Prospective motion correction in brain imaging: a review

MRI restoration using edge-guided adversarial learning

Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D) images present challenges when reformatted in orthogonal planes due to sparser sampling in the through-plane direction. Restoring the "missing" through-plane slices, or regions of an MRI image damaged by acquisition artifacts can be modeled as an image imputation task. In this work, we consider the damaged image data or missing through-plane slices as image masks and proposed an edge-guided generative adversarial network to restore brain MRI images. Inspired by the procedure of image inpainting, our proposed method decouples image repair into two stages: edge connection and contrast completion, both of which used general adversarial networks (GAN). We trained and tested on a dataset from the Human Connectome Project to test the application of our method for thick slice imputation, while we tested the artifact correction on clinical data and simulated datasets. Our Edge-Guided GAN had superior PSNR, SSIM, conspicuity and signal texture compared to traditional imputation tools, the Context Encoder and the Densely Connected Super Resolution Network with GAN (DCSRN-GAN). The proposed network may improve utilization of clinical 2D scans for 3D atlas generation and big-data comparative studies of brain morphometry.

Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain

PURPOSE

To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF).

METHODS

Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps.

RESULTS

A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T₁ and T₂ estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T₁ and 18 ms for T₂ values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations.

CONCLUSION

We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.

Improved motion correction of submillimetre 7T fMRI time series with Boundary-Based Registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network

BACKGROUND AND PURPOSE

Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network.

MATERIALS AND METHODS

The network was trained to identify motion artifacts in axial T2-weighted spin-echo images of the brain. Using an extensive data augmentation scheme and a motion artifact simulation pipeline, we created a synthetic training dataset of 93,600 images based on only 16 artifact-free clinical MRI cases. A blinded reader study using a unique test dataset of 28 additional clinical MRI cases with real patient motion was conducted to evaluate the performance of the network.

RESULTS

Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases (P < .03).

CONCLUSIONS

Retrospective correction of motion artifacts using a multiscale fully convolutional network is promising and may mitigate the substantial motion-related problems in the clinical MRI workflow.

Accelerating the Evolution of Nonhuman Primate Neuroimaging

Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions.

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T

PURPOSE

The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging.

METHODS

High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B₀ perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases.

RESULTS

Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization.

CONCLUSION

Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 ​mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Motion-corrected MRI with DISORDER: Distributed and incoherent sample orders for reconstruction deblurring using encoding redundancy

PURPOSE

To enable rigid body motion-tolerant parallel volumetric magnetic resonance imaging by retrospective head motion correction on a variety of spatiotemporal scales and imaging sequences.

THEORY AND METHODS

Tolerance against rigid body motion is based on distributed and incoherent sampling orders for boosting a joint retrospective motion estimation and reconstruction framework. Motion resilience stems from the encoding redundancy in the data, as generally provided by the coil array. Hence, it does not require external sensors, navigators or training data, so the methodology is readily applicable to sequences using 3D encodings.

RESULTS

Simulations are performed showing full inter-shot corrections for usual levels of in vivo motion, large number of shots, standard levels of noise and moderate acceleration factors. Feasibility of inter- and intra-shot corrections is shown under controlled motion in vivo. Practical efficacy is illustrated by high-quality results in most corrupted of 208 volumes from a series of 26 clinical pediatric examinations collected using standard protocols.

CONCLUSIONS

The proposed framework addresses the rigid motion problem in volumetric anatomical brain scans with sufficient encoding redundancy which has enabled reliable pediatric examinations without sedation.

Local and Interregional Neurochemical Associations Measured by Magnetic Resonance Spectroscopy for Studying Brain Functions and Psychiatric Disorders

Magnetic resonance spectroscopy (MRS) studies have found significant correlations among neurometabolites (e.g., between glutamate and GABA) across individual subjects and altered correlations in neuropsychiatric disorders. In this article, we discuss neurochemical associations among several major neurometabolites which underpin these observations by MRS. We also illustrate the role of spectral editing in eliminating unwanted correlations caused by spectral overlapping. Finally, we describe the prospects of mapping macroscopic neurochemical associations across the brain and characterizing excitation-inhibition balance of neural networks using glutamate- and GABA-editing MRS imaging.

