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Brackenier Y, Cordero-Grande L, McElroy S, Tomi-Tricot R, Barbaroux H, Bridgen P, Malik SJ, Hajnal JV. Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals. Magn Reson Med 2024. [PMID: 38860530 DOI: 10.1002/mrm.30161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/18/2024] [Accepted: 05/07/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast. THEORY AND METHODS PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. RESULTS The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach. CONCLUSIONS This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.
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Affiliation(s)
- Yannick Brackenier
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BNN, ISCIII, Madrid, Spain
| | - Sarah McElroy
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Raphael Tomi-Tricot
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Hugo Barbaroux
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Philippa Bridgen
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Shaihan J Malik
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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2
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Kang SH, Lee Y. Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images. Bioengineering (Basel) 2024; 11:227. [PMID: 38534500 DOI: 10.3390/bioengineering11030227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/20/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitting problem. The volume data were rotated and translated with random intensity and frequency, in three dimensions, and were iterated as the number of slices in the volume data. Then, for every slice, a portion of the motion-free k-space data was replaced with motion k-space data, respectively. In addition, based on the transposed k-space data, we acquired MR images with motion artifacts and residual maps and constructed datasets. For a quantitative evaluation, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient of correlation (CC), and universal image quality index (UQI) were measured. The U-Net models for motion artifact reduction with the residual map-based dataset showed the best performance across all evaluation factors. In particular, the RMSE, PSNR, CC, and UQI improved by approximately 5.35×, 1.51×, 1.12×, and 1.01×, respectively, and the U-Net model with the residual map-based dataset was compared with the direct images. In conclusion, our simulation-based dataset demonstrates that U-Net models can be effectively trained for motion artifact reduction.
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Affiliation(s)
- Seong-Hyeon Kang
- Department of Biomedical Engineering, Eulji University, Seongnam 13135, Republic of Korea
| | - Youngjin Lee
- Department of Radiological Science, Gachon University, Incheon 21936, Republic of Korea
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3
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Marchetto E, Murphy K, Glimberg SL, Gallichan D. Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques. Magn Reson Med 2023; 90:1297-1315. [PMID: 37183791 PMCID: PMC7615144 DOI: 10.1002/mrm.29705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE This study investigated the artifacts arising from different types of head motion in brain MR images and how well these artifacts can be compensated using retrospective correction based on two different motion-tracking techniques. METHODS MPRAGE images were acquired using a 3 T MR scanner on a cohort of nine healthy participants. Subjects moved their head to generate circular motion (4 or 6 cycles/min), stepwise motion (small and large) and "simulated realistic" motion (nodding and slow diagonal motion), based on visual instructions. One MPRAGE scan without deliberate motion was always acquired as a "no motion" reference. Three dimensional fat-navigator (FatNavs) and a Tracoline markerless device (TracInnovations) were used to obtain motion estimates and images were separately reconstructed retrospectively from the raw data based on these different motion estimates. RESULTS Image quality was recovered from both motion tracking techniques in our stepwise and slow diagonal motion scenarios in almost all cases, with the apparent visual image quality comparable to the no-motion case. FatNav-based motion correction was further improved in the case of stepwise motion using a skull masking procedure to exclude non-rigid motion of the neck from the co-registration step. In the case of circular motion, both methods struggled to correct for all motion artifacts. CONCLUSION High image quality could be recovered in cases of stepwise and slow diagonal motion using both motion estimation techniques. The circular motion scenario led to more severe image artifacts that could not be fully compensated by the retrospective motion correction techniques used.
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Affiliation(s)
- Elisa Marchetto
- CUBRIC/School of Engineering, Cardiff University, Cardiff, UK
| | - Kevin Murphy
- CUBRIC/School of Physics and Astronomy, Cardiff University, Cardiff, UK
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4
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Pollak C, Kügler D, Breteler MMB, Reuter M. Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts. Neuroimage 2023; 275:120176. [PMID: 37209757 DOI: 10.1016/j.neuroimage.2023.120176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023] Open
Abstract
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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Affiliation(s)
- Clemens Pollak
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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5
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Levitt J, van der Kouwe A, Jeong H, Lewis LD, Bonmassar G. The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:3539. [PMID: 37050598 PMCID: PMC10098760 DOI: 10.3390/s23073539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. METHODS The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. RESULTS MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. CONCLUSIONS The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.
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Affiliation(s)
- Joshua Levitt
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Hongbae Jeong
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
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6
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Gilbert KM, Nichols ES, Gati JS, Duerden EG. A radiofrequency coil for infants and toddlers. NMR IN BIOMEDICINE 2023:e4928. [PMID: 36939270 DOI: 10.1002/nbm.4928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Infants and toddlers are a challenging population upon which to perform magnetic resonance imaging (MRI) of the brain, both in research and clinical settings. Because of the large range in head size during the early years of development, paediatric neuro-MRI requires a radiofrequency (RF) coil, or set of coils, that is tailored to head size to provide the highest image quality. Mitigating techniques must also be employed to reduce and correct for subject motion. This manuscript describes an RF coil with a tailored mechanical-electrical design that can adapt to the head size of 3-month-old infants to 3-year-old toddlers. The RF coil was designed with tight-fitting coil elements to improve the signal-to-noise ratio (SNR) in comparison with commercially available adult head coils, while simultaneously aiding in immobilization. The coil was designed without visual obstruction to facilitate an unimpeded view of the child's face and the potential application of camera or motion-tracking systems. Despite the lack of elements over the face, the paediatric coil produced higher SNR over most of the brain compared with adult coils, including more than twofold in the periphery. Acceleration rates of fourfold in each Cartesian direction could be achieved. High SNR allowed for short acquisition times through accelerated imaging protocols and reduced the probability of motion during a scan. Modification of the acquisition protocol, with immobilization of the head through the adjustable coil geometry, and subsequently being combined with a motion-tracking system, provides a compelling platform for scanning paediatric populations without sedation and with improved image quality.
