A correction algorithm for improved magnetic field monitoring with distal field probes.

PURPOSE: A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations. METHODS: The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed. RESULTS: The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies. CONCLUSION: The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.

Single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing.

PURPOSE: The expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI. Here, we investigate the extent that ℓ 1 $$ {\ell}_1 $$ -wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade-offs between spatial resolution, readout time and SNR for single-shot spiral DWI at 7T. The reconstructions were performed using our open-source graphics processing unit-enabled reconstruction toolbox, "MatMRI," that allows inclusion of the different components of the expanded encoding model, with or without CS. METHODS: In vivo accelerated single-shot spirals were acquired with five acceleration factors (R) (2×-6×) and three in-plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS-based regularization on image quality when compared to a known ground truth. RESULTS: In vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean-squared error) over the range of R investigated. CONCLUSION: The expanded encoding model and CS regularization are complementary tools for single-shot spiral diffusion MRI, which enables both higher spatial resolutions and higher R.

Optimization in the space domain for density compensation with the nonuniform FFT.

The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced Fourier samples in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

Estimation of free water-corrected microscopic fractional anisotropy.

Water diffusion anisotropy MRI is sensitive to microstructural changes in the brain that are hallmarks of various neurological conditions. However, conventional metrics like fractional anisotropy are confounded by neuron fiber orientation dispersion, and the relatively low resolution of diffusion-weighted MRI gives rise to significant free water partial volume effects in many brain regions that are adjacent to cerebrospinal fluid. Microscopic fractional anisotropy is a recent metric that can report water diffusion anisotropy independent of neuron fiber orientation dispersion but is still susceptible to free water contamination. In this paper, we present a free water elimination (FWE) technique to estimate microscopic fractional anisotropy and other related diffusion indices by implementing a signal representation in which the MRI signal within a voxel is assumed to come from two distinct sources: a tissue compartment and a free water compartment. A two-part algorithm is proposed to rapidly fit a set of diffusion-weighted MRI volumes containing both linear- and spherical-tensor encoding acquisitions to the representation. Simulations and in vivo acquisitions with four healthy volunteers indicated that the FWE method may be a feasible technique for measuring microscopic fractional anisotropy and other indices with greater specificity to neural tissue characteristics than conventional methods.

A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla.

Multimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate microstructural changes in the healthy mouse brain. The dataset consists of structural T2-weighted imaging, magnetization transfer ratio and saturation imaging, and advanced quantitative diffusion MRI (dMRI) methods. The dMRI methods include oscillating gradient spin echo (OGSE) dMRI and microscopic anisotropy (µA) dMRI, which provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The technical skills required to analyze microstructural MRI data are complex and include MRI sequence development, acquisition, and computational neuroimaging expertise. Here, we share unprocessed and preprocessed data, and scalar maps of quantitative MRI metrics. We envision utility of this dataset in the microstructural MRI field to develop and test biophysical models, methods that model temporal brain dynamics, and registration and preprocessing pipelines.

High-resolution, respiratory-resolved coronary MRA using a Phyllotaxis-reordered variable-density 3D cones trajectory.

PURPOSE: To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory. METHODS: To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. RESULTS: In initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). CONCLUSION: We have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.

Tuned bipolar oscillating gradients for mapping frequency dispersion of diffusion kurtosis in the human brain.

PURPOSE: Oscillating gradient spin-echo (OGSE) sequences have demonstrated an ability to probe time-dependent microstructural features, although they often suffer from low SNR due to increased TEs. In this work we introduce frequency-tuned bipolar (FTB) gradients as a variation of oscillating gradients with reduced TE and demonstrate their utility by mapping the frequency dispersion of kurtosis in human subjects. METHODS: An FTB oscillating gradient waveform is presented that provides encoding of 1.5 net oscillation periods, thereby reducing the TE of the acquisition. Simulations were performed to determine an optimal protocol based on the SNR of kurtosis frequency dispersion-defined as the difference in kurtosis between pulsed and oscillating gradient acquisitions. Healthy human subjects were scanned at 7T using pulsed gradient and an optimized 23 Hz FTB protocol, which featured a maximum b-value of 2500 s/mm2 . In addition, to directly compare existing methods, measurements using traditional cosine OGSE were also acquired. RESULTS: FTB oscillating gradients demonstrated equivalent frequency-dependent diffusion measurements compared with cosine-modulated OGSE while enabling a significant reduction in TE. Optimization and in vivo results suggest that FTB gradients provide increased SNR of kurtosis dispersion maps compared with traditional cosine OGSE. The optimized FTB gradient protocol demonstrated consistent reductions in apparent kurtosis values and increased diffusivity in generated frequency dispersion maps. CONCLUSIONS: This work presents an alternative to traditional cosine OGSE sequences, enabling more time-efficient acquisitions of frequency-dependent diffusion quantities as demonstrated through in vivo kurtosis frequency dispersion maps.

Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms.

Purpose: To introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterise the performance of diffusion magnetic resonance imaging (MRI) models and representations in the presence of orientation dispersion. Methods: An extension to an open-source 3D printing package was created to produce a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A ground truth map of that phantom's crossing angles and/or arc radius was registered to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric. Results: The mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle but increased with fibre curvature. Axial diffusivity (AD) decreased with increasing crossing angle. DKI's diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI's intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was correlated to crossing angle. Bingham-NODDI's intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODI P ) was also correlated to crossing angle and its secondary ODI (ODI S ) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle. Conclusion: Inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. The dependence of several representations and models on fibre dispersion/crossing was investigated. As expected, Bingham-NODDI was best able to characterise planar fibre dispersion in the phantoms.

Test-Retest Reproducibility of In Vivo Magnetization Transfer Ratio and Saturation Index in Mice at 9.4 Tesla.

BACKGROUND: Magnetization transfer saturation (MTsat) imaging was developed to reduce T1 dependence and improve specificity to myelin, compared to the widely used MT ratio (MTR) approach, while maintaining a feasible scan time. As MTsat imaging is an emerging technique, the reproducibility of MTsat compared to MTR must be evaluated. PURPOSE: To assess the test-retest reproducibility of MTR and MTsat in the mouse brain at 9.4 T and calculate sample sizes potentially required to detect effect sizes ranging from 6% to 14%. STUDY TYPE: Prospective. SUBJECTS: Twelve healthy C57Bl/6 mice. FIELD STRENGTH/SEQUENCE: 9.4 T; magnetization transfer imaging using FLASH-3D Gradient Echo; T2-weighted TurboRARE spin echo. ASSESSMENT: All mice were scanned at two timepoints (5 days apart). MTR and MTsat maps were analyzed using mean region-of-interest (ROIs: corpus callosum [CC], internal capsule [IC], hippocampus [HC], cortex [CX], and thalamus [TH]), and whole brain voxel-wise analysis. STATISTICAL TESTS: Bland-Altman plots were used to assess biases between test-retest measurements. Test-retest reproducibility was evaluated via between and within-subject coefficients of variation (bsCV and wsCV, respectively). Sample sizes required were calculated (significance level: 95%; power: 80%), given effect sizes ranging from 6% to 14%, using both between and within-subject approaches. Results were considered statistically significant at P ≤ 0.05. RESULTS: Bland-Altman plots showed negligible biases between test-retest sessions (MTR: 0.0009; MTsat: 0). ROI-based and voxel-wise CVs revealed high reproducibility for both MTR (ROI-bsCV/wsCV: CC-4.5/2.8%; IC-6.1/5.2%; HC-5.7/4.6%; CX-5.1/2.3%; TH-7.4/4.9%) and MTsat (ROI-bsCV/wsCV: CC-6.3/4.8%; IC-7.3/5.1%; HC-9.5/6.4%; CX-6.7/6.5%; TH-7.2/5.3%). With a sample size of 6, changes on the order of 15% could be detected in MTR and MTsat, both between and within subjects, while smaller changes (6%-8%) required sample sizes of 10-15 for MTR, and 15-20 for MTsat. DATA CONCLUSION: MTsat exhibited comparable reproducibility to MTR, while providing sensitivity to myelin with less T1 dependence than MTR. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Integration of an RF coil and commercial field camera for ultrahigh-field MRI.

PURPOSE: To develop an RF coil with an integrated commercial field camera for ultrahigh field (7T) neuroimaging. The RF coil would operate within a head-only gradient coil and be subject to the corresponding design constraints. The RF coil can thereafter be used for subject-specific correction of k-space trajectories-notably in gradient-sensitive sequences such as single-shot spiral imaging. METHODS: The transmit and receive performance was evaluated before and after the integration of field probes, whereas field probes were evaluated when in an optimal configuration external to the coil and after their integration. Diffusion-weighted EPI and single-shot spiral acquisitions were employed to evaluate the efficacy of correcting higher order field perturbations and the consequent effect on image quality. RESULTS: Field probes had a negligible effect on RF-coil performance, including the transmit efficiency, transmit uniformity, and mean SNR over the brain. Modest reductions in field-probe signal lifetimes were observed, caused primarily by nonidealities in the gradient and shim fields of the head-only gradient coil at the probe positions. The field-monitoring system could correct up to second-order field perturbations in single-shot spiral imaging. CONCLUSION: The integrated RF coil and field camera was capable of concurrent-field monitoring within a 7T head-only scanner and facilitated the subsequent correction of k-space trajectories during spiral imaging.

Characterization and correction of time-varying eddy currents for diffusion MRI.

