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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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2
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Eisenmenger LB, Peret A, Roberts GS, Spahic A, Tang C, Kuner AD, Grayev AM, Field AS, Rowley HA, Kennedy TA. Focused Abbreviated Survey MRI Protocols for Brain and Spine Imaging. Radiographics 2023; 43:e220147. [PMID: 37167089 PMCID: PMC10262597 DOI: 10.1148/rg.220147] [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: 06/14/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 05/13/2023]
Abstract
There has been extensive growth in both the technical development and the clinical applications of MRI, establishing this modality as one of the most powerful diagnostic imaging tools. However, long examination and image interpretation times still limit the application of MRI, especially in emergent clinical settings. Rapid and abbreviated MRI protocols have been developed as alternatives to standard MRI, with reduced imaging times, and in some cases limited numbers of sequences, to more efficiently answer specific clinical questions. A group of rapid MRI protocols used at the authors' institution, referred to as FAST (focused abbreviated survey techniques), are designed to include or exclude emergent or urgent conditions or screen for specific entities. These FAST protocols provide adequate diagnostic image quality with use of accelerated approaches to produce imaging studies faster than traditional methods. FAST protocols have become critical diagnostic screening tools at the authors' institution, allowing confident and efficient confirmation or exclusion of actionable findings. The techniques commonly used to reduce imaging times, the imaging protocols used at the authors' institution, and future directions in FAST imaging are reviewed to provide a practical and comprehensive overview of FAST MRI for practicing neuroradiologists. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
| | | | - Grant S. Roberts
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Alma Spahic
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Chenwei Tang
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Anthony D. Kuner
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Allison M. Grayev
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Aaron S. Field
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Howard A. Rowley
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
| | - Tabassum A. Kennedy
- From the Departments of Radiology (L.B.E., A.P., A.D.K., A.M.G.,
A.S.F., H.A.R., T.A.K.) and Medical Physics (G.S.R., A.S., C.T.), University of
Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI
53792-3252
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3
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Viessmann O, Polimeni JR. High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies. Curr Opin Behav Sci 2021; 40:96-104. [PMID: 33816717 DOI: 10.1016/j.cobeha.2021.01.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Limited detection power has been a bottleneck for subject-specific functional MRI (fMRI) studies, however the higher signal-to-noise ratio afforded by ultra-high magnetic fields (≥ 7 Tesla) provides levels of sensitivity and resolution needed to study individual subjects. What may be surprising is that higher imaging resolution may provide both higher specificity and sensitivity due to reductions in partial volume effects and reduced physiological noise. However, challenges remain to ensure high data quality and to reduce variability in ultra-high field fMRI. We discuss session-specific biases including those caused by factors related to instrumentation, anatomy, and physiology-which can translate into variability across sessions-and how to minimize these to help ultra-high field fMRI reach its full potential for individual-focused studies.
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Affiliation(s)
- Olivia Viessmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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4
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Yuen NH, Osachoff N, Chen JJ. Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing. Front Neurosci 2019; 13:900. [PMID: 31551676 PMCID: PMC6738198 DOI: 10.3389/fnins.2019.00900] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 08/12/2019] [Indexed: 12/22/2022] Open
Abstract
The frequency characteristics of the resting-state BOLD fMRI (rs-fMRI) signal are of increasing scientific interest, as we discover more frequency-specific biological interpretations. In this work, we use variational mode decomposition (VMD) to precisely decompose the rs-fMRI time series into its intrinsic mode functions (IMFs) in a data-driven manner. The accuracy of the VMD decomposition of constituent IMFs is verified through simulations, with higher reconstruction accuracy and much-reduced mode mixing relative to previous methods. Furthermore, we examine the relative contribution of the VMD-derived modes (frequencies) to the rs-fMRI signal as well as functional connectivity measurements. Our primary findings are: (1) The rs-fMRI signal within the 0.01–0.25 Hz range can be consistently characterized by four intrinsic frequency clusters, centered at 0.028 Hz (IMF4), 0.080 Hz (IMF3), 0.15 Hz (IMF2) and 0.22 Hz (IMF1); (2) these frequency clusters were highly reproducible, and independent of rs-fMRI data sampling rate; (3) not all frequencies were associated with equivalent network topology, in contrast to previous findings. In fact, while IMF4 is most likely associated with physiological fluctuations due to respiration and pulse, IMF3 is most likely associated with metabolic processes, and IMF2 with vasomotor activity. Both IMF3 and IMF4 could produce the brain-network topology typically observed in fMRI, whereas IMF1 and IMF2 could not. These findings provide initial evidence of feasibility in decomposing the rs-fMRI signal into its intrinsic oscillatory frequencies in a reproducible manner.
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Affiliation(s)
- Nicole H Yuen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
| | | | - J Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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5
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Maknojia S, Churchill NW, Schweizer TA, Graham SJ. Resting State fMRI: Going Through the Motions. Front Neurosci 2019; 13:825. [PMID: 31456656 PMCID: PMC6700228 DOI: 10.3389/fnins.2019.00825] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/23/2019] [Indexed: 11/19/2022] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.
