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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Resting State fMRI: Going Through the Motions.

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.

Motion correction methods for MRS: experts' consensus recommendations

Purpose To investigate the feasibility of using an electromagnetic (EM) tracker to estimate rigid body head motion parameters, and using these measurements to retrospectively reduce motion artifacts. Theory and methods A clinically used MPRAGE sequence was modified to measure motion using the EM tracking system once per repetition time. A retrospective k-space based motion correction algorithm that corrects for phase ramps (translation in image domain) and rotation of 3D k-space (rotation in image domain) was developed, using the parameters recorded using an EM tracker. The accuracy of the EM tracker for the purpose of motion measurement and correction was tested in phantoms, volunteers, and pediatric patients. Results Position localization was accurate to the order of 200 microns compared with registration localization in a phantom study. The quality of reconstructed images was assessed by computing the root mean square error, the structural similarity metric and average edge strength. Image quality improved consistently when motion correction was applied in both volunteer scans with deliberate head motion and in pediatric patient scans. In patients, the average edge strength improved significantly with retrospective motion correction, compared with images with no correction applied. Conclusions EM tracking was effective in measuring head motion in the MRI scanner with high accuracy, and enabled retrospective reconstruction to improve image quality by reducing motion artifacts.

Retrospective correction of head motion using measurements from an electromagnetic tracker.

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 $880~\mu \textsf {s}$ . 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.

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

Reducing motion sensitivity in 3D high-resolution T2*-weighted MRI by navigator-based motion and nonlinear magnetic field correction.

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.

Toward "plug and play" prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields.

In MRI brain imaging, subject motion limits obtainable image clarity. Due to the hardware layout of an MRI scanner, gradient excitations can be used to rapidly detect position. Orientation, however, is more difficult to detect and is commonly calculated by comparing the position measurements of multiple spatially constrained points to a reference dataset. The result is increased size of the apparatus the subject must wear, which can influence the imaging workflow. In optical based methods marker attachment sites are limited due to the line of sight requirement between the camera and marker, and an external reference frame is introduced. To address these challenges a method called VectOrient is proposed for orientation measurement that is based on vector observations of gravity and the MRI scanner’s static magnetic field. A prototype device comprising of an accelerometer, magnetometer and angular rate sensor shows good MRI compatibility. Phantom scans of a pineapple with zero scanner specific calibration achieve comparable results to a rigid body registration algorithm with deviations less than 0.8 degrees over 28 degree changes in orientation. Dynamic performance shows potential for prospective motion correction as rapid changes in orientation (peak 20 degrees per second) can be corrected. The pulse sequence implemented achieves orientation updates with a latency estimated to be less than 12.7 ms, of which only a small fraction (<1 ms) is used for computing orientation from the raw sensor signals. The device is capable of quantifying subject respiration and heart rates. The proposed approach for orientation estimation could help address some limitations of existing methods such as orientation measurement range, temporal resolution, ease of use and marker placement.

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A Method for Measuring Orientation Within a Magnetic Resonance Imaging Scanner Using Gravity and the Static Magnetic Field (VectOrient)

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.

External Hardware and Sensors, for Improved MRI

An orientation tracking device includes a memory for storing data therein including magnetic reference data relating to the direction of a static magnetic field of a magnetic imaging scanner in a selected principle co-ordinate frame and earth gravitational reference data relating to the direction of the gravitational field of the earth in the principle co-ordinate frame. The device also includes an accelerometer, a magnetometer and a communication module. A processor is connected to the accelerometer, the magnetometer and communication module and is adapted for receiving from the magnetometer a measurement of a magnetic field vector in a co-ordinate frame of the orientation tracking device, within the magnetic imaging scanner in which the orientation tracking device is placed in use. The processor further receives from the accelerometer a measurement of an acceleration vector in the orientation tracking device's. co-ordinate frame, within the magnetic imaging scanner in which the orientation tracking device is placed in use and the processor then determines the orientation of the device with respect to the principle co-ordinate frame by comparing the measured vectors from the accelerometer and magnetometer with the stored gravitational reference data and magnetic reference data respectively.

Adam van Niekerk

Papers

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

Toward "plug and play" prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields.

A Method for Measuring Orientation Within a Magnetic Resonance Imaging Scanner Using Gravity and the Static Magnetic Field (VectOrient)

External Hardware and Sensors, for Improved MRI

An orientation tracking device for mri