Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

PURPOSE: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. METHODS: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via nonlinear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k-space-based SSDU loss on the region of interest. NLINV-Net-based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. RESULTS: NLINV-Net-based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. CONCLUSION: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models.

PURPOSE: We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. METHOD: Samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method, different from conventional deep learning-based MRI reconstruction techniques. In addition to the maximum a posteriori estimate for the image, which can be obtained by maximizing the log-likelihood indirectly or directly, the minimum mean square error estimate and uncertainty maps can also be computed from those drawn samples. The data-driven Markov chains are constructed with the score-based generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. RESULTS: We numerically investigate the framework from these perspectives: (1) the interpretation of the uncertainty of the image reconstructed from undersampled k-space; (2) the effect of the number of noise scales used to train the generative models; (3) using a burn-in phase in MCMC sampling to reduce computation; (4) the comparison to conventional ℓ 1 $$ {\ell}_1 $$ -wavelet regularized reconstruction; (5) the transferability of learned information; and (6) the comparison to fastMRI challenge. CONCLUSION: A framework is described that connects the diffusion process and advanced generative models with Markov chains. We demonstrate its flexibility in terms of contrasts and sampling patterns using advanced generative priors and the benefits of also quantifying the uncertainty for every pixel.

Deep, deep learning with BART.

PURPOSE: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. METHODS: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network and MoDL, were implemented. RESULTS: State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient-based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. CONCLUSION: By integrating nonlinear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.

Quantitative MRI by nonlinear inversion of the Bloch equations.

PURPOSE: Development of a generic model-based reconstruction framework for multiparametric quantitative MRI that can be used with data from different pulse sequences. METHODS: Generic nonlinear model-based reconstruction for quantitative MRI estimates parametric maps directly from the acquired k-space by numerical optimization. This requires numerically accurate and efficient methods to solve the Bloch equations and their partial derivatives. In this work, we combine direct sensitivity analysis and pre-computed state-transition matrices into a generic framework for calibrationless model-based reconstruction that can be applied to different pulse sequences. As a proof-of-concept, the method is implemented and validated for quantitative T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ mapping with single-shot inversion-recovery (IR) FLASH and IR bSSFP sequences in simulations, phantoms, and the human brain. RESULTS: The direct sensitivity analysis enables a highly accurate and numerically stable calculation of the derivatives. The state-transition matrices efficiently exploit repeating patterns in pulse sequences, speeding up the calculation by a factor of 10 for the examples considered in this work, while preserving the accuracy of native ordinary differential equations solvers. The generic model-based method reproduces quantitative results of previous model-based reconstructions based on the known analytical solutions for radial IR FLASH. For IR bSFFP it produces accurate T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ maps for the National Insitute of Standards and Technology (NIST) phantom in numerical simulations and experiments. Feasibility is also shown for human brain, although results are affected by magnetization transfer effects. CONCLUSION: By developing efficient tools for numerical optimizations using the Bloch equations as forward model, this work enables generic model-based reconstruction for quantitative MRI.

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction.

PURPOSE: To develop a free-breathing myocardial T 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. METHODS: Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incomplete T 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurate T 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction. RESULTS: In comparison to an IR spin-echo reference, phantom results confirm good T 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improves T 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardial T 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability. CONCLUSION: Motion-resolved myocardial T 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Exercise Stress Real-Time Cardiac Magnetic Resonance Imaging for Noninvasive Characterization of Heart Failure With Preserved Ejection Fraction: The HFpEF-Stress Trial.

