Showing papers in "Magnetic Resonance in Medicine in 2019"

Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Abstract: Purpose To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data Theory Robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data This enables nonlinear estimation of missing k-space lines from acquired k-space data with improved noise resilience, as opposed to conventional linear k-space interpolation-based methods, such as GRAPPA, which are based on linear convolutional kernels Methods The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm The neural network contains 3 layers of convolutional operators, with 2 of these including nonlinear activation functions The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo data sets Results Phantom imaging shows that the proposed RAKI method outperforms GRAPPA at high (≥4) acceleration rates, both visually and quantitatively Quantitative cardiac imaging shows improved noise resilience at high acceleration rates (rate 4:23% and rate 5:48%) over GRAPPA The same trend of improved noise resilience is also observed in high-resolution brain imaging at high acceleration rates Conclusion The RAKI method offers a training database-free deep learning approach for MRI reconstruction, with the potential to improve many existing reconstruction approaches, and is compatible with conventional data acquisition protocols

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Abstract: Proton MRS (1 H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0 ) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

Assessment of the generalization of learned image reconstruction and the potential for transfer learning

Abstract: Purpose Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. Methods Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Results Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. Conclusion This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

Abstract: Purpose Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. Methods This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. Results For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). Conclusions Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.

Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease

Abstract: Purpose Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real-time data in patients with congenital heart disease (CHD). Methods A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real-time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real-time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. Results It was feasible to train a CNN to remove artifact from highly undersampled radial real-time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. Conclusion This article has demonstrated the potential for the use of a CNN for reconstruction of real-time radial data within the clinical setting. Clinical measures of ventricular volumes using real-time data with CNN reconstruction are not statistically significantly different from gold-standard, cardiac-gated, breath-hold techniques.

Magnetic resonance fingerprinting: a technical review.

Abstract: Multiparametric quantitative imaging is gaining increasing interest due to its widespread advantages in clinical applications. Magnetic resonance fingerprinting is a recently introduced approach of fast multiparametric quantitative imaging. In this article, magnetic resonance fingerprinting acquisition, dictionary generation, reconstruction, and validation are reviewed.

MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping.

Abstract: Purpose To develop and evaluate a novel deep learning-based image reconstruction approach called MANTIS (Model-Augmented Neural neTwork with Incoherent k-space Sampling) for efficient MR parameter mapping. Methods MANTIS combines end-to-end convolutional neural network (CNN) mapping, incoherent k-space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k-space and estimated parameter maps to ensure that the parameter maps produced synthesized k-space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T2 mapping of the knee at different acceleration rates and was compared with 2 other CNN mapping methods and conventional sparsity-based iterative reconstruction approaches. Global quantitative assessment and regional T2 analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS. Results MANTIS achieved high-quality T2 mapping at both moderate (R = 5) and high (R = 8) acceleration rates. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors (normalized root mean square error of 6.1% for R = 5 and 7.1% for R = 8) and higher similarity (structural similarity index of 86.2% at R = 5 and 82.1% at R = 8) to the reference in the T2 estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2-step CNN method. Conclusion The MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.

Echo planar time-resolved imaging (EPTI).

Abstract: Purpose To develop an efficient distortion- and blurring-free multi-shot EPI technique for time-resolved multiple-contrast and/or quantitative imaging. Methods EPI is a commonly used sequence but suffers from geometric distortions and blurring. Here, we introduce a new multi-shot EPI technique termed echo planar time-resolved imaging (EPTI), which has the ability to rapidly acquire distortion- and blurring-free multi-contrast data set. The EPTI approach performs encoding in ky -t space and uses a new highly accelerated spatio-temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0 -informed parallel imaging reconstruction, hundreds of "time-resolved" distortion- and blurring-free images at different TEs across the EPI readout window can be created at sub-millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self-estimation and correction of shot-to-shot B0 variations was developed. Simultaneous multi-slice acquisition was also incorporated to further improve the acquisition efficiency. Results We evaluated EPTI under varying simulated acceleration factors, B0 -inhomogeneity, and shot-to-shot B0 variations to demonstrate its ability to provide distortion- and blurring-free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient- and spin-echo EPTI for quantitative mapping of T2 , T2 * , proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole-brain in 28 s (0.8 s/slice), and (2) a gradient-echo EPTI, for multi-echo and quantitative T2 * fMRI at 2 × 2 × 3 mm3 whole-brain at a 3.3 s temporal resolution. Conclusion EPTI is a new approach for multi-contrast and/or quantitative imaging that can provide fast acquisition of distortion- and blurring-free images at multiple TEs.

Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM

Abstract: Purpose To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements. Methods Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data. Results The experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time. Conclusions The results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient-specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework.

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.

Abstract: PURPOSE To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. METHODS With a combination of data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling-augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accelerated knee imaging and liver imaging using a Cartesian trajectory and a golden-angle radial trajectory, respectively. Quantitative metrics were used to assess its performance against different references. The feasibility of SANTIS in reconstructing dynamic contrast-enhanced images was also demonstrated using transfer learning. RESULTS Compared to conventional reconstruction that exploits image sparsity, SANTIS achieved consistently improved reconstruction performance (lower errors and greater image sharpness). Compared to standard learning-based methods without sampling augmentation (e.g., training with a fixed undersampling pattern), SANTIS provides comparable reconstruction performance, but significantly improved robustness, against sampling pattern discrepancy. SANTIS also achieved encouraging results for reconstructing liver images acquired at different contrast phases. CONCLUSION By extensively varying undersampling patterns, the sampling-augmented training strategy in SANTIS can remove undersampling artifacts more robustly. The novel concept behind SANTIS can particularly be useful for improving the robustness of deep learning-based image reconstruction against discrepancy between training and inference, an important, but currently less explored, topic.

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

Abstract: PURPOSE We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization. METHODS A convolutional neural network (CNN) trained to remove motion artifacts from 2D T2 -weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model-based data-consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line-by-line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model-based image reconstruction. The method is tested in simulations and in vivo motion experiments of in-plane motion corruption. RESULTS While the convolutional neural network alone provides some motion mitigation (at the expense of introduced blurring), allowing it to guide the iterative joint-optimization both improves the search convergence and renders the joint-optimization separable. This enables rapid mitigation within shots in addition to between shots. For 2D in-plane motion correction experiments, the result is a significant reduction of both image space root mean square error in simulations, and a reduction of motion artifacts in the in vivo motion tests. CONCLUSION The separability and convergence improvements afforded by the combined convolutional neural network+model-based method shows the potential for meaningful postacquisition motion mitigation in clinical MRI.

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

Abstract: Purpose Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion-free reacquisition can become time- and cost-intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a-priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a-priori knowledge, this problem is still challenging. Methods We propose the use of deep learning frameworks to perform retrospective motion correction in a reference-free setting by learning from pairs of motion-free and motion-affected images. For this image-to-image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics. Results We observed that generative adversarial network-based motion correction is feasible producing near-realistic motion-free images as confirmed by blinded subjective image quality assessment. Generative adversarial network-based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error 0.8, normalized mutual information >0.9). Conclusion Deep learning-based retrospective restoration of motion artifacts is feasible resulting in near-realistic motion-free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.

High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI

Abstract: Purpose To develop a new high-dimensionality undersampled patch-based reconstruction (HD-PROST) for highly accelerated 2D and 3D multi-contrast MRI. Methods HD-PROST jointly reconstructs multi-contrast MR images by exploiting the highly redundant information, on a local and non-local scale, and the strong correlation shared between the multiple contrast images. This is achieved by enforcing multi-dimensional low-rank in the undersampled images. 2D magnetic resonance fingerprinting (MRF) phantom and in vivo brain acquisitions were performed to evaluate the performance of HD-PROST for highly accelerated simultaneous T1 and T2 mapping. Additional in vivo experiments for reconstructing multiple undersampled 3D magnetization transfer (MT)-weighted images were conducted to illustrate the impact of HD-PROST for high-resolution multi-contrast 3D imaging. Results In the 2D MRF phantom study, HD-PROST provided accurate and precise estimation of the T1 and T2 values in comparison to gold standard spin echo acquisitions. HD-PROST achieved good quality maps for the in vivo 2D MRF experiments in comparison to conventional low-rank inversion reconstruction. T1 and T2 values of white matter and gray matter were in good agreement with those reported in the literature for MRF acquisitions with reduced number of time point images (500 time point images, ~2.5 s scan time). For in vivo MT-weighted 3D acquisitions (6 different contrasts), HD-PROST achieved similar image quality than the fully sampled reference image for an undersampling factor of 6.5-fold. Conclusion HD-PROST enables multi-contrast 2D and 3D MR images in a short acquisition time without compromising image quality. Ultimately, this technique may increase the potential of conventional parameter mapping.

Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI.

Abstract: PURPOSE Microscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate µFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for µFA estimation. This study aims to evaluate µFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. METHODS SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The µFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of µFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. RESULTS µFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). CONCLUSION Powder-averaged SMT and (2-component) SM are unable to accurately report µFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.

Cryogen-free dissolution dynamic nuclear polarization polarizer operating at 3.35 T, 6.70 T, and 10.1 T.

Abstract: PURPOSE A novel dissolution dynamic nuclear polarization (dDNP) polarizer platform is presented. The polarizer meets a number of key requirements for in vitro, preclinical, and clinical applications. METHOD It uses no liquid cryogens, operates in continuous mode, accommodates a wide range of sample sizes up to and including those required for human studies, and is fully automated. RESULTS It offers a wide operational window both in terms of magnetic field, up to 10.1 T, and temperature, from room temperature down to 1.3 K. The polarizer delivers a 13 C liquid state polarization for [1-13 C]pyruvate of 70%. The build-up time constant in the solid state is approximately 1200 s (20 minutes), allowing a sample throughput of at least one sample per hour including sample loading and dissolution. CONCLUSION We confirm the previously reported strong field dependence in the range 3.35 to 6.7 T, but see no further increase in polarization when increasing the magnetic field strength to 10.1 T for [1-13 C]pyruvate and trityl. Using a custom dry magnet, cold head and recondensing, closed-cycle cooling system, combined with a modular DNP probe, and automation and fluid handling systems, we have designed a unique dDNP system with unrivalled flexibility and performance.

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.

Abstract: Purpose To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy (1 H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broadening, and spectral baseline into noise-free, line-narrowed, baseline-removed intact metabolite spectra. Methods A CNN was trained (n = 40 000) and tested (n = 5000) on simulated brain spectra with wide ranges of SNR (6.90-20.74) and linewidth (10-20 Hz). The CNN was further tested on in vivo spectra (n = 40) from five healthy volunteers with substantially different SNR, and the results were compared with those from the LCModel analysis. A Student t test was performed for the comparison. Results Using the proposed method the mean-absolute-percent-errors (MAPEs) in the estimated metabolite concentrations were 12.49% ± 4.35% for aspartate, creatine (Cr), γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione (GSH), myo-Inositol (mI), N-acetylaspartate, phosphocreatine (PCr), phosphorylethanolamine, and taurine over the whole simulated spectra in the test set. The metabolite concentrations estimated from in vivo spectra were close to the reported ranges for the proposed method and the LCModel analysis except mI, GSH, and especially Cr/PCr for the LCModel analysis, and phosphorylcholine to glycerophosphorylcholine ratio (PC/GPC) for both methods. The metabolite concentrations estimated across the in vivo spectra with different SNR were less variable with the proposed method (~10% or less) than with the LCModel analysis. Conclusion The robust performance of the proposed method against low SNR may allow a subminute 1 H-MRS of human brain, which is an important technical development for clinical studies.

SPARKLING: variable-density k-space filling curves for accelerated T 2 * -weighted MRI

Abstract: Purpose To present a new optimition-driven design of optimal k-space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING). Theory The SPARKLING algorithm is a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non-Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples (e.g., variable density) and a locally uniform k-space coverage. Methods Ex vivo and in vivo prospective T 2 * -weighted acquisitions were performed on a 7-Tesla scanner using the SPARKLING trajectories for various setups and target densities. Our method was compared to radial and variable-density spiral trajectories for high-resolution imaging. Results Combining sampling efficiency with compressed sensing, the proposed sampling patterns allowed up to 20-fold reductions in MR scan time (compared to fully sampled Cartesian acquisitions) for two-dimensional T 2 * -weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in-plane resolution of 390 μm. In comparison to existing non-Cartesian sampling strategies, the proposed technique also yielded superior image quality. Conclusions The proposed optimization-driven design of k-space trajectories is a versatile framework that is able to enhance MR sampling performance in the context of compressed sensing.

