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Hewlett M, Petrov I, Johnson PM, Drangova M. Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI dataset. NMR IN BIOMEDICINE 2024; 37:e5179. [PMID: 38808752 DOI: 10.1002/nbm.5179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024]
Abstract
Deep learning presents a generalizable solution for motion correction requiring no pulse sequence modifications or additional hardware, but previous networks have all been applied to coil-combined data. Multichannel MRI data provide a degree of spatial encoding that may be useful for motion correction. We hypothesize that incorporating deep learning for motion correction prior to coil combination will improve results. A conditional generative adversarial network was trained using simulated rigid motion artifacts in brain images acquired at multiple sites with multiple contrasts (not limited to healthy subjects). We compared the performance of deep-learning-based motion correction on individual channel images (single-channel model) with that performed after coil combination (channel-combined model). We also investigate simultaneous motion correction of all channel data from an image volume (multichannel model). The single-channel model significantly (p < 0.0001) improved mean absolute error, with an average 50.9% improvement compared with the uncorrected images. This was significantly (p < 0.0001) better than the 36.3% improvement achieved by the channel-combined model (conventional approach). The multichannel model provided no significant improvement in quantitative measures of image quality compared with the uncorrected images. Results were independent of the presence of pathology, and generalizable to a new center unseen during training. Performing motion correction on single-channel images prior to coil combination provided an improvement in performance compared with conventional deep-learning-based motion correction. Improved deep learning methods for retrospective correction of motion-affected MR images could reduce the need for repeat scans if applied in a clinical setting.
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Affiliation(s)
- Miriam Hewlett
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Ivailo Petrov
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Patricia M Johnson
- Department of Radiology, New York Medicine School of Medicine, New York, New York, USA
| | - Maria Drangova
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
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2
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Sengupta S, Glenn A, Rogers BP. Prospective head motion correction at 3 Tesla with wireless NMR markers and ultrashort echo navigators. Magn Reson Imaging 2024; 114:110238. [PMID: 39276809 DOI: 10.1016/j.mri.2024.110238] [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: 06/26/2024] [Revised: 08/28/2024] [Accepted: 09/10/2024] [Indexed: 09/17/2024]
Abstract
PURPOSE Prospective motion correction (PMC) with inductively-coupled wireless NMR markers has been shown to be an effective plug-and-play method for dealing with head motion at 7 Tesla [29,30]. However, technical challenges such as one-to-one identification of three wireless markers, generation of hyper-intense marker artifacts and low marker peak SNR in the navigators has limited the adoption of this technique. The goal of this work is to introduce solutions to overcome these issues and extend this technique to PMC for brain imaging at 3 Tesla. METHODS PMC with 6 degrees of freedom (DOF) was implemented using a novel ∼8 ms, ultrashort echo time (UTE) navigator in concert with optimally chosen MnCl2 marker samples to minimize marker artifacts. Distinct head coil sensitivities were leveraged to enable identification and tracking of individual markers and a variable flip angle (VFA) scheme and real time filtering were used to boost marker SNR. PMC was performed in 3D T1 weighted brain imaging at 3 Tesla with voluntary head motions in adult volunteers. RESULTS PMC with wireless markers improved image quality in 3D T1 weighted images in all subjects compared to non-motion corrected images for similar motions with no noticeable marker artifacts. Precision of motion tracking was found to be in the range of 0.01-0.06 mm/degrees. Navigator execution had minimal impact on sequence duration. CONCLUSIONS Wireless NMR markers provide an accurate, calibration-free and economical option for 6 DOF PMC in brain imaging across field strengths. Challenges in this technique can be addressed by combining navigator design, sample selection and real time data processing strategies.
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Affiliation(s)
- Saikat Sengupta
- Vanderbilt University Institute of Imaging Science,Vanderbilt University Medical Center, Nashville, TN 37235, USA; Department of Radiology and Radiological Sciences,Vanderbilt University Medical Center, Nashville, TN 37235, USA.
| | - Antonio Glenn
- Department of Biomedical Engineering, Case Western Reserve University Cleveland, OH 44106, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science,Vanderbilt University Medical Center, Nashville, TN 37235, USA; Department of Radiology and Radiological Sciences,Vanderbilt University Medical Center, Nashville, TN 37235, USA
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Silic M, Tam F, Graham SJ. Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging. SENSORS (BASEL, SWITZERLAND) 2024; 24:3737. [PMID: 38931521 PMCID: PMC11207598 DOI: 10.3390/s24123737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.
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Affiliation(s)
- Marina Silic
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; (M.S.); (F.T.)
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Fred Tam
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; (M.S.); (F.T.)
| | - Simon J. Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; (M.S.); (F.T.)
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
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4
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Hewlett M, Oran O, Liu J, Drangova M. Prospective motion correction for brain MRI using spherical navigators. Magn Reson Med 2024; 91:1528-1540. [PMID: 38174443 DOI: 10.1002/mrm.29961] [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: 08/25/2023] [Revised: 10/24/2023] [Accepted: 11/20/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs). METHODS SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil. RESULTS When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%). CONCLUSION Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.
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Affiliation(s)
- Miriam Hewlett
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Omer Oran
- Siemens Healthcare Limited, Oakville, Ontario, Canada
| | - Junmin Liu
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Maria Drangova
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
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5
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Singh NM, Dey N, Hoffmann M, Fischl B, Adalsteinsson E, Frost R, Dalca AV, Golland P. Data Consistent Deep Rigid MRI Motion Correction. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2024; 227:368-381. [PMID: 39415845 PMCID: PMC11482239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone. Our network produces a reconstruction as a function of two inputs: corrupted k-space data and motion parameters. We train the network using simulated, motion-corrupted k-space data generated with known motion parameters. At test-time, we estimate unknown motion parameters by minimizing a data consistency loss between the motion parameters, the network-based image reconstruction given those parameters, and the acquired measurements. Intra-slice motion correction experiments on simulated and realistic 2D fast spin echo brain MRI achieve high reconstruction fidelity while providing the benefits of explicit data consistency optimization. Our code is publicly available at https://www.github.com/nalinimsingh/neuroMoCo.
