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Brackenier Y, Cordero-Grande L, McElroy S, Tomi-Tricot R, Barbaroux H, Bridgen P, Malik SJ, Hajnal JV. Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals. Magn Reson Med 2024; 92:1881-1897. [PMID: 38860530 DOI: 10.1002/mrm.30161] [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: 02/28/2024] [Revised: 04/18/2024] [Accepted: 05/07/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast. THEORY AND METHODS PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. RESULTS The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach. CONCLUSIONS This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.
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Affiliation(s)
- Yannick Brackenier
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BNN, ISCIII, Madrid, Spain
| | - Sarah McElroy
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Raphael Tomi-Tricot
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Hugo Barbaroux
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Philippa Bridgen
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Shaihan J Malik
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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Adanyeguh IM, Park YW, Henry PG, Deelchand DK. B 0-insensitive image navigators for prospective motion-corrected MRS with localized second-order shimming. Magn Reson Med 2024; 92:1338-1347. [PMID: 38704666 PMCID: PMC11262980 DOI: 10.1002/mrm.30151] [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: 01/08/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE Localized shimming in single-voxel MRS often results in large B0 inhomogeneity outside the volume-of-interest. This causes unacceptable degradation in motion navigator images. Switching back and forth between whole-brain shim and localized shim is possible for linear shims, but not for higher-order shims. Here we propose motion navigators largely insensitive to B0 inhomogeneity for prospective motion-corrected MRS with localized higher-order shimming. METHODS A recent fast high-resolution motion navigator based on spiral-in/out k-space trajectories and multislice-to-volume registration was modified by splitting the readout into multiple shot interleaves which shortened the echo time and reduced the effect of B0 inhomogeneity. The performance of motion correction was assessed in healthy subjects in the prefrontal cortex using a sLASER sequence at 3T (N = 5) and 7T (N = 5). RESULTS With multiple spatial interleaves, excellent quality navigator images were acquired in the whole brain in spite of large B0 inhomogeneity outside the MRS voxel. The total duration of the navigator in sLASER remained relatively short even with multiple shots (3T: 10 spatial interleaves 94 ms per slice; 7T: 15 spatial interleaves 103 ms per slice). Prospective motion correction using the multi-shot navigators yielded comparable spectral quality (water linewidth and metabolite SNR) with and without subject motion. CONCLUSION B0-insensitive motion navigators enable prospective motion correction for MRS with all first- and second-order shims adjusted in the MRS voxel, providing optimal spectral linewidth.
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Affiliation(s)
- Isaac M Adanyeguh
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Young Woo Park
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
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Li B, She H. Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis. Magn Reson Med 2024; 92:1617-1631. [PMID: 38775235 DOI: 10.1002/mrm.30159] [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: 02/05/2024] [Revised: 04/07/2024] [Accepted: 05/02/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis. METHODS An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison. RESULTS The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans. CONCLUSIONS The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.
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Affiliation(s)
- Bowen Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University, Shanghai, China
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Sengupta S, Glenn A, Rogers B. Prospective head motion correction at 3 Tesla with wireless NMR markers and ultrashort echo navigators. Magn Reson Imaging 2024:110238. [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 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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5
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Riedel M, Ulrich T, Pruessmann KP. Run-time motion and first-order shim control by expanded servo navigation. Magn Reson Med 2024. [PMID: 39188123 DOI: 10.1002/mrm.30262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 07/17/2024] [Accepted: 08/04/2024] [Indexed: 08/28/2024]
Abstract
PURPOSE To provide a navigator-based run-time motion and first-order field correction for three-dimensional human brain imaging with high precision, minimal calibration and acquisition, and fast processing. METHODS A complex-valued linear perturbation model with feedback control is extended to estimate and correct for gradient shim fields using orbital navigators (2.3 ms). Two approaches for sensitizing the model to gradient fields are presented, one based on finite differences with three additional navigators, and another projection-based approximation requiring no additional navigators. A mechanism for noise decorrelation of the matrix and the data is proposed and evaluated to reduce unwanted parameter biases. RESULTS The rigid motion and first-order field control achieves robust motion and gradient shim corrections improving image quality in a series of phantom and in vivo experiments with varying field conditions. In phantom scans, magnet drifts, forced gradient field perturbations and field distortions from shifts of a second bottle phantom are successfully corrected. Field estimates of the magnet drifts are in good agreement with concurrent field probe measurements. For in vivo scans, the proposed method mitigates field variations from torso motions while being robust to head motion. In vivo gradient field precisions were30 nT / m $$ 30\;\mathrm{nT}/\mathrm{m} $$ along with single-digit micrometer and millidegree rigid precisions. CONCLUSION The navigator-based method achieves accurate, high-precision run-time motion and field corrections with low sequence impact and calibration requirements.
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Affiliation(s)
- Malte Riedel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Switzerland
| | - Thomas Ulrich
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Switzerland
| | - Klaas P Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Switzerland
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Roecher E, Mösch L, Zweerings J, Thiele FO, Caspers S, Gaebler AJ, Eisner P, Sarkheil P, Mathiak K. Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks. Int J Neural Syst 2024:2450052. [PMID: 38989919 DOI: 10.1142/s0129065724500527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.
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Affiliation(s)
- Erik Roecher
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
| | - Lucas Mösch
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
| | | | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty & University, Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Arnim Johannes Gaebler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- JARA-BRAIN, Jülich Aachen Research Alliance (JARA), Translational Brain Medicine, Germany
- Institute of Neurophysiology, Faculty of Medicine, RWTH Aachen, Germany
| | - Patrick Eisner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
| | - Pegah Sarkheil
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- JARA-BRAIN, Jülich Aachen Research Alliance (JARA), Translational Brain Medicine, Germany
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Rovedo P, Meine H, Hucker P, Taghizadeh E, Izadpanah K, Zaitsev M, Lange T. Time-Resolved Quantification of Patellofemoral Cartilage Deformation in Response to Loading and Unloading via Dynamic MRI With Prospective Motion Correction. J Magn Reson Imaging 2024; 60:175-183. [PMID: 37668040 DOI: 10.1002/jmri.28986] [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: 05/08/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND In vivo cartilage deformation has been studied by static magnetic resonance imaging (MRI) with in situ loading, but knowledge about strain dynamics after load onset and release is scarce. PURPOSE To measure the dynamics of patellofemoral cartilage deformation and recovery in response to in situ loading and unloading by using MRI with prospective motion correction. STUDY TYPE Prospective. SUBJECTS Ten healthy male volunteers (age: [31.4 ± 3.2] years). FIELD STRENGTH/SEQUENCE T1-weighted RF-spoiled 2D gradient-echo sequence with a golden angle radial acquisition scheme, augmented with prospective motion correction, at 3 T. ASSESSMENT In situ knee loading was realized with a flexion angle of approximately 40° using an MR-compatible pneumatic loading device. The loading paradigm consisted of 2 minutes of unloaded baseline followed by a 5-minute loading bout with 50% body weight and an unloading period of 38 minutes. The cartilage strain was assessed as the mean distance between patellar and femoral bone-cartilage interfaces as a percentage of the initial (pre-load) distance. STATISTICAL TESTS Wilcoxon signed-rank tests (significance level: P < 0.05), Pearson correlation coefficient (r). RESULTS The cartilage compression and recovery behavior was characterized by a viscoelastic response. The elastic compression ([-12.5 ± 3.1]%) was significantly larger than the viscous compression ([-7.6 ± 1.5]%) and the elastic recovery ([10.5 ± 2.1]%) was significantly larger than the viscous recovery ([6.1 ± 1.8]%). There was a significant residual offset strain ([-3.6 ± 2.3]%) across the cohort. A significant negative correlation between elastic compression and elastic recovery was observed (r = -0.75). DATA CONCLUSION The in vivo cartilage compression and recovery time course in response to loading was successfully measured via dynamic MRI with prospective motion correction. The clinical relevance of the strain characteristics needs to be assessed in larger subject and patient cohorts. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Philipp Rovedo
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Hans Meine
- Medical Image Computing Group, Department of Informatics, University of Bremen, Bremen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Patrick Hucker
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elham Taghizadeh
- Medical Image Computing Group, Department of Informatics, University of Bremen, Bremen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Kaywan Izadpanah
- Department of Orthopedic and Trauma Surgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Lange
