Prospective real-time correction for arbitrary head motion using active markers

Run-time motion and first-order shim control by expanded servo navigation

PURPOSE

To provide a navigator-based run-time motion and first-order field correction for three-dimensional human brain imaging with high precision, minimal calibration and acquisition, and fast processing.

METHODS

A complex-valued linear perturbation model with feedback control is extended to estimate and correct for gradient shim fields using orbital navigators (2.3 ms). Two approaches for sensitizing the model to gradient fields are presented, one based on finite differences with three additional navigators, and another projection-based approximation requiring no additional navigators. A mechanism for noise decorrelation of the matrix and the data is proposed and evaluated to reduce unwanted parameter biases.

RESULTS

The rigid motion and first-order field control achieves robust motion and gradient shim corrections improving image quality in a series of phantom and in vivo experiments with varying field conditions. In phantom scans, magnet drifts, forced gradient field perturbations and field distortions from shifts of a second bottle phantom are successfully corrected. Field estimates of the magnet drifts are in good agreement with concurrent field probe measurements. For in vivo scans, the proposed method mitigates field variations from torso motions while being robust to head motion. In vivo gradient field precisions were30   nT / m $$ 30\;\mathrm{nT}/\mathrm{m} $$ along with single-digit micrometer and millidegree rigid precisions.

CONCLUSION

The navigator-based method achieves accurate, high-precision run-time motion and field corrections with low sequence impact and calibration requirements.

Prospective head motion correction at 3 Tesla with wireless NMR markers and ultrashort echo navigators

PURPOSE

Prospective motion correction (PMC) with inductively-coupled wireless NMR markers has been shown to be an effective plug-and-play method for dealing with head motion at 7 Tesla [29,30]. However, technical challenges such as one-to-one identification of three wireless markers, generation of hyper-intense marker artifacts and low marker peak SNR in the navigators has limited the adoption of this technique. The goal of this work is to introduce solutions to overcome these issues and extend this technique to PMC for brain imaging at 3 Tesla.

METHODS

PMC with 6 degrees of freedom (DOF) was implemented using a novel ∼8 ms, ultrashort echo time (UTE) navigator in concert with optimally chosen MnCl2 marker samples to minimize marker artifacts. Distinct head coil sensitivities were leveraged to enable identification and tracking of individual markers and a variable flip angle (VFA) scheme and real time filtering were used to boost marker SNR. PMC was performed in 3D T1 weighted brain imaging at 3 Tesla with voluntary head motions in adult volunteers.

RESULTS

PMC with wireless markers improved image quality in 3D T1 weighted images in all subjects compared to non-motion corrected images for similar motions with no noticeable marker artifacts. Precision of motion tracking was found to be in the range of 0.01-0.06 mm/degrees. Navigator execution had minimal impact on sequence duration.

CONCLUSIONS

Wireless NMR markers provide an accurate, calibration-free and economical option for 6 DOF PMC in brain imaging across field strengths. Challenges in this technique can be addressed by combining navigator design, sample selection and real time data processing strategies.

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis

PURPOSE

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis.

METHODS

An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison.

RESULTS

The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans.

CONCLUSIONS

The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

Stealth RF energy harvesting in MRI using selective shielding

PURPOSE

To utilize the transmit radiofrequency (RF) field in MRI as a power source, near or within the field of view but without affecting image quality or safety.

METHODS

Power harvesting is performed by RF induction in a resonant coil. Resulting RF field distortion in the subject is canceled by a selective shield that couples to the harvester while being transparent to the RF transmitter. Such shielding is designed with the help of electromagnetic simulation. A shielded harvester of 3 cm diameter is implemented, assessed on the bench, and tested in a 3T MRI system, recording power yield during typical scans.

RESULTS

The concept of selective shielding is confirmed by simulation. Bench tests show effective power harvesting in the presence of the shield. In the MRI system, it is confirmed that selective shielding virtually eliminates RF perturbation. In scans with the harvester immediately adjacent to a phantom, up to 100 mW of average power are harvested without affecting image quality.

CONCLUSION

Selective shielding enables stealthy RF harvesting which can be used to supply wireless power to on-body devices during MRI.

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.

