Head motion measurement and correction using FID navigators

Purpose To demonstrate, for the first time, the feasibility of obtaining low‐latency 3D rigid‐body motion information from spherical Lissajous navigators acquired at extremely small k‐space radii, which has significant advantages compared with previous techniques. Theory and Methods A spherical navigator concept is proposed in which the surface of a k‐space sphere is sampled on a 3D Lissajous curve at a radius of 0.1/cm. The navigator only uses a single excitation and is acquired in less than 5 ms. Rotation estimations were calculated with an algorithm from computer vision that exploits a rotation theorem of the spherical harmonics transform and has minimal computational cost. The effectiveness of the concept was investigated with phantom and in vivo measurements on a commercial 3T MRI scanner. Results Scanner‐induced in vivo motion was measured with maximum absolute errors of 0.58° and 0.33 mm for rotations and translations, respectively. In the case of real, in vivo motion, the proposed method showed good agreement with motion information from FSL image registrations (mean/maximum deviations of 0.37°/1.24° and 0.44 mm/1.35 mm). In addition, phantom measurements indicated precisions of 0.014° and 0.013 mm. The computations for complete motion information took, on average, 24 ms on an ordinary laptop. Conclusions This work demonstrates a proof of concept for obtaining accurate motion information from small‐radius spherical navigators. The method has the potential to overcome several previously reported problems and could help increase the utility of navigator‐based motion correction both in research and in the clinic.

Section: Discussionmentioning

confidence: 99%

3D rigid‐body motion information from spherical Lissajous navigators at small k‐space radii: A proof of concept

Buschbeck

Yun

Shah

2019

“…The motion can be measured using optical tracking techniques that use a camera system to track markers fixed to the head . MR navigators, which acquire a set of MR data, either in image space or k‐space, represent another category of motion measurement techniques used to measure head motion. Although these tools are able to effectively correct for head motion for certain applications, they often are limited to specific pulse sequences and scanner hardware; as such, they are not always available for routine clinical and research use.…”

Section: Introductionmentioning

confidence: 99%

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Johnson

Drangova

2019

westgrid.ca) and Compute Canada Calcul Canada (www.computecanada.ca).Purpose: Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data.Methods: An open source MRI data set comprising T 2 *-weighted, FLASH magnitude, and phase brain images for 53 patients was used to generate complex image data for motion simulation. To simulate rigid motion, rotations and translations were applied to the image data based on randomly generated motion profiles. A conditional generative adversarial network, comprising a generator and discriminator networks, was trained using the motion-corrupted and corresponding ground truth (original) images as training pairs. Results: The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images. The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4% of the image mean value, whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8% The network output also demonstrated improved peak SNR and structural similarity index for all test-set images. Conclusion: The images predicted by the conditional generative adversarial network have quantitatively and qualitatively improved image quality compared to the motioncorrupted images. K E Y W O R D S conditional generative adversarial networks, convolutional neural networks, deep learning, motion correction, MRI

“…This was shown in Subject A where only 2 unique gradient pulses (600 μs) were used to improve image quality for small involuntary motion. This is less than a third of the acquisition time (2 milliseconds) used to acquire a typical FID navigator and a 10 th of the time required to acquire a collapsed fat navigator …”

Section: Discussionmentioning

confidence: 99%

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

Niekerk

Kouwe

Meintjes

2019

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.