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Wu W. Dynamic field mapping and distortion correction using single-shot blip-rewound EPI and joint multi-echo reconstruction. Magn Reson Med 2024; 92:82-97. [PMID: 38308081 DOI: 10.1002/mrm.30038] [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: 08/17/2023] [Revised: 01/02/2024] [Accepted: 01/16/2024] [Indexed: 02/04/2024]
Abstract
PURPOSE To develop a method for dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping and distortion correction. METHODS A blip-rewound EPI trajectory was developed to acquire multiple 2D EPI images in a single readout with an interleaved order, which allows a short TE difference. A joint multi-echo reconstruction was utilized to exploit the shared information between EPI images. The reconstructed images from each readout are combined to produce a final magnitude image. A∆ B 0 $$ \Delta {B}_0 $$ map is calculated from the phase of these images for distortion correction. The efficacy of the proposed method is assessed with phantom and in vivo experiments. The performance of the proposed method in the presence of subject motion is also investigated. RESULTS Compared to conventional multi-echo EPI, the proposed method allows dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping at matched resolution with a much shorter TR. Phantom and in vivo results show that the proposed method can provide a comparable magnitude image as conventional single-shot EPI. The∆ B 0 $$ \Delta {B}_0 $$ maps calculated from the proposed method are consistent with conventional multi-echo EPI in the phantom experiment. For in vivo experiments, the proposed method provides a more accurate estimation of∆ B 0 $$ \Delta {B}_0 $$ than conventional multi-echo EPI, which is prone to phase wrapping problems due to the long TE difference. In-vivo scan with subject motion shows the proposed dynamic field mapping method can improve the temporal stability of EPI time series compared to gradient echo (GRE) based static field mapping. CONCLUSION The proposed method allows accurate dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping for robust distortion correction without compromising spatial or temporal resolution.
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Affiliation(s)
- Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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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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10
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Rizzuti G, Schakel T, Huttinga NRF, Dankbaar JW, van Leeuwen T, Sbrizzi A. Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01161-y. [PMID: 38758490 DOI: 10.1007/s10334-024-01161-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024]
Abstract
OBJECT In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine. MATERIALS AND METHODS We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed. RESULTS Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity. DISCUSSION While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements.
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Affiliation(s)
- Gabrio Rizzuti
- Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, The Netherlands
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Tim Schakel
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Niek R F Huttinga
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Jan Willem Dankbaar
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Tristan van Leeuwen
- Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, The Netherlands
- Centrum Wiskunde & Informatica, Science Park Amsterdam 123, 1098 XG, Amsterdam, The Netherlands
| | - Alessandro Sbrizzi
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
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11
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Wampl S, Körner T, Meyerspeer M, Zaitsev M, Wolf M, Trattnig S, Wolzt M, Bogner W, Schmid AI. A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy. Sci Rep 2024; 14:10781. [PMID: 38734781 PMCID: PMC11088657 DOI: 10.1038/s41598-024-61403-w] [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: 11/10/2023] [Accepted: 05/06/2024] [Indexed: 05/13/2024] Open
Abstract
Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.
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Affiliation(s)
- Stefan Wampl
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Tito Körner
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Meyerspeer
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Marcos Wolf
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Wolzt
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Albrecht Ingo Schmid
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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12
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Ulrich T, Riedel M, Pruessmann KP. Servo navigators: Linear regression and feedback control for rigid-body motion correction. Magn Reson Med 2024; 91:1876-1892. [PMID: 38234052 DOI: 10.1002/mrm.29967] [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: 04/21/2023] [Revised: 11/05/2023] [Accepted: 11/24/2023] [Indexed: 01/19/2024]
Abstract
PURPOSE Navigator-based correction of rigid-body motion reconciling high precision with minimal acquisition, minimal calibration and simple, fast processing. METHODS A short orbital navigator (2.3 ms) is inserted in a three-dimensional (3D) gradient echo sequence for human head imaging. Head rotation and translation are determined by linear regression based on a complex-valued model built either from three reference navigators or in a reference-less fashion, from the first actual navigator. Optionally, the model is expanded by global phase and field offset. Run-time scan correction on this basis establishes servo control that maintains validity of the linear picture by keeping its expansion point stable in the head frame of reference. The technique is assessed in a phantom and demonstrated by motion-corrected imaging in vivo. RESULTS The proposed approach is found to establish stable motion control both with and without reference acquisition. In a phantom, it is shown to accurately detect motion mimicked by rotation of scan geometry as well as change in global B0 . It is demonstrated to converge to accurate motion estimates after perturbation well beyond the linear signal range. In vivo, servo navigation achieved motion detection with precision in the single-digit range of micrometers and millidegrees. Involuntary and intentional motion in the range of several millimeters were successfully corrected, achieving excellent image quality. CONCLUSION The combination of linear regression and feedback control enables prospective motion correction for head imaging with high precision and accuracy, short navigator readouts, fast run-time computation, and minimal demand for reference data.
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Affiliation(s)
- Thomas Ulrich
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Malte Riedel
- 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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13
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Motyka S, Weiser P, Bachrata B, Hingerl L, Strasser B, Hangel G, Niess E, Niess F, Zaitsev M, Robinson SD, Langs G, Trattnig S, Bogner W. Predicting dynamic, motion-related changes in B 0 field in the brain at a 7T MRI using a subject-specific fine-trained U-net. Magn Reson Med 2024; 91:2044-2056. [PMID: 38193276 DOI: 10.1002/mrm.29980] [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: 08/04/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B0 ), which is a prerequisite for high quality data. Thus, characterization of changes to B0 , for example induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. METHODS We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo gradient-echo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. RESULTS Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. CONCLUSION It is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators.
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Affiliation(s)
- Stanislav Motyka
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Paul Weiser
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Beata Bachrata
- Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Eva Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Fabian Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Maxim Zaitsev
- Department of Radiology - Medical Physics, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg, Germany
| | - Simon Daniel Robinson
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
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14
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Hangel G, Kasprian G, Chambers S, Haider L, Lazen P, Koren J, Diehm R, Moser K, Tomschik M, Wais J, Winter F, Zeiser V, Gruber S, Aull-Watschinger S, Traub-Weidinger T, Baumgartner C, Feucht M, Dorfer C, Bogner W, Trattnig S, Pataraia E, Roessler K. Implementation of a 7T Epilepsy Task Force consensus imaging protocol for routine presurgical epilepsy work-up: effect on diagnostic yield and lesion delineation. J Neurol 2024; 271:804-818. [PMID: 37805665 PMCID: PMC10827812 DOI: 10.1007/s00415-023-11988-5] [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: 07/26/2023] [Accepted: 09/05/2023] [Indexed: 10/09/2023]
Abstract
OBJECTIVE Recently, the 7 Tesla (7 T) Epilepsy Task Force published recommendations for 7 T magnetic resonance imaging (MRI) in patients with pharmaco-resistant focal epilepsy in pre-surgical evaluation. The objective of this study was to implement and evaluate this consensus protocol with respect to both its practicability and its diagnostic value/potential lesion delineation surplus effect over 3 T MRI in the pre-surgical work-up of patients with pharmaco-resistant focal onset epilepsy. METHODS The 7 T MRI protocol consisted of T1-weighted, T2-weighted, high-resolution-coronal T2-weighted, fluid-suppressed, fluid-and-white-matter-suppressed, and susceptibility-weighted imaging, with an overall duration of 50 min. Two neuroradiologists independently evaluated the ability of lesion identification, the detection confidence for these identified lesions, and the lesion border delineation at 7 T compared to 3 T MRI. RESULTS Of 41 recruited patients > 12 years of age, 38 were successfully measured and analyzed. Mean detection confidence scores were non-significantly higher at 7 T (1.95 ± 0.84 out of 3 versus 1.64 ± 1.19 out of 3 at 3 T, p = 0.050). In 50% of epilepsy patients measured at 7 T, additional findings compared to 3 T MRI were observed. Furthermore, we found improved border delineation at 7 T in 88% of patients with 3 T-visible lesions. In 19% of 3 T MR-negative cases a new potential epileptogenic lesion was detected at 7 T. CONCLUSIONS The diagnostic yield was beneficial, but with 19% new 7 T over 3 T findings, not major. Our evaluation revealed epilepsy outcomes worse than ILAE Class 1 in two out of the four operated cases with new 7 T findings.