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Affiliation(s)
| | - Neel Dey
- Massachusetts Institute of Technology
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging
- Harvard Medical School
| | - Bruce Fischl
- Massachusetts Institute of Technology
- Athinoula A. Martinos Center for Biomedical Imaging
- Harvard Medical School
| | | | - Robert Frost
- Athinoula A. Martinos Center for Biomedical Imaging
- Harvard Medical School
| | - Adrian V Dalca
- Massachusetts Institute of Technology
- Athinoula A. Martinos Center for Biomedical Imaging
- Harvard Medical School
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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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Ryan ME, Jaju A. Revolutionizing pediatric neuroimaging: the era of CT, MRI, and beyond. Childs Nerv Syst 2023; 39:2583-2592. [PMID: 37380927 DOI: 10.1007/s00381-023-06041-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 06/17/2023] [Indexed: 06/30/2023]
Abstract
PURPOSE To review the evolution of cross-sectional imaging in pediatric neuroradiology from early developments to current advancements and future directions. METHODS Information was obtained through a PubMed literature search as well as referenced online resources and personal experience from radiologists currently practicing pediatric neuroimaging and those who experienced the era of nascent cross-sectional imaging. RESULTS The advent of computed tomography (CT) and magnetic resonance imaging (MRI) in the 1970s and 1980s brought about a revolutionary shift in the field of medical imaging, neurosurgical and neurological diagnosis. These cross-sectional imaging techniques ushered in a new era by enabling the visualization of soft tissue structures within the brain and spine. Advancements in these imaging modalities have continued at a remarkable pace, now providing not only high high-resolution and 3-dimensional anatomical imaging, but also functional assessment. With each stride forward, CT and MRI have provided clinicians with invaluable insights, improving the accuracy and precision of diagnoses, facilitating the identification of optimal surgical targets, and guiding the selection of appropriate treatment strategies. CONCLUSION This article traces the origins and early developments of CT and MRI, chronicling their journey from pioneering technologies to their current indispensable status in clinical applications and exciting possibilities that lie ahead in the realm of medical imaging and neurologic diagnosis.
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Affiliation(s)
- Maura E Ryan
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Ave, Chicago, IL, USA.
- Northwestern University Feinberg School of Medicine, 420 East Superior St, Chicago, IL, USA.
| | - Alok Jaju
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Ave, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, 420 East Superior St, Chicago, IL, USA
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8
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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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9
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Polak D, Hossbach J, Splitthoff DN, Clifford B, Lo WC, Tabari A, Lang M, Huang SY, Conklin J, Wald LL, Cauley S. Motion guidance lines for robust data consistency-based retrospective motion correction in 2D and 3D MRI. Magn Reson Med 2023; 89:1777-1790. [PMID: 36744619 PMCID: PMC10518424 DOI: 10.1002/mrm.29534] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a robust retrospective motion-correction technique based on repeating k-space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI. METHODS The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency-based motion estimation and reconstruction, which can be guided by a low-resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k-space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence. RESULTS Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on-the-fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware. CONCLUSION The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.
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Affiliation(s)
- Daniel Polak
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Siemens Healthcare GmbH, Erlangen, Germany
| | | | | | | | | | - Azadeh Tabari
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Min Lang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y. Huang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - John Conklin
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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10
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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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11
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Madore B, Hess AT, van Niekerk AMJ, Hoinkiss DC, Hucker P, Zaitsev M, Afacan O, Günther M. External Hardware and Sensors, for Improved MRI. J Magn Reson Imaging 2023; 57:690-705. [PMID: 36326548 PMCID: PMC9957809 DOI: 10.1002/jmri.28472] [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: 07/27/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Bruno Madore
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron T Hess
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Adam MJ van Niekerk
- Karolinska Institutet, Solna, Sweden
- Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Patrick Hucker
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University Bremen, Bremen, Germany
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12
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Chen H, Dai K, Zhong S, Zheng J, Zhang X, Yang S, Cao T, Wang C, Karasan E, Frydman L, Zhang Z. High-resolution multi-shot diffusion-weighted MRI combining markerless prospective motion correction and locally low-rank constrained reconstruction. Magn Reson Med 2023; 89:605-619. [PMID: 36198013 DOI: 10.1002/mrm.29468] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 08/10/2022] [Accepted: 09/04/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Subject head motion is a major challenge in DWI, leading to image blurring, signal losses, and biases in the estimated diffusion parameters. Here, we investigate a combined application of prospective motion correction and spatial-angular locally low-rank constrained reconstruction to obtain robust, multi-shot, high-resolution diffusion-weighted MRI under substantial motion. METHODS Single-shot EPI with retrospective motion correction can mitigate motion artifacts and resolve any mismatching of gradient encoding orientations; however, it is limited by low spatial resolution and image distortions. Multi-shot acquisition strategies could achieve higher resolution and image fidelity but increase the vulnerability to motion artifacts and phase variations related to cardiac pulsations from shot to shot. We use prospective motion correction with optical markerless motion tracking to remove artifacts and reduce image blurring due to bulk motion, combined with locally low-rank regularization to correct for remaining artifacts due to shot-to-shot phase variations. RESULTS The approach was evaluated on healthy adult volunteers at 3 Tesla under different motion patterns. In multi-shot DWI, image blurring due to motion with 20 mm translations and 30° rotations was successfully removed by prospective motion correction, and aliasing artifacts caused by shot-to-shot phase variations were addressed by locally low-rank regularization. The ability of prospective motion correction to preserve the orientational information in DTI without requiring a reorientation of the b-matrix is highlighted. CONCLUSION The described technique is proved to hold valuable potential for mapping brain diffusivity and connectivity at high resolution for studies in subjects/cohorts where motion is common, including neonates, pediatrics, and patients with neurological disorders.