BACKGROUND: Right heart catheterization using exercise stress is the reference standard for the diagnosis of heart failure with preserved ejection fraction (HFpEF) but carries the risk of the invasive procedure. We hypothesized that real-time cardiac magnetic resonance (RT-CMR) exercise imaging with pathophysiologic data at excellent temporal and spatial resolution may represent a contemporary noninvasive alternative for diagnosing HFpEF. METHODS: The HFpEF-Stress trial (CMR Exercise Stress Testing in HFpEF; URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621. URL: https://dzhk.de/; Unique identifier: DZHK-17) prospectively recruited 75 patients with echocardiographic signs of diastolic dysfunction and dyspnea on exertion (E/e'>8, New York Heart Association class ≥II) to undergo echocardiography, right heart catheterization, and RT-CMR at rest and during exercise stress. HFpEF was defined according to pulmonary capillary wedge pressure (≥15 mm Hg at rest or ≥25 mm Hg during exercise stress). RT-CMR functional assessments included time-volume curves for total and early (1/3) diastolic left ventricular filling, left atrial (LA) emptying, and left ventricular/LA long axis strain. RESULTS: Patients with HFpEF (n=34; median pulmonary capillary wedge pressure at rest, 13 mm Hg; at stress, 27 mm Hg) had higher E/e' (12.5 versus 9.15), NT-proBNP (N-terminal pro-B-type natriuretic peptide; 255 versus 75 ng/L), and LA volume index (43.8 versus 36.2 mL/m2) compared with patients with noncardiac dyspnea (n=34; rest, 8 mm Hg; stress, 18 mm Hg; P≤0.001 for all). Seven patients were excluded because of the presence of non-HFpEF cardiac disease causing dyspnea on imaging. There were no differences in RT-CMR left ventricular total and early diastolic filling at rest and during exercise stress (P≥0.164) between patients with HFpEF and noncardiac dyspnea. RT-CMR revealed significantly impaired LA total and early (P<0.001) diastolic emptying in patients with HFpEF during exercise stress. RT-CMR exercise stress LA long axis strain was independently associated with HFpEF (adjusted odds ratio, 0.657 [95% CI, 0.516-0.838]; P=0.001) after adjustment for clinical and imaging measures and emerged as the best predictor for HFpEF (area under the curve at rest 0.82 versus exercise stress 0.93; P=0.029). CONCLUSIONS: RT-CMR allows highly accurate identification of HFpEF during physiologic exercise and qualifies as a suitable noninvasive diagnostic alternative. These results will need to be confirmed in multicenter prospective research studies to establish widespread routine clinical use. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621. URL: https://dzhk.de/; Unique identifier: DZHK-17.

Real-time radial tagging for quantification of left ventricular torsion.

PURPOSE: To develop a real-time radial tagging MRI for accurate measurement of rotational motion and twist of the left ventricle (LV). METHODS: A FLASH-based radial tagging sequence with an undersampled radial reading scheme was developed for both single and double-slice imaging in real-time. The Polar Fourier Transform was used for reconstruction to push the undersampling artifacts out of a reduced FOV. The developed technique was used to image five normal subjects during rest, plus one during both exercise and rest conditions. LV rotational motions were estimated for five consecutive cardiac cycles in all cases. The process was validated using a numerical phantom. The real-time measurement of global rotational motion was compared with those measured from a non-real-time exam using linear regression analysis and the Bland-Altman plot. RESULTS: The real-time acquisition was performed successfully with a temporal resolution of 46.2 ms. Image quality was sufficient for the reproducible calculation of rotation at rest and exercise. The feasibility of double-slice acquisition on human was further studied and a real-time twist of the left ventricle was demonstrated. The difference between LV global rotations from real-time and non-real-time approaches was 0.27 degrees. A significant reverse recoiling, induced by exercise, was reproducibly measured by the technique. CONCLUSION: A real-time radial tagging MRI technique was developed based on the undersampled radial acquisition and Polar Fourier Transform reconstruction, for accurate measuring of the heart rotational motion and twist. The technique was able to extract a meaningful change of diastolic recoiling under stress test conditions during physical activities (cycling).

Model-based reconstruction for simultaneous multi-slice T1 mapping using single-shot inversion-recovery radial FLASH.

PURPOSE: To develop a single-shot multi-slice T1 mapping method by combing simultaneous multi-slice (SMS) excitations, single-shot inversion-recovery (IR) radial fast low-angle shot (FLASH), and a nonlinear model-based reconstruction method. METHODS: SMS excitations are combined with a single-shot IR radial FLASH sequence for data acquisition. A previously developed single-slice calibrationless model-based reconstruction is extended to SMS, formulating the estimation of parameter maps and coil sensitivities from all slices as a single nonlinear inverse problem. Joint-sparsity constraints are further applied to the parameter maps to improve T1 precision. Validations of the proposed method are performed for a phantom and for the human brain and liver in 6 healthy adult subjects. RESULTS: Phantom results confirm good T1 accuracy and precision of the simultaneously acquired multi-slice T1 maps in comparison to single-slice references. In vivo human brain studies demonstrate the better performance of SMS acquisitions compared to the conventional spoke-interleaved multi-slice acquisition using model-based reconstruction. Aside from good accuracy and precision, the results of 6 healthy subjects in both brain and abdominal studies confirm good repeatability between scan and re-scans. The proposed method can simultaneously acquire T1 maps for 5 slices of a human brain ( 0.75×0.75×5mm3 ) or 3 slices of the abdomen ( 1.25×1.25×6mm3 ) within 4 seconds. CONCLUSIONS: The IR SMS radial FLASH acquisition together with a nonlinear model-based reconstruction enable rapid high-resolution multi-slice T1 mapping with good accuracy, precision, and repeatability.

