A phase-sensitive diffusion tensor magnetic resonance imaging (MRI) sequence is proposed with pulse timing optimization scheme to achieve velocity resolution of less than 20 μm s −1 and an integrated image reconstruction and velocity map generation pipeline. The application of ultra-slow flow relevant to neurofluids is enabled by the use of a recently developed, ultra-high-performance brain MRI gradient system. By simultaneously reconstructing magnitude and phase data, both metrics that characterize diffusive fluid motion and coherent velocity maps are calculated non-invasively in human subjects, time-resolved over the entire cardiac cycle. The resulting acquisition and reconstruction of velocity maps in brain parenchyma, enabled by high-performance brain imaging systems, promises to be an important approach to investigating ultra-slow neurofluid flow and glymphatic circulation.

Oscillating gradient spin echo (OGSE) diffusion MRI (dMRI) can probe the diffusive dynamics on short time scales ≲10 ms, which translates into the sensitivity to tissue microstructure at the short length scales ≲ 10 μ $$ \lesssim 10\kern0.3em \upmu $$ m. OGSE-based tissue microstructure imaging techniques able to characterize the cell diameter and cellular density have been established in pre-clinical studies. The unique image contrast of OGSE dMRI has been shown to differentiate tumor types and malignancies, enable early diagnosis of treatment effectiveness, and reveal different pathophysiology of lesions in stroke and neurological diseases. Recent innovations in high-performance gradient human MRI systems provide an opportunity to translate OGSE research findings in pre-clinical studies to human research and the clinic. The implementation of OGSE dMRI in human studies has the promise to advance our understanding of human brain microstructure and improve patient care. Compared to the clinical standard (pulsed gradient spin echo), engineering OGSE diffusion encoding for human imaging is more challenging. This review summarizes the impact of hardware and human biophysical safety considerations on the waveform design, imaging parameter space, and image quality of OGSE dMRI. Here we discuss the effects of the gradient amplitude, slew rate, peripheral nerve stimulation, cardiac stimulation, gradient driver, acoustic noise and mechanical vibration, eddy currents, gradient nonlinearity, concomitant gradient, motion and flow, and signal-to-noise ratio. We believe that targeted engineering for safe, high-quality, and reproducible imaging will enable the translation of OGSE dMRI techniques into the clinic.

A compact 7.0 T MRI system (C7T) has been successfully constructed, cooled down, and ramped (energized) to 7.01 T at GE HealthCare Technology and Innovation Center. The C7T magnet is designed for a B0-field homogeneity of <1.0 ppm over a 26-cm field-of-view, dedicated for MRI brain imaging. The C7T magnet applies a fully closed-loop cryogenic cooling system which condenses only 12 liters of liquid helium at 4.2 K from high pressure helium gas charged at room temperature. The helium is permanently sealed inside the cooling system even after a quench. This newly developed 7.0 T MRI has similar size and stray magnetic field as a clinical whole-body 3.0 T MRI magnet, it can be easily installed in a 3T scanner bay. This C7T MRI system can greatly improve the access to high performance 7T brain imaging for more patients. The cooldown, ramp and quench protection performance of the compact 7T MRI magnet prototype is reported in this paper.

The use of higher-performance gradient coils results in stronger second-order concomitant magnetic fields, which can lead to image artifacts such as signal dropout, blurring, and phase errors that cannot be corrected by pre-emphasis of gradient waveforms and/or radio frequency modulation alone. We have developed an axially symmetric second-order field coil that is insertable, and demonstrate its ability to prospectively correct the additional phase generated by second-order concomitant fields in 2D phase contrast and spiral-out gradient echo imaging in a 3.0 T high-performance head-gradient (MAGNUS) system.