PURPOSE: To develop and test a method for reducing artifacts due to time-varying eddy currents in oscillating gradient spin-echo (OGSE) diffusion images. METHODS: An in-house algorithm (TVEDDY), that for the first time retrospectively models eddy current decay, was tested on pulsed gradient spin echo and OGSE brain images acquired at 7 T. Image pairs were acquired using opposite polarity diffusion gradients. A three-parameter exponential decay model (two amplitudes and a time constant) was used to characterize and correct eddy current distortions by minimizing the intensity difference between image pairs. Correction performance was compared with conventional correction methods by evaluating the mean squared error (MSE) between diffusion-weighted images acquired with opposite polarity diffusion gradients. As a ground-truth comparison, images were corrected using field dynamics up to third order in space, measured using a field monitoring system. RESULTS: Time-varying eddy currents were observed for OGSE, which introduced blurring that was not reduced using the traditional approach but was diminished considerably with TVEDDY and field monitoring-informed model-based reconstruction. No MSE difference was observed between the conventional approach and TVEDDY for pulsed gradient spin echo, but for OGSE TVEDDY resulted in significantly lower MSE than the conventional approach. The field-monitoring reconstruction had the lowest MSE for both pulsed gradient spin echo and OGSE. CONCLUSION: This work establishes that it is possible to estimate time-varying eddy currents from the actual diffusion data, which provides substantial image-quality improvements for gradient-intensive diffusion MRI acquisitions like OGSE.

Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla.

BACKGROUND AND PURPOSE: Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (µA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and µA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations. METHODS: Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and µA dMRI protocols. Metrics investigated included µA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and µA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes. RESULTS: Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for µA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%). CONCLUSION: Most of the µA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10-15), µA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4-8%) and moderate changes (> 6%), respectively.

Utilizing the Wavelet Transform's Structure in Compressed Sensing.

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.

Tractography in Curvilinear Coordinates.

Coordinate invariance of physical laws is central in physics, it grants us the freedom to express observations in coordinate systems that provide computational convenience. In the context of medical imaging there are numerous examples where departing from Cartesian to curvilinear coordinates leads to ease of visualization and simplicity, such as spherical coordinates in the brain's cortex, or universal ventricular coordinates in the heart. In this work we introduce tools that enhance the use of existing diffusion tractography approaches to utilize arbitrary coordinates. To test our method we perform simulations that gauge tractography performance by calculating the specificity and sensitivity of tracts generated from curvilinear coordinates in comparison with those generated from Cartesian coordinates, and we find that curvilinear coordinates generally show improved sensitivity and specificity compared to Cartesian. Also, as an application of our method, we show how harmonic coordinates can be used to enhance tractography for the hippocampus.

Design and characterization of a 3D-printed axon-mimetic phantom for diffusion MRI.

PURPOSE: To introduce and characterize inexpensive and easily produced 3D-printed axon-mimetic diffusion MRI phantoms in terms of pore geometry and diffusion kurtosis imaging metrics. METHODS: Phantoms were 3D-printed with a composite printing material that, after the dissolution of the polyvinyl alcohol, exhibits microscopic fibrous pores. Confocal microscopy and synchrotron phase-contrast micro-CT imaging were performed to visualize and assess the pore sizes. Diffusion MRI scans of four identical phantoms and phantoms with varying print parameters in water were performed at 9.4 T. Diffusion kurtosis imaging was fit to both data sets and used to assess the reproducibility between phantoms and effects of print parameters on diffusion kurtosis imaging metrics. Identical scans were performed 25 and 76 days later, to test their stability. RESULTS: Segmentation of pores in three microscopy images yielded a mean, median, and SD of equivalent pore diameters of 7.57 µm, 3.51 µm, and 12.13 µm, respectively. Phantoms had T1 /T2 = 2 seconds/180 ms, and those with identical parameters showed a low coefficient of variation (~10%) in mean diffusivity (1.38 × 10-3 mm2 /s) and kurtosis (0.52) metrics and radial diffusivity (1.01 × 10-3 mm2 /s) and kurtosis (1.13) metrics. Printing temperature and speed had a small effect on diffusion kurtosis imaging metrics (< 16%), whereas infill density had a larger and more variable effect (> 16%). The stability analysis showed small changes over 2.5 months (< 7%). CONCLUSION: Three-dimension-printed axon-mimetic phantoms can mimic the fibrous structure of axon bundles on a microscopic scale, serving as complex, anisotropic diffusion MRI phantoms.

Automatic determination of the regularization weighting for wavelet-based compressed sensing MRI reconstructions.

