MP2RAGEME: T1 , T2 * , and QSM mapping in one sequence at 7 tesla

Comparison and calibration of MP2RAGE quantitative T1 values to multi-TI inversion recovery T1 values

While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect structural changes in brain tissue. Magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) is an acquisition protocol that allows for efficient T1 mapping with a much lower scan time per slab compared to multi-TI inversion recovery (IR) protocols. We collect and register B1-corrected MP2RAGE acquisitions with an additional inversion time (MP3RAGE) alongside multi-TI selective inversion recovery acquisitions for four subjects. We use a maximum a posteriori (MAP) T1 estimation method for both MP2RAGE and compare to typical point estimate MP2RAGE T1 mapping, finding no bias from MAP MP2RAGE but a sensitivity to B1+ inhomogeneities with MAP MP3RAGE. We demonstrate a tissue-dependent bias between MAP MP2RAGE T1 estimates and the multi-TI inversion recovery T1 values. To correct this bias, we train a patch-based ResNet-18 to calibrate the MAP MP2RAGE T1 estimates to the multi-TI IR T1 values. Across four folds, our network reduces the RMSE significantly (white matter: from 0.30 ± 0.01 s to 0.11 ± 0.02 s, subcortical gray matter: from 0.26 ± 0.02 s to 0.10 ± 0.02 s, cortical gray matter: from 0.36 ± 0.02 s to 0.17 ± 0.03 s). Using limited paired training data from both sequences, we can reduce the error between quantitative imaging methods and calibrate to one of the protocols with a neural network.

Structural connectivity of thalamic subnuclei in major depressive disorder: An ultra-high resolution diffusion MRI study at 7-Tesla

BACKGROUND

The thalamus serves as a central relay station within the brain, and thalamic connectional anomalies are increasingly thought to be present in major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented a thorough examination of the thalamus and its subnuclear connectional profile. We combined ultra-high field diffusion MRI acquired at 7.0 Tesla to map the white matter connectivity of thalamic subnuclei.

METHODS

Fifty-three MDD patients and 12 healthy controls (HCs) were involved in the final analysis. FreeSurfer was used to segment the thalamic subnuclei, and MRtrix was used to perform the preprocessing and tractography. Fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity, and streamline count of thalamic subnuclear tracts were measured as proxies of white matter microstructure. Bayesian multilevel model was used to assess group differences in white matter metrics for each thalamic subnuclear tract and the association between these white matter metrics and clinical features in MDD.

RESULTS

Evidence was found for reduced whiter matter metrics of the tracts spanning from all thalamic subnuclei among MDD versus HC participants. Moreover, evidence was found that white matter in various thalamic subnuclear tracts is related to medication status, age of onset and recurrence in MDD.

CONCLUSIONS

Structural connectivity was generally reduced in thalamic subnuclei in MDD participants. Several clinical characteristics are related to perturbed subnuclear thalamic connectivity with cortical and subcortical circuits that govern sensory processing, emotional function, and goal-directed behavior.

QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength

PURPOSE

To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

METHODS

QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of TI and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging.

RESULTS

In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE.

CONCLUSION

QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Structural connectivity of dopaminergic pathways in major depressive disorder: An ultra-high resolution 7-Tesla diffusion MRI study

Accumulating evidence points to imbalanced dopamine (DA) signaling and circulating levels in the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented a thorough examination of DA neural pathways in MDD. We uniquely employed ultra-high field diffusion MRI at 7.0 Tesla to map the white matter architecture and integrity of several DA pathways in MDD patients. Fifty-three MDD patients and 12 healthy controls (HCs) were enrolled in the final analysis. Images were acquired using a 7.0 Tesla MRI scanner. FreeSurfer was used to segment components of DA pathways, and MRtrix was used to perform preprocessing and tractography of mesolimbic, mesocortical, nigrostriatal, and unconventional DA pathways. Bayesian analyses assessed the impact of MDD and clinical features on DA tracts. MDD was associated with perturbed white matter microstructural properties of the nigrostriatal pathway, while several MDD features (severity of depression/age of onset/insomnia) related to connectivity changes within mesocortical, nigrostriatal, and unconventional pathways. MDD is associated with microstructural differences in the nigrostriatal pathway. The findings provide insight into the structural architecture and integrity of several DA pathways in MDD, and implicate their involvement in the clinical manifestation of MDD.