Five-minute whole-heart coronary MRA with sub-millimeter isotropic resolution, 100% respiratory scan efficiency, and 3D-PROST reconstruction.

Abstract: PURPOSE To enable whole-heart 3D coronary magnetic resonance angiography (CMRA) with isotropic sub-millimeter resolution in a clinically feasible scan time by combining respiratory motion correction with highly accelerated variable density sampling in concert with a novel 3D patch-based undersampled reconstruction (3D-PROST). METHODS An undersampled variable density spiral-like Cartesian trajectory was combined with 2D image-based navigators to achieve 100% respiratory efficiency and predictable scan time. 3D-PROST reconstruction integrates structural information from 3D patch neighborhoods through sparse representation, thereby exploiting the redundancy of the 3D anatomy of the coronary arteries in an efficient low-rank formulation. The proposed framework was evaluated in a static resolution phantom and in 10 healthy subjects with isotropic resolutions of 1.2 mm3 and 0.9 mm3 and undersampling factors of ×5 and ×9. 3D-PROST was compared against fully sampled (1.2 mm3 only), conventional parallel imaging, and compressed sensing reconstructions. RESULTS Phantom and in vivo (1.2 mm3 ) reconstructions were in excellent agreement with the reference fully sampled image. In vivo average acquisition times (min:s) were 7:57 ± 1:18 (×5) and 4:35 ± 0:44 (×9) for 0.9 mm3 resolution. Sub-millimeter 3D-PROST resulted in excellent depiction of the left and right coronary arteries including small branch vessels, leading to further improvements in vessel sharpness and visible vessel length in comparison with conventional reconstruction techniques. Image quality rated by 2 experts demonstrated that 3D-PROST provides good image quality and is robust even at high acceleration factors. CONCLUSION The proposed approach enables free-breathing whole-heart 3D CMRA with isotropic sub-millimeter resolution in <5 min and achieves improved coronary artery visualization in a short and predictable scan time.

Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding.

Abstract: PURPOSE:Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients.METHODS:The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain.RESULTS:Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory.CONCLUSION:Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs. (Less)

Mapping water exchange across the blood-brain barrier using 3D diffusion-prepared arterial spin labeled perfusion MRI.

Abstract: Purpose To present a novel MR pulse sequence and modeling algorithm to quantify the water exchange rate (kw ) across the blood-brain barrier (BBB) without contrast, and to evaluate its clinical utility in a cohort of elderly subjects at risk of cerebral small vessel disease (SVD). Methods A diffusion preparation module with spoiling of non-Carr-Purcell-Meiboom-Gill signals was integrated with pseudo-continuous arterial spin labeling (pCASL) and 3D gradient and spin echo (GRASE) readout. The tissue/capillary fraction of the arterial spin labeling (ASL) signal was separated by appropriate diffusion weighting (b = 50 s/mm2 ). kw was quantified using a single-pass approximation (SPA) model with total generalized variation (TGV) regularization. Nineteen elderly subjects were recruited and underwent 2 MRIs to evaluate the reproducibility of the proposed technique. Correlation analysis was performed between kw and vascular risk factors, Clinical Dementia Rating (CDR) scale, neurocognitive assessments, and white matter hyperintensity (WMH). Results The capillary/tissue fraction of ASL signal can be reliably differentiated with the diffusion weighting of b = 50 s/mm2 , given ~100-fold difference between the (pseudo-)diffusion coefficients of the 2 compartments. Good reproducibility of kw measurements (intraclass correlation coefficient = 0.75) was achieved. Average kw was 105.0 ± 20.6, 109.6 ± 18.9, and 94.1 ± 19.6 min-1 for whole brain, gray and white matter. kw was increased by 28.2%/19.5% in subjects with diabetes/hypercholesterolemia. Significant correlations between kw and vascular risk factors, CDR, executive/memory function, and the Fazekas scale of WMH were observed. Conclusion A diffusion prepared 3D GRASE pCASL sequence with TGV regularized SPA modeling was proposed to measure BBB water permeability noninvasively with good reproducibility. kw may serve as an imaging marker of cerebral SVD and associated cognitive impairment.

Aortic 4D flow MRI in 2 minutes using compressed sensing, respiratory controlled adaptive k-space reordering, and inline reconstruction.

