Magnetic Resonance Imaging

PurposeThis study aims to reduce Diffusion Tensor MRI (DT-MRI) scan time by minimizing diffusion-weighted measurements. Using machine learning, DT-MRI parameters are accurately estimated with just four tetrahedrally-arranged diffusion-encoded measurements, instead of the usual six or more. This significantly shortens scan duration and is particularly useful in ultra-low field (ULF) MRI studies and for non-compliant populations (e.g., children, the elderly, or those with movement disorders) where long scan times are impractical. MethodsTo improve upon a previous tetrahedral encoding approach, this study used a deep learning (DL) model to predict parallel and radial diffusivities and the principal eigenvector of the diffusion tensor with four tetrahedrally-arranged diffusion-weighted measurements. Synthetic data were generated for model training, covering a range of diffusion tensors with uniformly distributed eigenvectors and eigenvalues. Separate DL models were trained to predict diffusivities and principal eigenvectors, then evaluated on a digital phantom and in vivo data collected at 64 mT. ResultsThe DL models outperformed the previous tetrahedral encoding method in estimating diffusivities, fractional anisotropy, and principal eigenvectors, with significant improvements in ULF experiments, confirming the DL approachs feasibility in low SNR scenarios. However, the models had limitations when the tensors principal eigenvector aligned with the scanners axes ConclusionThe study demonstrates the potential of using DL to perform DT-MRI with only four directions in ULF environments, effectively reducing scan durations and addressing numerical instability seen in previous methods. These findings open new possibilities for ULF DT-MRI applications in research and clinical settings, particularly in pediatric neuroimaging

The Fluid And White matter Suppression (FLAWS) MRI sequence allows for the acquisition of multiple T1-weighted contrasts in a single sequence acquisition. However, its acquisition time is prohibitive for use in clinical practice when the k-space is linearly downsampled and reconstructed using the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique. This study proposes a FLAWS sequence optimization tailored to allow for the acquisition of FLAWS images with a Cartesian phyllotaxis k-space undersampling and compressed sensing (CS) reconstruction at 3T. The CS FLAWS sequence parameters were determined using a method previously employed to optimize FLAWS imaging at 1.5T and 7T. In-vivo experiments show that the proposed CS FLAWS optimization allows to reduce the FLAWS sequence acquisition time from 8 mins to 6 mins without decreasing the FLAWS image quality. In addition, this study demonstrates for the first time that T1-weighted imaging with low B1 sensitivity and T1 mapping can be performed with the FLAWS sequence at 3T for both GRAPPA and CS reconstructions. The FLAWS T1 mapping was validated using in-silico, in-vitro and in-vivo experiments with comparison against the inversion recovery turbo spin echo and MP2RAGE T1 mappings. These new results suggest that the recent advances in FLAWS imaging allow to combine the MP2RAGE imaging benefits (T1-weigthed imaging with low B1 sensitivity and T1 mapping) and with the previous version of FLAWS imaging benefits (multi T1-weighted contrast imaging) in a single 6 mins sequence acquisition. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

Intravoxel incoherent motion (IVIM) theory in MRI was proposed to account for the effect of vessel/capillary perfusion on the aggregate diffusion weighted MR signal. The prevalent IVIM modeling is based on equation-1: SI(b)/SI(0) = (1 -PF) x exp(-b x Dslow) + PF x exp(-b x Dfast) [1] where SI(b) and SI(0) denote the signal intensity of images acquired with the b-factor value of b and b=0 s/mm2, respectively. We recently reported that, for the liver and likely for other organs as well, IVIM modeling of the perfusion component is constrained by the diffusion component, with a reduced Dslow measure leading to artificially higher PF and Dfast measures. With higher b-value associated lower image signal of the targeted tissue, Euqation-1 is focused on describing the signal decay pattern along increasingly higher b-values by three IVIM parameters. Signal intensity at each b-value (i.e., SI(b)) is normalised by the signal intensity of b=0 image (i.e., SI(0)). We noted an apparent problem for Euqation-1. For example, if we want to compare the IVIM parameters of the normal liver parenchyma and a liver tumor, following Euqation-1 we will take the assumption that the SI(0) of the normal parenchyma and the tumor are the same and considered equally as 1 (or 100) for the biexponential decay modelling. However, this assumption is invalid for many scenarios. From our liver IVIM database of 27 healthy female subjects, we chose six of the youngest subjects (20-27 yrs) and six of the oldest subjects (58-71 yrs) and measured the signals of the liver and left erector spinae muscle on b=0 and 2 s/mm2 images. The results show, while there was no apparent difference of left erector spinae muscle signal among the young and elderly groups, the elderly groups liver SI(0) is approximately 20 % lower than that of young group. This difference skewed the ratios of various SI(b)/SI(0) and the followed IVIM parameter determination. The general trend is that lower liver SI(0) is associated with lower Dslow and higher PF and Dfast. If IVIM bi-exponential decay fitting starts from a very low non-zero b images (such as b=2 s/mm2 images), this problem persists. We performed an additional analysis of our IVIM database of five cirrhotic livers and the results show SI(b=2) of cirrhotic right liver is positively associated Dslow (Pearson r=0.687), and negatively associated with PF (Pearson r=-0.733). Though the examples we used in this letter are on liver aging and liver fibrosis, the points discussed are expected to be generalisable to other pathologies and to other organs.