A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

The thalamus has a key role in mediating cortical-subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.

ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging

BACKGROUND AND OBJECTIVES

Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization. This work introduces the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), a novel open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using deep learning (DL) models and enables MultiTask Learning (MTL) to perform related tasks in an integrated manner, targeting generalization in the MRI domain.

METHODS

We conducted a comprehensive literature review and analyzed 12,479 GitHub repositories to assess the current landscape of AI frameworks for MRI. Subsequently, we demonstrate how ATOMMIC standardizes workflows and improves data interoperability, enabling effective benchmarking of various DL models across MRI tasks and datasets. To showcase ATOMMIC's capabilities, we evaluated twenty-five DL models on eight publicly available datasets, focusing on accelerated MRI reconstruction, segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and segmentation using MTL.

RESULTS

ATOMMIC's high-performance training and testing capabilities, utilizing multiple GPUs and mixed precision support, enable efficient benchmarking of multiple models across various tasks. The framework's modular architecture implements each task through a collection of data loaders, models, loss functions, evaluation metrics, and pre-processing transformations, facilitating seamless integration of new tasks, datasets, and models. Our findings demonstrate that ATOMMIC supports MTL for multiple MRI tasks with harmonized complex-valued and real-valued data support while maintaining active development and documentation. Task-specific evaluations demonstrate that physics-based models outperform other approaches in reconstructing highly accelerated acquisitions. These high-quality reconstruction models also show superior accuracy in estimating quantitative parameter maps. Furthermore, when combining high-performing reconstruction models with robust segmentation networks through MTL, performance is improved in both tasks.

CONCLUSIONS

ATOMMIC advances MRI reconstruction and analysis by leveraging MTL and ensuring consistency across tasks, models, and datasets. This comprehensive framework serves as a versatile platform for researchers to use existing AI methods and develop new approaches in medical imaging.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping

PURPOSE

Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed-sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging.

METHODS

VFA, MP2RAGE, and csMP2RAGE were compared against inversion-recovery T1 in an aqueous phantom at 3 T. The same 1-mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability.

RESULTS

The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization-transfer effects. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging.

CONCLUSIONS

We demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case-control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0 of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.

High SNR full brain relaxometry at 7T by accelerated MR-STAT

PURPOSE

To demonstrate the feasibility and robustness of the Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) framework for fast, high SNR relaxometry at 7T.

METHODS

To deploy MR-STAT on 7T-systems, we designed optimized flip-angles using the BLAKJac-framework that incorporates the SAR-constraints. Transmit RF-inhomogeneities were mitigated by including a measuredB 1 + $$ {B}_1^{+} $$ -map in the reconstruction. Experiments were performed on a gel-phantom and on five volunteers to explore the robustness of the sequence and its sensitivity toB 1 + $$ {B}_1^{+} $$ inhomogeneities. The SNR-gain at 7T was explored by comparing phantom and in vivo results to MR-STAT at 3T in terms of SNR-efficiency.

RESULTS

The higher SNR at 7T enabled two-fold acceleration with respect to current 2D MR-STAT protocols at lower field strengths. The resulting scan had whole-brain coverage, with 1 x 1 x 3 mm3 resolution (1.5 mm slice-gap) and was acquired within 3 min including theB 1 + $$ {B}_1^{+} $$ -mapping. AfterB 1 + $$ {B}_1^{+} $$ -correction, the estimated T1 and T2 in a phantom showed a mean relative error of, respectively, 1.7% and 4.4%. In vivo, the estimated T1 and T2 in gray and white matter corresponded to the range of values reported in literature with a variation over the subjects of 1.0%-2.1% (WM-GM) for T1 and 4.3%-5.3% (WM-GM) for T2. We measured a higher SNR-efficiency at 7T (R = 2) than at 3T for both T1 and T2 with, respectively, a 4.1 and 2.3 times increase in SNR-efficiency.