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge.

PURPOSE: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). RESULTS: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. DISCUSSION AND CONCLUSION: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

Physics-based reconstruction methods for magnetic resonance imaging.

Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction-addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Real-time cardiovascular magnetic resonance T1 and extracellular volume fraction mapping for tissue characterisation in aortic stenosis.

BACKGROUND: Myocardial fibrosis is a major determinant of outcome in aortic stenosis (AS). Novel fast real-time (RT) cardiovascular magnetic resonance (CMR) mapping techniques allow comprehensive quantification of fibrosis but have not yet been compared against standard techniques and histology. METHODS: Patients with severe AS underwent CMR before (n = 110) and left ventricular (LV) endomyocardial biopsy (n = 46) at transcatheter aortic valve replacement (TAVR). Midventricular short axis (SAX) native, post-contrast T1 and extracellular volume fraction (ECV) maps were generated using commercially available modified Look-Locker Inversion recovery (MOLLI) (native: 5(3)3, post-contrast: 4(1)3(1)2) and RT single-shot inversion recovery Fast Low-Angle Shot (FLASH) with radial undersampling. Focal late gadolinium enhancement was excluded from T1 and ECV regions of interest. ECV and LV mass were used to calculate LV matrix volumes. Variability and agreements were assessed between RT, MOLLI and histology using intraclass correlation coefficients, coefficients of variation and Bland Altman analyses. RESULTS: RT and MOLLI derived ECV were similar for midventricular SAX slice coverage (26.2 vs. 26.5, p = 0.073) and septal region of interest (26.2 vs. 26.5, p = 0.216). MOLLI native T1 time was in median 20 ms longer compared to RT (p < 0.001). Agreement between RT and MOLLI was best for ECV (ICC > 0.91), excellent for post-contrast T1 times (ICC > 0.81) and good for native T1 times (ICC > 0.62). Diffuse collagen volume fraction by biopsies was in median 7.8%. ECV (RT r = 0.345, p = 0.039; MOLLI r = 0.40, p = 0.010) and LV matrix volumes (RT r = 0.45, p = 0.005; MOLLI r = 0.43, p = 0.007) were the only parameters associated with histology. CONCLUSIONS: RT mapping offers fast and sufficient ECV and LV matrix volume calculation in AS patients. ECV and LV matrix volume represent robust and universally comparable parameters with associations to histologically assessed fibrosis and may emerge as potential targets for clinical decision making.

Simple auto-calibrated gradient delay estimation from few spokes using Radial Intersections (RING).

PURPOSE: To develop a simple and robust tool for the estimation of gradient delays from highly undersampled radial k-space data. THEORY: In radial imaging gradient delays induce parallel and orthogonal trajectory shifts, which can be described using an ellipse model. The intersection points of the radial spokes, which can be estimated by spoke-by-spoke comparison of k-space samples, distinctly determine the parameters of the ellipse. Using the proposed method (RING), these parameters can be obtained using a least-squares fit and utilized for the correction of gradient delays. METHODS: The functionality and accuracy of the proposed RING method is validated and compared to correlation-based gradient-delay estimation from opposing spokes using numerical simulations, phantom and in vivo heart measurements. RESULTS: In all experiments, RING robustly provides accurate gradient delay estimations even for as few as three radial spokes. CONCLUSIONS: The simple and straightforward to implement RING method provides accurate gradient delay estimation for highly undersampled radial imaging.

Frequency-modulated SSFP with radial sampling and subspace reconstruction: A time-efficient alternative to phase-cycled bSSFP.

PURPOSE: A novel subspace-based reconstruction method for frequency-modulated balanced steady-state free precession (fmSSFP) MRI is presented. In this work, suitable data acquisition schemes, subspace sizes, and efficiencies for banding removal are investigated. THEORY AND METHODS: By combining a fmSSFP MRI sequence with a 3D stack-of-stars trajectory, scan efficiency is maximized as spectral information is obtained without intermediate preparation phases. A memory-efficient reconstruction routine is implemented by introducing the low-frequency Fourier transform as a subspace which allows for the formulation of a convex reconstruction problem. The removal of banding artifacts is investigated by comparing the proposed acquisition and reconstruction technique to phase-cycled bSSFP MRI. Aliasing properties of different undersampling schemes are analyzed and water/fat separation is demonstrated by reweighting the reconstructed subspace coefficients to generate virtual spectral responses in a post-processing step. RESULTS: A simple root-of-sum-of-squares combination of the reconstructed subspace coefficients yields high-SNR images with the characteristic bSSFP contrast but without banding artifacts. Compared to Golden-Angle trajectories, turn-based sampling schemes were superior in minimizing aliasing across reconstructed subspace coefficients. Water/fat separated images of the human knee were obtained by reweighting subspace coefficients. CONCLUSIONS: The novel subspace-based fmSSFP MRI technique emerges as a time-efficient alternative to phase-cycled bSFFP. The method does not need intermediate preparation phases, offers high SNR and avoids banding artifacts. Reweighting of the reconstructed subspace coefficients allows for generating virtual spectral responses with applications to water/fat separation.