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Affiliation(s)
- Gilbert Hangel
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria.
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria.
- Medical Imaging Cluster, Medical University of Vienna, Vienna, Austria.
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stefanie Chambers
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Lukas Haider
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- NMR Research Unit, Faculty of Brain Science, Queens Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands
| | - Philipp Lazen
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Johannes Koren
- Department of Neurology, Klinik Hietzing, Vienna, Austria
| | - Robert Diehm
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Katharina Moser
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Matthias Tomschik
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Jonathan Wais
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Fabian Winter
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Vitalij Zeiser
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Stephan Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Martha Feucht
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Christian Dorfer
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | - Siegfried Trattnig
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Karl Roessler
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
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15
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Elliott ML, Nielsen JA, Hanford LC, Hamadeh A, Hilbert T, Kober T, Dickerson BC, Hyman BT, Mair RW, Eldaief MC, Buckner RL. Precision Brain Morphometry Using Cluster Scanning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.23.23300492. [PMID: 38234845 PMCID: PMC10793507 DOI: 10.1101/2023.12.23.23300492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (~1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as "cluster scanning." Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer's Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jared A Nielsen
- Department of Psychology, Neuroscience Center, Brigham Young University, Provo, UT, 84602, USA
| | - Lindsay C Hanford
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Aya Hamadeh
- Baylor College of Medicine, Houston, TX 77030
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit
- Alzheimer's Disease Research Center
- Athinoula A. Martinos Center for Biomedical Imaging
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Bradley T Hyman
- Alzheimer's Disease Research Center
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
| | - Ross W Mair
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Athinoula A. Martinos Center for Biomedical Imaging
| | - Mark C Eldaief
- Frontotemporal Disorders Unit
- Alzheimer's Disease Research Center
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Alzheimer's Disease Research Center
- Athinoula A. Martinos Center for Biomedical Imaging
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
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16
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Li B, Li N, Wang Z, Balan R, Ernst T. Simultaneous multislice EPI prospective motion correction by real-time receiver phase correction and coil sensitivity map interpolation. Magn Reson Med 2023; 90:1932-1948. [PMID: 37448116 PMCID: PMC10795703 DOI: 10.1002/mrm.29789] [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: 09/29/2022] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 07/15/2023]
Abstract
PURPOSE To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated. METHODS A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation. RESULTS Brain SMS-EPI scans in the presence of motion with PMC and no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p = 0.0007) for scans with large movements (up to ˜35 mm and 30°). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol. CONCLUSION Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion.
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Affiliation(s)
- Bo Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, United States
| | - Ningzhi Li
- U.S. Food Drug Administration, Silver Spring, MD, United States
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, United States
| | - Radu Balan
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - Thomas Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, United States
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17
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Robinson SD, Bachrata B, Eckstein K, Bollmann S, Bollmann S, Hodono S, Cloos M, Tourell M, Jin J, O'Brien K, Reutens DC, Trattnig S, Enzinger C, Barth M. Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL). Hum Brain Mapp 2023; 44:5095-5112. [PMID: 37548414 PMCID: PMC10502646 DOI: 10.1002/hbm.26440] [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: 01/26/2023] [Revised: 06/01/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023] Open
Abstract
The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.
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Affiliation(s)
- Simon Daniel Robinson
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Department of NeurologyMedical University of GrazGrazAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
| | - Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
- Department of Medical EngineeringCarinthia University of Applied SciencesKlagenfurtAustria
| | - Korbinian Eckstein
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Saskia Bollmann
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Steffen Bollmann
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
| | - Shota Hodono
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Martijn Cloos
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Monique Tourell
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | - Jin Jin
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | | | - David C. Reutens
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | | | - Markus Barth
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
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18
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Siegel M, Taghizadeh E, Fuchs A, Maier P, Schmal H, Lange T, Yilmaz T, Meine H, Izadpanah K. [Influence of the quadriceps muscles on the patellofemoral contact in patients with low flexion patellofemoral instability after MPFL reconstruction]. ORTHOPADIE (HEIDELBERG, GERMANY) 2023; 52:834-842. [PMID: 37567919 PMCID: PMC10539450 DOI: 10.1007/s00132-023-04413-2] [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] [Accepted: 05/29/2023] [Indexed: 08/13/2023]
Abstract
INTRODUCTION MPFL reconstruction represents one of the most important surgical treatment options for recurrent patellar dislocations at low flexion angles associated with low flexion patellofemoral instability. Nevertheless, the role of quadriceps muscles in patients with patellofemoral instability before and after patellofemoral stabilization using MPFL reconstruction has not been fully elucidated. The present study investigates the influence of quadriceps muscles on the patellofemoral contact in patients with low flexion patellofemoral instability (PFI) before and after surgical patellofemoral stabilization using MPFL reconstruction using 3 T MRI datasets in early degrees of flexion (0-30°). METHODS In this prospective cohort study, 15 patients with low flexion PFI before and after MPFL reconstruction and 15 subjects with healthy knee joints were studied using dynamic MRI scans. MRI scans were performed in a custom-made pneumatic knee loading device to determine the patellofemoral cartilage contact area (CCA) with and without quadriceps activation (50 N). Comparative measurements were performed using 3D cartilage and bone meshes in 0-30° knee flexion in the patients with patellofemoral instability preoperatively and postoperatively. RESULTS The preoperative patellofemoral CCA of patients with low flexion PFI was 67.3 ± 47.3 mm2 in 0° flexion, 118.9 ± 56.6 mm2 in 15° flexion, and 267.6 ± 96.1 mm2 in 30° flexion. With activated quadriceps muscles (50 N), the contact area was 72.4 ± 45.9 mm2 in extension, 112.5 ± 54.9 mm2 in 15° flexion, and 286.1 ± 92.7 mm2 in 30° flexion without statistical significance. Postoperatively determined CCA revealed 159.3 ± 51.4 mm2 , 189.6 ± 62.2 mm2 and 347.3 ± 52.1 mm2 in 0°, 15° and 30° flexion. Quadriceps activation with 50 N showed a contact area in extension of 141.0 ± 63.8 mm2, 206.6 ± 67.7 mm2 in 15° flexion, and 353.5 ± 64.6 mm2 in 30° flexion, also without statistical difference compared with unloaded CCAs. Subjects with healthy knee joints showed an increase of 10.3% in CCA at 30° of flexion (p = 0.003). CONCLUSION Although patellofemoral CCA increases significantly after isolated MPFL reconstruction in patients with low flexion patellofemoral instability, there is no significant influence of quadriceps muscles either preoperatively or postoperatively.