CONCLUSION

We presented an accelerated version of MR-STAT tailored to high field (7T) MRI using a low-SAR flip-angle train and showed high quality parameter maps with an increased SNR-efficiency compared to MR-STAT at 3T.

Quantitative MRI at 7-Tesla reveals novel frontocortical myeloarchitecture anomalies in major depressive disorder

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p's < 0.05, Cohen's D = 0.55-0.66) and symptom severity (p's < 0.01, r = 0.271-0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping

PURPOSE

To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO).

METHODS

A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard.

RESULTS

The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal.

CONCLUSION

mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Combining arterial blood contrast with BOLD increases fMRI intracortical contrast

BOLD fMRI is widely applied in human neuroscience but is limited in its spatial specificity due to a cortical-depth-dependent venous bias. This reduces its localization specificity with respect to neuronal responses, a disadvantage for neuroscientific research. Here, we modified a submillimeter BOLD protocol to selectively reduce venous and tissue signal and increase cerebral blood volume weighting through a pulsed saturation scheme (dubbed Arterial Blood Contrast) at 7 T. Adding Arterial Blood Contrast on top of the existing BOLD contrast modulated the intracortical contrast. Isolating the Arterial Blood Contrast showed a response free of pial-surface bias. The results suggest that Arterial Blood Contrast can modulate the typical fMRI spatial specificity, with important applications in in-vivo neuroscience.

Affine transformation edited and refined deep neural network for quantitative susceptibility mapping

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by a Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds.

Clipped DeepControl: Deep neural network two-dimensional pulse design with an amplitude constraint layer

Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (convolutional) neural network pulse prediction time (a few milliseconds) was in comparison more than three orders of magnitude faster than the conventional optimal control computation. The network pulses were from the supervised training capable of compensating scan-subject dependent inhomogeneities of B₀ and B₁⁺ fields. Unfortunately, the network presented with a small percentage of pulse amplitude overshoots in the test subset, despite the optimal control pulses used in training were fully constrained. Here, we have extended the convolutional neural network with a custom-made clipping layer that completely eliminates the risk of pulse amplitude overshoots, while preserving the ability to compensate for the inhomogeneous field conditions.

A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R₂^*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R₂^* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R₂^*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R₂^* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R₂^*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R₂^* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Empirical transmit field bias correction of T1w/T2w myelin maps

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.

qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.

Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking

PURPOSE

To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T 2 ∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan.

METHODS

The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T 2 ∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined.

RESULTS

High quality, co-registered T1 , T2 , and T 2 ∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T 2 ∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images.

CONCLUSION

The MR Multitasking-based 3D brain mapping of T1 , T2 , T 2 ∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.

Charting human subcortical maturation across the adult lifespan with in vivo 7 T MRI

The human subcortex comprises hundreds of unique structures. Subcortical functioning is crucial for behavior, and disrupted function is observed in common neurodegenerative diseases. Despite their importance, human subcortical structures continue to be difficult to study in vivo. Here we provide a detailed account of 17 prominent subcortical structures and ventricles, describing their approximate iron and myelin contents, morphometry, and their age-related changes across the normal adult lifespan. The results provide compelling insights into the heterogeneity and intricate age-related alterations of these structures. They also show that the locations of many structures shift across the lifespan, which is of direct relevance for the use of standard magnetic resonance imaging atlases. The results further our understanding of subcortical morphometry and neuroimaging properties, and of normal aging processes which ultimately can improve our understanding of neurodegeneration.

Automatic segmentation of Deep Grey Nuclei using a high-resolution 7T MRI Atlas - quantification of T1 values in healthy volunteers

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (0.6mm)³ in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T₁ -weighted brain template (0.5mm)³ using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and twelve thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T₁ values derived from the 3D-MP2RAGE volumes, after correction for residual B₁ ⁺ biases. Three metrics (The Dice and the volumetric similarity coefficients, and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration and their impact on the measurements of the quantitative T₁ values was highlighted. This represents a fully-automated segmentation process permitting the extraction of unbiased normative T₁ values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

A local multi-transmit coil combined with a high-density receive array for cerebellar fMRI at 7 T