Dynamic water/fat separation and B0 inhomogeneity mapping-joint estimation using undersampled triple-echo multi-spoke radial FLASH.

PURPOSE: To achieve dynamic water/fat separation and B0 field inhomogeneity mapping via model-based reconstructions of undersampled triple-echo multi-spoke radial FLASH acquisitions. METHODS: This work introduces an undersampled triple-echo multi-spoke radial FLASH sequence, which uses (i) complementary radial spokes per echo train for faster spatial encoding, (ii) asymmetric echoes for flexible and nonuniform echo spacing, and (iii) a golden angle increment across frames for optimal k-space coverage. Joint estimation of water, fat, B0 inhomogeneity, and coil sensitivity maps from undersampled triple-echo data poses a nonlinear and non-convex inverse problem which is solved by a model-based reconstruction with suitable regularization. The developed methods are validated using phantom experiments with different degrees of undersampling. Real-time MRI studies of the knee, liver, and heart are conducted without prospective gating or retrospective data sorting at temporal resolutions of 70, 158, and 40 ms, respectively. RESULTS: Up to 18-fold undersampling is achieved in this work. Even in the presence of rapid physiological motion, large B0 field inhomogeneities, and phase wrapping, the model-based reconstruction yields reliably separated water/fat maps in conjunction with spatially smooth inhomogeneity maps. CONCLUSIONS: The combination of a triple-echo acquisition and joint reconstruction technique provides a practical solution to time-resolved and motion robust water/fat separation at high spatial and temporal resolution.

Real-time MRI for the dynamic assessment of fundoplication failure in patients with gastroesophageal reflux disease.

PURPOSE: To assess the diagnostic potential of dynamic real-time MRI for fundoplication failure in patients with persistent or recurrent GERD-like (gastroesophageal reflux disease) complaints. MATERIAL AND METHODS: Twenty-two consecutive patients (male n = 11; female n = 11; median age 59 years) with recurrent or persistent GERD-like symptom after fundoplication were enrolled between 2015 and 2017. Median duration of GERD-like symptoms was 21 months. Real-time MRI (3 Tesla) was performed at 40 ms temporal resolution using undersampled radial fast low-angle shot acquisitions with nonlinear inverse image reconstruction. MRI movies dynamically visualized bolus transit of pineapple juice through the gastroesophageal junction, position of the fundoplication wrap and recurring hernia or reflux during Valsalva maneuver. MRI results were compared to endoscopic findings. RESULTS: Real-time MRI was successfully completed in all patients without adverse events (average examination time 15 min). Morphological correlates for GERD-like symptoms were evident in 20 patients (90.1%) with gastric reflux in 19 cases. Nine patients (40.1%) had wrap disruption and recurrent gastric hernia. Wrap migration or telescoping hernia was detected in nine patients (40.1%). One patient presented with continued reflux despite intact fundoplication wrap. Esophageal dysmotility with delayed bolus passage was observed in one case. On endoscopy, gastric hernia or wrap disruption was diagnosed in seven cases, and esophagitis or Barret's metaplasia in nine cases. CONCLUSION: Real-time MRI is a fast and safe modality for dynamic imaging after fundoplication, without radiation exposure or administration of gadolinium-based contrast media. In a relevant number of cases, real-time MRI reveals correlates for GERD-like symptoms. KEY POINTS: • Real-time MRI reliably visualizes the gastroesophageal junction after fundoplication surgery. • Patients with recurring GERD-like symptoms have a high rate of morphological failure patterns that can be identified by real-time MRI. • Dynamic assessment of gastroesophageal junction by real-time MRI is a perspective diagnostic tool for detection of fundoplication failure.

Hiatal hernias in patients with GERD-like symptoms: evaluation of dynamic real-time MRI vs endoscopy.

