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Thomas Küstner¹, Jan Borst¹, Dominik Nickel², Fabian Bamberg³, Marcel Früh¹, and Sergios Gatidis^1,4
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, ²Siemens Healthineers, Erlangen, Germany, ³Department of Diagnostic and Interventional Radiology, University of Freiburg, Freiburg, Germany, ⁴Max Planck Institute for Intelligent Systems, Tuebingen, Germany

Keywords: Machine Learning/Artificial Intelligence, Motion Correction, Motion Detection, Self-Supervised Learning

Motion is still one of the major extrinsic sources for imaging artifacts in MRI that can strongly deteriorate image quality. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion‐free reacquisition can become time‐ and cost‐intensive. Furthermore, in large-scale epidemiological cohorts, manual quality screening becomes impracticable. An automated quality assessment is thus of interest. Reliable motion estimation in varying domains (imaging sequences, multiple scanners, sites) is however challenging. In this work, we propose an attention-based transformer that can detect motion in various MR imaging scenarios.

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Zhuoyang Gu^1,2, Lianghu Guo¹, Qing Yang¹, Xinyi Cai¹, Tianli Tao¹, Sifan He¹, Hua Jiang¹, Haifeng Tang¹, Qian Wang¹, Xiaopeng Zong¹, Dinggang Shen¹, Qiang He², and Han Zhang^1
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, ²United Imaging Healthcare Co., Ltd., Shanghai, China

Keywords: Motion Correction, Brain, MRI acquisition

Head motion monitoring and compensation during MRI is essential to imaging quality and success rates of acquisition. MoCAP is a novel real-time head motion monitoring and correction technique utilizing structured light to perform prospective motion correction by adjusting MR gradient. We perform effectiveness and reliability assessment on MoCAP in structural and functional MRI. MoCAP can significantly improve quality of the two modalities, reducing motion artifacts and enhancing validity and reliability of post-processing results. MoCAP is promising in the MRI field for special populations like children and patients with difficulty in keeping still during scan.

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Thomas Ulrich¹, Malte Riedel¹, and Klaas Prüssmann^1
1Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zürich, Switzerland

Keywords: Motion Correction, fMRI (resting state)

We propose to apply a navigator-based prospective motion correction method to 3D-EPI fMRI scans. The method is investigated during in-vivo volunteer experiments with and without intentional motion. Post-processing with SPM12 shows that head motion is largely compensated by the navigator method. We show that the technique is sensitive to breathing motion and cardioballistic motion by comparing to physiological measurements from a breathing belt and pulse oximeter, and we show that it strongly reduces inter-volume displacements.

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Wanyong Shin¹ and Mark J Lowe^1
1Radiology, Cleveland Clinic, Cleveland, OH, United States

Keywords: Motion Correction, Brain, Head motion, fMRI

We investigate the source of residual motion artifact after volumetric motion correction using a custom MR sequence acquisition with prospectively injected motion (SIMPACE). We injected various patterns of motion during scanning ex-vivo brain phantoms at 3T to synthesize head motion in an fMRI dataset. We propose voxelwise retrospective motion regressors in addition to 6 rigid body motion parameters, then compare it with the “standard” 6 motion parameters and their derivatives regressor models. The proposed model improves tSNR by 40% and 11% in linear drift of motion and the realistic motion case, respectively, compared to 6 motion parameter model.

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Miriam Hewlett^1,2, Junmin Liu², and Maria Drangova^1,2
1Medical Biophysics, Western University, London, ON, Canada, ²Robarts Research Institute, London, ON, Canada

Keywords: Motion Correction, Motion Correction

Jointly acquired FID and spherical navigators were applied for prospective correction of motion and retrospective correction of zeroth order field offsets in a susceptibility weighted imaging protocol. Initial results showed a significant reduction in motion artifacts with prospective motion correction, quantified using structural similarity index. Retrospective B0 correction provided an additional improvement in image quality (also statistically significant). Future work will investigate methods to reduce residual artifacts, as well as incorporating the navigator within deadtime in the sequence to reduce scan time.