PURPOSE: To present a method that automatically, rapidly, and in a noniterative manner determines the regularization weighting for wavelet-based compressed sensing reconstructions. This method determines level-specific regularization weighting factors from the wavelet transform of the image obtained from zero-filling in k-space. METHODS: We compare reconstruction results obtained by our method, λauto , to the ones obtained by the L-curve, λLcurve , and the minimum NMSE, λNMSE . The comparisons are done using in vivo data; then, simulations are used to analyze the impact of undersampling and noise. We use NMSE, Pearson's correlation coefficient, high-frequency error norm, and structural similarity as reconstruction quality indices. RESULTS: Our method, λauto , provides improved reconstructed image quality to that obtained by λLcurve regardless of undersampling or SNR and comparable quality to λNMSE at high SNR. The method determines the regularization weighting prospectively with negligible computational time. CONCLUSION: Our main finding is an automatic, fast, noniterative, and robust procedure to determine the regularization weighting. The impact of this method is to enable prospective and tuning-free wavelet-based compressed sensing reconstructions.

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit.

BACKGROUND: Diffusion kurtosis imaging (DKI) quantifies the non-Gaussian diffusion of water within tissue microstructure. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes. PURPOSE: To assess the reproducibility in whole-brain high-resolution DKI at varying b-values. STUDY TYPE: Retrospective. SUBJECTS AND PHANTOMS: In all, 44 individuals from the test-retest Human Connectome Project (HCP) database and 12 3D-printed phantoms. FIELD STRENGTH/SEQUENCE: Diffusion-weighted multiband echo-planar imaging sequence at 3T and 9.4T. magnetization-prepared rapid acquisition gradient echo at 3T for in vivo structural data only. ASSESSMENT: From HCP data with b-values = 1000, 2000, 3000 s/mm2 (dataset A), two additional datasets with b-values = 1000, 3000 s/mm2 (dataset B) and b-values = 1000, 2000 s/mm2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in white matter (WM) and gray matter (GM) based on whole-brain and regions of interest (ROIs). STATISTICAL TESTS: DKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate the presence of systematic biases. Repeated measures analysis of variance was used for statistical comparison. RESULTS: Datasets A and B exhibited lower DKI CoVs (<20%) compared to C (<50%) in both WM and GM ROIs (all P < 0.05). This effect varies between DKI and DTI parameters (P < 0.005). Whole-brain fitting residuals were consistent across datasets (P > 0.05), but lower residuals in dataset B were detected for the WM ROIs (P < 0.001). A similar trend was observed for the phantom data CoVs (<7.5%) at varying fiber orientations for datasets A and B. Finally, dataset C was characterized by higher residuals across the different fiber crossings (P < 0.05). DATA CONCLUSION: The study demonstrates that high reproducibility can still be achieved within a reasonable scan time, specifically dataset B, supporting the potential of DKI for aiding clinical tools in detecting microstructural changes.

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI.

We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS: 3D iNAVs reconstructed using the DL model-based approach and conventional l1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.

Diffusion time dependency along the human corpus callosum and exploration of age and sex differences as assessed by oscillating gradient spin-echo diffusion tensor imaging.

Conventional diffusion imaging uses pulsed gradient spin echo (PGSE) waveforms with diffusion times of tens of milliseconds (ms) to infer differences of white matter microstructure. The combined use of these long diffusion times with short diffusion times (<10 â€‹ms) enabled by oscillating gradient spin echo (OGSE) waveforms can enable more sensitivity to changes of restrictive boundaries on the scale of white matter microstructure (e.g. membranes reflecting the axon diameters). Here, PGSE and OGSE images were acquired at 4.7 â€‹T from 20 healthy volunteers aged 20-73 years (10 males). Mean, radial, and axial diffusivity, as well as fractional anisotropy were calculated in the genu, body and splenium of the corpus callosum (CC). Monte Carlo simulations were also conducted to examine the relationship of intra- and extra-axonal radial diffusivity with diffusion time over a range of axon diameters and distributions. The results showed elevated diffusivities with OGSE relative to PGSE in the genu and splenium (but not the body) in both males and females, but the OGSE-PGSE difference was greater in the genu for males. Females showed positive correlations of OGSE-PGSE diffusivity difference with age across the CC, whereas there were no such age correlations in males. Simulations of radial diffusion demonstrated that for axon sizes in human brain both OGSE and PGSE diffusivities were dominated by extra-axonal water, but the OGSE-PGSE difference nonetheless increased with area-weighted outer-axon diameter. Therefore, the lack of OGSE-PGSE difference in the body is not entirely consistent with literature that suggests it is composed predominantly of axons with large diameter. The greater OGSE-PGSE difference in the genu of males could reflect larger axon diameters than females. The OGSE-PGSE difference correlation with age in females could reflect loss of smaller axons at older ages. The use of OGSE with short diffusion times to sample the microstructural scale of restriction implies regional differences of axon diameters along the corpus callosum with preliminary results suggesting a dependence on age and sex.