Expansion of diffusion MRI (dMRI) both into the realm of strong gradients, and into accessible imaging with portable low-field devices, brings about the challenge of gradient nonlinearities. Spatial variations of the diffusion gradients make diffusion weightings and directions non-uniform across the field of view, and deform perfect shells in the q-space designed for isotropic directional coverage. Such imperfections hinder parameter estimation: Anisotropic shells hamper the deconvolution of fiber orientation distribution function (fODF), while brute-force retraining of a nonlinear regressor for each unique set of directions and diffusion weightings is computationally inefficient. Here we propose a protocol-independent parameter estimation (PIPE) method that enables fast parameter estimation for the most general case where the scan in each voxel is acquired with a different protocol in q-space. PIPE applies for any spherical convolution-based dMRI model, irrespective of its complexity, which makes it suitable both for white and gray matter in the brain or spinal cord, and for other tissues where fiber bundles have the same properties within a voxel (fiber response), but are distributed with an arbitrary fODF. In vivo human MRI experiments on a high-performance system show that PIPE can map fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 minutes. PIPE enables fast parameter estimation in the presence of arbitrary gradient nonlinearities, eliminating the need to arrange dMRI in shells or to retrain the estimator for different protocols in each voxel. PIPE applies for any model based on a convolution of a voxel-wise fiber response and fODF, and data from varying b-tensor shapes, diffusion/echo times, and other scan parameters.

The MAGNUS high-performance MRI gradient platform delivers Gmax = 200–300 mT/m, and SRmax = 500–750 T/m/s using standard clinical 3.0T system power electronics. This enables the exploration of an expanded diffusion parameter space (b~7–≥30 ms/μm2) with reasonable SNR, along with substantially shorter diffusion encoding pulse-widths, echo times, reduced distortion, and blurring from shorter echo spacing. The choice of high b-value diffusion-encoding space can effectively suppress contributions from extra-axonal water, allowing for simplified biophysical models to be explored for non-invasive mapping of intra-axonal content. In this study, the feasibility and reproducibility of mapping in-vivo whole-brain effective intra-axonal radius (reff), using MAGNUS was assessed. By making use of a test-retest paradigm, reproducibility and sensitivity were evaluated for this new biomarker. Six healthy volunteers were imaged, after obtaining written informed consent, under local IRB-approved protocols with a focus on utilizing the maximum gradient strength of 300 mT/m. Multi-shell dMRI protocols, with a lower bound b = 7 ms/μm2 were used for feasibility analysis and short (same-day) and long-term (7-days) test-retest repeatability. To aid in increased precision, a framework for rigorous post-processing incorporating real-valued diffusion data handling and gradient non-linearity correction was integrated. At 300 mT/m, simulations highlight a lower bound threshold for robust detectability of reff >1.41 μm. The simulated distribution function was consistent with in-vivo measurements, where a mean reff = 2.75 ± 0.15 μm was observed for whole-brain white matter (WM) across all volunteers. Left-Right brain white matter asymmetry as a function of reff was noted with segmentations of well-reported parcels, such as the corpus callosum and corticospinal tract, demonstrating good agreement with prior literature. Data highlighted good repeatability in voxel-wise and parcel-based estimates for short- and long-term test-retest analysis. A mean coefficient of variance of 3.2% for WM parcels across all volunteers was noted, with a reproducibility coefficient of 0.16 μm (6.6%) highlighting a lack of systemic bias. This study reports on the feasibility of investigating reff using MAGNUS. The analysis of repeatability established the floor of changes in the brain that can be observed in studies leveraging reff as a neuroimaging biomarker for white matter integrity or for investigating neuroplastic processes in the brain.

Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (patient clinical benefits are maximized and adverse effects are minimized) of the large number of possible DBS parameter settings (signal frequency, voltage, pulse width and contact locations) using the current empirical protocol requires numerous clinical visits, which substantially increases the time to reach optimal DBS stimulation, patient cost burden and ultimately limits the number of patients who can undergo DBS treatment. These issues became even more problematic with the recent introduction of electrode models with stimulation directionality thereby enabling more complex stimulation paradigms. These difficulties have necessitated the search for a biomarker-based optimization method that will streamline the DBS optimization process. Our recently published functional magnetic resonance imaging (fMRI) and machine learning-assisted DBS parameter optimization for PD treatment has provided a way to rapidly classify DBS parameters using parcel-based features that were extracted from DBS-fMRI response maps. However, the parcel-based method had limited accuracy as the parcels are based on subjective literature review. Here, we propose an unsupervised autoencoder (AE) based extraction of features from the DBS-fMRI responses to improve this accuracy. We demonstrate the usage of the extracted features in classification methods such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN) and LDA. We trained and tested these five classification algorithms using 122 fMRI response maps of 39 PD patients with a priori clinically optimized DBS parameters. Further, we investigated the robustness of the AE-based feature extraction method to changes in the activation patterns of the DBS-fMRI responses, which may be caused by difference in stimulation side and disease condition. Changes in the locations of activated and deactivated brain regions was simulated using a left-right horizontal flipping of the original left-sided (or nominal) DBS-fMRI response maps. The visualization of AE-based features extracted from the nominal and flipped DBS-fMRI response maps formed optimal and non-optimal clusters in a neuro-functionally meaningful manner, which indicate robustness of the AE-based feature extraction to subtle differences in the activated regions of DBS-fMRI response maps. The MLP, RF, SVM and LDA methods gave an overall DBS parameter classification accuracy of 96%, 94%, 92% and 93% respectively when trained using the AE-extracted features from the nominal DBS-fMRI maps. The AE-based MLP, RF, SVM and LDA accuracies were higher than the overall accuracy (81%) of our initial parcel-based LDA method. The performance of an AE-MLP model trained using the nominal DBS-fMRI maps did not change significantly when the model was tested on the flipped DBS-fMRI responses. We showed that the MLP method combined with AE-based feature extraction is best suited for fMRI-based DBS parameter optimization and represents another step towards a proposed digital tool for rapid semi-automated biomarker-based DBS optimization.### Competing Interest StatementThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Afis Ajala, Jianwei Qiu, John Karigiannis, Radhika Madhavan, Desmond Yeo, Luca Marinelli and Thomas Foo are salaried employees of GE Global Research. Andres Lozano is a consultant and advisor to Functional Neuromodulation, Medtronic, Boston Scientific, Abbott and Insightech.### Clinical TrialNCT03153670### Funding StatementThis work was supported by the Michael J. Fox foundation [grant number MJFF-008877, 2019]; the Canadian Institutes of Health Research Banting fellowship [grant number 471913, 2022].### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Data used in this work were acquired after protocols were approved by the institutional research ethics board at the University Health Network, Toronto, Canada.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe datasets analyzed in this research are not publicly available due to data privacy regulations of patient data. Upon reasonable request, the study protocol and individual de-identified participants' raw fMRI data will be available to investigators from the corresponding author using private online cloud storage. Researchers wishing to validate or replicate this work using the same datasets would need to be approved by the research boards of University of Toronto, University Health Network and GE Global Research.

Objective: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson’s disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit.Methods and procedures: We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization.Results: The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively.Conclusion: The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement—A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.

We report use of a dual-density dielectric barrier surrounding a detachable high-pass radiofrequency (RF) birdcage coil to achieve an order-of-magnitude reduction of acoustic noise in a high-performance head gradient system. The barrier consisted of a 4.5 mm-thick mass-loaded vinyl and a 6 mm-thick polyurethane foam. It was inserted into the radial gap between the birdcage coil and the RF shield in a prototype head-only gradient system at 3 T. More than 9 dBA reduction of sound pressure level was achieved on the average with representative, high acoustic-noise imaging sequences. Increased acoustic damping was apparent from acoustic impulse response functions. High dielectric constant of the mass-loaded vinyl effectively added distributed capacitance to the birdcage coil, lowering the resonance frequency, but not seriously degrading the RF transmission performance. The barrier occupied the radial space normally used for air cooling of the RF coil and the RF shield. The resulting omission of air cooling was found to be acceptable with efficient gradient thermal management and use of a highresistivity RF shield for eddy current reduction. The proposed method can improve patient experience while preserving image quality in a high-power head-only gradient system.