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Bo Li¹ and Thomas Ernst^1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, Baltimore, MD, United States

Keywords: Motion Correction, Diffusion/other diffusion imaging techniques, velocity navigator

Uncorrected head rotations during diffusion weighted imaging (DWI) can induce gradient imbalances that may shift the echo outside of the k-space window and cause signal dropouts. Therefore, we developed a rotational velocity navigator (~10ms duration) that is acquired immediately after each excitation and estimates the rotational velocities perpendicular to the diffusion gradient. The accuracy of estimated velocities was 4.1°/s relative to an optical tracking system (“gold standard”). Ultimately, the rotational velocities can predict the gradient moment error in first order, and a gradient blip can be applied to recenter the echo in k-space. 

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Guanhua Wang¹, Shouchang Guo², Jeffrey A. Fessler², and Douglas C. Noll^1
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²EECS, University of Michigan, Ann Arbor, MI, United States

Keywords: Motion Correction, Motion Correction

This abstract presents a retrospective motion correction method for fMRI. The method alternates between motion estimation and motion-informed model-based reconstruction. Compared to registration-based correction, this approach resolves intra-frame, inter-shot motion without additional navigators. The open-source GPU-based implementation enables efficient correction/reconstruction for large-scale non-Cartesian fMRI data. With prospective experiments, we demonstrate that our approach outperformed retrospective registration by providing higher-resolution images with reduced false positives in activation maps.

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Ulrich Lindberg¹, Stefan Glimberg², Gerard Crelier³, Martin Buehrer³, and Henrik Bo Wiberg Larsson^1
1Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark, ²TracInnovations, Ballerup, Denmark, ³GyroTools, Zurich, Switzerland

Keywords: Image Reconstruction, Motion Correction

Using an external motion tracking system for recording head movement during MR-acquisition and a modified version of the Philips reconstruction software (Recon2.0) that enables phase re-alignment and re-gridding of acquisition profiles during the standard immediate reconstruction process, we present a fully integrated retrospective motion correction solution that deliver both corrected and non-corrected images to the user and allows for the immediate visual assessment of the correction quality upon acquisition.

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Julian Hossbach^1,2, Daniel Nicolas Splitthoff², Bryan Clifford³, Daniel Polak^4,5, Stephan Cauley⁵, and Andreas Maier^1
1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Siemens Medical Solutions, Boston, MA, United States, ⁴Siemens Healthcare Gmbh, Erlangen, Germany, ⁵Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States

Keywords: Machine Learning/Artificial Intelligence, Motion Correction

Motion artifacts can pose a difficult challenge in the clinical workflow. For addressing this issue, we here investigate the performance of two Deep Learning based motion mitigation strategies, MoPED and NAMER, and demonstrate that both approaches can readily be combined. This allows for the correction of severely corrupted images.

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Jose de Arcos¹, Ana Beatriz Solana², Jonathan Weir-McCall^3,4, Emil Ljungberg^5,6, Joshua D Kaggie³, and Florian Wiesinger^2
1GE HealthCare, Little Chalfont, Amersham, United Kingdom, ²ASL Europe, GE HealthCare, Munich, Germany, ³Department of Radiology, University of Cambridge, Cambridge, United Kingdom, ⁴University of Cambridge School of Clinical Medicine, Royal Papworth Hospital, Cambridge, United Kingdom, ⁵Medical Radiation Physics, Lund University, Lund, Sweden, ⁶Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom

Keywords: Motion Correction, Motion Correction, Self-navigation

Here we propose a self-navigated technique, based on the extraction of respiratory motion estimates using interleaved spiral phyllotaxis trajectories in 3D radial zero echo time (ZTE) acquisitions. These are particularly well-suited to capture the short T2* signal, characteristic of lung parenchyma, and have high acquisition efficiency allowing to generate fast temporal resolution navigators. The self-navigation technique worked robustly for different respiratory patterns and on 1.5T and 3.0T fields strengths, obtaining high quality images comparable to the ones obtained with bellows-gating.

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Laura Bortolotti¹, Olivier Mougin¹, Paul Glover¹, Richard Bowtell¹, and Penny Gowland^1
1SPMIC, University of Nottingham, Nottingham, United Kingdom

Keywords: Motion Correction, Motion Correction, Marker-based, calibration

Open MRI scanner improves patient comfort but allows movement during scanning. Hence, implementation of MoCo techniques is crucial to maximize image quality. Here, we established a method to use a standard commercial optical motion tracking set-up in an Open MRI scanner. The optical system has been calibrated to provide tracking measurements in the image reference frame. Then, a simple phantom movement was tested and corrected using NUFFT algorithm in BART.