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Affiliation(s)
- Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Emily S Nichols
- Applied Psychology, Faculty of Education, The University of Western Ontario, London, Ontario, Canada
- Western Institute for Neuroscience, The University of Western Ontario, London, Ontario, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Emma G Duerden
- Applied Psychology, Faculty of Education, The University of Western Ontario, London, Ontario, Canada
- Western Institute for Neuroscience, The University of Western Ontario, London, Ontario, Canada
- Department of Pediatrics, Schulich School of Medicine and Dentistry, London, Ontario, Canada
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7
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Zhang K, Triphan SMF, Ziener CH, Jende JME, Kauczor HU, Schlemmer HP, Sedlaczek O, Kurz FT. Navigator-based slice tracking for kidney pCASL using spin-echo EPI acquisition. Magn Reson Med 2023; 90:231-239. [PMID: 36806110 DOI: 10.1002/mrm.29621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/26/2023] [Accepted: 02/06/2023] [Indexed: 02/21/2023]
Abstract
PURPOSE To apply a navigator-based slice-tracking method to prospectively compensate respiratory motion for kidney pseudo-continuous arterial spin labeling (pCASL), using spin-echo (SE) EPI acquisition. METHODS A single gradient-echo slice selection and projection readout at the location of the diaphragm along the inferior-superior direction was applied as a navigator. Navigator acquisition and fat suppression were inserted before each transverse imaging slice of the readouts of a 2D-SE-EPI-based pCASL sequence. Motion information was calculated after exclusion of the signal saturation in the navigator signal caused by EPI excitations. The motion information was then used to directly adjust the slice positioning in real time. RESULTS The respiratory motion from the navigator signal was calculated, and slice positioning was changed in real time based on the motion information. We could show that motion compensation reduces kidney movement, and that the coefficients of variation across renal perfusion values were significantly reduced when motion correction was applied. The average reduction of coefficients of variation was approximately 20%, resulting in a more accurate and detailed structure of the respective perfusion maps. CONCLUSIONS This study demonstrates the feasibility of a navigator-based slice-tracking technique in kidney imaging with a SE-EPI readout pCASL sequence to reduce kidney motion.
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Affiliation(s)
- Ke Zhang
- Department of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Simon M F Triphan
- Department of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian H Ziener
- Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Johann M E Jende
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Oliver Sedlaczek
- Department of Diagnostic & Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Felix T Kurz
- Division of Radiology, German Cancer Research Center, Heidelberg, Germany.,Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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8
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Vossough A. Newer MRI Techniques in Pediatric Neuroimaging. Semin Roentgenol 2023; 58:131-144. [PMID: 36732007 DOI: 10.1053/j.ro.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Affiliation(s)
- Arastoo Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA..
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9
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Brackenier Y, Cordero‐Grande L, Tomi‐Tricot R, Wilkinson T, Bridgen P, Price A, Malik SJ, De Vita E, Hajnal JV. Data‐driven motion‐corrected brain
MRI
incorporating pose‐dependent
B
0
fields. Magn Reson Med 2022; 88:817-831. [PMID: 35526212 PMCID: PMC9324873 DOI: 10.1002/mrm.29255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022]
Abstract
Purpose To develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B0) fields. Theory and Methods Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics‐inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo. Results The applicability of the physics‐inspired B0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B0 dependencies cannot be neglected. The use of a compact physics‐inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.
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Affiliation(s)
- Yannick Brackenier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Lucilio Cordero‐Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación Universidad Politécnica de Madrid and CIBER‐BNN Madrid Spain
| | - Raphael Tomi‐Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- MR Research Collaborations Siemens Healthcare Limited Frimley United Kingdom
| | - Thomas Wilkinson
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Philippa Bridgen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Anthony Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
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10
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Laustsen M, Andersen M, Xue R, Madsen KH, Hanson LG. Tracking of rigid head motion during MRI using an EEG system. Magn Reson Med 2022; 88:986-1001. [PMID: 35468237 PMCID: PMC9325421 DOI: 10.1002/mrm.29251] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 02/26/2022] [Accepted: 03/08/2022] [Indexed: 11/21/2022]
Abstract
Purpose To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients. Theory and Methods Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators. Results Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees). Conclusion Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.
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Affiliation(s)
- Malte Laustsen
- Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs. Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.,Sino-Danish Centre for Education and Research, Aarhus, Denmark.,University of Chinese Academic of Sciences, Beijing, China
| | - Mads Andersen
- Philips Healthcare, Copenhagen, Denmark.,Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Rong Xue
- University of Chinese Academic of Sciences, Beijing, China.,State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.,DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars G Hanson
- Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs. Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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