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Affiliation(s)
- Markus Siegel
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland.
| | - Elham Taghizadeh
- Institute for Medical Image Computing, Fraunhofer MEVIS, Universitätsallee 29, 28359, Bremen, Deutschland
| | - Andreas Fuchs
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Philipp Maier
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Hagen Schmal
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
- Dep. Of Orthopedic Surgery, University Hospital Odense, Sdr. Boulevard 29, 5000, Odense, Dänemark
| | - Thomas Lange
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Killianstraße 5a, 79106, Freiburg, Deutschland
| | - Tayfun Yilmaz
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Hans Meine
- Institute for Medical Image Computing, Fraunhofer MEVIS, Universitätsallee 29, 28359, Bremen, Deutschland
| | - Kaywan Izadpanah
- Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital, Albert Ludwigs University Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
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19
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Zhou J, Hagberg GE, Aghaeifar A, Bause J, Zaitsev M, Scheffler K. Prediction of motion induced magnetic fields for human brain MRI at 3 T. MAGMA (NEW YORK, N.Y.) 2023; 36:797-813. [PMID: 36964797 PMCID: PMC10504152 DOI: 10.1007/s10334-023-01076-0] [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: 08/05/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVE Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we propose a forward simulation strategy to obtain B0 predictions at different head-positions. METHODS FM were predicted by combining (1) a multi-class tissue model for estimation of tissue-induced fields, (2) a linear k-space model for capturing gradient imperfections, (3) a dipole estimation for quantifying lower-body perturbing fields (4) and a position-dependent tissue mask to model FM alterations caused by large motion effects. The performance of the combined simulation strategy was compared with an approach based on a rigid body transformation of the FM measured in the reference position to the new position. RESULTS The transformed FM provided inconsistent results for large head movements (> 5° rotation, approximately), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach. CONCLUSION The proposed simulation strategy is able to predict movement-induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM.
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Affiliation(s)
- Jiazheng Zhou
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany.
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.
| | - Gisela E Hagberg
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Ali Aghaeifar
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Jonas Bause
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Maxim Zaitsev
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Scheffler
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
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20
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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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21
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Wu B, Li C, Zhang J, Lai H, Feng Q, Huang M. Unsupervised dual-domain disentangled network for removal of rigid motion artifacts in MRI. Comput Biol Med 2023; 165:107373. [PMID: 37611424 DOI: 10.1016/j.compbiomed.2023.107373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/28/2023] [Accepted: 08/12/2023] [Indexed: 08/25/2023]
Abstract
Motion artifacts in magnetic resonance imaging (MRI) have always been a serious issue because they can affect subsequent diagnosis and treatment. Supervised deep learning methods have been investigated for the removal of motion artifacts; however, they require paired data that are difficult to obtain in clinical settings. Although unsupervised methods are widely proposed to fully use clinical unpaired data, they generally focus on anatomical structures generated by the spatial domain while ignoring phase error (deviations or inaccuracies in phase information that are possibly caused by rigid motion artifacts during image acquisition) provided by the frequency domain. In this study, a 2D unsupervised deep learning method named unsupervised disentangled dual-domain network (UDDN) was proposed to effectively disentangle and remove unwanted rigid motion artifacts from images. In UDDN, a dual-domain encoding module was presented to capture different types of information from the spatial and frequency domains to enrich the information. Moreover, a cross-domain attention fusion module was proposed to effectively fuse information from different domains, reduce information redundancy, and improve the performance of motion artifact removal. UDDN was validated on a publicly available dataset and a clinical dataset. Qualitative and quantitative experimental results showed that our method could effectively remove motion artifacts and reconstruct image details. Moreover, the performance of UDDN surpasses that of several state-of-the-art unsupervised methods and is comparable with that of the supervised method. Therefore, our method has great potential for clinical application in MRI, such as real-time removal of rigid motion artifacts.
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Affiliation(s)
- Boya Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Caixia Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Jiawei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Haoran Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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22
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Siegel M, Taghizadeh E, Lange T, Fuchs A, Yilmaz T, Maier P, Meine H, Schmal H, Izadpanah K. Influence of Medial Patellofemoral Ligament Reconstruction on Patellofemoral Contact in Patients With Low-Flexion Patellar Instability: An MRI Study. Orthop J Sports Med 2023; 11:23259671231160215. [PMID: 37213660 PMCID: PMC10192662 DOI: 10.1177/23259671231160215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/19/2023] [Indexed: 05/23/2023] Open
Abstract
Background Medial patellofemoral ligament (MPFL) reconstruction is a well-established procedure for the treatment of patients with patellofemoral instability (PFI) at low flexion angles (0°-30°). Little is known about the effect of MPFL surgery on patellofemoral cartilage contact area (CCA) during the first 30° of knee flexion. Purpose/Hypothesis The purpose of this study was to investigate the effect of MPFL reconstruction on CCA using magnetic resonance imaging (MRI). We hypothesized that patients with PFI would have a lower CCA than patients with healthy knees and that CCA would increase after MPFL reconstruction over the course of low knee flexion. Study Design Cohort study; Level of evidence, 2. Methods In a prospective matched-paired cohort study, the CCA of 13 patients with low-flexion PFI was determined before and after MPFL reconstruction, and the data were compared with those of 13 healthy volunteers (controls). MRI was performed with the knee at 0°, 15°, and 30° of flexion in a custom-designed knee-positioning device. To suppress motion artifacts, motion correction was performed using a Moiré Phase Tracking system via a tracking marker attached to the patella. The CCA was calculated on the basis of semiautomatic cartilage and bone segmentation and registration. Results The CCA (mean ± SD) at 0°, 15°, and 30° of flexion for the control participants was 1.38 ± 0.62, 1.91 ± 0.98, and 3.68 ± 0.92 cm2, respectively. In patients with PFI, the CCA at 0°, 15°, and 30° of flexion was 0.77 ± 0.49, 1.26 ± 0.60, and 2.89 ± 0.89 cm2 preoperatively and 1.65 ± 0.55, 1.97 ± 0.68, and 3.52 ± 0.57 cm2 postoperatively. Patients with PFI exhibited a significantly reduced preoperative CCA at all 3 flexion angles when compared with controls (P ≤ .045 for all). Postoperatively, there was a significant increase in CCA at 0° of flexion (P = .001), 15° of flexion (P = .019) and 30° of flexion (P = .026). There were no significant postoperative differences in CCA between patients with PFI and controls at any flexion angle. Conclusion Patients with low-flexion patellar instability showed a significant reduction in patellofemoral CCA at 0°, 15°, and 30° of flexion. MPFL reconstruction increased the contact area significantly at all angles.
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Affiliation(s)
- Markus Siegel
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
- Markus Siegel, MD,
Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital,
Albert Ludwigs University Freiburg, Hugstetter Strasse 55, Freiburg, 79098
Germany ()
| | - Elham Taghizadeh
- Fraunhofer Institute for Digital
Medicine MEVIS, Bremen, Germany
| | - Thomas Lange
- Division of Medical Physics, Department
of Diagnostic and Interventional Radiology, Medical Center–University of Freiburg,
Faculty of Medicine, Albert Ludwigs University of Freiburg, Freiburg, Germany
| | - Andreas Fuchs
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
| | - Tayfun Yilmaz
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
| | - Philipp Maier
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
| | - Hans Meine
- Fraunhofer Institute for Digital
Medicine MEVIS, Bremen, Germany
| | - Hagen Schmal
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
- Department of Orthopedic Surgery,
University Hospital Odense, Odense, Denmark
| | - Kaywan Izadpanah
- Department of Orthopedic Surgery and
Traumatology, Freiburg University Hospital, Albert Ludwigs University of Freiburg,
Freiburg, Germany
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23
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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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24
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van Gelderen P, Li X, de Zwart JA, Beck ES, Okar SV, Huang Y, Lai K, Sulam J, van Zijl PCM, Reich DS, Duyn JH, Liu J. Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R 2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T. Neuroimage 2023; 270:119992. [PMID: 36858332 PMCID: PMC10278242 DOI: 10.1016/j.neuroimage.2023.119992] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 02/17/2023] [Accepted: 02/25/2023] [Indexed: 03/03/2023] Open
Abstract
MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0.3 × 0.3 × 0.4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s-1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s-1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0.5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.