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Cemre Ariyurek¹, Serge Vasylechko¹, Xiaodong Zhong², Vibhas Deshpande³, Michael Bush⁴, Stephan Voss¹, Onur Afacan¹, and Sila Kurugol^1
1Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States, ²MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States, ³MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Austin, TX, United States, ⁴MR R&D Collaborations, Siemens Medical Solutions USA, Inc., New York, NY, United States

Keywords: Motion Correction, Diffusion/other diffusion imaging techniques

Diffusion-weighted MRI (DW-MRI) is capable of detecting and characterizing liver tumors and following-up treatments. Unfortunately, respiratory motion during DW-MRI scan causes misalignments between slices and reduces image quality. 3D slice-to-volume registration (SVR) can be employed to correct for motion. However, motion estimates may be inaccurate for high b-value images where SNR decreases. In this work, we propose to use PT estimated motion correction, which is calibrated on 3D SVR motion parameters obtained from low b-value images. We showed misalignments between slices are reduced by the proposed PT-based motion correction compared to SVR-based motion correction and no correction.

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Steven Winata¹, Daniel Christopher Hoinkiss², Graeme Alexander Keith¹, Salim Mohammed al-Wasity¹, and David Andrew Porter^1
1Imaging Centre of Excellence, University of Glasgow, Glasgow, Scotland, ²Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany

Keywords: Motion Correction, fMRI, Prospective motion correction; Markerless; Real-time

7 Tesla MRI provides higher signal-to-noise ratio (SNR) and spatial resolution, but is also more sensitive to motion-induced artefacts. The restricted environment within 7T scanners make non-hardware options for motion correction attractive. In this abstract, we investigate the use of the markerless, image-based, real-time Multislice Prospective Acquisition Correction (MS-PACE) technique for functional MRI protocols at 7T. This multislice scheme allows for a sub-TR motion detection and correction. It is demonstrated that the technique is able to correct longer-term motion components occurring during the acquisition.

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Yan Chen¹, James H. Holmes¹, Curtis A. Corum², Vincent Magnotta¹, and Mathews Jacob^1
1University of Iowa, Iowa City, IA, United States, ²Champaign Imaging, LLC, Minneapolis, MN, United States

Keywords: Motion Correction, Multi-Contrast

Recent quantitative parameter mapping methods including MR fingerprinting collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as deep factor model, which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the representation enables the acquisition of the images in a highly undersampled fashion, which translates to reduced scan time in 3D high-resolution multi-contrast applications. The approach integrates motion estimation and compensation, making the approach robust to subject motion during the scan.

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Daniel Polak¹, Daniel Nicolas Splitthoff¹, Bryan Clifford², Wei-Ching Lo², Yantu Huang³, Susie Huang⁴, John Conklin⁴, Lawrence L. Wald⁵, and Stephen F. Cauley^5
1Siemens Healthcare GmbH, Erlangen, Germany, ²Siemens Medical Solutions, Boston, MA, United States, ³Shenzhen Magnetic Resonance, Shenzhen, China, ⁴Massachusetts General Hospital, Boston, MA, United States, ⁵A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States

Keywords: Motion Correction, Brain, Value, Clinical Application

Retrospective motion correction for 2D TSE/FSE is challenging due to interpolation through slices with gaps, interleaved slice orderings, and spin history effects. Optimized Cartesian sampling trajectories provide decreased motion sensitivity in specific situations but can also exacerbate motion sensitivity under typical patient motion. In this work, we introduce a dynamic acquisition strategy that determines the k-space lines acquired in the next shot based on the prior patient motion. Specifically, each TR dynamically encodes available lines which minimize motion variance. We show that this dynamic acquisition strategy results in improved reconstruction robustness under typical clinical motion scenarios.