Development of innovative non-invasive neuroimaging methods and biomarkers are critical for studying brain disease. In this work, we have developed a methodology to characterize the frequency responses and spatial localization of oscillations and movements of cerebrospinal fluid (CSF) flow in the human brain. Using 7 Tesla human MRI and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture CSF oscillations and movements. Physiological data was simultaneously collected and correlated with the 7T MR data. The primary components of CSF oscillations were identified using spectral analysis (with frequency bands identified around 0.3Hz, 1.2Hz and 2.4Hz) and were mapped spatially and temporally onto the MR image domain and temporally onto the physiological domain. The developed methodology shows a good consistency and repeatability (standard deviation of 0.052 and 0.078 for 0.3Hz and 1.2Hz bands respectively) in-vivo for potential brain dynamics and CSF flow and clearance studies.

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed. HighlightsO_LInovel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling. C_LIO_LIutilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron. C_LIO_LImatter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts. C_LIO_LIacquired myelin and iron maps may be utilized to monitor longitudinal disease progress. C_LI

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion MRI methods for detecting such effects. In this work, the sensitivities of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at baseline and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in water diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a sensitive single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

This work aims to characterize fiber-tract-specific orientation-dependent R2 from the seven callosal segments (CC1-CC7) based on human brain Connectome high-resolution DTI datasets. WM voxels from each segment were masked by the thresholds of FA and mode of anisotropy. Encoded in T2W images (b-value = 0), an orientation-dependent R2 profile was constructed based on voxel-wise fiber orientations and characterized by a "cone" model. This allowed [Formula], an orientation-dependent or anisotropic R2, to be separated from its orientation-independent or isotropic counterpart. Except for [Formula], no discernible differences were found for the fits between the two b-values. On average, [Formula] increased from 2.4{+/-}0.2 (1/s) to 3.2{+/-}0.3 (1/s) as the b-value increased. Furthermore, [Formula] showed an increasing trend from CC1 to CC7 and the open angle of the cone fluctuated around 65{degrees}. As usual, FA was found increasing from CC1 to CC7 and from a higher to a lower b-value. In conclusion, we have shown that fiber-tract-specific Anisotropic R2 profiles from the callosal segments can be characterized by a cone model. The proposed method offers a unique opportunity to reevaluate existing clinical DTI studies and optimize new ones for characterizing specific WM tracts in both healthy and diseased subjects.

The draining-vein bias of T2*-weighted sequences, like gradient echo echo-planar imaging (GRE-EPI), can limit the spatial specificity of functional MRI (fMRI). The underlying extravascular signal changes increase with field strength (B0) and the perpendicularity of draining veins to the main axis of B0, and are therefore particularly problematic at ultra-high field (UHF). In contrast, simulations showed that T2-weighted sequences are less affected by the draining-vein bias, depending on the amount of rephasing of extravascular signal. As large pial veins on the cortical surface follow the cortical folding tightly, their orientation can be approximated by the cortical orientation to [Formula]. In our work, we compare the influence of the cortical orientation to [Formula] on the resting-state fMRI signal of three sequences aiming to understand their macrovascular contribution. While 2D GRE-EPI and 3D GRE-EPI (both T2*-weighted) showed a high dependence on the cortical orientation to [Formula], especially on the cortical surface, this was not the case for 3D balanced steady-state free precession (bSSFP) (T2/T1-weighted). Here, a slight increase of orientation dependence was shown in depths closest to WM. And while orientation dependence decreased with increased distance to the veins for both EPI sequences, no change in orientation dependence was observed in bSSFP. This indicates the low macrovascular contribution to the bSSFP signal, making it a promising sequence for layer fMRI at UHF.