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Affiliation(s)
- Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jacco A de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Erin S Beck
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States of America
| | - Serhat V Okar
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America
| | - Yujia Huang
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - KuoWei Lai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America; Department of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Peter C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America; Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - Daniel S Reich
- Translational Neurology Section, NINDS, NIH, Bethesda, MD, United States of America
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, NIH, Bethesda, MD, United States of America
| | - Jiaen Liu
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States of America.
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25
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Levitt J, van der Kouwe A, Jeong H, Lewis LD, Bonmassar G. The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:3539. [PMID: 37050598 PMCID: PMC10098760 DOI: 10.3390/s23073539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. METHODS The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. RESULTS MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. CONCLUSIONS The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.
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Affiliation(s)
- Joshua Levitt
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Hongbae Jeong
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Engineering, Massachusetts General Hospital, Charlestown, MA 02129, USA
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26
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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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27
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Steinbrenner M, McDowell A, Centeno M, Moeller F, Perani S, Lorio S, Maziero D, Carmichael DW. Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States. Brain Topogr 2023; 36:319-337. [PMID: 36939987 PMCID: PMC10164016 DOI: 10.1007/s10548-023-00945-0] [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: 09/01/2022] [Accepted: 02/14/2023] [Indexed: 03/21/2023]
Abstract
BACKGROUND EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction. METHODS Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other. RESULTS Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7. CONCLUSION To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.
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Affiliation(s)
- Mirja Steinbrenner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.,Department of Neurology and Experimental Neurology, Epilepsy Center Berlin-Brandenburg, Charité-Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Amy McDowell
- Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK
| | - Maria Centeno
- Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK.,Epilepsy Unit, Neurology Department, Hospital Clinic Barcelona/IDIBAPS, Villarroel 170., Barcelona, 08036, Spain
| | - Friederike Moeller
- Department of Clinical Neurophysiology, Great Ormond Street Hospital, Great Ormond Street, London, WC1N 3JH, UK
| | - Suejen Perani
- Department of Basic and Clinical Neuroscience, KCL Institute of Psychiatry, Psychology & Neuroscience, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Sara Lorio
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Danilo Maziero
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego Health, San Diego, CA, USA
| | - David W Carmichael
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK. .,Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK.
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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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29
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Solomon E, Lotan E, Zan E, Sodickson DK, Block KT, Chandarana H. MP-RAVE: IR-Prepared T 1 -Weighted Radial Stack-of-Stars 3D GRE imaging with retrospective motion correction. Magn Reson Med 2023; 90:202-210. [PMID: 36763847 DOI: 10.1002/mrm.29614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/17/2022] [Accepted: 01/24/2023] [Indexed: 02/12/2023]
Abstract
PURPOSE To describe an inversion-recovery T1 -weighted radial stack-of-stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP-RAGE and to demonstrate how the radial acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion. METHODS The proposed sequence, named MP-RAVE, has been derived from a previously described radial stack-of-stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness. The effectiveness has been evaluated in phantoms and healthy volunteers and compared to conventional MP-RAGE acquisition. RESULTS MP-RAGE and MP-RAVE anatomical images were rated "good" to "excellent" in overall image quality, with artifact level between "mild" and "no artifacts", and with no statistically significant difference between methods. During head motion, MP-RAVE showed higher inherent robustness with artifacts confined to local brain regions. In combination with motion correction, MP-RAVE provided noticeably improved image quality during different head motion and showed statistically significant improvement in image sharpness. CONCLUSION MP-RAVE provides comparable image quality and contrast to conventional MP-RAGE with improved robustness to head motion. In combination with retrospective 3D motion correction, MP-RAVE can be a useful alternative to MP-RAGE, especially in non-cooperative or pediatric patients.
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Affiliation(s)
- Eddy Solomon
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
| | - Eyal Lotan
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
| | - Elcin Zan
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, University Grossman School of Medicine, New York, New York, USA
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30
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Lang M, Tabari A, Polak D, Ford J, Clifford B, Lo WC, Manzoor K, Splitthoff DN, Wald LL, Rapalino O, Schaefer P, Conklin J, Cauley S, Huang SY. Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings. AJNR Am J Neuroradiol 2023; 44:125-133. [PMID: 36702502 PMCID: PMC9891324 DOI: 10.3174/ajnr.a7777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/11/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations. MATERIALS AND METHODS Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5). RESULTS The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively. CONCLUSIONS SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.
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Affiliation(s)
- M Lang
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - A Tabari
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - D Polak
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Siemens Healthcare GmbH (D.P., D.N.S.), Erlangen, Germany
| | - J Ford
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - B Clifford
- Siemens Medical Solutions (B.C., W.-C.L.), Boston, Massachusetts
| | - W-C Lo
- Siemens Medical Solutions (B.C., W.-C.L.), Boston, Massachusetts
| | - K Manzoor
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - D N Splitthoff
- Siemens Healthcare GmbH (D.P., D.N.S.), Erlangen, Germany
| | - L L Wald
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
- Harvard-MIT Health Sciences and Technology (L.L.W.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - O Rapalino
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - P Schaefer
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - J Conklin
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - S Cauley
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - S Y Huang
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
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Solomon O, Patriat R, Braun H, Palnitkar TE, Moeller S, Auerbach EJ, Ugurbil K, Sapiro G, Harel N. Motion robust magnetic resonance imaging via efficient Fourier aggregation. Med Image Anal 2023; 83:102638. [PMID: 36257133 DOI: 10.1016/j.media.2022.102638] [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: 02/01/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 02/04/2023]
Abstract
We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.
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Affiliation(s)
- Oren Solomon
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America.
| | - Rémi Patriat
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Henry Braun
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Tara E Palnitkar
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Steen Moeller
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Edward J Auerbach
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Kamil Ugurbil
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, NC, United States of America; Department of Biomedical Engineering, Duke University, NC, United States of America; Department of Computer Science, Duke University, NC, United States of America; Department of Mathematics, Duke University, NC, United States of America
| | - Noam Harel
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America; Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States of America
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32
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Seginer A, Keith GA, Porter DA, Schmidt R. Artifact suppression in readout-segmented consistent K-t space EPSI (RS-COKE) for fast 1 H spectroscopic imaging at 7 T. Magn Reson Med 2022; 88:2339-2357. [PMID: 35975965 PMCID: PMC9804880 DOI: 10.1002/mrm.29373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 01/09/2023]
Abstract
PURPOSE Fast proton (1 H) MRSI is an important diagnostic tool for clinical investigations, providing metabolic and spatial information. MRSI at 7 T benefits from increased SNR and improved separation of peaks but requires larger spectral widths. RS-COKE (Readout-Segmented Consistent K-t space Epsi) is an echo planar spectroscopic imaging (Epsi) variant capable to support the spectral width required for human brain metabolites spectra at 7 T. However, mismatches between readout segments lead to artifacts, particularly when subcutaneous lipid signals are not suppressed. In this study, these mismatches and their effects are analyzed and reduced. METHODS The following corrections to the data were performed: i) frequency-dependent phase corrections; ii) k-space trajectory corrections, derived from short reference scans; and iii) smoothing of data at segment transitions to mitigate the effect of residual mismatches. The improvement was evaluated by performing single-slice RS-COKE on a head-shaped phantom with a "lipid" layer and healthy subjects, using varying resolutions and durations ranging from 4.1 × 4.7 × 15 mm3 in 5:46 min to 3.1 × 3.3 × 15 mm3 in 13:07 min. RESULTS Artifacts arising from the readout-segmented acquisition were substantially reduced, thus providing high-quality spectroscopic imaging in phantom and human scans. LCModel fitting of the human data resulted in a relative Cramer-Rao lower bounds within 6% for NAA, Cr, and Cho images in the majority of the voxels. CONCLUSION Using the new reference scans and reconstruction steps, RS-COKE was able to deliver fast 1 H MRSI at 7 T, overcoming the spectral width limitation of standard EPSI at this field strength.