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Affiliation(s)
- Hao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ke Dai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Sijie Zhong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jiaxu Zheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.,United Imaging Healthcare, Shanghai, People's Republic of China
| | - Xinyue Zhang
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Shasha Yang
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Tuoyu Cao
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Chaohong Wang
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Ekin Karasan
- Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California
| | - Lucio Frydman
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Zhiyong Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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13
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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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14
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Liu J, Wang C, Wang J, Zhang C, Wu Y, Balu N, Qi H, Zhang Q, Yuan C, Chen H. Motion detection and correction for carotid MRI using a markerless optical system. Magn Reson Imaging 2022; 94:161-167. [PMID: 36191857 DOI: 10.1016/j.mri.2022.09.010] [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: 05/13/2021] [Revised: 04/29/2022] [Accepted: 09/27/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Motion related artifact is a challenge for MRI, especially when imaging regions like the carotid artery where complex motion (abrupt and bulk motion) may occur. This study aims to develop a non-contact motion detection and correction system for carotid MRI using a markerless optical tracking system. METHODS The proposed markerless optical tracking system consisted of a cross-line laser, an MRI-compatible camera and plastic holders mounted inside the scanner bore. The neck motion of the subject can be captured by monitoring the change of the projected laser position in real-time. The system was used to correct both abrupt motion and bulk motion for carotid MRI. The abrupt motion (e.g. coughing) was compensated by discarding the corrupted k-space lines and re-estimating the missing lines using SPIRiT algorithm. The bulk motion was corrected by phase adjustment of k-space lines according to the measured 1D-translational bulk motion (along anterior-posterior direction) and optimized in-plane translation parameters. Ten volunteers underwent carotid MRI with real-time neck motion detection and retrospective motion correction. Artery sharpness, vessel wall thickness and overall image quality score were compared between the motion-corrupted image and motion-corrected images of different correction strategies. RESULTS Both the abrupt motion and the bulk motion during carotid scanning were successfully detected and corrected. The results of ten volunteers demonstrated significant improvement in carotid artery sharpness, vessel wall thickness measurement, and overall image quality score using the proposed markerless optical tracking system and motion correction strategies. CONCLUSION The proposed markerless structured light based motion detection and correction system can sensitively detect both abrupt and bulk motion during carotid MR scans. By correcting for both abrupt and bulk motion, vessel wall delineation was improved in carotid MR images, which could potentially facilitate carotid plaque identification and atherosclerosis diagnosis in the future.
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Affiliation(s)
- Jin Liu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America
| | - Chunyao Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jinnan Wang
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Chen Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yifan Wu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America
| | - Niranjan Balu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Haikun Qi
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qiang Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
| | - Chun Yuan
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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15
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Pardoe HR, Martin SP. In-scanner head motion and structural covariance networks. Hum Brain Mapp 2022; 43:4335-4346. [PMID: 35593313 PMCID: PMC9435006 DOI: 10.1002/hbm.25957] [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: 03/29/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 11/08/2022] Open
Abstract
In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.
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Affiliation(s)
- Heath R Pardoe
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA.,Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Samantha P Martin
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
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16
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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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17
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Gagoski B, Xu J, Wighton P, Tisdall MD, Frost R, Lo WC, Golland P, van der Kouwe A, Adalsteinsson E, Grant PE. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T. Magn Reson Med 2022; 87:1914-1922. [PMID: 34888942 PMCID: PMC8810713 DOI: 10.1002/mrm.29106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/19/2021] [Accepted: 11/12/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction. METHODS A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired. RESULTS The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899. CONCLUSION The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.
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Affiliation(s)
- Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Paul Wighton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Frost
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Inc, Charlestown, Massachusetts, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - P. Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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18
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Slipsager JM, Glimberg SL, Højgaard L, Paulsen RR, Wighton P, Tisdall MD, Jaimes C, Gagoski BA, Grant PE, van der Kouwe A, Olesen OV, Frost R. Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI. Magn Reson Med 2022; 87:629-645. [PMID: 34490929 PMCID: PMC8635810 DOI: 10.1002/mrm.28991] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 07/17/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE To compare prospective motion correction (PMC) and retrospective motion correction (RMC) in Cartesian 3D-encoded MPRAGE scans and to investigate the effects of correction frequency and parallel imaging on the performance of RMC. METHODS Head motion was estimated using a markerless tracking system and sent to a modified MPRAGE sequence, which can continuously update the imaging FOV to perform PMC. The prospective correction was applied either before each echo train (before-ET) or at every sixth readout within the ET (within-ET). RMC was applied during image reconstruction by adjusting k-space trajectories according to the measured motion. The motion correction frequency was retrospectively increased with RMC or decreased with reverse RMC. Phantom and in vivo experiments were used to compare PMC and RMC, as well as to compare within-ET and before-ET correction frequency during continuous motion. The correction quality was quantitatively evaluated using the structural similarity index measure with a reference image without motion correction and without intentional motion. RESULTS PMC resulted in superior image quality compared to RMC both visually and quantitatively. Increasing the correction frequency from before-ET to within-ET reduced the motion artifacts in RMC. A hybrid PMC and RMC correction, that is, retrospectively increasing the correction frequency of before-ET PMC to within-ET, also reduced motion artifacts. Inferior performance of RMC compared to PMC was shown with GRAPPA calibration data without intentional motion and without any GRAPPA acceleration. CONCLUSION Reductions in local Nyquist violations with PMC resulted in superior image quality compared to RMC. Increasing the motion correction frequency to within-ET reduced the motion artifacts in both RMC and PMC.