Feasibility of tracking involuntary head movement for MRI using a coil as a magnetic dipole in a time-varying gradient

Accurate tracking involuntary head movements is fairly a challenging problem in MR imaging of the brain. Though there are few techniques available to monitor the head movement of the subject for a prospective motion correction, it is still an unsolved problem in MRI. In this theoretical study, we aim to describe an analytical investigation to track head movement inside an MR scanner by calculating the change in induced voltage in the head-mounted coils during the execution of time-varying gradients. We derive an expression to calculate the change in induced voltage in a coil placed in a time-varying gradient. We also derive a general equation to investigate the changes in the induced voltage in a set of coils mounted onto the head for the planar position and orientation of the coils. Each coil is considered as a magnetic dipole with location and sensitivity vectors. The changes of the vectors can track the head movement in the MR scanner by measuring the changes in the induced voltage in the coils. The dipole concept is valid for a wide range of coils. The changes in induced voltage in the coils are linear due to small changes in pose of the head. Movement parameters are estimated from the induced voltage changes. If the random noise voltage is less than 100 μV, it does not significantly affect movement parameters because the change in induced voltage in the coils dominates over the small noise voltage. This method and array of the coils may provide a real-life solution to the long-standing problem of head motion during MRI.

Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings

BACKGROUND AND PURPOSE

A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations.

MATERIALS AND METHODS

Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5).

RESULTS

The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively.

CONCLUSIONS

SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis

Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2-weighted images from the IXI public dataset were split into three datasets, i.e., training (50 cases/6500 slices), validation (5/650), and test (12/1560) sets. First, motion-corrupted k-spaces and images were simulated using a pseudo-random sampling order and random motion tracks. A convolutional neural network (CNN) model was trained to filter the motion-corrupted images. Then, the k-space of the filtered image was compared with the motion-corrupted k-space line-by-line, to detect the PE lines affected by motion. Finally, the unaffected PE lines were used to reconstruct the final image using compressed sensing (CS). For the simulated images with 35%, 40%, 45%, and 50% unaffected PE lines, the mean peak signal-to-noise ratio (PSNRs) of resulting images (mean±standard deviation) were 36.129±3.678, 38.646±3.526, 40.426±3.223, and 41.510±3.167, respectively, and the mean structural similarity (SSIMs) were 0.950±0.046, 0.964±0.035, 0.975±0.025, and 0.979±0.023, respectively. For images with more than 35% PE lines unaffected by motion, images reconstructed with proposed algorithm exhibited better quality than those images reconstructed with CS using 35% under-sampled data (PSNR 37.678±3.261, SSIM 0.964±0.028). It was proved that deep learning and k-space analysis can detect the k-space PE lines affected by motion and CS can be used to reconstruct images from unaffected data, effectively alleviating the motion artifacts.

Interleaved and simultaneous multi-nuclear magnetic resonance in vivo. Review of principles, applications and potential

Magnetic resonance signals from different nuclei can be excited or received at the same time,rendering simultaneous or rapidly interleaved multi-nuclear acquisitions feasible. The advan-tages are a reduction of total scan time compared to sequential multi-nuclear acquisitions or that additional information from heteronuclear data is obtained at thesame time and anatomical position. Information content can be qualitatively increased by delivering a more comprehensive MR-based picture of a transient state (such as an exercise bout). Also, combiningnon-proton MR acquisitions with 1 Hinformation (e.g., dynamic shim updates and motion correction) can be used to improve data quality during long scans and benefits image coregistration. This work reviews the literature on interleaved and simultaneous multi-nuclear MRI and MRS in vivo. Prominent use cases for this methodology in clinical and research applications are brain and muscle, but studies have also been carried out in other targets, including the lung, knee, breast and heart. Simultaneous multi-nuclear measurements in the liver and kidney have also been performed, but exclusively in rodents. In this review, a consistent nomenclature is proposed, to help clarify the terminology used for this principle throughout the literature on in-vivo MR. An overview covers the basic principles, the technical requirements on the MR scanner and the implementations realised either by MR system vendors or research groups, from the early days until today. Considerations regarding the multi-tuned RF coils required and heteronuclear polarisation interactions are briefly discussed, and fields for future in-vivo applications for interleaved multi-nuclear MR pulse sequences are identified.

