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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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Andronesi OC, Bhattacharyya PK, Bogner W, Choi IY, Hess AT, Lee P, Meintjes E, Tisdall MD, Zaitzev M, van der Kouwe A. Motion correction methods for MRS: experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4364. [PMID: 33089547 PMCID: PMC7855523 DOI: 10.1002/nbm.4364] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 05/07/2023]
Abstract
Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.
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Affiliation(s)
- Ovidiu C. Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding Author: Ovidiu C. Andronesi, MD, PhD, Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Thirteenth Street, Charlestown, MA 02129, USA;
| | | | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - In-Young Choi
- Department of Neurology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Aaron T. Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, University of Oxford
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ernesta Meintjes
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Maxim Zaitzev
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- High Field Magnetic Resonance Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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van Niekerk A, Meintjes E, van der Kouwe A. A Wireless Radio Frequency Triggered Acquisition Device (WRAD) for Self-Synchronised Measurements of the Rate of Change of the MRI Gradient Vector Field for Motion Tracking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1610-1621. [PMID: 30629498 PMCID: PMC7192240 DOI: 10.1109/tmi.2019.2891774] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
In this paper, we present a device that is capable of wireless synchronization to the MRI pulse sequence time frame with sub-microsecond precision. This is achieved by detecting radio frequency pulses in the parent pulse sequence using a small resonant circuit. The device incorporates a 3-axis pickup coil, constructed using conventional printed circuit board (PCB) manufacturing techniques, to measure the rate of change of the gradient waveforms with respect to time. Using Maxwell's equations, assuming negligible rates of change of curl and divergence, a model of the expected gradient derivative (slew) vector field is presented. A 3-axis Hall effect magnetometer allows for the measurement of the direction of the static magnetic field in the device co-ordinate frame. By combining the magnetometer measurement with the pickup coil voltages and slew vector field model, the orientation and position can be determined to within a precision of 0.1 degrees and 0.1 mm, respectively, using a pulse series lasting 880 μs . The gradient pulses are designed to be sinusoidal, enabling the detection of a phase shift between the time frame of the pickup coil digitization circuit and the gradient amplifiers. The signal processing is performed by a low power micro-controller on the device and the results are transmitted out of the scanner bore using a low latency 2.4 GHz radio link. The device identified an unexpected 40 kHz oscillation relating to the pulse width modulation frequency of the gradient amplifiers that is predominantly in the direction of the static magnetic field. The proposed wireless radio frequency triggered acquisition device enables users to probe the scanner gradient slew vector field with minimal hardware set-up and shows promise for the future developments in the prospective motion correction.
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van Niekerk A, van der Kouwe A, Meintjes E. Toward "plug and play" prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields. Magn Reson Med 2019; 82:1214-1228. [PMID: 31066109 DOI: 10.1002/mrm.27790] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/22/2019] [Accepted: 04/08/2019] [Indexed: 11/05/2022]
Abstract
PURPOSE The efficacy of a Wireless Radio frequency triggered Acquisition Device (WRAD) is evaluated for high frequency (50 Hz) prospective motion correction in a 3-dimensional spoiled gradient echo pulse sequence. METHODS The device measures the rate of change in the gradient vector fields (slew) using a 3-dimensional assembly of Printed Circuit Board (PCB) inductors and the direction of the static magnetic field using a 3-axis Hall effect magnetometer. The slew vector encoding is highly efficient, because the Maxwell-term position encoding is observable, allowing overconstrained pose measurement using 3 sinusoidal gradient pulses lasting 880 μs. Since small offsets in the magnetometer can introduce bias into the pose estimates, sensor/system biases are tracked using a lightweight Kalman filter. The only calibration required is determining a geometric scaling factor for the pickup coils, which is specific to the device and will therefore be valid in any scanner. RESULTS The device was used to perform prospective motion correction in 3 subjects, resulting in an increase in Average Edge Strength (AES) for involuntary and deliberate motion. CONCLUSIONS The WRAD is simple to set up and use, with well-defined measurement variance. This could enable "plug and play" prospective motion correction if pulse sequence independence is achieved.
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Affiliation(s)
- Adam van Niekerk
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Andre van der Kouwe
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts.,Radiology, Harvard Medical School, Boston, Massachusetts
| | - Ernesta Meintjes
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Faculty of Health Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa.,Cape Universities Body Imaging Centre (CUBIC) at UCT, Cape Town, South Africa
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