The human cerebellum is involved in a wide array of functions, ranging from motor control to cognitive control, and as such is of great neuroscientific interest. However, its function is underexplored in vivo, due to its small size, its dense structure and its placement at the bottom of the brain, where transmit and receive fields are suboptimal. In this study, we combined two dense coil arrays of 16 small surface receive elements each with a transmit array of three antenna elements to improve BOLD sensitivity in the human cerebellum at 7 T. Our results showed improved B₁⁺ and SNR close to the surface as well as g-factor gains compared with a commercial coil designed for whole-head imaging. This resulted in improved signal stability and large gains in the spatial extent of the activation close to the surface (<3.5 cm), while good performance was retained deeper in the cerebellum. Modulating the phase of the transmit elements of the head coil to constructively interfere in the cerebellum improved the B₁⁺ , resulting in a temporal SNR gain. Overall, our results show that a dedicated transmit array along with the SNR gains of surface coil arrays can improve cerebellar imaging, at the cost of a decreased field of view and increased signal inhomogeneity.

Manual delineation approaches for direct imaging of the subcortex

The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of protocols for the anatomical delineation of 21 individual subcortical structures. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients for ten healthy volunteers. In addition, dilated Dice coefficients showed that manual parcellations created using these protocols can provide high-quality training data for automated algorithms. Here, we share the protocols, together with three example MRI datasets and the created manual delineations. The protocols can be applied to create high-quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.

Simulating Local Deformations in the Human Cortex Due to Blood Flow-Induced Changes in Mechanical Tissue Properties: Impact on Functional Magnetic Resonance Imaging

Investigating human brain tissue is challenging due to the complexity and the manifold interactions between structures across different scales. Increasing evidence suggests that brain function and microstructural features including biomechanical features are related. More importantly, the relationship between tissue mechanics and its influence on brain imaging results remains poorly understood. As an important example, the study of the brain tissue response to blood flow could have important theoretical and experimental consequences for functional magnetic resonance imaging (fMRI) at high spatial resolutions. Computational simulations, using realistic mechanical models can predict and characterize the brain tissue behavior and give us insights into the consequent potential biases or limitations of in vivo, high-resolution fMRI. In this manuscript, we used a two dimensional biomechanical simulation of an exemplary human gyrus to investigate the relationship between mechanical tissue properties and the respective changes induced by focal blood flow changes. The model is based on the changes in the brain’s stiffness and volume due to the vasodilation evoked by neural activity. Modeling an exemplary gyrus from a brain atlas we assessed the influence of different potential mechanisms: (i) a local increase in tissue stiffness (at the level of a single anatomical layer), (ii) an increase in local volume, and (iii) a combination of both effects. Our simulation results showed considerable tissue displacement because of these temporary changes in mechanical properties. We found that the local volume increase causes more deformation and consequently higher displacement of the gyrus. These displacements introduced considerable artifacts in our simulated fMRI measurements. Our results underline the necessity to consider and characterize the tissue displacement which could be responsible for fMRI artifacts.

Variability in subthalamic nucleus targeting for deep brain stimulation with 3 and 7 Tesla magnetic resonance imaging

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective surgical treatment for Parkinson's disease (PD). Side-effects may, however, be induced when the DBS lead is placed suboptimally. Currently, lower field magnetic resonance imaging (MRI) at 1.5 or 3 Tesla (T) is used for targeting. Ultra-high-field MRI (7 T and above) can obtain superior anatomical information and might therefore be better suited for targeting. This study aims to test whether optimized 7 T imaging protocols result in less variable targeting of the STN for DBS compared to clinically utilized 3 T images. Three DBS-experienced neurosurgeons determined the optimal STN DBS target site on three repetitions of 3 T-T2, 7 T-T2*, 7 T-R2* and 7 T-QSM images for five PD patients. The distance in millimetres between the three repetitive coordinates was used as an index of targeting variability and was compared between field strength, MRI contrast and repetition with a Bayesian ANOVA. Further, the target coordinates were registered to MNI space, and anatomical coordinates were compared between field strength, MRI contrast and repetition using a Bayesian ANOVA. The results indicate that the neurosurgeons are stable in selecting the DBS target site across MRI field strength, MRI contrast and repetitions. The analysis of the coordinates in MNI space however revealed that the actual selected location of the electrode is seemingly more ventral when using the 3 T scan compared to the 7 T scans.