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Tracking of rigid head motion during MRI using an EEG system

Purpose

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.

Theory and Methods

Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators.

Results

Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees).

Conclusion

Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

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.

Self-navigated prospective motion correction for 3D-EPI acquisition

PURPOSE

Although 3D EPI is more susceptible to motion artifacts than 2D EPI, it presents some benefits for functional MRI, including the absence of spin-history artifacts, greater potential for parallel imaging acceleration, and better functional sensitivity in high-resolution imaging. Here we present a self-navigated 3D-EPI sequence suitable for prospective motion-corrected functional MRI without additional hardware or pulses.

METHODS

For each volume acquisition, the first 24 of the 52 partitions being acquired are accumulated to a new feedback block that was added to the image reconstruction pipeline. After zero-filling the remaining partitions, the feedback block constructs a volumetric self-navigator (vSNav), co-registers it to the reference vSNav acquired during the first volume acquisition, and sends motion estimates to the sequence. The sequence then updates its FOV and acquires subsequent partitions with the adjusted FOV, until the next update is received. The sequence was validated without and with intentional motion in phantom and in vivo on a 3T Skyra.

RESULTS

For phantom scans, the FOV was updated 0.704 s after acquisition of the vSNav partitions, and for in vivo scans after 0.768 s. Both phantom and in vivo data demonstrated stable motion estimates in the absence of motion. For in vivo acquisitions, prospective head-pose estimates using the vSNav's and retrospective estimates with FLIRT (FMRIB's Linear Image Registration Tool) agreed to within 0.23 mm (< 10% of the slice thickness) and 0.14° in all directions.

CONCLUSION

Depending when motion occurs during a volume acquisition, the proposed method fully corrects the FOV and recovers image quality within one volume acquisition.

Learning-based motion artifact removal networks for quantitative R 2 ∗ mapping

PURPOSE

To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data.

METHODS

We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B 0 -inhomogeneity-corrected R 2 ∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R 2 ∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B 0 -inhomogeneity-corrected quantitative R 2 ∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay.

RESULTS

We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R 2 ∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models.

CONCLUSION

Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R 2 ∗ maps, while LEARN-BIO directly performs motion- and B 0 -inhomogeneity-corrected R 2 ∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI

PURPOSE

To compare prospective motion correction (PMC) and retrospective motion correction (RMC) in Cartesian 3D-encoded MPRAGE scans and to investigate the effects of correction frequency and parallel imaging on the performance of RMC.

METHODS

Head motion was estimated using a markerless tracking system and sent to a modified MPRAGE sequence, which can continuously update the imaging FOV to perform PMC. The prospective correction was applied either before each echo train (before-ET) or at every sixth readout within the ET (within-ET). RMC was applied during image reconstruction by adjusting k-space trajectories according to the measured motion. The motion correction frequency was retrospectively increased with RMC or decreased with reverse RMC. Phantom and in vivo experiments were used to compare PMC and RMC, as well as to compare within-ET and before-ET correction frequency during continuous motion. The correction quality was quantitatively evaluated using the structural similarity index measure with a reference image without motion correction and without intentional motion.

RESULTS

PMC resulted in superior image quality compared to RMC both visually and quantitatively. Increasing the correction frequency from before-ET to within-ET reduced the motion artifacts in RMC. A hybrid PMC and RMC correction, that is, retrospectively increasing the correction frequency of before-ET PMC to within-ET, also reduced motion artifacts. Inferior performance of RMC compared to PMC was shown with GRAPPA calibration data without intentional motion and without any GRAPPA acceleration.

CONCLUSION

Reductions in local Nyquist violations with PMC resulted in superior image quality compared to RMC. Increasing the motion correction frequency to within-ET reduced the motion artifacts in both RMC and PMC.

Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking

PURPOSE

To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping.

METHODS

MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T₁ /T₂ /T_1ρ mapping in the brain was investigated.

RESULTS

For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T₁ /T₂ /T1_ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T₁ /T₂ /T_1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance.

CONCLUSION

Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.

Scout accelerated motion estimation and reduction (SAMER)

PURPOSE

To demonstrate a navigator/tracking-free retrospective motion estimation technique that facilitates clinically acceptable reconstruction time.