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b-values up to 17.5 ms/m2. The anisotropic Karger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median{+/-}interquartile range) 13{+/-}8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast ([&le;]20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Karger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups. Highlights- The Connectome 2.0 scanner equipped with 500 mT/m gradients enables high-sensitivity diffusion MRI for imaging exchange times in vivo - The global exchange time across the living human cortex was estimated to be about 13 ms on Connectome 2.0, faster than measured using a protocol compatible to Connectome 1.0 - Spatially varying exchange times were observed across the cortex - A GPU-accelerated tool was developed to speed up parameter estimation by 100 times - The narrow pulse approximation used in neurite exchange imaging does not affect estimation performance in high gradient performance MRI systems

PurposeLung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem. Material & MethodsConsidering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results. ResultsThe proposed system yielded mean Dice coefficients of 0.96{+/-}0.02 and 0.89{+/-}0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations. ConclusionsThe proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

Brain tissue microstructure is characterized by heterogeneous diffusivity and transversal relaxation rates. Standard diffusion MRI (dMRI) is acquired using a single echo time (TE) and only provides information about heterogeneous diffusivity in the underlying tissue. Combined relaxation diffusion MRI (rdMR) integrates dMRI with multiple TEs to probe the coupling between relaxation rate and diffusivity. This work introduces a method to model rdMRI data signals by characterizing the apparent relaxation rate related to dMRI with different b-values. The proposed approach can extrapolate dMRI signals to ultra-long or ultra-short TEs to increase or reduce signals from intra-neurite water to improve the characterization of neurite microstructure without solving multi-compartment models. The performance of the proposed method was examined using an in vivo dataset acquired from a clinical scanner to estimate neurite sizes.

The application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to its superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine the fMRI time-series derived at different echo times to improve the data quality. Here we evaluated three multi-echo combination schemes, i.e. optimal combination (T2*-weighted), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio (tCNR) weighted combination. For the first time, the effect of these multi-echo combinations on functional resting-state networks was assessed in the temporal and spatial domain, and compared to networks derived from the second echo (35 ms) functional images. Sixteen healthy volunteers were scanned during a 5 minutes resting-state fMRI session. After obtaining the networks, several temporal and spatial metrics were calculated for their time-series and spatial maps. Our results showed that, compared to the second echo network time-series, the Pearson correlation and root mean square error were the most consistent for the optimal combination time-series and the least with those derived from tSNR-weighted combination. The frequency analysis further suggested that the time-series from the tSNR-weighted combination method reduced hardware- and physiological-related artifacts as reflected by the reduced power for the associated frequencies in almost all networks. Moreover, the spatial stability and extent of the networks significantly increased after multi-echo combination, primarily for the optimal combination, followed by the tSNR-weighted combination. The performance of the tCNR-weighted combination lacked robustness and instead varied remarkedly between resting-state networks in both the temporal and spatial domain. The results highlight the benefits of multi-echo sequences on resting-state networks as well as the importance of adjusting the choice of multi-echo combination method to the research question and domain of interest.

PurposeTo estimate dynamic off-resonance due to vigorous body motion in accelerated fMRI of awake behaving non-human primates (NHPs) using the standard EPI 3-line navigator, in order to attenuate the effects of time-varying off-resonance on the reconstruction. MethodsIn NHP fMRI the animals head is usually head-posted, and the dynamic off-resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off-resonance at each frame can be approximated as a spatially linear perturbation of the off-resonance at a reference frame, and is manifested as a relative linear shift in k-space. Using GRAPPA operators, we estimated these shifts by comparing the 3-line navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data. ResultsThe proposed method is shown to successfully estimate low-spatial order dynamic off-resonance perturbations, including induced linear off-resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multi-slice in vivo acquisitions in awake-behaving NHPs. ConclusionA method is proposed that does not need any sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state-of-the-art human imaging.