Motion prediction enables simulated MR-imaging of freely moving model organisms

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

Magnetic resonance imaging (MRI) requires its subjects not to move, since movement will cause image artifacts. This is hard to achieve for adult humans, whom we can ask to comply, but can currently only be achieved by sedation for other freely moving biological specimens. Because of the importance of non-invasive MRI as a technique to also capture metabolic information during activity, this is a huge deficiency of the methodology that is hampering progress. In our paper we ask the question whether it is possible to computationally combine optical information on specimen movement with MRI. Our approach is to predict the future movement and position of the specimen and thereby anticipate where it will be so as to specify correct MRI parameters. Our computer simulations show, for a freely moving worm, that a reasonable prediction is already possible for a short time window, and that we can control the amount of error of the resulting MRI image. Importantly, with the continuous speedup of computation, our simulations suggest that it is opportune now to implement such a system in hardware.

European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND)

INTRODUCTION

The goal of European Ultrahigh-Field Imaging Network in Neurodegenerative Diseases (EUFIND) is to identify opportunities and challenges of 7 Tesla (7T) MRI for clinical and research applications in neurodegeneration. EUFIND comprises 22 European and one US site, including over 50 MRI and dementia experts as well as neuroscientists.

METHODS

EUFIND combined consensus workshops and data sharing for multisite analysis, focusing on 7 core topics: clinical applications/clinical research, highest resolution anatomy, functional imaging, vascular systems/vascular pathology, iron mapping and neuropathology detection, spectroscopy, and quality assurance. Across these topics, EUFIND considered standard operating procedures, safety, and multivendor harmonization.

RESULTS

The clinical and research opportunities and challenges of 7T MRI in each subtopic are set out as a roadmap. Specific MRI sequences for each subtopic were implemented in a pilot study presented in this report. Results show that a large multisite 7T imaging network with highly advanced and harmonized imaging sequences is feasible and may enable future multicentre ultrahigh-field MRI studies and clinical trials.

DISCUSSION

The EUFIND network can be a major driver for advancing clinical neuroimaging research using 7T and for identifying use-cases for clinical applications in neurodegeneration.

Prospective motion and B0 shim correction for MR spectroscopy in human brain at 7T

PURPOSE

To demonstrate feasibility and performance of prospective motion and B₀ shim correction for MRS in human brain at 7T.

METHODS

Prospective motion correction using an optical camera and linear B₀ shim correction using FASTMAP-like navigators were implemented into a semi-LASER sequence. The effect of motion on spectral quality was assessed without and with prospective correction in prefrontal cortex in 11 subjects.

RESULTS

Without prospective motion and shim correction, motion resulted in considerable degradation of MR spectra (broader linewidth, lower signal-to-noise ratio, degraded water suppression). With prospective motion and shim correction, spectral quality remained excellent despite motion. Prospective motion correction alone was not sufficient to prevent degradation of spectral quality.

CONCLUSION

Prospective motion and B₀ shim correction is feasible at 7T and should help improve the robustness of MRS, particularly in motion-prone populations.

Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.

A comparison of in vivo MRI based cortical myelin mapping using T1w/T2w and R1 mapping at 3T

In this manuscript, we compare two commonly used methods to perform cortical mapping based on myelination of the human neocortex. T1w/T2w and R1 maps with matched total acquisition times were obtained from a young cohort in randomized order and using a test–retest design. Both methodologies showed cortical myelin maps that enhanced similar anatomical features, namely primary sensory regions known to be myelin rich. T1w/T2w maps showed increased robustness to movement artifacts in comparison to R1 maps, while the test re-test reproducibility of both methods was comparable. Based on Brodmann parcellation, both methods showed comparable variability within each region. Having parcellated cortical myelin maps into VDG11b areas of 4a, 4p, 3a, 3b, 1, 2, V2, and MT, both methods behave identically with R1 showing an increased variability between subjects. In combination with the test re-test evaluation, we concluded that this increased variability between subjects reflects relevant tissue variability. A high level of correlation was found between the R1 and T1w/T2w regions with regions of higher deviations being co-localized with those where the transmit RF field deviated most from its nominal value. We conclude that R1 mapping strategies might be preferable when studying different population cohorts where cortical properties are expected to be altered while T1w/T2w mapping will have advantages when performing cortical based segmentation.