Left-right asymmetric and smaller right habenula volume in major depressive disorder on high-resolution 7-T magnetic resonance imaging

The habenula (Hb) has been hypothesized to play an essential role in major depressive disorder (MDD) as it is considered to be an important node between fronto-limbic areas and midbrain monoaminergic structures based on animal studies. In this study, we aimed to investigate the differences in volume and T1 value of the Hb between patients with MDD and healthy control (HC) subjects. Analysis for the Hb volumes was performed using high-resolution 7-T magnetic resonance (MR) image data from 33 MDD patients and 36 healthy subjects. Two researchers blinded to the clinical data manually delineated the habenular nuclei and Hb volume, and T1 values were calculated based on overlapping voxels. We compared the Hb volume and T1 value between the MDD and HC groups and compared the volume and T1 values between the left and right Hbs in each group. Compared to HC subjects, MDD patients had a smaller right Hb volume; however, there was no significant volume difference in the left Hb between groups. In the MDD group, the right Hb was smaller in volume and lower in T1 value than the left Hb. The present findings suggest a smaller right Hb volume and left-right asymmetry of Hb volume in MDD. Future high-resolution 7-T MR imaging studies with larger sample sizes will be needed to derive a more definitive conclusion.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

PURPOSE

To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM.

METHODS

We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms.

RESULTS

The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel.

CONCLUSION

The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

MESMERISED: Super-accelerating T1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging

There is an increasing interest in quantitative imaging of T₁, T₂ and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T₂ and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T₁, T₂ and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T₁ and diffusion imaging, while also retaining the capacity to perform quantitative T₂ and B₁ mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T₁ relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT₁ imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm² can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm² MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T₂ and B₁⁺ mapping to super-accelerated qT₁ and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T₁, T₂ and diffusion contrast for multi-component modeling of tissue microstructure.

Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI

An efficient multi-slice inversion–recovery EPI (MS-IR-EPI) sequence for fast, high spatial resolution, quantitative T₁ mapping is presented, using a segmented simultaneous multi-slice acquisition, combined with slice order shifting across multiple acquisitions. The segmented acquisition minimises the effective TE and readout duration compared to a single-shot EPI scheme, reducing geometric distortions to provide high quality T₁ maps with a narrow point-spread function. The precision and repeatability of MS-IR-EPI T₁ measurements are assessed using both T₁-calibrated and T₂-calibrated ISMRM/NIST phantom spheres at 3 and 7 T and compared with single slice IR and MP2RAGE methods. Magnetization transfer (MT) effects of the spectrally-selective fat-suppression (FS) pulses required for in vivo imaging are shown to shorten the measured in-vivo T₁ values. We model the effect of these fat suppression pulses on T₁ measurements and show that the model can remove their MT contribution from the measured T₁, thus providing accurate T₁ quantification. High spatial resolution T₁ maps of the human brain generated with MS-IR-EPI at 7 T are compared with those generated with the widely implemented MP2RAGE sequence. Our MS-IR-EPI sequence provides high SNR per unit time and sharper T₁ maps than MP2RAGE, demonstrating the potential for ultra-high resolution T₁ mapping and the improved discrimination of functionally relevant cortical areas in the human brain.

A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data

Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

T1-Based Synthetic Magnetic Resonance Contrasts Improve Multiple Sclerosis and Focal Epilepsy Imaging at 7 T

OBJECTIVES

Ultra-high field magnetic resonance imaging (MRI) (≥7 T) is a unique opportunity to improve the clinical diagnosis of brain pathologies, such as multiple sclerosis or focal epilepsy. However, several shortcomings of 7 T MRI, such as radiofrequency field inhomogeneities, could degrade image quality and hinder radiological interpretation. To address these challenges, an original synthetic MRI method based on T1 mapping achieved with the magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence was developed. The radiological quality of on-demand T1-based contrasts generated by this technique was evaluated in multiple sclerosis and focal epilepsy imaging at 7 T.