PURPOSE:MRI using 3D stack-of-spirals (SoS) readout on a high-performance gradient system is subject to strong second-order, spatially varying concomitant fields, which can lead to signal dropout and blurring artifacts that become more significant at locations farther from the gradient isocenter. A method for compensating for second-order concomitant fields in 3D axial SoS image reconstruction is described. METHODS:We retrospectively correct for second-order concomitant field-induced phase error in the 3D SoS data by slice-dependent k-space phase compensation based on the nominal spiral readout trajectories. The effectiveness of the method was demonstrated in phantom and healthy volunteer scans in which 3D pseudo-continuous arterial spin labeling imaging was performed with SoS fast spin-echo readout at 3 T. RESULTS:Substantial reduction in blurring was observed with the proposed method. In phantom scans, blurring was reduced by about 53% at 98 mm from the gradient isocenter. In the in vivo 3D pseudo-continuous arterial spin labeling scans, differences of up to 10% were observed at 78 mm from the isocenter, especially around the white-matter and gray-matter interfaces, between the corrected and uncorrected proton density images, perfusion-weighted images, and cerebral blood flow maps. CONCLUSIONS:The described retrospective correction method provides a means to correct erroneous phase accruals due to second-order concomitant fields in 3D axial stack-of-spirals imaging.

PURPOSE:To study implant lead tip heating because of the RF power deposition by developing mathematical models and comparing them with measurements acquired at 1.5 T and 3 T, especially to predict resonant length. THEORY AND METHODS:A simple exponential model and an adapted transmission line model for the electric field transfer function were developed. A set of wavenumbers, including that calculated from insulated antenna theory (King wavenumber) and that of the embedding medium were considered. Experiments on insulated, capped wires of varying lengths were performed to determine maximum temperature rise under RF exposure. The results are compared with model predictions from analytical expressions derived under the assumption of a constant electric field, and with those numerically calculated from spatially varying, simulated electric fields from body coil transmission. Simple expressions for the resonant length bounded between one-quarter and one-half wavelength are developed based on the roots of transcendental equations. RESULTS:The King wavenumber for both models more closely matched the experimental data with a maximum root mean square error of 9.81°C at 1.5 T and 5.71°C at 3 T compared to other wavenumbers with a maximum root mean square error of 27.52°C at 1.5 T and 22.01°C for 3 T. Resonant length was more accurately predicted compared to values solely based on the embedding medium. CONCLUSION:Analytical expressions were developed for implanted lead heating and resonant lengths under specific assumptions. The value of the wavenumber has a strong effect on the model predictions. Our work could be used to better manage implanted device lead tip heating.

A newly developed compact 7.0 T MRI system (C7T) is a dedicated brain imaging system with the weight and footprint of a clinical whole-body 3.0 T MRI scanner. This new 7T MRI scanner will be easier for deployment and installation than a whole-body 7T thanks to lighter weight and more compact size. Its superconducting coils wound with NbTi wires are mounted inside of aluminum support cylinders. The magnet has a fully sealed cooling system that comprises of 3 cryocoolers, liquid and gas helium tanks and stainless-steel cooling tubes thermally attached to aluminum support cylinders. It needs only about 1% of liquid helium as that of conventional superconducting magnet. Helium will be permanently sealed inside the helium tanks and cooling tubes even after a quench. As such, no vent stack is needed for this magnet. The C7T magnet is designed for a B0-field homogeneity of <1.0 ppm over a 26-cm field-of-view. This magnet incorporates a superconducting shim assembly comprising 6 axial and 12 transverse shim coil sets, which reduces the amount of magnetic material required in the passive shimming system. The design and construction of this novel compact 7T MRI magnet is reported in this paper.

In blood-oxygen-level-dependent (BOLD)-based resting-state functional (RS-fMRI) studies, usage of multi-echo echo-planar-imaging (ME-EPI) is limited due to unacceptable late echo times when high spatial resolution is used. Equipped with high-performance gradients, the compact 3T MRI system (C3T) enables a three-echo whole-brain ME-EPI protocol with smaller than 2.5 mm isotropic voxel and shorter than 1 s repetition time, as required in landmark fMRI studies. The performance of the ME-EPI was comprehensively evaluated with signal variance reduction and region-of-interest-, seed- and independent-component-analysis-based functional connectivity analyses and compared with a counterpart of single-echo EPI with the shortest TR possible. Through the multi-echo combination, the thermal noise level is reduced. Functional connectivity, as well as signal intensity, are recovered in the medial orbital sulcus and anterior transverse collateral sulcus in ME-EPI. It is demonstrated that ME-EPI provides superior sensitivity and accuracy for detecting functional connectivity and/or brain networks in comparison with single-echo EPI. In conclusion, the high-performance gradient enabled high-spatial-temporal resolution ME-EPI would be the method of choice for RS-fMRI study on the C3T.