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Affiliation(s)
- Sanam Maknojia
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Nathan W Churchill
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Tom A Schweizer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.,Division of Neurosurgery, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - S J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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6
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Fast imaging for mapping dynamic networks. Neuroimage 2018; 180:547-558. [DOI: 10.1016/j.neuroimage.2017.08.029] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/21/2017] [Accepted: 08/09/2017] [Indexed: 01/22/2023] Open
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7
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Functional magnetic resonance imaging: Basic principles and application in the neurosciences. RADIOLOGIA 2018. [DOI: 10.1016/j.rxeng.2018.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Labbé Atenas T, Ciampi Díaz E, Cruz Quiroga JP, Uribe Arancibia S, Cárcamo Rodríguez C. Functional magnetic resonance imaging: basic principles and application in the neurosciences. RADIOLOGIA 2018; 60:368-377. [PMID: 29544987 DOI: 10.1016/j.rx.2017.12.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 12/25/2017] [Accepted: 12/26/2017] [Indexed: 11/25/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is an advanced tool for the study of brain functions in healthy subjects and in neuropsychiatric patients. This tool makes it possible to identify and locate specific phenomena related to neuronal metabolism and activity. Starting with the detection of changes in the blood supply to a region that participates in a function, more complex approaches have been developed to study the dynamics of neuronal networks. Studies examining the brain at rest or involved in different tasks have provided evidence related to the onset, development, and/or response to treatment in various diseases. The diversity of the possible artifacts associated with image registration as well as the complexity of the analytical experimental designs has generated abundant debate about the technique behind fMRI. This article aims to introduce readers to the fundamentals underlying fMRI, to explain how fMRI studies are interpreted, and to discuss fMRI's contributions to the study of the mechanisms underlying diverse diseases of the nervous system.
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Affiliation(s)
- T Labbé Atenas
- Centro Interdisciplinario de Neurociencias, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - E Ciampi Díaz
- Departamento de Neurología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - J P Cruz Quiroga
- Departamento de Radiología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - S Uribe Arancibia
- Departamento de Radiología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Centro de Imágenes Biomédicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - C Cárcamo Rodríguez
- Centro Interdisciplinario de Neurociencias, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Departamento de Neurología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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9
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Golestani AM, Faraji-Dana Z, Kayvanrad M, Setsompop K, Graham SJ, Chen JJ. Simultaneous Multislice Resting-State Functional Magnetic Resonance Imaging at 3 Tesla: Slice-Acceleration-Related Biases in Physiological Effects. Brain Connect 2018; 8:82-93. [PMID: 29226689 DOI: 10.1089/brain.2017.0491] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Simultaneous multislice echo-planar imaging (SMS-EPI) can enhance the spatiotemporal resolution of resting-state functional MRI (rs-fMRI) by encoding and simultaneously imaging "groups" of slices. However, phenomena, including respiration, cardiac pulsatility, respiration volume per time (RVT), and cardiac rate variation (CRV), referred to as "physiological processes," impact SMS-EPI rs-fMRI in a manner that is yet to be well characterized. In particular, physiological noise may incur aliasing and introduce spurious signals from one slice into another within the "slice group" in rs-fMRI data, resulting in a deleterious effect on resting-state functional connectivity MRI (rs-fcMRI) maps. In the present work, we aimed to quantitatively compare the effects of physiological noise on regular EPI and SMS-EPI in terms of rs-fMRI data and resulting functional connectivity measurements. We compare SMS-EPI and regular EPI data acquired from 11 healthy young adults with matching parameters. The physiological noise characteristics were compared between the two data sets through different combinations of physiological regression steps. We observed that the physiological noise characteristics differed between SMS-EPI and regular EPI, with cardiac pulsatility contributing more to noise in regular EPI data but low-frequency heart rate variability contributing more to SMS-EPI. In addition, a significant slice-group bias was observed in the functional connectivity density maps derived from SMS-EPI data. We conclude that making appropriate corrections for physiological noise is likely more important for SMS-EPI than for regular EPI acquisitions.
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Affiliation(s)
- Ali M Golestani
- 1 Rotman Research Institute at Baycrest Centre , Toronto, Canada
| | - Zahra Faraji-Dana
- 2 Department of Medical Biophysics, University of Toronto , Toronto, Canada .,3 Sunnybrook Research Institute , Sunnybrook Health Sciences Centre, Toronto, Canada
| | | | - Kawin Setsompop
- 4 Department of Radiology, Harvard Medical School , Boston, Massachusetts
| | - Simon J Graham
- 2 Department of Medical Biophysics, University of Toronto , Toronto, Canada .,3 Sunnybrook Research Institute , Sunnybrook Health Sciences Centre, Toronto, Canada
| | - J Jean Chen
- 1 Rotman Research Institute at Baycrest Centre , Toronto, Canada .,2 Department of Medical Biophysics, University of Toronto , Toronto, Canada
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10
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Caballero-Gaudes C, Reynolds RC. Methods for cleaning the BOLD fMRI signal. Neuroimage 2017; 154:128-149. [PMID: 27956209 PMCID: PMC5466511 DOI: 10.1016/j.neuroimage.2016.12.018] [Citation(s) in RCA: 320] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 12/05/2016] [Accepted: 12/08/2016] [Indexed: 01/13/2023] Open
Abstract
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.
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Affiliation(s)
| | - Richard C Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, USA
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11
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Herbst M, Poser BA, Singh A, Deng W, Knowles B, Zaitsev M, Stenger VA, Ernst T. Motion correction for diffusion weighted SMS imaging. Magn Reson Imaging 2016; 38:33-38. [PMID: 27988191 DOI: 10.1016/j.mri.2016.12.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 12/13/2016] [Accepted: 12/13/2016] [Indexed: 10/20/2022]
Affiliation(s)
- M Herbst
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA; Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - B A Poser
- Maastricht Brain Imaging Centre, Faculty of Psychology & Neuroscience, Maastricht University, Netherlands
| | - A Singh
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - W Deng
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - B Knowles
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - M Zaitsev
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - V A Stenger
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - T Ernst
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
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