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

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.

Prospective motion correction in functional MRI using simultaneous multislice imaging and multislice-to-volume image registration

The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50% when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion.

Active head motion reduction in magnetic resonance imaging using tactile feedback

Head motion is a common problem in clinical as well as empirical (functional) magnetic resonance imaging applications, as it can lead to severe artefacts that reduce image quality. The scanned individuals themselves, however, are often not aware of their head motion. The current study explored whether providing subjects with this information using tactile feedback would reduce their head motion and consequently improve image quality. In a single session that included six runs, 24 participants performed three different cognitive tasks: (a) passive viewing, (b) mental imagery, and (c) speeded responses. These tasks occurred in two different conditions: (a) with a strip of medical tape applied from one side of the magnetic resonance head coil, via the participant's forehead, to the other side, and (b) without the medical tape being applied. Results revealed that application of medical tape to the forehead of subjects to provide tactile feedback significantly reduced both translational as well as rotational head motion. While this effect did not differ between the three cognitive tasks, there was a negative quadratic relationship between head motion with and without feedback. That is, the more head motion a subject produced without feedback, the stronger the motion reduction given the feedback. In conclusion, the here tested method provides a simple and cost-efficient way to reduce subjects' head motion, and might be especially beneficial when extensive head motion is expected a priori.

A method for correcting breathing-induced field fluctuations in T2*-weighted spinal cord imaging using a respiratory trace

PURPOSE

Spinal cord MRI at ultrahigh field is hampered by time-varying magnetic fields associated with the breathing cycle, giving rise to ghosting artifacts in multi-shot acquisitions. Here, we suggest a correction approach based on linking the signal from a respiratory bellows to field changes inside the spinal cord. The information is used to correct the data at the image reconstruction level.

METHODS

The correction was demonstrated in the context of multi-shot T2*-weighted imaging of the cervical spinal cord at 7T. A respiratory trace was acquired during a high-resolution multi-echo gradient-echo sequence, used for structural imaging and quantitative T2* mapping, and a multi-shot EPI time series, as would be suitable for fMRI. The coupling between the trace and the breathing-induced fields was determined by a short calibration scan in each individual. Images were reconstructed with and without trace-based correction.

RESULTS

In the multi-echo acquisition, breathing-induced fields caused severe ghosting in images with long TE, which led to a systematic underestimation of T2* in the spinal cord. The trace-based correction reduced the ghosting and increased the estimated T2* values. Breathing-related ghosting was also observed in the multi-shot EPI images. The correction largely removed the ghosting, thereby improving the temporal signal-to-noise ratio of the time series.

CONCLUSIONS

Trace-based retrospective correction of breathing-induced field variations can reduce ghosting and improve quantitative metrics in multi-shot structural and functional T2*-weighted imaging of the spinal cord. The method is straightforward to implement and does not rely on sequence modifications or additional hardware beyond a respiratory bellows.

Low-field MRI: An MR physics perspective

Historically, clinical MRI started with main magnetic field strengths in the ∼0.05-0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state-of-the-art of low-field systems (defined as 0.25-1T), both with respect to its low cost, low foot-print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher-field MRI. In the first section, the signal-to-noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low-field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019.