Abstract: Purpose To evaluate the accuracy and feasibility of a free-breathing 4D flow technique using compressed sensing (CS), where 4D flow imaging of the thoracic aorta is performed in 2 min with inline image reconstruction on the MRI scanner in less than 5 min. Methods The 10 in vitro 4D flow MRI scans were performed with different acceleration rates on a pulsatile flow phantom (9 CS acceleration factors [R = 5.4-14.1], 1 generalized autocalibrating partially parallel acquisition [GRAPPA] R = 2). Based on in vitro results, CS-accelerated 4D flow of the thoracic aorta was acquired in 20 healthy volunteers (38.3 ± 15.2 years old) and 11 patients with aortic disease (61.3 ± 15.1 years) with R = 7.7. A conventional 4D flow scan was acquired with matched spatial coverage and temporal resolution. Results CS depicted similar hemodynamics to conventional 4D flow in vitro, and in vivo, with >70% reduction in scan time (volunteers: 1:52 ± 0:25 versus 7:25 ± 2:35 min). Net flow values were within 3.5% in healthy volunteers, and voxel-by-voxel comparison demonstrated good agreement. CS significantly underestimated peak velocities (vmax ) and peak flow (Qmax ) in both volunteers and patients (volunteers: vmax , -16.2% to -9.4%, Qmax : -11.6% to -2.9%, patients: vmax , -11.2% to -4.0%; Qmax , -10.2% to -5.8%). Conclusion Aortic 4D flow with CS is feasible in a two minute scan with less than 5 min for inline reconstruction. While net flow agreement was excellent, CS with R = 7.7 produced underestimation of Qmax and vmax ; however, these were generally within 13% of conventional 4D flow-derived values. This approach allows 4D flow to be feasible in clinical practice for comprehensive assessment of hemodynamics.

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

Abstract: Purpose A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T 2 * and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time. Methods We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T 2 * -ADC spectra using an inverse Laplace transform. Results T 2 * -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. Conclusions Combined T 2 * -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T 2 * -ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.

Creatine and phosphocreatine mapping of mouse skeletal muscle by a polynomial and Lorentzian line-shape fitting CEST method.

Abstract: Purpose To obtain high-resolution Cr and PCr maps of mouse skeletal muscle using a polynomial and Lorentzian line-shape fitting (PLOF) CEST method. Methods Wild-type mice and guanidinoacetate N-methyltransferase-deficient (GAMT-/-) mice that have low Cr and PCr concentrations in muscle were used to assign the Cr and PCr peaks in the Z-spectrum at 11.7 T. A PLOF method was proposed to simultaneously extract and quantify the Cr and PCr by assuming a polynomial function for the background and 2 Lorentzian functions for the CEST peaks at 1.95 ppm and 2.5 ppm. Results The Z-spectra of phantoms revealed that PCr has 2 CEST peaks (2 ppm and 2.5 ppm), whereas Cr only showed 1 peak at 2 ppm. Comparison of the Z-spectra of wild-type and GAMT-/- mice indicated that, contrary to brain, there was no visible protein guanidinium peak in the skeletal-muscle Z-spectrum, which allowed us to extract clean PCr and Cr CEST signals. High-resolution PCr and Cr concentration maps of mouse skeletal muscle were obtained by the PLOF CEST method after calibration with in vivo MRS. Conclusions The PLOF method provides an efficient way to map Cr and PCr concentrations simultaneously in the skeletal muscle at high MRI field.

Translation of Carbon‐13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients

Abstract: Purpose To develop and translate a metabolite-specific imaging sequence using a symmetric echo planar readout for clinical hyperpolarized (HP) Carbon-13 (13 C) applications. Methods Initial data were acquired from patients with prostate cancer (N = 3) and high-grade brain tumors (N = 3) on a 3T scanner. Samples of [1-13 C]pyruvate were polarized for at least 2 h using a 5T SPINlab system operating at 0.8 K. Following injection of the HP substrate, pyruvate, lactate, and bicarbonate (for brain studies) were sequentially excited with a singleband spectral-spatial RF pulse and signal was rapidly encoded with a single-shot echo planar readout on a slice-by-slice basis. Data were acquired dynamically with a temporal resolution of 2 s for prostate studies and 3 s for brain studies. Results High pyruvate signal was seen throughout the prostate and brain, with conversion to lactate being shown across studies, whereas bicarbonate production was also detected in the brain. No Nyquist ghost artifacts or obvious geometric distortion from the echo planar readout were observed. The average error in center frequency was 1.2 ± 17.0 and 4.5 ± 1.4 Hz for prostate and brain studies, respectively, below the threshold for spatial shift because of bulk off-resonance. Conclusion This study demonstrated the feasibility of symmetric EPI to acquire HP 13 C metabolite maps in a clinical setting. As an advance over prior single-slice dynamic or single time point volumetric spectroscopic imaging approaches, this metabolite-specific EPI acquisition provided robust whole-organ coverage for brain and prostate studies while retaining high SNR, spatial resolution, and dynamic temporal resolution.