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Affiliation(s)
- Jakob M. Slipsager
- DTU Compute, Technical University of Denmark, Denmark
- Dept. of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
- TracInnovations, Ballerup, Denmark
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | | | - Liselotte Højgaard
- Dept. of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | | | - Paul Wighton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Camilo Jaimes
- Boston Children’s Hospital, Boston, Massachusetts
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Borjan A. Gagoski
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, Massachusetts
| | - P. Ellen Grant
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, Massachusetts
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Oline V. Olesen
- DTU Compute, Technical University of Denmark, Denmark
- Dept. of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
- TracInnovations, Ballerup, Denmark
| | - Robert Frost
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts
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Henry D, Fulton R, Maclaren J, Aksoy M, Bammer R, Kyme A. Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3063260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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20
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Sprenger T, Kits A, Norbeck O, van Niekerk A, Berglund J, Rydén H, Avventi E, Skare S. NeuroMix-A single-scan brain exam. Magn Reson Med 2021; 87:2178-2193. [PMID: 34904751 DOI: 10.1002/mrm.29120] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 01/15/2023]
Abstract
PURPOSE Implement a fast, motion-robust pulse sequence that acquires T1 -weighted, T2 -weighted, T2 * -weighted, T2 fluid-attenuated inversion recovery, and DWI data in one run with only one prescription and one prescan. METHODS A software framework was developed that configures and runs several sequences in one main sequence. Based on that framework, the NeuroMix sequence was implemented, containing motion robust single-shot sequences using EPI and fast spin echo (FSE) readouts (without EPI distortions). Optional multi-shot sequences that provide better contrast, higher resolution, or isotropic resolution could also be run within the NeuroMix sequence. An optimized acquisition order was implemented that minimizes times where no data is acquired. RESULTS NeuroMix is customizable and takes between 1:20 and 4 min for a full brain scan. A comparison with the predecessor EPIMix revealed significant improvements for T2 -weighted and T2 fluid-attenuated inversion recovery, while taking only 8 s longer for a similar configuration. The optional contrasts were less motion robust but offered a significant increase in quality, detail, and contrast. Initial clinical scans on 1 pediatric and 1 adult patient showed encouraging image quality. CONCLUSION The single-shot FSE readouts for T2 -weighted and T2 fluid-attenuated inversion recovery and the optional multishot FSE and 3D-EPI contrasts significantly increased diagnostic value compared with EPIMix, allowing NeuroMix to be considered as a standalone brain MRI application.
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Affiliation(s)
- Tim Sprenger
- MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Annika Kits
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ola Norbeck
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Adam van Niekerk
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Berglund
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Henric Rydén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Avventi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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21
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Eichhorn H, Vascan AV, Nørgaard M, Ellegaard AH, Slipsager JM, Keller SH, Marner L, Ganz M. Characterisation of Children's Head Motion for Magnetic Resonance Imaging With and Without General Anaesthesia. FRONTIERS IN RADIOLOGY 2021; 1:789632. [PMID: 37492164 PMCID: PMC10365093 DOI: 10.3389/fradi.2021.789632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/08/2021] [Indexed: 07/27/2023]
Abstract
Head motion is one of the major reasons for artefacts in Magnetic Resonance Imaging (MRI), which is especially challenging for children who are often intimidated by the dimensions of the MR scanner. In order to optimise the MRI acquisition for children in the clinical setting, insights into children's motion patterns are essential. In this work, we analyse motion data from 61 paediatric patients. We compare structural MRI data of children imaged with and without general anaesthesia (GA), all scanned using the same hybrid PET/MR scanner. We analyse several metrics of motion based on the displacement relative to a reference, decompose the transformation matrix into translation and rotation, as well as investigate whether different regions in the brain are affected differently by the children's motion. Head motion for children without GA was significantly higher, with a median of the mean displacements of 2.19 ± 0.93 mm (median ± standard deviation) during 41.7±7.5 min scans; however, even anaesthetised children showed residual head motion (mean displacement of 1.12±0.35 mm). For both patient groups translation along the z-axis (along the scanner bore) was significantly larger in absolute terms (GA / no GA: 0.87±0.29/0.92 ± 0.49 mm) compared to the other directions. Considering directionality, both patient groups were moving in negative z-direction and thus, out of the scanner. The awake children additionally showed significantly more nodding rotation (0.33±0.20°). In future studies as well as in the clinical setting, these predominant types of motion need to be taken into consideration to limit artefacts and reduce re-scans due to poor image quality.
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Affiliation(s)
- Hannah Eichhorn
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Andreea-Veronica Vascan
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Martin Nørgaard
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Center for Reproducible Neuroscience, Department of Psychology, Stanford University, Stanford, CA, United States
| | | | - Jakob M. Slipsager
- TracInnovations, Ballerup, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Department of Clinical Physiology, Nuclear Medicine & PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Sune Høgild Keller
- Department of Clinical Physiology, Nuclear Medicine & PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Lisbeth Marner
- Department of Clinical Physiology, Nuclear Medicine & PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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22
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van Niekerk A, Berglund J, Sprenger T, Norbeck O, Avventi E, Rydén H, Skare S. Control of a wireless sensor using the pulse sequence for prospective motion correction in brain MRI. Magn Reson Med 2021; 87:1046-1061. [PMID: 34453458 DOI: 10.1002/mrm.28994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE To synchronize and pass information between a wireless motion-tracking device and a pulse sequence and show how this can be used to implement customizable navigator interleaving schemes that are part of the pulse sequence design. METHODS The device tracks motion by sampling the voltages induced in 3 orthogonal pickup coils by the changing gradient fields. These coils were modified to also detect RF-transmit events using a 3D RF-detection circuit. The device could then detect and decode a set RF signatures while ignoring excitations in the parent pulse sequence. A set of unique RF signatures were then paired with a collection of navigators and used to trigger readouts on the wireless device synchronous to the pulse sequence execution. Navigator interleaving schemes were then demonstrated in 3D RF-spoiled gradient echo, T1 -FLAIR (fluid-attenuated inversion recovery) PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction), and T2 -FLAIR PROPELLER pulse sequences. RESULTS Excitations in the parent pulse sequences were successfully rejected and the RF signatures successfully decoded. For the 3D gradient echo sequence, distortions were removed by interleaving flipped polarity navigators and taking the difference between consecutive readouts. The impact on scan duration was reduced by 54% by breaking up the navigators into smaller parts. Successful motion correction was performed using the PROPELLER pulse sequences in 3 Tesla and 1.5 Tesla MRI scanners without modifications to the device hardware or software. CONCLUSION The proposed RF signature-based triggering scheme enables complex interactions between the pulse sequence and a wireless device. Thus, enabling prospective motion correction that is repeatable, versatile, and minimally invasive with respect to hardware setup.
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Affiliation(s)
- Adam van Niekerk
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Johan Berglund
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tim Sprenger
- MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden
| | - Ola Norbeck
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Avventi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Henric Rydén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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Pardoe HR, Martin SP, Zhao Y, George A, Yuan H, Zhou J, Liu W, Devinsky O. Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video. Magn Reson Imaging 2021; 81:101-108. [PMID: 34147591 DOI: 10.1016/j.mri.2021.06.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/06/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. METHODS 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. RESULTS Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. CONCLUSIONS We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.