A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data

It has been known for decades that head motion/other artifacts affect the blood oxygen level-dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind-source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event-related functional magnetic resonance imaging (erfMRI) and block-design (bdfMRI) datasets collected with multiband 32- (repetition time [TR] = 460 ms) or older 12-channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind-source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind-source denoising approaches appear to remove signal as well as noise from task-related data at individual subject and group levels.

Improvement in diagnostic quality of structural and angiographic MRI of the brain using motion correction with interleaved, volumetric navigators

INTRODUCTION

Subject movements lead to severe artifacts in magnetic resonance (MR) brain imaging. In this study we evaluate the diagnostic image quality in T1-weighted, T2-weighted, and time-of-flight angiographic MR sequences when using a flexible, navigator-based prospective motion correction system (iMOCO).

METHODS

Five healthy volunteers were scanned during different movement scenarios with and without (+/-) iMOCO activated. An experienced neuroradiologist graded images for image quality criteria (grey-white-matter discrimination, basal ganglia, and small structure and vessel delineation), and general image quality on a four-grade scale.

RESULTS

In scans with deliberate motion, there was a significant improvement in the image quality with iMOCO compared to the scans without iMOCO in both general image impression (T1 p<0.01, T2 p<0.01, TOF p = 0.03) and in anatomical grading (T1 p<0.01, T2 p<0.01, TOF p = 0.01). Subjective image quality was considered non-diagnostic in 91% of the scans with motion -iMOCO, but only in 4% of the scans with motion +iMOCO. iMOCO performed best in the T1-weighted sequence and least well in the angiography sequence. iMOCO was not shown to have any negative effect on diagnostic image quality, as no significant difference in diagnostic quality was seen between scans -iMOCO and +iMOCO with no deliberate movement.

CONCLUSION

The evaluation showed that iMOCO enables substantial improvements in image quality in scans affected by subject movement, recovering important diagnostic information in an otherwise unusable scan.

Retrospective correction of motion-affected MR images using deep learning frameworks

PURPOSE

Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion-free reacquisition can become time- and cost-intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a-priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a-priori knowledge, this problem is still challenging.

METHODS

We propose the use of deep learning frameworks to perform retrospective motion correction in a reference-free setting by learning from pairs of motion-free and motion-affected images. For this image-to-image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics.

RESULTS

We observed that generative adversarial network-based motion correction is feasible producing near-realistic motion-free images as confirmed by blinded subjective image quality assessment. Generative adversarial network-based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error <0.08, structural similarity index >0.8, normalized mutual information >0.9).

CONCLUSION

Deep learning-based retrospective restoration of motion artifacts is feasible resulting in near-realistic motion-free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

A multifunction digital receiver suitable for real-time frequency detection and compensation in fast magnetic resonance imaging

We describe the development and implementation of a multifunction digital receiver suitable for magnetic resonance imaging with capability of real-time frequency detection and compensation. The digital receiver consists primarily of firmware modules that combine the functionalities of signal acquisition, frequency detection and compensation, and data correction and image reconstruction. The receiver was developed based on a single multiple-input multiple-output radio-frequency electronic board equipped with a reconfigurable Field Programmable Gate Array (FPGA) device. A simple and practical algorithm was developed and implemented on the FPGA to accelerate the data processing for frequency determination. The simplified frequency detection and the higher system integration enable the receiver to reduce dramatically the time for frequency detection and compensation. With this receiver, we are able to detect the frequency of short-duration signals in the bandwidth of 10 MHz centered at 400 MHz within 75 ns after the signal acquisition. We describe the designs of the key FPGA modules and how these modules integrate into a multifunction receiver. We also present testing data that validate the simplified algorithm for frequency determination, demonstrate frequency detection and compensation, and demonstrate how real-time data correction is performed during image acquisition and reconstruction.

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.

Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging

Estimating a time-varying signal, such as head motion from magnetic resonance imaging data, becomes particularly challenging in the face of other temporal dynamics such as functional activation. This paper describes a new Kalman filter-like framework that includes a sparse residual term in the measurement model. This additional term allows the extended Kalman filter to generate real-time motion estimates suitable for prospective motion correction when such dynamics occur. An iterative augmented Lagrangian algorithm similar to the alterating direction method of multipliers implements the update step for this Kalman filter. This paper evaluates the accuracy and convergence rate of this iterative method for small and large motion in terms of its sensitivity to parameter selection. The included experiment on a simulated functional magnetic resonance imaging acquisition demonstrates that the resulting method improves the maximum Youden's J index of the time series analysis by 2-3% versus retrospective motion correction, while the sensitivity index increases from 4.3 to 5.4 when combining prospective and retrospective correction.

Rigid motion-corrected magnetic resonance fingerprinting

PURPOSE

Develop a method for rigid body motion-corrected magnetic resonance fingerprinting (MRF).

METHODS

MRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC-MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high-quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase shifts; and (4) motion-corrected data are reconstructed with low-rank inversion. MC-MRF was validated in a standard T1 /T2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through-plane motion in 2D MC-MRF was investigated.

RESULTS

Simulation results show that motion in MRF can introduce artifacts in T1 and T2 maps, depending when it occurs. MC-MRF improved parametric map quality in all phantom and in vivo experiments with in-plane motion, comparable to the no-motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through-plane motion, particularly for smaller structures in T2 maps.

CONCLUSION

Here, a novel method for motion correction in MRF (MC-MRF) is proposed, which improves parametric map quality and accuracy in comparison to no-motion correction approaches. Future work will include validation of 3D MC-MRF to enable also through-plane motion correction.

Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI

Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n = 464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9–11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5–6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2–12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.

MR-PET head motion correction based on co-registration of multicontrast MR images

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Exploring the sensitivity of magnetic resonance fingerprinting to motion

PURPOSE

To explore the motion sensitivity of magnetic resonance fingerprinting (MRF), we performed experiments with different types of motion at various time intervals during multiple scans. Additionally, we investigated the possibility to correct the motion artifacts based on redundancy in MRF data.

METHODS

A radial version of the FISP-MRF sequence was used to acquire one transverse slice through the brain. Three subjects were instructed to move in different patterns (in-plane rotation, through-plane wiggle, complex movements, adjust head position, and pretend itch) during different time intervals. The potential to correct motion artifacts in MRF by removing motion-corrupted data points from the fingerprints and dictionary was evaluated.

RESULTS

Morphological structures were well preserved in multi-parametric maps despite subject motion. Although the bulk T1 values were not significantly affected by motion, fine structures were blurred when in-plane motion was present during the first part of the scan. On the other hand, T2 values showed a considerable deviation from the motion-free results, especially when through-plane motion was present in the middle of the scan (-44% on average). Explicitly removing the motion-corrupted data from the scan partially restored the T2 values (-10% on average).

CONCLUSION

Our experimental results showed that different kinds of motion have distinct effects on the precision and effective resolution of the parametric maps measured with MRF. Although MRF-based acquisitions can be relatively robust to motion effects occurring at the beginning or end of the sequence, relying on redundancy in the data alone is not sufficient to assure the accuracy of the multi-parametric maps in all cases.

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.

Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging

PURPOSE

To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI).

METHODS

A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets.

RESULTS

Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS.

CONCLUSION

The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.

A Simple Measure for Acuity in Medical Images

An automatic and objective assessment of image quality is important in an era, where large-scale processing of imaging data from multi-center studies becomes commonplace. Based on a comprehensive statistical image model that includes noise and blur, a measure for image acuity is derived here as the ratio of the maximal gradient magnitude and the intensity difference at a boundary. Acuity may be affected by the object under study, the image acquisition, reconstruction processes, and any post-processing steps. The acuity measure presented here is post-hoc, intuitive to understand, simple to compute, and easily integrates with other standard measures of image quality. Three applications in medical imaging are included where our acuity measure is useful in the objective and automatic assessment of image quality.

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.

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.