METHODS

Scout accelerated motion estimation and reduction (SAMER) uses a single 3-5 s, low-resolution scout scan and a novel sequence reordering to independently determine motion states by minimizing the data-consistency error in a SENSE plus motion forward model. This eliminates time-consuming alternating optimization as no updates to the imaging volume are required during the motion estimation. The SAMER approach was assessed quantitatively through extensive simulation and was evaluated in vivo across multiple motion scenarios and clinical imaging contrasts. Finally, SAMER was synergistically combined with advanced encoding (Wave-CAIPI) to facilitate rapid motion-free imaging.

RESULTS

The highly accelerated scout provided sufficient information to achieve accurate motion trajectory estimation (accuracy ~0.2 mm or degrees). The novel sequence reordering improved the stability of the motion parameter estimation and image reconstruction while preserving the clinical imaging contrast. Clinically acceptable computation times for the motion estimation (~4 s/shot) are demonstrated through a fully separable (non-alternating) motion search across the shots. Substantial artifact reduction was demonstrated in vivo as well as corresponding improvement in the quantitative error metric. Finally, the extension of SAMER to Wave-encoding enabled rapid high-quality imaging at up to R = 9-fold acceleration.

CONCLUSION

SAMER significantly improved the computational scalability for retrospective motion estimation and correction.

High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)

PURPOSE

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.

THEORY AND METHODS

Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.

RESULTS

The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.

CONCLUSION

Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI

Purpose

To investigate the ability of free induction decay navigator (FIDnav)‐based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort.

Methods

A study was carried out in 102 pediatric patients (aged 0‐18 years) at 3T using a 32‐channel head coil array. Subjects were scanned with an FID‐navigated MPRAGE sequence and images were graded by two radiologists using a five‐point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav‐based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores.

Results

A total of 12% of images were rated as non‐diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID‐navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross‐correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non‐diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study.

Conclusions

Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.

Closed-loop control of k-space sampling via physiologic feedback for cine MRI

BACKGROUND

Segmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling (ARKS) to increase sampling uniformity.

METHODS

The closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and the ability to detect cardiac phase in vivo was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach.

RESULTS

The autonomous trajectory increased k-space sampling uniformity by 15±7%, main lobe point spread function (PSF) signal intensity by 6±4%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.01 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 10±11%, decreased uniformity variability by 44±12%, and increased PSF signal ratio by 6±6% relative to golden angle sampling.

CONCLUSION

The closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

Appearance Learning for Image-Based Motion Estimation in Tomography

In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Geometric information within this process is usually depending on the system setting only, i.e., the scanner position or readout direction. Patient motion therefore corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object. To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach. The RPE measures the motion-induced geometric deviations independent of the object based on virtual marker positions, which are available during training. We train our network using 27 patients and deploy a 21-4-2 split for training, validation and testing. In average, we achieve a residual mean RPE of 0.013mm with an inter-patient standard deviation of 0.022mm. This is twice the accuracy compared to previously published results. In a motion estimation benchmark the proposed approach achieves superior results in comparison with two state-of-the-art measures in nine out of twelve experiments. The clinical applicability of the proposed method is demonstrated on a motion-affected clinical dataset.

Simultaneous feedback control for joint field and motion correction in brain MRI

T2*-weighted gradient-echo sequences count among the most widely used techniques in neuroimaging and offer rich magnitude and phase contrast. The susceptibility effects underlying this contrast scale with B₀, making T2*-weighted imaging particularly interesting at high field. High field also benefits baseline sensitivity and thus facilitates high-resolution studies. However, enhanced susceptibility effects and high target resolution come with inherent challenges. Relying on long echo times, T2*-weighted imaging not only benefits from enhanced local susceptibility effects but also suffers from increased field fluctuations due to moving body parts and breathing. High resolution, in turn, renders neuroimaging particularly vulnerable to motion of the head. This work reports the implementation and characterization of a system that aims to jointly address these issues. It is based on the simultaneous operation of two control loops, one for field stabilization and one for motion correction. The key challenge with this approach is that the two loops both operate on the magnetic field in the imaging volume and are thus prone to mutual interference and potential instability. This issue is addressed at the levels of sensing, timing, and control parameters. Performance assessment shows the resulting system to be stable and exhibit adequate loop decoupling, precision, and bandwidth. Simultaneous field and motion control is then demonstrated in examples of T2*-weighted in vivo imaging at 7T.