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Affiliation(s)
| | - Graeme A. Keith
- Imaging Centre of ExcellenceUniversity of GlasgowGlasgowUnited Kingdom
| | - David A. Porter
- Imaging Centre of ExcellenceUniversity of GlasgowGlasgowUnited Kingdom
| | - Rita Schmidt
- Department of Brain SciencesWeizmann Institute of ScienceRehovotIsrael,The Azrieli National Institute for Human Brain Imaging and ResearchWeizmann Institute of ScienceRehovotIsrael
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33
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Dabrowski O, Courvoisier S, Falcone JL, Klauser A, Songeon J, Kocher M, Chopard B, Lazeyras F. Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets. Phys Med 2022; 102:79-87. [PMID: 36137403 DOI: 10.1016/j.ejmp.2022.09.005] [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: 03/14/2022] [Revised: 08/26/2022] [Accepted: 09/12/2022] [Indexed: 11/19/2022] Open
Abstract
MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either prospective or retrospective. Benchmarking the latter methods requires labeled motion-corrupted datasets, which are uncommon. Up to our best knowledge, no protocol for generating labeled datasets of MRI images corrupted by controlled motion has yet been proposed. Hence, we present a methodology allowing the acquisition of reproducible motion-corrupted MRI images as well as validation of the system's performance by motion estimation through rigid-body volume registration of fast 3D echo-planar imaging (EPI) time series. A proof-of-concept is presented, to show how the protocol can be implemented to provide qualitative and quantitative results. An MRI-compatible video system displays a moving target that volunteers equipped with customized plastic glasses must follow to perform predefined head choreographies. Motion estimation using rigid-body EPI time series registration demonstrated that head position can be accurately determined (with an average standard deviation of about 0.39 degrees). A spatio-temporal upsampling and interpolation method to cope with fast motion is also proposed in order to improve motion estimation. The proposed protocol is versatile and straightforward. It is compatible with all MRI systems and may provide insights on the origins of specific motion artifacts. The MRI and artificial intelligence research communities could benefit from this work to build in-vivo labeled datasets of motion-corrupted MRI images suitable for training/testing any retrospective motion correction or machine learning algorithm.
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Affiliation(s)
- Oscar Dabrowski
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland.
| | - Sébastien Courvoisier
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Jean-Luc Falcone
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland
| | - Antoine Klauser
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Julien Songeon
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Michel Kocher
- Biomedical Imaging Group (BIG), School of Engineering, EPFL, Lausanne, Switzerland
| | - Bastien Chopard
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
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34
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Manfredi-Lozano M, Leysen V, Adamo M, Paiva I, Rovera R, Pignat JM, Timzoura FE, Candlish M, Eddarkaoui S, Malone SA, Silva MSB, Trova S, Imbernon M, Decoster L, Cotellessa L, Tena-Sempere M, Claret M, Paoloni-Giacobino A, Plassard D, Paccou E, Vionnet N, Acierno J, Maceski AM, Lutti A, Pfrieger F, Rasika S, Santoni F, Boehm U, Ciofi P, Buée L, Haddjeri N, Boutillier AL, Kuhle J, Messina A, Draganski B, Giacobini P, Pitteloud N, Prevot V. GnRH replacement rescues cognition in Down syndrome. Science 2022; 377:eabq4515. [PMID: 36048943 PMCID: PMC7613827 DOI: 10.1126/science.abq4515] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
At the present time, no viable treatment exists for cognitive and olfactory deficits in Down syndrome (DS). We show in a DS model (Ts65Dn mice) that these progressive nonreproductive neurological symptoms closely parallel a postpubertal decrease in hypothalamic as well as extrahypothalamic expression of a master molecule that controls reproduction-gonadotropin-releasing hormone (GnRH)-and appear related to an imbalance in a microRNA-gene network known to regulate GnRH neuron maturation together with altered hippocampal synaptic transmission. Epigenetic, cellular, chemogenetic, and pharmacological interventions that restore physiological GnRH levels abolish olfactory and cognitive defects in Ts65Dn mice, whereas pulsatile GnRH therapy improves cognition and brain connectivity in adult DS patients. GnRH thus plays a crucial role in olfaction and cognition, and pulsatile GnRH therapy holds promise to improve cognitive deficits in DS.
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Affiliation(s)
- Maria Manfredi-Lozano
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Valerie Leysen
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Michela Adamo
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Isabel Paiva
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR 7364, Université de Strasbourg-CNRS, Strasbourg, France
| | - Renaud Rovera
- Univ. Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Jean-Michel Pignat
- Department of Clinical Neurosciences, Neurorehabilitation Unit, University Hospital CHUV, Lausanne, Switzerland
| | - Fatima Ezzahra Timzoura
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Michael Candlish
- Experimental Pharmacology, Center for Molecular Signaling (PZMS), Saarland University School of Medicine, 66421, Homburg, Germany
| | - Sabiha Eddarkaoui
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
| | - Samuel A. Malone
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Mauro S. B. Silva
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Sara Trova
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Monica Imbernon
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Laurine Decoster
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Ludovica Cotellessa
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Manuel Tena-Sempere
- Univ. Cordoba, IMIBC/HURS, CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Cordoba, Spain
| | - Marc Claret
- Neuronal Control of Metabolism Laboratory, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain; Centro de Investigación Biomédica en Red (CIBER) de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
| | - Ariane Paoloni-Giacobino
- Department of Genetic Medicine, University Hospitals of Geneva, 4 rue Gabrielle-Perret-Gentil, 1211, Genève 14, Switzerland
| | - Damien Plassard
- CNRS UMR 7104, INSERM U1258, GenomEast Platform, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, Illkirch, France
| | - Emmanuelle Paccou
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Nathalie Vionnet
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - James Acierno
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Aleksandra Maleska Maceski
- Neurologic Clinic and Polyclinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel; University Hospital Basel, University of Basel, Basel Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Frank Pfrieger
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, 67000 Strasbourg, France
| | - S. Rasika
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Federico Santoni
- Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Ulrich Boehm
- Experimental Pharmacology, Center for Molecular Signaling (PZMS), Saarland University School of Medicine, 66421, Homburg, Germany
| | - Philippe Ciofi
- Univ. Bordeaux, Inserm, U1215, Neurocentre Magendie, Bordeaux, France
| | - Luc Buée
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
| | - Nasser Haddjeri
- Univ. Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Anne-Laurence Boutillier
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR 7364, Université de Strasbourg-CNRS, Strasbourg, France
| | - Jens Kuhle
- Neurologic Clinic and Polyclinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel; University Hospital Basel, University of Basel, Basel Switzerland
| | - Andrea Messina
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paolo Giacobini
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Nelly Pitteloud
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Vincent Prevot
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
- Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
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Breitling J, Korzowski A, Kempa N, Boyd PS, Paech D, Schlemmer HP, Ladd ME, Bachert P, Goerke S. Motion correction for three-dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts. NMR IN BIOMEDICINE 2022; 35:e4720. [PMID: 35233847 DOI: 10.1002/nbm.4720] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
In chemical exchange saturation transfer (CEST) MRI, motion correction is compromised by the drastically changing image contrast at different frequency offsets, particularly at the direct water saturation. In this study, a simple extension for conventional image registration algorithms is proposed, enabling robust and accurate motion correction of CEST-MRI data. The proposed method uses weighted averaging of motion parameters from a conventional rigid image registration to identify and mitigate erroneously misaligned images. Functionality of the proposed method was verified by ground truth datasets generated from 10 three-dimensional in vivo measurements at 3 T with simulated realistic random rigid motion patterns and noise. Performance was assessed using two different criteria: the maximum image misalignment as a measure for the robustness against direct water saturation artifacts, and the spectral error as a measure of the overall accuracy. For both criteria, the proposed method achieved the best scores compared with two motion-correction algorithms specifically developed to handle the varying contrasts in CEST-MRI. Compared with a straightforward linear interpolation of the motion parameters at frequency offsets close to the direct water saturation, the proposed method offers better performance in the absence of artifacts. The proposed method for motion correction in CEST-MRI allows identification and mitigation of direct water saturation artifacts that occur with conventional image registration algorithms. The resulting improved robustness and accuracy enable reliable motion correction, which is particularly crucial for an automated and carefree evaluation of spectral CEST-MRI data, e.g., for large patient cohorts or in clinical routines.