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Affiliation(s)
- Heath R Pardoe
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA.
| | - Samantha P Martin
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
| | | | - Allan George
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
| | - Hui Yuan
- Fordham University, New York, USA
| | | | - Wei Liu
- Fordham University, New York, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
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Nikulin AV, Vignaud A, Avdievich NI, Berrahou D, de Rosny J, Ourir A. Open birdcage coil for head imaging at 7T. Magn Reson Med 2021; 86:2290-2300. [PMID: 34080734 DOI: 10.1002/mrm.28845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/07/2021] [Accepted: 04/26/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE To theoretically describe, design, and test the new geometry of the birdcage coil for 7 Tesla anatomical brain imaging, which includes a large window on top, without deliberately jeopardizing its homogeneity and efficiency. This opencage will not only improve patient comfort but also enable the volunteer to follow functional MRI stimuli. This design could also facilitate the tracking of patient compliance and enable better correction of the movement. METHODS Via the transfer matrix approach, a birdcage-like coil with a nonperiodic distribution of rungs is constructed with optimized currents in the coil rungs. Subsequently, the coil is adjusted in full-wave simulations. Then, the coil is assembled, fine-tuned, and matched on the bench. Finally, these results are confirmed experimentally on a phantom and in vivo. RESULTS Indeed, the computed isolation of -14.9 dB between the feeding ports of the coil and the symmetry of the circular polarized mode pattern transmit RF magnetic field ( B 1 + ) showed that the coil was properly optimized. An experimental assessment of the developed coil showed competitive transmit efficiency and coverage compared with the conventional birdcage coil of similar size. CONCLUSION The proposed opencage coil can be designed and work without a dramatic drop of performance in terms of the B 1 + field homogeneity, transmit efficiency ( B 1 + / P ref ), peak local specific absorption rate ( S A R 10 g ) and SAR efficiency ( B 1 + / S A R 10 g ).
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Affiliation(s)
- Anton V Nikulin
- Institut Langevin, ESPCI Paris, CNRS, PSL University, Paris, France
| | - Alexandre Vignaud
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Nikolai I Avdievich
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | - Julien de Rosny
- Institut Langevin, ESPCI Paris, CNRS, PSL University, Paris, France
| | - Abdelwaheb Ourir
- Institut Langevin, ESPCI Paris, CNRS, PSL University, Paris, France
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Berglund J, Sprenger T, van Niekerk A, Rydén H, Avventi E, Norbeck O, Skare S. Motion-insensitive susceptibility weighted imaging. Magn Reson Med 2021; 86:1970-1982. [PMID: 34076922 DOI: 10.1002/mrm.28850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/07/2021] [Accepted: 04/29/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE To enable SWI that is robust to severe head movement. METHODS Prospective motion correction using a markerless optical tracker was applied to all pulse sequences. Three-dimensional gradient-echo and 3D EPI were used as reference sequences, but were expected to be sensitive to motion-induced B0 changes, as the long TE required for SWI allows phase discrepancies to accumulate between shots. Therefore, 2D interleaved snapshot EPI was investigated for motion-robust SWI and compared with conventional 2D EPI. Repeated signal averages were retrospectively corrected for motion. The sequences were evaluated at 3 T through controlled motion experiments involving two cooperative volunteers and SWI of a tumor patient. RESULTS The performed continuous head motion was in the range of 5-8° rotations. The image quality of the 3D sequences and conventional 2D EPI was poor unless the rotational motion axis was parallel to B0 . Interleaved snapshot EPI had minimal intraslice phase discrepancies due to its small temporal footprint. Phase inconsistency between signal averages was well tolerated due to the high-pass filter effect of the SWI processing. Interleaved snapshot EPI with prospective and retrospective motion correction demonstrated similar image quality, regardless of whether motion was present. Lesion depiction was equal to 3D EPI with matching resolution. CONCLUSION Susceptibility-based imaging can be severely corrupted by head movement despite accurate prospective motion correction. Interleaved snapshot EPI is a superior alternative for patients who are prone to move and offers SWI which is insensitive to motion when combined with prospective and retrospective motion correction.
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Affiliation(s)
- Johan Berglund
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tim Sprenger
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,MR Applied Science Laboratory, GE Healthcare, Stockholm, Sweden
| | - Adam van Niekerk
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Henric Rydén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Avventi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ola Norbeck
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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26
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Artificial intelligence in child abuse imaging. Pediatr Radiol 2021; 51:1061-1064. [PMID: 33904953 DOI: 10.1007/s00247-021-05073-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 03/08/2021] [Accepted: 03/24/2021] [Indexed: 12/22/2022]
Abstract
There have been rapid advances in artificial intelligence (AI) technology in recent years, and the field of diagnostic imaging is no exception. Just as digital technology revolutionized how radiology is practiced, so these new technologies also appear poised to bring sweeping change. As AI tools make the transition from the theoretical to the everyday, important decisions need to be made about how they will be applied and what their role will be in the practice of radiology. Pediatric radiology presents distinct challenges and opportunities for the application of these tools, and in this article we discuss some of these, specifically as they relate to the prediction, identification and investigation of child abuse.
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Parker DB, Spincemaille P, Razlighi QR. Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT). Magn Reson Med 2021; 86:1586-1599. [PMID: 33797118 DOI: 10.1002/mrm.28723] [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: 05/13/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems. THEORY AND METHODS Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner. RESULTS The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data. CONCLUSION Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.
