴, Stephan Cauley⁵, and Andreas Maier^1
1Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, ²Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany, ³Siemens Healthcare, Erlangen, Germany, ⁴Siemens Medical Solutions, Malden, MA, United States, ⁵Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States

Estimating intrascan subject motion enables the reduction of motion artifacts but often requires further calibration data e.g. from an additional motion-free reference. In this work, we explore how the reuse of supplementary scans in the imaging workflow can be used as motion calibration data. More specifically, the preceding parallel imaging calibration scan is reutilized to support a Deep Learning (DL) approach for estimating motion. Results are presented which indicate that DL, in contrast to a conventional optimization approach, can extract the motion and improve the image quality despite contrast differences between the calibration and imaging scans.

Nalini M Singh^1,2, Malte Hoffmann^3,4, Daniel C Moyer¹, Ikbeom Jang^3,4, Lina Chen⁵, Marcio Aloisio Bezerra Cavalcanti Rockenbach⁵, Arnaud Guidon⁶, Iman Aganj^3,4, Jayashree Kalpathy-Cramer^3,4,5, Elfar Adalsteinsson^2,7,8, Bruce Fischl^2,3,4, Adrian V Dalca^1,3,4, Polina Golland*^1,7,8, and Robert Frost*^3,4
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁵MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, United States, ⁶GE Healthcare, Boston, MA, United States, ⁷Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁸Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

We demonstrate a deep learning approach for fast retrospective intraslice rigid motion correction in segmented multislice MRI. The proposed neural network architecture combines convolutions on frequency and image space representations of the acquired data to produce high quality reconstructions. Unlike many prior techniques, our method does not require auxiliary information on the subject head motion during the scan. The resulting reconstruction procedure is more accurate and is an order of magnitude faster than GRAPPA. Our work offers the first step toward fast motion correction in any setting with substantial, unpredictable, challenging to track motion.

Yulin Chang¹, Daniel Nicolas Splitthoff², Wei-ching Lo¹, M. Dylan Tisdall³, and Andre van der Kouwe^4
1Siemens Medical Solutions USA Inc., Malvern, PA, United States, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States

We show that for navigator-based prospective motion correction MRI, acceleration of 3D EPI acquisition increases sequence flexibility and improves the navigator image quality without sacrificing the quality of motion correction. 

Tom Wilkinson^1,2, Yannick Brackenier^1,2, Felipe Godinez³, Raphael Tomi-Tricot^1,2,4, Jan Sedlacik^1,2, Philippa Bridgen^1,2, Sharon Giles^1,2, Joseph V Hajnal^1,2, and Shaihan J Malik^1,2
1Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²London Collaborative Ultra high field System (LoCUS), London, United Kingdom, ³Department of Radiology, University of California Davis, Sacramento, CA, United States, ⁴MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

3D FatNavs were used to provide a rapid, robust pre-scan to calibrate motion estimation from pilot-tone. The amount of training data required to make reliable forward predictions was investigated. This method robustly predicts motion within a dataset and can be applied to other datasets with lower accuracy. Improving the accuracy and speed of this method is ongoing work. This independent method of motion estimation shows promise for rapid calibration as part of routine examinations and paves the way to offer motion correction at ultra-high field where motion-correction is particularly relevant.

Jonathan R. Polimeni^1,2,3, M. Dylan Tisdall⁴, Daniel J. Park¹, Paul Wighton¹, S. Robert Frost^1,2, Christine L. Tardif^5,6, and Andre 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, ³Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, ⁵Department of Biomedical Engineering, Department of Neurology & Neurosurgery, McGill University, Montreal, QC, Canada, ⁶McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada

In multi-inversion EPI (MI-EPI), each slice samples a distinct inversion time during each inversion recovery, providing an efficient method for estimating T₁. MI-EPI is vulnerable to through-plane motion, which results in slices sampling a subset of the desired inversion times and wrong TIs will be attributed to the slices. This cannot be corrected retrospectively. We introduce prospective motion correction in MI-EPI using volumetric navigators (vNavs). vNavs are acquired at the beginning of the inversion recovery thus the effects of their excitation pulses must be modeled for T₁ estimation. This provides improved T₁ estimation accuracy in the presence of subject motion.

Gabrio Rizzuti¹, Niek Huttinga², Alessandro Sbrizzi², and Tristan van Leeuwen^3
1Utrecht University, Utrecht, Netherlands, ²Universitair Medisch Centrum Utrecht, Utrecht, Netherlands, ³Centrum Wiskunde & Informatica, Amsterdam, Netherlands

We present a retrospective rigid motion correction and reconstruction scheme for brain MRI with the aid of structural priors. The proposed framework is designed to be applied to a clinical protocol including multiple scans for multiple contrasts, of which some scans are corrupted due to motion during the acquisitions, but at least one is uncorrupted and exploited as an additional prior. We envision a practical workflow that can easily fit into the existing clinical practice without the need for changing the MRI acquisition protocols.

Aziz Kocanaogullari¹, Cemre Ariyurek¹, Onur Afacan¹, and Sila Kurugol^1
1Radiology, Boston Children's Hospital, Boston, MA, United States

Radial DCE-MRI is robust to motion. However, bulk motion or heavy breathing causes 1) irrecoverably deteriorated k-space lines acquired during motion events reducing image quality, 2) misaligned volumes in a dynamic sequence. In this work we propose to solve the first problem by fitting a Gaussian process to the k-space center of each spoke over time and using it to determine outlier spokes corrupted by motion. We solve the second problem by clustering the dynamic data to respective motionless phases before and after each motion event and registering volumes between phases for computationally efficient correction of motion with fewer registrations.

Mohammed A. Al-masni¹, Seul Lee¹, Jaeuk Yi¹, Sewook Kim¹, Sung-Min Gho², Young Hun Choi³, and Dong-Hyun Kim^1
1Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea, Republic of, ²GE Healthcare, Seoul, Korea, Republic of, ³Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

MRI is sensitive to motion caused by patient movement. It may cause severe degradation of image quality. We develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to reduce the rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. The experimental results prove the feasibility of self-assisted priors since it does not require any further data scans. 

Adam George Tattersall^1,2, Keith A Goatman², Scott Semple¹, and Lucy E Kershaw^1
1University of Edinburgh, Edinburgh, United Kingdom, ²Canon Medical Research Europe, Edinburgh, United Kingdom

Before registration can take place, an approach to pairing images must be chosen. We used synthetic data to provide a ground truth to evaluate four approaches to pairing images.  Our synthetic data was created using tracer kinetics modelling of abdominal DCE-MRI to create motionless data. Then local distortions were applied to the motionless data. We found that using one image as a reference image produced the best result to eliminate the propagation of errors. The reference image must have a similar intensity distribution to the other images in the sequence.