Frequency-adjustable magnetic field probes

PURPOSE

Nuclear Magnetic Resonance field probes provide exciting possibilities for enhancing MR image quality by allowing for calibration of k-space trajectories and/or dynamic measurement of local field changes. The purpose of this study is to design and build field probes, which are easier to manufacture and more flexible to use than existing probes.

METHODS

A new manufacturing method is presented based on light-activated resin to encase the coil assembly and the 1H sample. This method allows for realizing field probes with tightly integrated orthogonal coils, whereby the local resonance frequency of protons can be adjusted during the MR experiment, by applying a DC current to the integrated B 0 -field modification coil.

RESULTS

The apparent field probe position in a gradient echo experiment was shifted within the field of view by changing its Larmor frequency using an integrated micro-coil with 5.5 windings. The measured frequency modulation induced by the B 0 -field modification coil was 113 Hz/mA. The probe was tested with currents up to 100 mA. The DC current in the local field modification coil did not introduce visible artifacts in the MR images. Furthermore selective off-resonant excitation of the new field probes at 2 kHz above the main RF frequency was demonstrated. Gradient impulse response functions measured with a traditional and proposed probe show similar gradient imperfections.

CONCLUSIONS

The presented approach opens up new possibilities for concurrent field monitoring during MR experiments using standard RF capabilities of clinical scanners.

Correction of out-of-FOV motion artifacts using convolutional neural network

PURPOSE

Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem.

METHODS AND MATERIALS

A modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

RESULTS

The proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P < .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P < .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P < .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P < .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P < .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P < .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion.

CONCLUSION

In conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.

A Reconfigurable Platform for Magnetic Resonance Data Acquisition and Processing

Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have been addressed by moving data acquisition closer to the coil. However, with the increasing number of radio-frequency (RF) channels and trend towards higher acquisition duty-cycles, the data amount is growing, which poses challenges for throughput and data handling. As it is becoming a limitation, early compression and preprocessing is becoming ever more important. Additionally, sensors deliver diverse data, which require distinct and often low-latency processing for run-time updates of scanner operation. To address these challenges, we propose the transition to reconfigurable hardware with an application tailored assembly of interfaces and real-time processing resources. We present an integrated solution based on a system-on-chip (SoC), which offers sufficient throughput and hardware-based parallel processing power for very challenging applications. It is equipped with fiber-optical modules serving as versatile interfaces for modular systems with in-field operation. We demonstrate the utility of the platform on the example of concurrent imaging and field sensing with hardware-based coil compression and trajectory extraction. The preprocessed data are then used in expanded encoding model based image reconstruction of single-shot and segmented spirals as used in time-series and anatomical imaging respectively.

A within-coil optical prospective motion-correction system for brain imaging at 7T

PURPOSE

Motion artifact limits the clinical translation of high-field MR. We present an optical prospective motion correction system for 7 Tesla MRI using a custom-built, within-coil camera to track an optical marker mounted on a subject.

METHODS

The camera was constructed to fit between the transmit-receive coils with direct line of sight to a forehead-mounted marker, improving upon prior mouthpiece work at 7 Tesla MRI. We validated the system by acquiring a 3D-IR-FSPGR on a phantom with deliberate motion applied. The same 3D-IR-FSPGR and a 2D gradient echo were then acquired on 7 volunteers, with/without deliberate motion and with/without motion correction. Three neuroradiologists blindly assessed image quality. In 1 subject, an ultrahigh-resolution 2D gradient echo with 4 averages was acquired with motion correction. Four single-average acquisitions were then acquired serially, with the subject allowed to move between acquisitions. A fifth single-average 2D gradient echo was acquired following subject removal and reentry.

RESULTS

In both the phantom and human subjects, deliberate and involuntary motion were well corrected. Despite marked levels of motion, high-quality images were produced without spurious artifacts. The quantitative ratings confirmed significant improvements in image quality in the absence and presence of deliberate motion across both acquisitions (P < .001). The system enabled ultrahigh-resolution visualization of the hippocampus during a long scan and robust alignment of serially acquired scans with interspersed movement.