MATERIALS AND METHODS

This retrospective study was carried out from October 2017 to September 2019 and included 21 patients with different phenotypes of multiple sclerosis and 35 patients with focal epilepsy who underwent MRI brain examinations using a whole-body investigative 7 T magnetic resonance system. The quality of 2 proposed synthetic contrast images were assessed and compared with conventional images acquired at 7 T using the MP2RAGE sequence by 4 radiologists, evaluating 3 qualitative criteria: signal homogeneity, contrast intensity, and lesion visualization. Statistical analyses were performed on reported quality scores using Wilcoxon rank tests and further multiple comparisons tests. Intraobserver and interobserver reliabilities were calculated as well.

RESULTS

Radiological quality scores were reported higher for synthetic images when compared with original images, regardless of contrast, pathologies, or raters considered, with significant differences found for all 3 criteria (P < 0.0001, Wilcoxon rank test). None of the 4 radiologists ever rated a synthetic image "markedly worse" than an original image. Synthetic images were rated slightly less satisfying for only 3 epileptic patients, without precluding lesion identification.

CONCLUSION

T1-based synthetic MRI with the MP2RAGE sequence provided on-demand contrasts and high-quality images to the radiologist, facilitating lesion visualization in multiple sclerosis and focal epilepsy, while reducing the magnetic resonance examination total duration by removing an additional sequence.

Sensitive detection of extremely small iron oxide nanoparticles in living mice using MP2RAGE with advanced image co-registration

Magnetic resonance imaging (MRI) is a widely used non-invasive methodology for both preclinical and clinical studies. However, MRI lacks molecular specificity. Molecular contrast agents for MRI would be highly beneficial for detecting specific pathological lesions and quantitatively evaluating therapeutic efficacy in vivo. In this study, an optimized Magnetization Prepared—RApid Gradient Echo (MP-RAGE) with 2 inversion times called MP2RAGE combined with advanced image co-registration is presented as an effective non-invasive methodology to quantitatively detect T1 MR contrast agents. The optimized MP2RAGE produced high quality in vivo mouse brain T1 (or R1 = 1/T1) map with high spatial resolution, 160 × 160 × 160 µm³ voxel at 9.4 T. Test–retest signal to noise was > 20 for most voxels. Extremely small iron oxide nanoparticles (ESIONPs) having 3 nm core size and 11 nm hydrodynamic radius after polyethylene glycol (PEG) coating were intracranially injected into mouse brain and detected as a proof-of-concept. Two independent MP2RAGE MR scans were performed pre- and post-injection of ESIONPs followed by advanced image co-registration. The comparison of two T1 (or R1) maps after image co-registration provided precise and quantitative assessment of the effects of the injected ESIONPs at each voxel. The proposed MR protocol has potential for future use in the detection of T1 molecular contrast agents.

Ultra-high field fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns

Ultra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents, (b) have a width (~1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation.

Multi-contrast anatomical subcortical structures parcellation

The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita.

Fast automatic segmentation of thalamic nuclei from MP2RAGE acquisition at 7 Tesla

PURPOSE

Thalamic nuclei are largely invisible in conventional MRI due to poor contrast. Thalamus Optimized Multi-Atlas Segmentation (THOMAS) provides automatic segmentation of 12 thalamic nuclei using white-matter-nulled (WMn) Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence at 7T, but increases overall scan duration. Routinely acquired, bias-corrected Magnetization Prepared 2 Rapid Gradient Echo (MP2RAGE) sequence yields superior tissue contrast and quantitative T1 maps. Application of THOMAS to MP2RAGE has been investigated in this study.

METHODS

Eight healthy volunteers and five pediatric-onset multiple sclerosis patients were recruited at Children's Hospital of Philadelphia and scanned at Siemens 7T with WMn-MPRAGE and multi-echo-MP2RAGE (ME-MP2RAGE) sequences. White-matter-nulled contrast was synthesized (MP2-SYN) from T₁ maps from ME-MP2RAGE sequence. Thalamic nuclei were segmented using THOMAS joint label fusion algorithm from WMn-MPRAGE and MP2-SYN datasets. THOMAS pipeline was modified to use majority voting to segment bias corrected T1-weighted uniform (MP2-UNI) images. Thalamic nuclei from MP2-SYN and MP2-UNI images were evaluated against corresponding nuclei obtained from WMn-MPRAGE images using dice coefficients, volume similarity indices (VSIs) and distance between centroids.