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Affiliation(s)
- Johannes Breitling
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Korzowski
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Neele Kempa
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Philip S Boyd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Peter Bachert
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Steffen Goerke
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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36
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Backhausen LL, Herting MM, Tamnes CK, Vetter NC. Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders. Neuropsychol Rev 2022; 32:400-418. [PMID: 33893904 PMCID: PMC9090677 DOI: 10.1007/s11065-021-09496-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
Abstract
Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.
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Affiliation(s)
- Lea L. Backhausen
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
| | - Megan M. Herting
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Christian K. Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora C. Vetter
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
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Plumley A, Watkins L, Treder M, Liebig P, Murphy K, Kopanoglu E. Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design. Magn Reson Med 2022; 87:2254-2270. [PMID: 34958134 PMCID: PMC7613077 DOI: 10.1002/mrm.29132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/30/2021] [Accepted: 12/03/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B 1 + distributions following within-slice motion, which can then be used for tailored pTx pulse redesign. METHODS Using simulated data, conditional generative adversarial networks were trained to predict B 1 + distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B1 maps. Tailored pTx pulses were designed using B1 maps at the original position (simulated, no motion) and evaluated using simulated B1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method. RESULTS Predicted B 1 + maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. CONCLUSION We propose a framework for predicting B 1 + maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.
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Affiliation(s)
- Alix Plumley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Luke Watkins
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Matthias Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | - Kevin Murphy
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Emre Kopanoglu
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12051032. [PMID: 35626188 PMCID: PMC9140169 DOI: 10.3390/diagnostics12051032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/08/2022] [Accepted: 04/19/2022] [Indexed: 11/18/2022] Open
Abstract
Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.
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Musa M, Sengupta S, Chen Y. MRI-Compatible Soft Robotic Sensing Pad for Head Motion Detection. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3147892] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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40
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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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Pawar K, Chen Z, Shah NJ, Egan GF. Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation. NMR IN BIOMEDICINE 2022; 35:e4225. [PMID: 31865624 DOI: 10.1002/nbm.4225] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/24/2019] [Accepted: 10/24/2019] [Indexed: 06/10/2023]
Abstract
The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.
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Affiliation(s)
- Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - N Jon Shah
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Research Centre Jülich, Institute of Medicine, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
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Bayih SG, Jankiewicz M, Alhamud A, van der Kouwe AJW, Meintjes EM. Self-navigated prospective motion correction for 3D-EPI acquisition. Magn Reson Med 2022; 88:211-223. [PMID: 35344618 DOI: 10.1002/mrm.29202] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/31/2021] [Accepted: 01/29/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE Although 3D EPI is more susceptible to motion artifacts than 2D EPI, it presents some benefits for functional MRI, including the absence of spin-history artifacts, greater potential for parallel imaging acceleration, and better functional sensitivity in high-resolution imaging. Here we present a self-navigated 3D-EPI sequence suitable for prospective motion-corrected functional MRI without additional hardware or pulses. METHODS For each volume acquisition, the first 24 of the 52 partitions being acquired are accumulated to a new feedback block that was added to the image reconstruction pipeline. After zero-filling the remaining partitions, the feedback block constructs a volumetric self-navigator (vSNav), co-registers it to the reference vSNav acquired during the first volume acquisition, and sends motion estimates to the sequence. The sequence then updates its FOV and acquires subsequent partitions with the adjusted FOV, until the next update is received. The sequence was validated without and with intentional motion in phantom and in vivo on a 3T Skyra. RESULTS For phantom scans, the FOV was updated 0.704 s after acquisition of the vSNav partitions, and for in vivo scans after 0.768 s. Both phantom and in vivo data demonstrated stable motion estimates in the absence of motion. For in vivo acquisitions, prospective head-pose estimates using the vSNav's and retrospective estimates with FLIRT (FMRIB's Linear Image Registration Tool) agreed to within 0.23 mm (< 10% of the slice thickness) and 0.14° in all directions. CONCLUSION Depending when motion occurs during a volume acquisition, the proposed method fully corrects the FOV and recovers image quality within one volume acquisition.
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Affiliation(s)
- Samuel Getaneh Bayih
- Biomedical Engineering Research Center, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Marcin Jankiewicz
- Biomedical Engineering Research Center, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Cape Universities Body Imaging Center, University of Cape Town, Cape Town, South Africa
| | - A Alhamud
- Biomedical Engineering Research Center, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Cape Universities Body Imaging Center, University of Cape Town, Cape Town, South Africa.,The Modern Pioneer Center and ArSMRM for MRI Training and Development, Tripoli, Libya
| | - André J W van der Kouwe
- Biomedical Engineering Research Center, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Ernesta M Meintjes
- Biomedical Engineering Research Center, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Cape Universities Body Imaging Center, University of Cape Town, Cape Town, South Africa
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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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Sciarra A, Mattern H, Yakupov R, Chatterjee S, Stucht D, Oeltze-Jafra S, Godenschweger F, Speck O. Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI. Magn Reson Med 2022; 87:646-657. [PMID: 34463376 PMCID: PMC8663924 DOI: 10.1002/mrm.28998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/28/2021] [Accepted: 08/16/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion. METHODS Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking system was used to monitor head motion and consequently update the imaging volume. For all subjects, high-resolution T1 (3D-MPRAGE), T2 (2D turbo spin echo), proton density (2D turbo spin echo), and T2∗ (2D gradient echo) weighted images were acquired with and without PMC. The images were evaluated through subjective and objective analysis. RESULTS Subjective evaluation overall has shown a statistically significant improvement (5.5%) in terms of image quality with PMC ON. In a separate evaluation of every contrast, three of the four contrasts (T1 , T2 , and proton density) have shown a statistically significant improvement (9.62%, 9.85%, and 9.26%), whereas the fourth one ( T2∗ ) has shown improvement, although not statistically significant. In the evaluation with objective metrics, average edge strength has shown an overall improvement of 6% with PMC ON, which was statistically significant; and gradient entropy has shown an overall improvement of 2%, which did not reach statistical significance. CONCLUSION Based on subjective assessment, PMC improved image quality in high-resolution images of healthy compliant subjects in the absence of intentional motion for all contrasts except T2∗ , in which no significant differences were observed. Quantitative metrics showed an overall trend for an improvement with PMC, but not all differences were significant.