PurposeHead-motion tracking and correction remains a key area of research in MRI, but the lack of rigorous and standardized evaluation approaches hinders their optimization and comparison. We introduce an in-vivo framework for assessing the accuracy of intra-MRI head motion tracking, and demonstrates its effectiveness by comparing two methods based on a markerless optical system (MOS) and a fat signal navigator (FatNav). MethodsSix participants underwent 3T brain MRI using a T1-weighted (T1w) pulse-sequence with a fat- navigator module. Participants performed head-rotations of 2{degrees} or 4{degrees}, each visually guided by MOS feedback around a single primary axis (X or Z). MOS and FatNav estimations were evaluated against rigid-registration of T1w-images, as gold-standard, across seven different head positions. ResultsThe proposed approach revealed that MOS outperforms FatNav in estimating translation and large head rotations (2-4{degrees}), while FatNav shows better accuracy for subtle rotations. Image quality assessments following correction for three head rotations (rightward, upward, and leftward) confirmed that MOS outperformed FatNav in restoring image fidelity, as evidenced by the higher Structural Similarity Index, Peak Signal-to-Noise Ratio, and Focus Measure. Unlike the traditional image quality- based comparisons, the proposed framework demonstrated sensitivity to subtle improvements in FatNav performance, achieved by applying a neck mask to the fat-navigator images. ConclusionThe proposed framework enabled a precise in-vivo evaluation and comparison of MOS and FatNav for head-motions estimation. It was sufficiently sensitive to reveal a slight improvement in FatNav performance when neck was masked in fat-navigator images. In parallel, the conventional image quality-based approach confirmed the superior performance of MOS in restoring T1W image quality, though it did not capture the improvement achieved by FatNav with neck-masking. Together, these two complementary approaches provide a comprehensive assessment of both head-motions estimation and correction in MRI.

PurposeThis study aimed to develop a new 3D dual-echo rosette k-space trajectory, specifically for ultra-short echo time (UTE) magnetic resonance imaging (MRI) applications. The direct imaging of the myelin bilayer, which has ultra-short transverse relaxation time (uT2), was acquired to test the performance of the proposed UTE sequence. Theory and MethodsThe rosette trajectory was developed based on rotations of a petal-like pattern in the kx-ky plane, with oscillated extensions in kz-direction for 3D coverage. Five healthy volunteers were recruited and underwent ten dual-echo 3D rosette UTE scans with various echo times (TEs). Dual-exponential complex model fitting was performed on the magnitude data to separate uT2 signals, with the output of uT2 fraction, uT2 value, and long T2 value. ResultsThe reconstructed images signal contrast between whiate matter (WM) and grey matter (GM) increased with longer TEs. The WM regions had higher uT2 fraction values than GM (10.9%{+/-}1.9% vs. 5.7%{+/-}2.4%). The uT2 value was approximately 0.15 milliseconds in WM. ConclusionThe higher uT2 fraction value in WM compared to GM demonstrated the ability of the proposed sequence to capture rapidly decaying signals.

In this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.

BackgroundLow-field open magnetic resonance imaging (MRI) systems, typically operating at magnetic field strengths below 1 Tesla, has greatly expanded the accessibility of MRI technology to meet a wide range of patient needs. However, the inherent challenges of low-field MRI, such as limited signal-to-noise ratios and limited availability of dedicated radiofrequency (RF) coils, have prompted the need for innovative coil designs that can improve imaging quality and diagnostic capabilities. PurposeIn response to these challenges, we introduce the coupled stack-up volume coil, a novel RF coil design that addresses the shortcomings of conventional birdcage in the context of low-field open MRI. MethodsThe proposed coupled stack-up volume coil design utilizes a unique architecture that optimizes both transmit/receive efficiency and RF field homogeneity and offers the advantage of a simple design and construction, making it a practical and feasible solution for low-field MRI applications. This paper presents a comprehensive exploration of the theoretical framework, design considerations, and experimental validation of this innovative coil design. ResultsWe demonstrate the superior performance of the coupled stack-up volume coil in achieving 47.7% higher transmit/receive efficiency and 68% more uniform magnetic field distribution compared to traditional birdcage coils in electromagnetic simulations. Bench tests results show that the B1 field efficiency of coupled stack-up volume coil is 57.3% higher compared with that of conventional birdcage coil. ConclusionsThe proposed coupled stack-up volume coil outperforms the conventional birdcage coil in terms of B1 efficiency, imaging coverage, and low-frequency operation capability. This design provides a robust and simple solution to low-field MR RF coil design.