Separating fetal and maternal placenta circulations using multiparametric MRI

Abstract: Purpose The placenta is a vital organ for the exchange of oxygen, nutrients, and waste products between fetus and mother The placenta may suffer from several pathologies, which affect this fetal‐maternal exchange, thus the flow properties of the placenta are of interest in determining the course of pregnancy In this work, we propose a new multiparametric model for placental tissue signal in MRI Methods We describe a method that separates fetal and maternal flow characteristics of the placenta using a 3‐compartment model comprising fast and slowly circulating fluid pools, and a tissue pool is fitted to overlapping multiecho T2 relaxometry and diffusion MRI with low b‐values We implemented the combined model and acquisition on a standard 15 Tesla clinical system with acquisition taking less than 20 minutes Results We apply this combined acquisition in 6 control singleton placentas Mean myometrial T2 relaxation time was 12363 (±671) ms Mean T2 relaxation time of maternal blood was 20217 (±9298) ms In the placenta, mean T2 relaxation time of the fetal blood component was 14489 (±5442) ms Mean ratio of maternal to fetal blood volume was 116 (±06), and mean fetal blood saturation was 7293 (±2011)% across all 6 cases Conclusion The novel acquisition in this work allows the measurement of histologically relevant physical parameters, such as the relative proportions of vascular spaces In the placenta, this may help us to better understand the physiological properties of the tissue in disease

Free-breathing, non-ECG, continuous myocardial T 1 mapping with cardiovascular magnetic resonance multitasking.

Abstract: Author(s): Shaw, Jaime L; Yang, Qi; Zhou, Zhengwei; Deng, Zixin; Nguyen, Christopher; Li, Debiao; Christodoulou, Anthony G | Abstract: PurposeTo evaluate the accuracy and repeatability of a free-breathing, non-electrocardiogram (ECG), continuous myocardial T1 and extracellular volume (ECV) mapping technique adapted from the Multitasking framework.MethodsThe Multitasking framework is adapted to quantify both myocardial native T1 and ECV with a free-breathing, non-ECG, continuous acquisition T1 mapping method. We acquire interleaved high-spatial resolution image data and high-temporal resolution auxiliary data following inversion-recovery pulses at set intervals and perform low-rank tensor imaging to reconstruct images at 344 inversion times, 20 cardiac phases, and 6 respiratory phases. The accuracy and repeatability of Multitasking T1 mapping in generating native T1 and ECV maps are compared with conventional techniques in a phantom, a simulation, 12 healthy subjects, and 10 acute myocardial infarction patients.ResultsIn phantoms, Multitasking T1 mapping correlated strongly with the gold-standard spin-echo inversion recovery (R2 = 0.99). A simulation study demonstrated that Multitasking T1 mapping has similar myocardial sharpness to the fully sampled ground truth. In vivo native T1 and ECV values from Multitasking T1 mapping agree well with conventional MOLLI values and show good repeatability for native T1 and ECV mapping for 60 seconds, 30 seconds, or 15 seconds of data. Multitasking native T1 and ECV in myocardial infarction patients correlate positively with values from MOLLI.ConclusionMultitasking T1 mapping can quantify native T1 and ECV in the myocardium with free-breathing, non-ECG, continuous scans with good image quality and good repeatability in vivo in healthy subjects, and correlation with MOLLI T1 and ECV in acute myocardial infarction patients.

Multi-modal functional MRI to explore placental function over gestation.