CONCLUSION

We demonstrate the use of a within-coil camera to perform optical prospective motion correction and ultrahigh-resolution imaging at 7 Tesla MRI. The setup does not require a mouthpiece, which could improve accessibility of motion correction during 7 Tesla MRI exams.

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

PURPOSE

We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization.

METHODS

A convolutional neural network (CNN) trained to remove motion artifacts from 2D T2 -weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model-based data-consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line-by-line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model-based image reconstruction. The method is tested in simulations and in vivo motion experiments of in-plane motion corruption.

RESULTS

While the convolutional neural network alone provides some motion mitigation (at the expense of introduced blurring), allowing it to guide the iterative joint-optimization both improves the search convergence and renders the joint-optimization separable. This enables rapid mitigation within shots in addition to between shots. For 2D in-plane motion correction experiments, the result is a significant reduction of both image space root mean square error in simulations, and a reduction of motion artifacts in the in vivo motion tests.

CONCLUSION

The separability and convergence improvements afforded by the combined convolutional neural network+model-based method shows the potential for meaningful postacquisition motion mitigation in clinical MRI.

Resting State fMRI: Going Through the Motions

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.

Retrospective correction of head motion using measurements from an electromagnetic tracker

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.

Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI

OBJECTIVE

We demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging.

METHODS

PET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability.

RESULTS

For both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC.

CONCLUSION

The first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity.

SIGNIFICANCE

As simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand.

Rigid-body motion correction in hybrid PET/MRI using spherical navigator echoes

Integrated positron emission tomography and magnetic resonance imaging (PET/MRI) is an imaging technology that provides complementary anatomical and functional information for medical diagnostics. Both PET and MRI are highly susceptible to motion artifacts due, in part, to long acquisition times. The simultaneous acquisition of the two modalities presents the opportunity to use MRI navigator techniques for motion correction of both PET and MRI data. For this task, we propose spherical navigator echoes (SNAVs)-3D k-space navigators that can accurately and rapidly measure rigid body motion in all six degrees of freedom. SNAVs were incorporated into turbo FLASH (tfl)-a product fast gradient echo sequence-to create the tfl-SNAV pulse sequence. Acquiring in vivo brain images from a healthy volunteer with both sequences first compared the tfl-SNAV and product tfl sequences. It was observed that incorporation of the SNAVs into the image sequence did not have any detrimental impact on the image quality. The SNAV motion correction technique was evaluated using an anthropomorphic brain phantom. Following a stationary reference image where the tfl-SNAV sequence was acquired along with simultaneous list-mode PET, three identical PET/MRI scans were performed where the phantom was moved several times throughout each acquisition. This motion-up to 11° and 14 mm-resulted in motion artifacts in both PET and MR images. Following SNAV motion correction of the MRI and PET list-mode data, artifact reduction was achieved for both the PET and MR images in all three motion trials. The corrected images have improved image quality and are quantitatively more similar to the ground truth reference images.

Markerless high-frequency prospective motion correction for neuroanatomical MRI

PURPOSE

To integrate markerless head motion tracking with prospectively corrected neuroanatomical MRI sequences and to investigate high-frequency motion correction during imaging echo trains.

METHODS

A commercial 3D surface tracking system, which estimates head motion by registering point cloud reconstructions of the face, was used to adapt the imaging FOV based on head movement during MPRAGE and T₂ SPACE (3D variable flip-angle turbo spin-echo) sequences. The FOV position and orientation were updated every 6 lines of k-space (< 50 ms) to enable "within-echo-train" prospective motion correction (PMC). Comparisons were made with scans using "before-echo-train" PMC, in which the FOV was updated only once per TR, before the start of each echo train (ET). Continuous-motion experiments with phantoms and in vivo were used to compare these high-frequency and low-frequency correction strategies. MPRAGE images were processed with FreeSurfer to compare estimates of brain structure volumes and cortical thickness in scans with different PMC.

RESULTS

The median absolute pose differences between markerless tracking and MR image registration were 0.07/0.26/0.15 mm for x/y/z translation and 0.06º/0.02º/0.12° for rotation about x/y/z. The PMC with markerless tracking substantially reduced motion artifacts. The continuous-motion experiments showed that within-ET PMC, which minimizes FOV encoding errors during ETs that last over 1 second, reduces artifacts compared with before-ET PMC. T₂ SPACE was found to be more sensitive to motion during ETs than MPRAGE. FreeSurfer morphometry estimates from within-ET PMC MPRAGE images were the most accurate.