The high temporal resolution of echo-planar imaging (EPI) has driven its widespread use in functional MRI (fMRI), and recent advancements in EPI have enabled the mapping of cortical layer-specific functional activities. Notwithstanding these technical innovations, geometric distortion correction in EPI remains essential to ensure accurate functional mapping onto anatomical references. A common approach for distortion correction involves the acquisition of an additional scan, with the phase-encoding direction reversed. However, this extra scan necessitates redundant acquisitions of calibration scans, significantly increasing acquisition time and energy deposition. To address this concern, this work presents a novel EPI scheme that simultaneously acquires both the original and reversed phase-encoding data within a single fMRI session. Furthermore, despite the widespread use of distortion correction, methods for qualitative and quantitative evaluation of its impact on submillimetre fMRI analysis have remained largely unexplored. This study acquires submillimetre visual fMRI data (0.73 x 0.73 mm2) at 7T using the suggested EPI scheme and evaluates the impact of distortion correction through various metrics proposed here, including spatial resolution, co-registration accuracy, functional mapping fidelity, and distribution of functional voxels. Our fMRI acquisition scheme effectively reduced redundant acquisition time and total energy deposition to subjects. Distortion-corrected fMRI data demonstrated significant improvements in co-registration with anatomical scans, thereby enhancing functional mapping accuracy. The improvements were achieved without significant degradation of spatial resolution or alteration of the functional activation distribution. These findings were verified through qualitative and quantitative assessments, highlighting the effectiveness of distortion correction in submillimetre fMRI. HighlightsO_LIWe present a novel fMRI scheme that effectively acquires reversed-PE EPI data C_LIO_LIOur fMRI scheme substantially reduces extra acquisition time and energy deposition C_LIO_LIThe impact of EPI distortion correction is evaluated for submillimetre fMRI C_LIO_LIWe propose diverse qualitative and quantitative metrics, enabling thorough analysis C_LIO_LIDistortion correction demonstrates its effectiveness for high-fidelity fMRI mapping C_LI

Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging (MRI) technique that supports data acquisition at spatial and temporal resolutions suitable for brain function studies. However, susceptibility artifacts are unavoidable distortions in EPI. These distortions are especially strong in high spatial resolution images and can lead to misrepresentation of brain function in fMRI experiments. A common method for correcting susceptibility artifacts is based on a registration scheme which uses two EPI images acquired using identical sequences but with inverse phase-encoding (PE) directions. In this paper, we present a new method for correcting susceptibility artifacts by integrating a T1-weighted (T1w) image into the inverse-PE based registration, since the T1w structural image is considered as a ground-truth measurement of the brain. Furthermore, the T1w image is used as a criterion to select automatically the regularization parameters of the proposed image registration. Evaluations on two high-resolution EPI-fMRI datasets, acquired at 3T and 7T scanners, confirm that the proposed method provides more robust and sharper corrections and runs faster compared with two other state-of-the-art inverse-PE based susceptibility artifact correction methods, i.e. HySCO and TOPUP.

PurposeWe present an observational study involving free-breathing short-scan-time dynamic MRI (dMRI) method that can be routinely used for computing dynamic lung volumes accurately. Materials and Methods(i) Full-resolution free-breathing sagittally-acquired 2D dMRI scans are gathered from 45 normal children via True-FISP sequence. Sparse dMRI (s-dMRI) scans are simulated from these datasets by subsampling in the spatio-temporal domains via a limited number NSS of selected sagittal locations and TSS of time instances (respectively, NFS and TFS for full scan). (ii) A 4D image is constructed from both full and sparse scans. Lungs are segmented from 4D image, and their volumes from full (VF) and sparse dMRI (VS) scans are computed. (iii) A regression model is fit for VF as a function of VS on a training set, and the full-resolution volume VP predicted by the model is estimated from VS. (iv) The deviation of VP from VF is analyzed on both synthesized sparse dMRI scans from a separate full-resolution test set and actual s-dMRI scans prospectively acquired from 10 normal children. ResultsWith NSS=5 (per lung) and TSS=40, the deviation of VP from VF was [~]2% with a total scan-time of [~]9 min (45-60 min for the full scan with NFS=15-22 (per lung) and TFS=80). These metrics become 0.4%, and <20 min for s-dMRI with NSS=15-22 (per lung) and TSS=40. Conclusions-dMRI is a practical approach for computing dynamic lung volumes that can be used routinely with no radiation concern, especially on patients who cannot tolerate long scan times.