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

Servo navigators: Linear regression and feedback control for rigid-body motion correction

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.

Prospective motion correction for brain MRI using spherical navigators

PURPOSE

To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs).

METHODS

SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil.

RESULTS

When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%).

CONCLUSION

Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.

Development of a novel 10-echo multi-contrast sequence based on EPIK to deliver simultaneous quantification of T2 and T2 * with application to oxygen extraction fraction

PURPOSE

The simultaneous quantification of T₂ and T₂ * maps based on fast sequences for combined GE and SE acquisition has rekindled research and clinical interest by offering a wide range of attractive applications, e.g., dynamic tracking of oxygen extraction fraction (OEF). However, previously published methods based on EPI-readouts have been hindered by resolution and the number of acquired echoes.

METHODS

This work presents a novel 10-echo GE-SE EPIK (EPI with keyhole) sequence for the rapid quantification of T₂ '. T₂ /T₂ * maps from the GE-SE EPIK sequence were validated using three phantoms and 15 volunteers at 3T. The incorporation of a sliding window approach, combined with the full sampling of the k-space center inherent to EPIK, enables a high effective temporal resolution. That is, for an eight-slice breath-hold experiment, a temporal sampling rate of eight reconstructed slices per 1.1 s.

RESULTS

In comparison with repeated single-echo SE, multi-echo GE, and spectroscopy methods, the GE-SE EPIK sequence shows good agreement in quantifying T₂ /T₂ * values, while the gray matter/white matter separation yielded the expected contrast differentiation. The OEF was calculated with a view to an initial application with clinical relevance, producing results comparable to those in the literature and with good sensitivity in breath-hold experiments.

CONCLUSIONS

GE-SE EPIK provides increased resolution and more echoes, including two SEs, than comparable sequences. Moreover, GE-SE EPIK achieves this within an acquisition time of 57 s for 20 slices (matrix size = 128×128; FOV = 24 cm) and with a reasonably short TE for the final echo (114 ms). The sequence can dynamically track OEF changes in a breath-hold experiment.

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation

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.