RESULTS

For MP2-SYN, dice > 0.85 and VSI > 0.95 was achieved for five larger nuclei and dice > 0.6 and VSI > 0.7 was achieved for seven smaller nuclei. The dice and VSI were slightly higher, whereas the distance between centroids were smaller for MP2-SYN compared to MP2-UNI, indicating improved performance using the MP2-SYN image.

CONCLUSIONS

THOMAS algorithm can successfully segment thalamic nuclei in MP2RAGE images with essentially equivalent quality as WMn-MPRAGE, widening its applicability in studies focused on thalamic involvement in aging and disease.

3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings

7 Tesla (7T) magnetic resonance imaging holds great promise for improved visualization of the human brain for clinical purposes. To assess whether 7T is superior regarding localization procedures of small brain structures, we compared manual parcellations of the red nucleus, subthalamic nucleus, substantia nigra, globus pallidus interna and externa. These parcellations were created on a commonly used clinical anisotropic clinical 3T with an optimized isotropic (o)3T and standard 7T scan. The clinical 3T MRI scans did not allow delineation of an anatomically plausible structure due to its limited spatial resolution. o3T and 7T parcellations were directly compared. We found that 7T outperformed the o3T MRI as reflected by higher Dice scores, which were used as a measurement of interrater agreement for manual parcellations on quantitative susceptibility maps. This increase in agreement was associated with higher contrast to noise ratios for smaller structures, but not for the larger globus pallidus segments. Additionally, control-analyses were performed to account for potential biases in manual parcellations by assessing semi-automatic parcellations. These results showed a higher consistency for structure volumes for 7T compared to optimized 3T which illustrates the importance of the use of isotropic voxels for 3D visualization of the surgical target area. Together these results indicate that 7T outperforms c3T as well as o3T given the constraints of a clinical setting.

3D magnetic resonance fingerprinting with quadratic RF phase

PURPOSE

To implement 3D magnetic resonance fingerprinting (MRF) with quadratic RF phase (qRF-MRF) for simultaneous quantification of T₁ , T₂ , ΔB₀ , and T 2 ∗ .

METHODS

3D MRF data with effective undersampling factor of 3 in the slice direction were acquired with quadratic RF phase patterns for T₁ , T₂ , and T 2 ∗ sensitivity. Quadratic RF phase encodes the off-resonance by modulating the on-resonance frequency linearly in time. Transition to 3D brings practical limitations for reconstruction and dictionary matching because of increased data and dictionary sizes. Randomized singular value decomposition (rSVD)-based compression in time and reduction in dictionary size with a quadratic interpolation method are combined to be able to process prohibitively large data sets in feasible reconstruction and matching times.

RESULTS

Accuracy of 3D qRF-MRF maps in various resolutions and orientations are compared to 3D fast imaging with steady-state precession (FISP) for T₁ and T₂ contrast and to 2D qRF-MRF for T 2 ∗ contrast and ΔB₀ . The precision of 3D qRF-MRF was 1.5-2 times higher than routine clinical scans. 3D qRF-MRF ΔB₀ maps were further processed to highlight the susceptibility contrast.

CONCLUSION

Natively co-registered 3D whole brain T₁ , T₂ , T 2 ∗ , ΔB₀ , and QSM maps can be acquired in as short as 5 min with 3D qRF-MRF.

Elimination of low-inversion-efficiency induced artifacts in whole-brain MP2RAGE using multiple RF-shim configurations at 7 T