CONCLUSION

Markerless head tracking can be used for PMC, and high-frequency within-ET PMC can reduce sensitivity to motion during long imaging ETs.

Reproducibility of brain MRS in older healthy adults at 7T

To date, the majority of MRS reproducibility studies have been conducted in healthy younger adults, with only a few conducted in older adults at 3 T. With the growing interest in applying MRS methods to study the longitudinal course and effects of treatments in neurodegenerative disease, it is important to establish reproducibility in age-matched controls, especially in older individuals. In this study, spectroscopic data were acquired using a stimulated echo acquisition mode (STEAM) localization technique in two regions (anterior and posterior cingulate cortices-ACC, PCC, respectively) in 10 healthy, cognitively normal older adults (64 ± 8.1 years). Reproducibility was assessed via mean coefficients of variation (CVs) and relative differences (RDs) calculated across two visits performed 2-3 months apart. Metabolites with high signal-to-noise ratio (SNR) such as NAA, tCho, and Glu had mean CVs of 10% or less and mean RDs of 15% or less across both regions. Metabolites with lower SNR such as GABA and Gln had slightly higher mean CVs of 22% or less and mean RDs of 27% or less across both regions. These results demonstrate the feasibility of acquiring MRS data at 7 T in older subjects, and establish that the spectroscopic data are reproducible in both the ACC and PCC in older, healthy subjects to the same extent as in previous studies in young subjects.

Pros and cons of ultra-high-field MRI/MRS for human application

Magnetic resonance imaging and spectroscopic techniques are widely used in humans both for clinical diagnostic applications and in basic research areas such as cognitive neuroimaging. In recent years, new human MR systems have become available operating at static magnetic fields of 7 T or higher (≥300 MHz proton frequency). Imaging human-sized objects at such high frequencies presents several challenges including non-uniform radiofrequency fields, enhanced susceptibility artifacts, and higher radiofrequency energy deposition in the tissue. On the other side of the scale are gains in signal-to-noise or contrast-to-noise ratio that allow finer structures to be visualized and smaller physiological effects to be detected. This review presents an overview of some of the latest methodological developments in human ultra-high field MRI/MRS as well as associated clinical and scientific applications. Emphasis is given to techniques that particularly benefit from the changing physical characteristics at high magnetic fields, including susceptibility-weighted imaging and phase-contrast techniques, imaging with X-nuclei, MR spectroscopy, CEST imaging, as well as functional MRI. In addition, more general methodological developments such as parallel transmission and motion correction will be discussed that are required to leverage the full potential of higher magnetic fields, and an overview of relevant physiological considerations of human high magnetic field exposure is provided.

Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration

Magnetic resonance fingerprinting (MRF) can be used to simultaneously obtain multiple parameter maps from a single pulse sequence. However, patient motion during MRF acquisition may result in blurring and artifacts in estimated parameter maps. In this work, a novel motion correction method was proposed to correct for rigid motion in MRF. The proposed method involved sliding-window reconstruction to obtain intermediate images followed by image registration to estimate rigid motion information between these images. Finally, the motion-corrupted k-space data were corrected with the estimated motion parameters and then reconstructed to obtain the parameter maps via the conventional MRF processing pipeline. The proposed method was evaluated using both simulations and in vivo MRF experiments with intently different types of motion. For motion-corrupted data, the proposed method yielded brain T₁, T₂ and proton density maps with obviously reduced blurring and artifacts and lower normalized root-mean-square error, compared to MRF without motion correction. In conclusion, motion-corrected MRF using the proposed method has the potential to produce accurate parameter maps in the presence of in-plane rigid motion.