Abstract: Purpose: To investigate, visualize and quantify the physiology of the human placenta in several dimensions ‐ functional, temporal over gestation, and spatial over the whole organ Methods: Bespoke MRI techniques, combining a rich diffusion protocol, anatomical data and T2* mapping together with a multi‐modal pipeline including motion correction and extracted quantitative features were developed and employed on pregnant women between 22 and 38 weeks gestational age including two pregnancies diagnosed with pre‐eclampsia Results: A multi‐faceted assessment was demonstrated showing trends of increasing lacunarity, and decreasing T2* and diffusivity over gestation Conclusions: The obtained multi‐modal acquisition and quantification shows promising opportunities for studying evolution, adaptation and compensation processes

An automated approach to fully self-gated free-running cardiac and respiratory motion-resolved 5D whole-heart MRI.

Abstract: PURPOSE To develop a previously reported, electrocardiogram (ECG)-gated, motion-resolved 5D compressed sensing whole-heart sparse MRI methodology into an automated, optimized, and fully self-gated free-running framework in which external gating or triggering devices are no longer needed. METHODS Cardiac and respiratory self-gating signals were extracted from raw image data acquired in 12 healthy adult volunteers with a non-ECG-triggered 3D radial golden-angle 1.5 T balanced SSFP sequence. To extract cardiac self-gating signals, central k-space coefficient signal analysis (k0 modulation), as well as independent and principal component analyses were performed on selected k-space profiles. The procedure yielding triggers with the smallest deviation from those of the reference ECG was selected for the automated protocol. Thus, optimized cardiac and respiratory self-gating signals were used for binning in a compressed sensing reconstruction pipeline. Coronary vessel length and sharpness of the resultant 5D images were compared with image reconstructions obtained with ECG-gating. RESULTS Principal component analysis-derived cardiac self-gating triggers yielded a smaller deviation ( 17.4±6.1ms ) from the reference ECG counterparts than k0 modulation ( 26±7.5ms ) or independent component analysis ( 19.8±5.2ms ). Cardiac and respiratory motion-resolved 5D images were successfully reconstructed with the automated and fully self-gated approach. No significant difference was found for coronary vessel length and sharpness between images reconstructed with the fully self-gated and the ECG-gated approach (all P≥.06 ). CONCLUSION Motion-resolved 5D compressed sensing whole-heart sparse MRI has successfully been developed into an automated, optimized, and fully self-gated free-running framework in which external gating, triggering devices, or navigators are no longer mandatory. The resultant coronary MRA image quality was equivalent to that obtained with conventional ECG-gating.

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

Abstract: Purpose To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging. Methods Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures. Results The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations. Conclusions Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.

3D gradient echo snapshot CEST MRI with low power saturation for human studies at 3T

Abstract: Purpose For clinical implementation, a chemical exchange saturation transfer (CEST) imaging sequence must be fast, with high signal-to-noise ratio (SNR), 3D coverage, and produce robust contrast. However, spectrally selective CEST contrast requires dense sampling of the Z-spectrum, which increases scan duration. This article proposes a compromise: using a 3D snapshot gradient echo (GRE) readout with optimized CEST presaturation, sampling, and postprocessing, highly resolved Z-spectroscopy at 3T is made possible with 3D coverage at almost no extra time cost. Methods A 3D snapshot CEST sequence was optimized for low-power CEST MRI at 3T. Pulsed saturation was optimized for saturation power and saturation duration. Spectral sampling and postprocessing (B0 correction, denoising) was optimized for spectrally selective Lorentzian CEST effect extraction. Reproducibility was demonstrated in 3 healthy volunteers and feasibility was shown in 1 tumor patient. Results Low-power saturation was achieved by a train of 80 pulses of duration tp = 20 ms (total saturation time tsat = 3.2 seconds at 50% duty cycle) with B1 = 0.6 μT at 54 irradiation frequency offsets. With the 3D snapshot CEST sequence, a 180 × 220 × 54 mm field of view was acquired in 7 seconds per offset. Spectrally selective CEST effects at +3.5 and -3.5 ppm were quantified using multi-Lorentzian fitting. Reproducibility was high with an intersubject coefficient of variation below 10% in CEST contrasts. Amide and nuclear overhauser effect CEST effects showed similar correlations in tumor and necrosis as show in previous ultra-high field work. Conclusion A sophisticated CEST tool ready for clinical application was developed and tested for feasibility.