Patients with abandoned pacemaker leads require special attention during MR exams due to the risk of lead tip heating from RF energy deposition. The electromagnetic fields on the compact 3T (C3T) scanner fall off rapidly caudal to the head, and therefore present a reduced risk of lead tip heating during brain imaging. We compared the images from a whole-body 1.5T scanner and a high-performance C3T scanner. The C3T images show substantial improvements in image quality and greater cortical detail. This work establishes the feasibility of C3T brain MRI with a 32-channel receive coil for patients with abandoned leads.

The MAGNUS ultra-high-performance gradient coil delivers simultaneous 200 mT/m and 500 T/m/s performance on each axis, with higher PNS thresholds than whole-body gradient coils, which is particularly useful for diffusion microstructure imaging. Our initial clinical experience with the MAGNUS research scanner has successfully identified white matter abnormalities (? intra-axonal edema) using multi-shell DTI (b max = 4000 s/mm 2 ) and OGSE ( f max = 100 Hz) in an acute symptomatic mTBI subject. It has also successfully identified differences in intracellular volume fraction using multi-shell multi-frequency OGSE (b max = 2000 s/mm 2 , f max = 100 Hz) between a low-grade diffuse astrocytoma and a high-grade glioblastoma.

Purpose: We hypothesized that the time-dependent diffusivity at short diffusion times, as measured by oscillating gradient spin echo (OGSE) diffusion MRI, can characterize tissue microstructures in glioma patients. Theory and Methods: Five adult patients with known diffuse glioma, including two pre-surgical and three with new enhancing lesions after treatment for high-grade glioma, were scanned in an ultra-high-performance gradient 3.0T MRI system. OGSE diffusion MRI at 30-100 Hz and pulsed gradient spin echo diffusion imaging (approximated as 0 Hz) were obtained. The ADC and trace-diffusion-weighted image at each acquired frequencywere calculated, that is, ADC (f) and TraceDWI (f). Results: In pre-surgical patients, biopsy-confirmed solid enhancing tumor in a high-grade glioblastoma showed higher (ADC (f))| (ADC (0 Hz)) and lower (TraceDWI (f)) | (TraceDWI (0 Hz)), compared to that at same OGSE frequency in a low- grade astrocytoma. In post-treatment patients, the enhancing lesions of two patients who were diagnosed with tumor progression contained more voxels with high (ADC (f)) |(ADC (0 Hz)) and low (TraceDWI(f)) |(TraceDWI(0 Hz)), compared to the enhancing lesions of a patient who was diagnosed with treatment effect. Non- enhancing T-2 signal abnormality lesions in both the pre-surgical high-grade glioblastoma and post-treatment tumor progressions showed regions with high (ADC (f))|(ADC (0 Hz)) and low (TraceDWI (f))| (TraceDWI (0 Hz)), consistent with infiltrative tumor. The solid tumor of the glioblastoma, the enhancing lesions of post-treatment tumor progressions, and the suspected infiltrative tumors showed high diffusion time-dependency from 30 to 100 Hz, consistent with high intra-tumoral volume fraction (cellular density). Conclusion: Different characteristics of OGSE-based time-dependent diffusivity can reveal heterogenous tissue microstructures that indicate cellular density in glioma patients.