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Affiliation(s)
- David B Parker
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA
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Gottwald LM, Blanken CPS, Tourais J, Smink J, Planken RN, Boekholdt SM, Meijboom LJ, Coolen BF, Strijkers GJ, Nederveen AJ, van Ooij P. Retrospective Camera-Based Respiratory Gating in Clinical Whole-Heart 4D Flow MRI. J Magn Reson Imaging 2021; 54:440-451. [PMID: 33694310 PMCID: PMC8359364 DOI: 10.1002/jmri.27564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 12/17/2022] Open
Abstract
Background Respiratory gating is generally recommended in 4D flow MRI of the heart to avoid blurring and motion artifacts. Recently, a novel automated contact‐less camera‐based respiratory motion sensor has been introduced. Purpose To compare camera‐based respiratory gating (CAM) with liver‐lung‐navigator‐based gating (NAV) and no gating (NO) for whole‐heart 4D flow MRI. Study Type Retrospective. Subjects Thirty two patients with a spectrum of cardiovascular diseases. Field Strength/Sequence A 3T, 3D‐cine spoiled‐gradient‐echo‐T1‐weighted‐sequence with flow‐encoding in three spatial directions. Assessment Respiratory phases were derived and compared against each other by cross‐correlation. Three radiologists/cardiologist scored images reconstructed with camera‐based, navigator‐based, and no respiratory gating with a 4‐point Likert scale (qualitative analysis). Quantitative image quality analysis, in form of signal‐to‐noise ratio (SNR) and liver‐lung‐edge (LLE) for sharpness and quantitative flow analysis of the valves were performed semi‐automatically. Statistical Tests One‐way repeated measured analysis of variance (ANOVA) with Wilks's lambda testing and follow‐up pairwise comparisons. Significance level of P ≤ 0.05. Krippendorff's‐alpha‐test for inter‐rater reliability. Results The respiratory signal analysis revealed that CAM and NAV phases were highly correlated (C = 0.93 ± 0.09, P < 0.01). Image scoring showed poor inter‐rater reliability and no significant differences were observed (P ≥ 0.16). The image quality comparison showed that NAV and CAM were superior to NO with higher SNR (P = 0.02) and smaller LLE (P < 0.01). The quantitative flow analysis showed significant differences between the three respiratory‐gated reconstructions in the tricuspid and pulmonary valves (P ≤ 0.05), but not in the mitral and aortic valves (P > 0.05). Pairwise comparisons showed that reconstructions without respiratory gating were different in flow measurements to either CAM or NAV or both, but no differences were found between CAM and NAV reconstructions. Data Conclusion Camera‐based respiratory gating performed as well as conventional liver‐lung‐navigator‐based respiratory gating. Quantitative image quality analysis showed that both techniques were equivalent and superior to no‐gating‐reconstructions. Quantitative flow analysis revealed local flow differences (tricuspid/pulmonary valves) in images of no‐gating‐reconstructions, but no differences were found between images reconstructed with camera‐based and navigator‐based respiratory gating. Level of Evidence 3 Technical Efficacy Stage 2
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Affiliation(s)
- Lukas M Gottwald
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Carmen P S Blanken
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - João Tourais
- MR R&D-Clinical Science, Philips Healthcare, Best, The Netherlands.,Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Jouke Smink
- MR R&D-Clinical Science, Philips Healthcare, Best, The Netherlands
| | - R Nils Planken
- Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | - Lilian J Meijboom
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Gustav J Strijkers
- Biomedical Engineering and Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Aart J Nederveen
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Pim van Ooij
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
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Singh A, Salehi SSM, Gholipour A. Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3523-3534. [PMID: 32746102 PMCID: PMC7787194 DOI: 10.1109/tmi.2020.2998600] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held-out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.
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Berglund J, van Niekerk A, Rydén H, Sprenger T, Avventi E, Norbeck O, Glimberg SL, Olesen OV, Skare S. Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker. Magn Reson Med 2020; 85:1427-1440. [PMID: 32989859 PMCID: PMC7756594 DOI: 10.1002/mrm.28524] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/31/2020] [Accepted: 08/28/2020] [Indexed: 01/25/2023]
Abstract
PURPOSE To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques. METHODS An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off. RESULTS Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased. CONCLUSIONS Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.
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Affiliation(s)
- Johan Berglund
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Adam van Niekerk
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Henric Rydén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Tim Sprenger
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,MR Applied Science Laboratory, GE Healthcare, Stockholm, Sweden
| | - Enrico Avventi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ola Norbeck
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | | | | | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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Simultaneous feedback control for joint field and motion correction in brain MRI. Neuroimage 2020; 226:117286. [PMID: 32992003 DOI: 10.1016/j.neuroimage.2020.117286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/21/2020] [Accepted: 08/14/2020] [Indexed: 11/23/2022] Open
Abstract
T2*-weighted gradient-echo sequences count among the most widely used techniques in neuroimaging and offer rich magnitude and phase contrast. The susceptibility effects underlying this contrast scale with B0, making T2*-weighted imaging particularly interesting at high field. High field also benefits baseline sensitivity and thus facilitates high-resolution studies. However, enhanced susceptibility effects and high target resolution come with inherent challenges. Relying on long echo times, T2*-weighted imaging not only benefits from enhanced local susceptibility effects but also suffers from increased field fluctuations due to moving body parts and breathing. High resolution, in turn, renders neuroimaging particularly vulnerable to motion of the head. This work reports the implementation and characterization of a system that aims to jointly address these issues. It is based on the simultaneous operation of two control loops, one for field stabilization and one for motion correction. The key challenge with this approach is that the two loops both operate on the magnetic field in the imaging volume and are thus prone to mutual interference and potential instability. This issue is addressed at the levels of sensing, timing, and control parameters. Performance assessment shows the resulting system to be stable and exhibit adequate loop decoupling, precision, and bandwidth. Simultaneous field and motion control is then demonstrated in examples of T2*-weighted in vivo imaging at 7T.
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Lee H, Zhao X, Song HK, Wehrli FW. Self-Navigated Three-Dimensional Ultrashort Echo Time Technique for Motion-Corrected Skull MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2869-2880. [PMID: 32149683 PMCID: PMC7484857 DOI: 10.1109/tmi.2020.2978405] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ultrashort echo time (UTE) MRI is capable of detecting signals from protons with very short T2 relaxation times, and thus has potential for skull-selective imaging as a radiation-free alternative to computed tomography. However, relatively long scan times make the technique vulnerable to artifacts from involuntary subject motion. Here, we developed a self-navigated, three-dimensional (3D) UTE pulse sequence, which builds on dual-RF, dual-echo UTE imaging, and a retrospective motion correction scheme for motion-resistant skull MRI. Full echo signals in the second readout serve as a self-navigator that yields a time-course of center of mass, allowing for adaptive determination of motion states. Furthermore, golden-means based k-space trajectory was employed to achieve a quasi-uniform distribution of sampling views on a spherical k-space surface for any subset of the entire data collected, thereby allowing reconstruction of low-resolution images pertaining to each motion state for subsequent estimation of rigid-motion parameters. Finally, the extracted trajectory of the head was used to make the whole k-space datasets motion-consistent, leading to motion-corrected, high-resolution images. Additionally, we posit that hardware-related k-space trajectory errors, if uncorrected, result in obscured bone contrast. Thus, a calibration scan was performed once to measure k-space encoding locations, subsequently used during image reconstruction of actual imaging data. In vivo studies were performed to evaluate the effectiveness of the proposed correction schemes in combination with approaches to accelerated bone-selective imaging. Results illustrating effective removal of motion artifacts and clear depiction of skull bone voxels suggest that the proposed method is robust to intermittent head motions during scanning.