Evaluation of prospective motion correction of high-resolution 3D-T2-FLAIR acquisitions in epilepsy patients

T2-FLAIR is the single most sensitive MRI contrast to detect lesions underlying focal epilepsies but 3D sequences used to obtain isotropic high-resolution images are susceptible to motion artefacts. Prospective motion correction (PMC) - demonstrated to improve 3D-T1 image quality in a pediatric population - was applied to high-resolution 3D-T2-FLAIR scans in adult epilepsy patients to evaluate its clinical benefit. Coronal 3D-T2-FLAIR scans were acquired with a 1mm isotropic resolution on a 3T MRI scanner. Two expert neuroradiologists reviewed 40 scans without PMC and 40 with navigator-based PMC. Visual assessment addressed six criteria of image quality (resolution, SNR, WM-GM contrast, intensity homogeneity, lesion conspicuity, diagnostic confidence) on a seven-point Likert scale (from non-diagnostic to outstanding). SNR was also objectively quantified within the white matter. PMC scans had near-identical scores on the criteria of image quality to non-PMC scans, with the notable exception that intensity homogeneity was generally worse. Using PMC, the percentage of scans with bad image quality was substantially lower than without PMC (3.25% vs. 12.5%) on the other five criteria. Quantitative SNR estimates revealed that PMC and non-PMC had no significant difference in SNR (P=0.07). Application of prospective motion correction to 3D-T2-FLAIR sequences decreased the percentage of low-quality scans, reducing the number of scans that need to be repeated to obtain clinically useful data.

Hybrid PET/MRI Methodology

The hybrid PET/MR scanner represents the first implementation of the effective integration of two modalities allowing truly synchronous/simultaneous acquisition of their imaging signals. This integration, resulting from the innovation and development of specific hardware components has paved the way for new approaches in the study of neurodegenerative diseases. This chapter will describe the hardware development that has led to the availability of different clinical solutions for PET/MR imaging as well as the still-open technological challenges and opportunities related to the processing and exploitation of the simultaneous acquisition in neurological studies.

From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.

Head motion measurement and correction using FID navigators

PURPOSE

To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array.

METHODS

FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms.

RESULTS

FIDnav motion estimates achieved mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion.

CONCLUSIONS

Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.

Comparison of prospective head motion correction with NMR field probes and an optical tracking system

PURPOSE

The aim of this study was to compare prospective head motion correction and motion tracking abilities of two tracking systems: Active NMR field probes and a Moiré phase tracking camera system using an optical marker.

METHODS

Both tracking systems were used simultaneously on human subjects. The prospective head motion correction was compared in an MP2RAGE and a gradient echo sequence. In addition, the motion tracking trajectories for three subjects were compared against each other and their correlation and deviations were analyzed.

RESULTS

With both tracking systems motion artifacts were visibly reduced. The precision of the field probe system was on the order of 50 µm for translations and 0.03° for rotations while the camera's was approximately 5 µm and 0.007°. The comparison of the measured trajectories showed close correlation and an average absolute deviation below 500 µm and 0.5°.

CONCLUSION

This study presents the first in vivo comparison between NMR field probes and Moiré phase tracking. For the gradient echo images, the field probes had a similar motion correction performance as the optical tracking system. For the MP2RAGE measurement, however, the camera yielded better results. Still, both tracking systems substantially decreased image artifacts in the presence of subject motion. Thus, the motion tracking modality should be chosen according to the specific requirements of the experiment while considering the desired image resolution, refresh rate, and head coil constraints.

Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF

PURPOSE

The purpose of this study is to increase the robustness of MR fingerprinting (MRF) toward subject motion.

METHODS

A novel reconstruction algorithm, MOtion insensitive MRF (MORF), was developed, which uses an iterative reconstruction based retrospective motion correction approach. Each iteration loops through the following steps: pattern recognition, metric based identification of motion corrupted frames, registration based motion estimation, and motion compensated data consistency verification. The proposed algorithm was validated using in vivo 2D brain MRF data with retrospective in-plane motion introduced at different stages of the acquisition. The validation was performed using qualitative and quantitative comparisons between results from MORF, the iterative multi-scale (IMS) algorithm, and with the IMS results using data without motion for a ground truth comparison. Additionally, the MORF algorithm was evaluated in prospectively motion corrupted in vivo 2D brain MRF datasets.

RESULTS

For datasets corrupted by in-plane motion both prospectively and retrospectively, MORF noticeably reduced motion artifacts compared with iterative multi-scale and closely resembled the results from data without motion, even when ∼54% of data was motion corrupted during different parts of the acquisition.

CONCLUSIONS

MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization

We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.