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Affiliation(s)
- A. Sciarra
- Medicine and Digitalization - MedDigit, Medical Faculty, Univ. Dept. of Neurology, Otto von Guericke University, Magdeburg, 39120, Germany, Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Institute for Physics, Otto von Guericke University, Magdeburg, 39106, Germany
| | - H. Mattern
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - R. Yakupov
- German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany
| | - S. Chatterjee
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University, Magdeburg
| | - D. Stucht
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - S. Oeltze-Jafra
- Medicine and Digitalization - MedDigit, Medical Faculty, Univ. Dept. of Neurology, Otto von Guericke University, Magdeburg, 39120, Germany, German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany, Center for Behavioral Brain Sciences, Magdeburg, 39120, Germany
| | - F. Godenschweger
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - O. Speck
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Institute for Physics, Otto von Guericke University, Magdeburg, 39106, Germany, German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany, Leibniz Institute for Neurobiology, Magdeburg, 39120, Germany, Center for Behavioral Brain Sciences, Magdeburg, 39120, Germany
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45
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Snyder J, McPhee KC, Wilman AH. T 2 quantification in brain using 3D fast spin-echo imaging with long echo trains. Magn Reson Med 2021; 87:2145-2160. [PMID: 34894641 PMCID: PMC9299830 DOI: 10.1002/mrm.29113] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 11/15/2022]
Abstract
Purpose Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T2 quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods. Methods The FSE sequence design was explored to use long echo trains while minimizing T2 fitting error and maintaining typical proton density and T2‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts. Results Phantom and healthy in vivo brain T2 measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T2 maps. Optimization resulted in better T2 fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis). Conclusion T2 mapping using 3D FSE with long echo trains and variable refocusing angles provides T2 accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T2 mapping methods for high anatomic detail.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | | | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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46
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Deelchand DK, Ho ML, Nestrasil I. Ultra-High-Field Imaging of the Pediatric Brain and Spinal Cord. Magn Reson Imaging Clin N Am 2021; 29:643-653. [PMID: 34717851 DOI: 10.1016/j.mric.2021.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Neuroimaging with ultra-high field magnets (≥7T) provides superior signal-to-noise, spatial resolution and tissue contrast; but also greater safety concerns, longer scanning times, and increased distortion and field inhomogeneity. Brain and spinal cord anatomic microstructure and function imaged in greater detail offers improved lesion detection, delineation, and characterization. The ongoing development of novel imaging contrasts and translation of cutting-edge sequences will aid more accurate, sensitive, and precise diagnosis, interventional planning, and follow-up for a variety of pathologic conditions.
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Affiliation(s)
- Dinesh Kumar Deelchand
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street Southeast, Minneapolis, MN 55455, USA
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH 43205, USA
| | - Igor Nestrasil
- Masonic Institute for the Developing Brain, Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55414, USA.
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47
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Dalili D, Fritz J, Isaac A. 3D MRI of the Hand and Wrist: Technical Considerations and Clinical Applications. Semin Musculoskelet Radiol 2021; 25:501-513. [PMID: 34547815 DOI: 10.1055/s-0041-1731652] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In the last few years, major developments have been observed in the field of magnetic resonance imaging (MRI). Advances in both scanner hardware and software technologies have witnessed great leaps, enhancing the diagnostic quality and, therefore, the value of MRI. In musculoskeletal radiology, three-dimensional (3D) MRI has become an integral component of the diagnostic pathway at our institutions. This technique is particularly relevant in patients with hand and wrist symptoms, due to the intricate nature of the anatomical structures and the wide range of differential diagnoses for most presentations. We review the benefits of 3D MRI of the hand and wrist, commonly used pulse sequences, clinical applications, limitations, and future directions. We offer guidance for enhancing the image quality and tips for image interpretation of 3D MRI of the hand and wrist.
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Affiliation(s)
- Danoob Dalili
- Epsom and St Helier University Hospitals, London, United Kingdom
| | - Jan Fritz
- NYU Grossman School of Medicine, New York University, New York, New York
| | - Amanda Isaac
- Guy's and St. Thomas' Hospitals NHS Foundation Trust, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London (KCL), London, United Kingdom
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48
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Yedavalli V, DiGiacomo P, Tong E, Zeineh M. High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping. Magn Reson Imaging Clin N Am 2021; 29:13-39. [PMID: 33237013 DOI: 10.1016/j.mric.2020.09.002] [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] [Indexed: 10/22/2022]
Abstract
High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.
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Affiliation(s)
- Vivek Yedavalli
- Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA 94305-5105, USA; Division of Neuroradiology, Johns Hopkins University, 600 N. Wolfe St. B-112 D, Baltimore, MD 21287, USA
| | - Phillip DiGiacomo
- Department of Bioengineering, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA
| | - Elizabeth Tong
- Department of Radiology, 300 Pasteur Drive, Room S031, Stanford, CA 94305-5105, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA.
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49
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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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50
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Liu J, Beck ES, Filippini S, van Gelderen P, de Zwart JA, Norato G, Sati P, Al-Louzi O, Kolb H, Donadieu M, Morrison M, Duyn JH, Reich DS. Navigator-Guided Motion and B0 Correction of T2*-Weighted Magnetic Resonance Imaging Improves Multiple Sclerosis Cortical Lesion Detection. Invest Radiol 2021; 56:409-416. [PMID: 34086012 PMCID: PMC8269363 DOI: 10.1097/rli.0000000000000754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Cortical lesions are common in multiple sclerosis (MS). T2*-weighted (T2*w) imaging at 7 T is relatively sensitive for cortical lesions, but quality is often compromised by motion and main magnetic field (B0) fluctuations. PURPOSE The aim of this study was to determine whether motion and B0 correction with a navigator-guided gradient-recalled echo sequence can improve cortical lesion detection in T2*w magnetic resonance imaging. MATERIALS AND METHODS In this prospective study, a gradient-recalled echo sequence incorporating a navigator allowing for motion and B0 field correction was applied to collect T2*w images at 7 T from adults with MS between August 2019 and March 2020. T2*-weighted images were acquired in 1 to 3 partially overlapping scans per individual and were reconstructed using global average B0 correction ("uncorrected") or motion correction and spatially linear B0 correction ("corrected"). Image quality rating and manual segmentation of cortical lesions were performed on uncorrected and corrected images. Lesions seen on a single scan were retrospectively evaluated on the complementary scan. The association of cortical lesions with clinical disability was assessed. Mixed models were used to determine the effect of correction on lesion detection as well as on the relationship between disability and lesion count. RESULTS A total of 22 T2*w scans were performed on 11 adults with MS (mean [SD] age, 49 [11] years; 8 women). Quality improved for 20 of 22 scans (91%) after correction. A total of 69 cortical lesions were identified on uncorrected images (median per scan, 2; range, 0-11) versus 148 on corrected images (median per scan, 4.5; range, 0-25; rate ratio [RR], 2.1; P < 0.0001). For low-quality uncorrected scans with moderate to severe motion artifact (18/22, 82%), there was an improvement in cortical lesion detection with correction (RR, 2.5; P < 0.0001), whereas there was no significant change in cortical lesion detection for high-quality scans (RR, 1.3; P = 0.43). CONCLUSIONS Navigator-guided motion and B0 correction substantially improves the overall image quality of T2*w magnetic resonance imaging at 7 T and increases its sensitivity for cortical lesions.
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Affiliation(s)
- Jiaen Liu
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Erin S Beck
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
| | - Stefano Filippini
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
- Department of Neurosciences, Drug, and Child Health, University of Florence, Florence, Italy
| | - Peter van Gelderen
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Jacco A de Zwart
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Gina Norato
- Clinical Trials Unit, NINDS, NIH, Bethesda, MD, USA
| | - Pascal Sati
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Omar Al-Louzi
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
| | - Hadar Kolb
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
| | - Maxime Donadieu
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
| | - Mark Morrison
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
| | - Jeff H Duyn
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, NINDS, NIH, Bethesda, MD, USA
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