Access to high-quality MR exams is severely limited for patients with some implanted devices due to labeled MR safety conditions, but small-bore systems can overcome this limitation. For example, a compact 3 T MR scanner (C3T) with high-performance gradients can acquire exams of the head, extremities, and infants. Because of its reduced bore size and the patient being advanced only partially into the bore, the associated electromagnetic (EM) fields drop off rapidly caudal to the head, compared to whole-body systems. Therefore, some patients with MR conditional implanted devices can safely receive 3 T brain exams on the C3T using its strong gradients and a multiple-channel receive coil, while a corresponding exam on whole-body MR is precluded. The purpose of this study is to evaluate the performance of a small-bore scanner for subjects with MR conditional spinal or sacral nerve stimulators, or abandoned cardiac implantable electronic device (CIED) leads. The spatial dependence of specific absorption rate (SAR) on the C3T was compared to whole-body scanners. A device assessment tool was developed and applied to evaluate MR safety individually on the C3T for 12 subjects with implanted devices or abandoned CIED leads. Once MR safety was established, the subjects received a C3T brain exam along with their clinical, 1.5 T exam. The resulting images were graded by three board-certified neuroradiologists. The C3T exams were well-tolerated with no adverse events, and significantly outperformed the whole-body 1.5 T exams in terms of overall image quality.

The heterogeneity of hepatocellular carcinoma may be better seen with magnetic resonance imaging (MRI) compared with CT due to its high soft tissue contrast, providing precise tumor targets during therapy, while ultrasound (US) remains as the imaging modality for real-time guidance. Image fusion methods existing in clinical workflows involve rigid registration only and fail to compensate for liver motion in US. In this work, we present a hybrid deformable fusion method to align pre-interventional 3D MRI and interventional 3D US in real-time. Multimodal pre-interventional MRI (pMRI) and US (pUS) volumes were obtained from 3 human volunteers using a simultaneous MRI-US acquisition system, with an MR-compatible, hands-free US probe. pMRI and pUS volumes were aligned using conventional deformable registration, as it is not time critical. Deep learning (DL)–based registration was used for real-time fusion of pUS to iUS, and consecutive iUS volumes in near real time. The predicted DL deformation fields were used to deform the pMRI to match each US volume. US data with respiration were collected at a temporal resolution of 4.2 volumes/sec. 1600 US volumes from one volunteer was used for DL training. The hybrid deformable registration method was evaluated for pMRI and 20 US volume alignments for each volunteer. Mean Euclidean distance error between expert placed landmarks and predicted positions of landmarks after image alignment were computed. Table 1 shows the mean landmark error (LE) and computation times for the hybrid (HDR) and conventional deformable registration (CDR) method aligning pMRI and 20 US volumes. Feasibility of a multimodal hybrid deformable registration method with clinically acceptable registration accuracy and low latency was shown. The method for motion compensation may improve tumor targeting in interventional procedures including liver ablation.

An ultra-high diffusion b-value protocol ( b =7,000-30,000s/mm 2 ) was acquired in a high-performance 3.0 T MAGNUS head gradient system (max gradient amplitude=200 mT/m and max slew rate=500 T/m/s) for white matter microstructure imaging.With shorter TE enabled by MAGNUS, measurements of intra-axonal diffusivities are demonstrated at ultra-high b-values. At these ultra-high b-values, signal from extra-axonal water is effectively suppressed but SNR is also reduced. We show that real-valued image reconstruction maintains Gaussian noise properties and reduces Rician bias, allowing better quantification of intra-axonal diffusivities and effective axonal radii in healthy volunteers and in ongoing human subject research studies of mild traumatic brain injury.

The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms that are selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of gradient waveforms with different sensitivities to restricted diffusion and exchange (150 samples), our results reveal unique time-dependence signatures in grey and white matter, where the former is characterised by both restricted diffusion and exchange and the latter predominantly exhibits restricted diffusion. Furthermore, we show that gradient waveforms with independently varying sensitivities to restricted diffusion and exchange can be used to map exchange in the human brain. We consistently find that exchange in grey matter is at least twice as fast as in white matter, across all subjects and all gradient strengths. The shortest exchange times observed in this study were in the cerebellar cortex (115 ms). We also assess the feasibility of future clinical applications of the method used in this work, where we find that the grey-white matter exchange contrast obtained with a 25-minute 300 mT/m protocol is preserved by a 4-minute 300 mT/m and a 10-minute 80 mT/m protocol. Our work underlines the utility of free waveforms for detecting time-dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.