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Norbeck O, van Niekerk A, Avventi E, Rydén H, Berglund J, Sprenger T, Skare S. T 1 -FLAIR imaging during continuous head motion: Combining PROPELLER with an intelligent marker. Magn Reson Med 2020; 85:868-882. [PMID: 32871026 DOI: 10.1002/mrm.28477] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/03/2020] [Accepted: 07/24/2020] [Indexed: 11/08/2022]
Abstract
PURPOSE The purpose of this work is to describe a T1 -weighted fluid-attenuated inversion recovery (FLAIR) sequence that is able to produce sharp magnetic resonance images even if the subject is moving their head throughout the acquisition. METHODS The robustness to motion artifacts and retrospective motion correction capabilities of the PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) trajectory were combined with prospective motion correction. The prospective correction was done using an intelligent marker attached to the subject. This marker wirelessly synchronizes to the pulse sequence to measure the directionality and magnitude of the magnetic fields present in the MRI machine during a short navigator, thus enabling it to determine its position and orientation in the scanner coordinate frame. Three approaches to incorporating the marker-navigator into the PROPELLER sequence were evaluated. The specific absorption rate, and subsequent scan time, of the T1 -weighted FLAIR PROPELLER sequence, was reduced using a variable refocusing flip-angle scheme. Evaluations of motion correction performance were done with 4 volunteers and 3 types of head motion. RESULTS During minimal out-of-plane movement, retrospective PROPELLER correction performed similarly to the prospective correction. However, the prospective clearly outperformed the retrospective correction when there was out-of-plane motion. Finally, the combination of retrospective and prospective correction produced the sharpest images even during large continuous motion. CONCLUSION Prospective motion correction of a PROPELLER sequence makes it possible to handle continuous, large, and high-speed head motions with only minor reductions in image quality.
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Affiliation(s)
- Ola Norbeck
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Adam van Niekerk
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Enrico Avventi
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Henric Rydén
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Johan Berglund
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tim Sprenger
- MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden
| | - Stefan Skare
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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Balasubramanian M, Mulkern RV, Neil JJ, Maier SE, Polimeni JR. Probing in vivo cortical myeloarchitecture in humans via line-scan diffusion acquisitions at 7 T with 250-500 micron radial resolution. Magn Reson Med 2020; 85:390-403. [PMID: 32738088 DOI: 10.1002/mrm.28419] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 06/15/2020] [Accepted: 06/18/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE The goal of this study was to measure diffusion signals within the cerebral cortex using the line-scan technique to achieve extremely high resolution in the radial direction (ie, perpendicular to the cortical surface) and to demonstrate the utility of these measurements for investigating laminar architecture in the living human brain. METHODS Line-scan diffusion data with 250-500 micron radial resolution were acquired at 7 T on 8 healthy volunteers, with each line prescribed perpendicularly to primary somatosensory cortex (S1) and primary motor cortex (M1). Apparent diffusion coefficients, fractional anisotropy values, and radiality indices were measured as a function of cortical depth. RESULTS In the deep layers of S1, we found evidence for high anisotropy and predominantly tangential diffusion, with low anisotropy observed in superficial S1. In M1, moderate anisotropy and predominantly radial diffusion was seen at almost all cortical depths. These patterns were consistent across subjects and were conspicuous without averaging data across different locations on the cortical sheet. CONCLUSION Our results are in accord with the myeloarchitecture of S1 and M1, known from prior histology studies: in S1, dense bands of tangential myelinated fibers run through the deep layers but not the superficial ones, and in M1, radial myelinated fibers are prominent at most cortical depths. This work therefore provides support for the idea that high-resolution diffusion signals, measured with the line-scan technique and receiving a boost in SNR at 7 T, may serve as a sensitive probe of in vivo laminar architecture.
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Affiliation(s)
- Mukund Balasubramanian
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Robert V Mulkern
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Stephan E Maier
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Institute of Clinical Sciences, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jonathan R Polimeni
- Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Liu Y, Yan Z. A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI. SENSORS 2020; 20:s20133628. [PMID: 32605230 PMCID: PMC7374374 DOI: 10.3390/s20133628] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 11/16/2022]
Abstract
Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer's disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.
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Affiliation(s)
- Yingqian Liu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;
- School of Electrical Engineering, Binzhou University, Binzhou 256600, China
- Correspondence: ; Tel.: +86-13581150864
| | - Zhuangzhi Yan
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;
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Schlüter M, Glandorf L, Gromniak M, Saathoff T, Schlaefer A. Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography. SENSORS 2020; 20:s20092678. [PMID: 32397153 PMCID: PMC7248981 DOI: 10.3390/s20092678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/21/2020] [Accepted: 05/05/2020] [Indexed: 11/16/2022]
Abstract
Optical tracking systems are widely used, for example, to navigate medical interventions. Typically, they require the presence of known geometrical structures, the placement of artificial markers, or a prominent texture on the target’s surface. In this work, we propose a 6D tracking approach employing volumetric optical coherence tomography (OCT) images. OCT has a micrometer-scale resolution and employs near-infrared light to penetrate few millimeters into, for example, tissue. Thereby, it provides sub-surface information which we use to track arbitrary targets, even with poorly structured surfaces, without requiring markers. Our proposed system can shift the OCT’s field-of-view in space and uses an adaptive correlation filter to estimate the motion at multiple locations on the target. This allows one to estimate the target’s position and orientation. We show that our approach is able to track translational motion with root-mean-squared errors below 0.25 mm and in-plane rotations with errors below 0.3°. For out-of-plane rotations, our prototypical system can achieve errors around 0.6°.