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Veronika J Spieker^1,2, Jonathan K Stelter³, Veronika A Zimmer², Kilian Weiss⁴, Rickmer F Braren³, Dimitrios Karampinos³, and Julia A Schnabel^1,2,5
1Helmholtz Center Munich, Munich, Germany, ²School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ³Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany, ⁴Philips GmbH Market DACH, Hamburg, Germany, ⁵School of Biomedical Imaging and Imaging Sciences, King's College, London, United Kingdom

Keywords: Motion Correction, Liver

Respiratory motion is a major source of artefacts in abdominal MRI and varies considerably across subjects. Motion-resolved strategies utilize the periodic nature of respiratory motion, but do not always consider absolute patient-specific motion information. The purpose of the present work is to develop a framework incorporating the patient-specific absolute body motion into a motion-resolved reconstruction (XD-GRASP), while adapting different motion binning strategies. To explore the potential of respiratory motion knowledge for MR reconstruction, individual absolute body motion, i.e. of the liver, is identified.

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Matthias Serger¹, Ruediger Stirnberg¹, Philipp Ehses¹, and Tony Stoecker^1,2
1German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ²Department of Physics and Astronomy, University of Bonn, Bonn, Germany

Keywords: Motion Correction, Motion Correction

FID navigators and field probes can measure local motion-induced magnetic field changes at different positions. Both methods are suited for marker-less prospective motion correction but with limited accuracy. A small 7T study with five subjects was performed to compare their performance for small involuntary and large voluntary motion. It is demonstrated that errors of regression models, calibrated between magnetic field measurements and motion parameters, are consistently reduced by combining both techniques. Furthermore, a proof-of-concept prospective motion correction experiment based on FID navigators is presented, which will be extended to include field probe measurements in the near future.  

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Mohammed A. Al-masni¹, Seul Lee², Sewook Kim², Sung-Min Gho³, Young Hun Choi⁴, and Dong-Hyun Kim^2
1Department of Artificial Intelligence, Sejong University, Seoul, Korea, Republic of, ²Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ³GE Healthcare, Korea, Seoul, Korea, Republic of, ⁴Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

Keywords: Motion Correction, Artifacts

Patient movement during MRI scan can cause severe degradation of image quality. In Susceptibility-Weighted Imaging (SWI), several echoes are measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the higher echoes are more susceptible to artifacts and signal dropout. This paper proposes a data-driven retrospective deep learning method by taking the advantage of interactively learning multiple echoes together through sharing their knowledge using unified training parameters. The proposed method allows to share information and gain an understanding of the correlations between multiple echoes towards generating high-resolution susceptibility enhanced contrast images.

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Daniel Nicolas Splitthoff¹, Stephen Cauley², Tobias Kober³, Julian Hossbach¹, Bryan Clifford⁴, Wei-Ching Lo⁴, Yan Tu Huang⁵, Susie Y. Huang⁶, John Conklin⁶, Lawrence L. Wald², and Daniel Polak^1
1Siemens Healthcare GmbH, Erlangen, Germany, ²A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Siemens Healthineers International AG, Lausanne, Switzerland, ⁴Siemens Medical Solutions, Boston, MA, United States, ⁵Shenzhen Magnetic Resonance Ltd.,, Shenzhen, China, ⁶Massachusetts General Hospital, Charlestown, MA, United States

Keywords: Motion Correction, Data Acquisition, Fast imaging, Compressed Sensing

Long clinical acquisitions can face the challenge of motion during the scan. One approach to address this problem is the recently introduced SAMER retrospective motion correction technique for 3D MPRAGE. As an alternative, it has been suggested to shorten the scan time by acceleration techniques, e.g., using Compressed Sensing, and thus reduce the likelihood of patient discomfort and severe motion. We here combine SAMER with Compressed Sensing for high acceleration factors (R=6).

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Laura Bortolotti¹, Isabel Clennell ¹, Amy Bradbury¹, Olivier Mougin¹, Paul Glover¹, Richard Bowtell¹, and Penny Gowland^1
1SPMIC, University of Nottingham, Nottingham, United Kingdom

Keywords: Motion Correction, Motion Correction, Marker based tracking

Body motion at nine typical upright MRI poses (standing, seated, supine), have been characterised for several body positions (head, shoulder, sternum, hip) for 20 subjects over 30 s (free breathing); and 20 s (breath hold) and for 9 subjects over 10 minutes. Free-standing caused most motion; lying supine caused least. The use of a body support and breath-holding reduced motion. Net motion was similar over 50-1000 ms sampling periods. Respiratory-related motion could be removed from tracking data. The degree of motion identified in this work will inform future respiratory triggering and motion correction work.