The magnetization-prepared two-rapid-gradient-echo (MP2RAGE) sequence is used for structural T₁ -weighted imaging and T₁ mapping of the human brain. In this sequence, adiabatic inversion RF pulses are commonly used, which require the B₁⁺ magnitude to be above a certain threshold. Achieving this threshold in the whole brain may not be possible at ultra-high fields because of the short RF wavelength. This results in low-inversion regions especially in the inferior brain (eg cerebellum and temporal lobes), which is reflected as regions of bright signal in MP2RAGE images. This study aims at eliminating the low-inversion-efficiency induced artifacts in MP2RAGE images at 7 T. The proposed technique takes advantage of parallel RF transmission systems by splitting the brain into two overlapping slabs and calculating the complex weights of transmit channels (ie RF shims) on these slabs for excitation and inversion independently. RF shims were calculated using fast methods implemented in the standard workflow. The excitation RF pulse was designed to obtain slabs with flat plateaus and sharp edges. These slabs were joined into a single volume during the online image reconstruction. The two-slab strategy naturally results in a signal-to-noise ratio loss; however, it allowed the use of independent shims to make the B₁⁺ field exceed the adiabatic threshold in the inferior brain, eliminating regions of low inversion efficiency. Accordingly, the normalized root-mean-square errors in the inversion were reduced to below 2%. The two-slab strategy was found to outperform subject-specific k_T -point inversion RF pulses in terms of inversion error. The proposed strategy is a simple yet effective method to eliminate low-inversion-efficiency artifacts; consequently, MP2RAGE-based, artifact-free T₁ -weighted structural images were obtained in the whole brain at 7 T.

7 Tesla MRI Followed by Histological 3D Reconstructions in Whole-Brain Specimens

Post mortem magnetic resonance imaging (MRI) studies on the human brain are of great interest for the validation of in vivo MRI. It facilitates a link between functional and anatomical information available from MRI in vivo and neuroanatomical knowledge available from histology/immunocytochemistry. However, linking in vivo and post mortem MRI to microscopy techniques poses substantial challenges. Fixation artifacts and tissue deformation of extracted brains, as well as co registration of 2D histology to 3D MRI volumes complicate direct comparison between modalities. Moreover, post mortem brain tissue does not have the same physical properties as in vivo tissue, and therefore MRI approaches need to be adjusted accordingly. Here, we present a pipeline in which whole-brain human post mortem in situ MRI is combined with subsequent tissue processing of the whole human brain, providing a 3-dimensional reconstruction via blockface imaging. To this end, we adapted tissue processing procedures to allow both post mortem MRI and subsequent histological and immunocytochemical processing. For MRI, tissue was packed in a susceptibility matched solution, tailored to fit the dimensions of the MRI coil. Additionally, MRI sequence parameters were adjusted to accommodate T1 and T2^∗ shortening, and scan time was extended, thereby benefiting the signal-to-noise-ratio that can be achieved using extensive averaging without motion artifacts. After MRI, the brain was extracted from the skull and subsequently cut while performing optimized blockface imaging, thereby allowing three-dimensional reconstructions. Tissues were processed for Nissl and silver staining, and co-registered with the blockface images. The combination of these techniques allows direct comparisons across modalities.

Sharpness in motion corrected quantitative imaging at 7T

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R₁, R₂^* and Quantitative Susceptibility Maps and T₁-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

Enhanced POCS reconstruction for partial Fourier imaging in multi-echo and time-series acquisitions

PURPOSE

To improve partial Fourier (PF) imaging reconstruction in time-series or multi-echo acquisitions.

METHODS

Many PF methods use a phase estimate to restore Hermitian symmetry before filling missing k-space entries with measured data from the opposite half. This estimate is obtained from the symmetrically sampled, central part of k-space and its low-resolution results in artifacts near high-frequency phase effects (eg, tissue boundaries, vessels), limiting PF undersampling. Enhanced projection onto convex sets (POCS) uses full-resolution phase estimates and relies on alternating the half of k-space that is acquired in time series or multi-echo acquisitions. This enables full-resolution phase estimates to be calculated for each volume/echo, which are fed into the POCS framework. We apply enhanced POCS to high-resolution multi-echo FLASH and 3D-EPI functional MRI time-series data.

RESULTS

Reconstruction errors and their bias dramatically reduce compared with existing methods, without leading to temporal blurring in time-series acquisitions. This allows for higher PF acceleration factors at virtually no cost.

CONCLUSION

Enhanced POCS results in superior PF reconstructions. Furthermore, as the resolution of the phase estimate used for symmetry correction no longer depends on the PF factor, enhanced POCS is more robust against larger PF omission.