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Huang P, Carlin JD, Henson RN, Correia MM. Improved motion correction of submillimetre 7T fMRI time series with Boundary-Based Registration (BBR). Neuroimage 2020; 210:116542. [PMID: 31958583 PMCID: PMC7068704 DOI: 10.1016/j.neuroimage.2020.116542] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/09/2020] [Accepted: 01/10/2020] [Indexed: 11/16/2022] Open
Abstract
Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.
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Affiliation(s)
- Pei Huang
- MRC-Cognition and Brain Sciences Unit, University of Cambridge, UK.
| | - Johan D Carlin
- MRC-Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Richard N Henson
- MRC-Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK
| | - Marta M Correia
- MRC-Cognition and Brain Sciences Unit, University of Cambridge, UK
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Kozak BM, Jaimes C, Kirsch J, Gee MS. MRI Techniques to Decrease Imaging Times in Children. Radiographics 2020; 40:485-502. [PMID: 32031912 DOI: 10.1148/rg.2020190112] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Long acquisition times can limit the use of MRI in pediatric patients, and the use of sedation or general anesthesia is frequently necessary to facilitate diagnostic examinations. The use of sedation or anesthesia has disadvantages including increased cost and imaging time and potential risks to the patient. Reductions in imaging time may decrease or eliminate the need for sedation or general anesthesia. Over the past decade, a number of imaging techniques that can decrease imaging time have become commercially available. These products have been used increasingly in clinical practice and include parallel imaging, simultaneous multisection imaging, radial k-space acquisition, compressed sensing MRI reconstruction, and automated protocol selection software. The underlying concepts, supporting data, current clinical applications, and available products for each of these strategies are reviewed in this article. In addition, emerging techniques that are still under investigation may provide further reductions in imaging time, including artificial intelligence-based reconstruction, gradient-controlled aliasing sampling and reconstruction, three-dimensional MR spectroscopy, and prospective motion correction. The preliminary results for these techniques are also discussed. ©RSNA, 2020 See discussion on this article by Greer and Vasanawala.
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Affiliation(s)
- Benjamin M Kozak
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - John Kirsch
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Michael S Gee
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
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Aranovitch A, Haeberlin M, Gross S, Dietrich BE, Reber J, Schmid T, Pruessmann KP. Motion detection with NMR markers using real‐time field tracking in the laboratory frame. Magn Reson Med 2019; 84:89-102. [DOI: 10.1002/mrm.28094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 11/01/2019] [Accepted: 11/01/2019] [Indexed: 01/13/2023]
Affiliation(s)
- Alexander Aranovitch
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Maximilian Haeberlin
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Simon Gross
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Benjamin E. Dietrich
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Jonas Reber
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Thomas Schmid
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
| | - Klaas P. Pruessmann
- Institute for Biomedical Engineering ETH Zurich and University of Zurich Zurich Switzerland
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Afacan O, Wallace TE, Warfield SK. Retrospective correction of head motion using measurements from an electromagnetic tracker. Magn Reson Med 2019; 83:427-437. [PMID: 31400036 DOI: 10.1002/mrm.27934] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/25/2019] [Accepted: 07/15/2019] [Indexed: 11/06/2022]
Abstract
PURPOSE To investigate the feasibility of using an electromagnetic (EM) tracker to estimate rigid body head motion parameters, and using these measurements to retrospectively reduce motion artifacts. THEORY AND METHODS A clinically used MPRAGE sequence was modified to measure motion using the EM tracking system once per repetition time. A retrospective k-space based motion correction algorithm that corrects for phase ramps (translation in image domain) and rotation of 3D k-space (rotation in image domain) was developed, using the parameters recorded using an EM tracker. The accuracy of the EM tracker for the purpose of motion measurement and correction was tested in phantoms, volunteers, and pediatric patients. RESULTS Position localization was accurate to the order of 200 microns compared with registration localization in a phantom study. The quality of reconstructed images was assessed by computing the root mean square error, the structural similarity metric and average edge strength. Image quality improved consistently when motion correction was applied in both volunteer scans with deliberate head motion and in pediatric patient scans. In patients, the average edge strength improved significantly with retrospective motion correction, compared with images with no correction applied. CONCLUSIONS EM tracking was effective in measuring head motion in the MRI scanner with high accuracy, and enabled retrospective reconstruction to improve image quality by reducing motion artifacts.
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Affiliation(s)
- Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
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van Niekerk A, van der Kouwe A, Meintjes E. Toward "plug and play" prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields. Magn Reson Med 2019; 82:1214-1228. [PMID: 31066109 DOI: 10.1002/mrm.27790] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/22/2019] [Accepted: 04/08/2019] [Indexed: 11/05/2022]
Abstract
PURPOSE The efficacy of a Wireless Radio frequency triggered Acquisition Device (WRAD) is evaluated for high frequency (50 Hz) prospective motion correction in a 3-dimensional spoiled gradient echo pulse sequence. METHODS The device measures the rate of change in the gradient vector fields (slew) using a 3-dimensional assembly of Printed Circuit Board (PCB) inductors and the direction of the static magnetic field using a 3-axis Hall effect magnetometer. The slew vector encoding is highly efficient, because the Maxwell-term position encoding is observable, allowing overconstrained pose measurement using 3 sinusoidal gradient pulses lasting 880 μs. Since small offsets in the magnetometer can introduce bias into the pose estimates, sensor/system biases are tracked using a lightweight Kalman filter. The only calibration required is determining a geometric scaling factor for the pickup coils, which is specific to the device and will therefore be valid in any scanner. RESULTS The device was used to perform prospective motion correction in 3 subjects, resulting in an increase in Average Edge Strength (AES) for involuntary and deliberate motion. CONCLUSIONS The WRAD is simple to set up and use, with well-defined measurement variance. This could enable "plug and play" prospective motion correction if pulse sequence independence is achieved.
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Affiliation(s)
- Adam van Niekerk
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Andre van der Kouwe
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts.,Radiology, Harvard Medical School, Boston, Massachusetts
| | - Ernesta Meintjes
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Cape Universities Body Imaging Centre (CUBIC) at UCT, Cape Town, South Africa
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