Integrated radio-frequency/wireless coil design for simultaneous MR image acquisition and wireless communication

PURPOSE

An innovative radio-frequency (RF) coil design that allows RF currents both at the Larmor frequency and in a wireless communication band to flow on the same coil is proposed to enable simultaneous MRI signal reception and wireless data transfer, thereby minimizing the number of wired connections in the scanner without requiring any modifications or additional hardware within the scanner bore.

METHODS

As a first application, the proposed integrated RF/wireless coil design was further combined with an integrated RF/shim coil design to perform not only MR image acquisition and wireless data transfer, but also localized B₀ shimming with a single coil. Proof-of-concept phantom experiments were conducted with such a coil to demonstrate its ability to simultaneously perform these three functions, while maintaining the RF performance, wireless data integrity, and B₀ shimming performance.

RESULTS

Performing wirelessly controlled shimming of localized B₀ inhomogeneities with the coil substantially reduced the B₀ root-mean-square error (>70%) and geometric distortions in echo-planar images without degrading the image quality, signal-to-noise ratio (<1.7%), or wireless data throughput (maximum variance = 0.04 Mbps) of the coil.

CONCLUSIONS

The RF/wireless coil design can provide a solution for wireless data transfer that can be easily integrated into existing MRI scanners for a variety of applications.

Parallel 2D FFT implementation on FPGA suitable for real-time MR image processing

We report the design and implementation of a parallel two-dimensional fast Fourier transform (2D FFT) algorithm on a Field Programmable Gate Array (FPGA) for real-time MR image processing. Although a number of architectures of 2D FFT hardware processors have been reported, these generic processors or IP cores are not always effective for processing MRI data. The key feature of our design is that our processors are customized solely for real-time MRI applications. We demonstrate that by considering the unique features of real-time MRI data streams, we were able to develop and implement the 2D FFT processors that are resource-efficient and flexible enough to handle both regular and irregular data. Using a data-driven approach, we were able to simplify the inter-processor data communication while maintaining data synchronization without a synchronous clock signal bus and complex interconnection network. We experimentally verified our designs by processing multi-slice image data sets with 128 × 128 and 256 × 256 in-plane resolution. The results demonstrate the effectiveness of our 2D FFT processors and show that image reconstruction can be accelerated in proportion to the parallel processing factor. We achieved image-reconstruction processing rates up to 3000 and 800 slices per second for images with 128 × 128 and 256 × 256 in-plane resolution, respectively. The results also indicate that the image-reconstruction acceleration is primarily limited by the speed of the data transfer between the FPGA device and external sensors.

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.

Age differences in head motion and estimates of cortical morphology

Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.

Cerebral effects of music during isometric exercise: An fMRI study

A block-design experiment was conducted using fMRI to examine the brain regions that activate during the execution of an isometric handgrip exercise performed at light-to-moderate-intensity in the presence of music. Nineteen healthy adults (7 women and 12 men; M_age = 24.2, SD = 4.9 years) were exposed to an experimental condition (music [MU]) and a no-music control condition (CO) in a randomized order within a single session. Each condition lasted for 10 min and participants were required to execute 30 exercise trials (i.e., 1 trial = 10 s exercise + 10 s rest). Attention allocation, exertional responses, and affective changes were assessed immediately after each condition. The BOLD response was compared between conditions to identify the combined effects of music and exercise on neural activity. The findings indicate that music reallocated attention toward task-unrelated thoughts (d = 0.52) and upregulated affective arousal (d = 0.72) to a greater degree when compared to a no-music condition. The activity of the left inferior frontal gyrus (lIFG) also increased when participants executed the motor task in the presence of music (F = 24.65), and a significant negative correlation was identified between lIFG activity and perceived exertion for MU (limb discomfort: r = -0.54; overall exertion: r = -0.62). The authors hypothesize that the lIFG activates in response to motor tasks that are executed in the presence of environmental sensory stimuli. Activation of this region might also moderate processing of interoceptive signals - a neurophysiological mechanism responsible for reducing exercise consciousness and ameliorating fatigue-related symptoms.