PURPOSE: To assess the diagnostic potential of real-time MRI for assessment of hiatal hernias in patients with GERD-like symptoms compared to endoscopy. MATERIAL AND METHODS: One hundred eight patients with GERD-like symptoms were included in this observational cohort study between 2015 and 2017. Real-time MRI was performed at 3.0 Tesla with temporal resolution of 40 ms, dynamically visualizing the esophageal transport of a pineapple juice bolus, its passage through the gastroesophageal junction, and functional responses during Valsalva maneuver. Hernia detection on MRI and endoscopy was calculated using contingency tables with diagnosis of hernia on either modality as reference. RESULTS: Of 108 patients, 107 underwent successful MRI without adverse events; 1 examination was aborted to inability to swallow pineapple juice in supine position. No perforation or acute bleeding occurred during endoscopy. Median examination time was 15 min. Eighty-five patients (79.4%) were diagnosed with hiatal hernia on either real-time MRI or endoscopy. Forty-six hernias were visible on both modalities. Seventeen hernias were evident exclusively on MRI, and 22 exclusively on endoscopy. Sixteen of the 63 MRI-detected hernias (25.4%) were detectable only during Valsalva maneuver, which were smaller compared to hernias at rest (median - 13.5 vs - 33.0 mm, p < 0.001). Diagnostic accuracy for hernia detection was comparable for MRI and endoscopy (sensitivity 74% vs 80%, p = 0.4223; specificity 100% vs 100%, p > 0.99). CONCLUSION: Real-time MRI is a fast and safe modality for assessment of the gastroesophageal junction, without radiation exposure or administration of gadolinium-based contrast media. Although MRI and endoscopy yield comparable diagnostic accuracy, dynamic MRI sequences are able to visualize hiatal hernias that were occult on static MRI sequences or endoscopy in a relevant number of cases. KEY POINTS: • Real-time MRI is a safe and fast imaging modality for examination of the gastroesophageal junction, combining anatomical and functional information for enhanced detection of hiatal hernias. • Real-time MRI and endoscopy yield comparably high diagnostic accuracy: real-time MRI visualizes hiatal hernias that were occult on endoscopy in a relevant number of patients; however, several hiatal hernias detected on endoscopy were occult on real-time MRI. • There is clinical potential of real-time MR imaging in patients with GERD-like symptoms and equivocal findings on endoscopy or pH-metry, for anatomical visualization in patients planned for surgical intervention, or those with suspected fundoplication failures.

Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance.

BACKGROUND: This study develops a model-based myocardial T1 mapping technique with sparsity constraints which employs a single-shot inversion-recovery (IR) radial fast low angle shot (FLASH) cardiovascular magnetic resonance (CMR) acquisition. The method should offer high resolution, accuracy, precision and reproducibility. METHODS: The proposed reconstruction estimates myocardial parameter maps directly from undersampled k-space which is continuously measured by IR radial FLASH with a 4 s breathhold and retrospectively sorted based on a cardiac trigger signal. Joint sparsity constraints are imposed on the parameter maps to further improve T1 precision. Validations involved studies of an experimental phantom and 8 healthy adult subjects. RESULTS: In comparison to an IR spin-echo reference method, phantom experiments with T1 values ranging from 300 to 1500 ms revealed good accuracy and precision at simulated heart rates between 40 and 100 bpm. In vivo T1 maps achieved better precision and qualitatively better preservation of image features for the proposed method than a real-time CMR approach followed by pixelwise fitting. Apart from good inter-observer reproducibility (0.6% of the mean), in vivo results confirmed good intra-subject reproducibility (1.05% of the mean for intra-scan and 1.17, 1.51% of the means for the two inter-scans, respectively) of the proposed method. CONCLUSION: Model-based reconstructions with sparsity constraints allow for single-shot myocardial T1 maps with high spatial resolution, accuracy, precision and reproducibility within a 4 s breathhold. Clinical trials are warranted.

Accelerated whole-heart MR angiography using a variable-density poisson-disc undersampling pattern and compressed sensing reconstruction.

PURPOSE: To accelerate whole-heart three-dimension MR angiography (MRA) by using a variable-density Poisson-disc undersampling pattern and a compressed sensing (CS) reconstruction algorithm, and compare the results with sensitivity encoding (SENSE). METHODS: For whole-heart MRA, a prospective variable-density Poisson-disc k-space undersampling pattern was developed in which 1-2% of central part of k-space was fully sampled, and sampling in the remainder decreased exponentially toward the periphery. The undersampled data were then estimated using CS reconstruction. In patients, images using this sequence with an undersampling rate of ≈6 were compared with those using a SENSE rate of 2 (n = 15) and a SENSE rate of 6 (n = 13). RESULTS: Compared with SENSE rate 2, CS rate 6 images had similar objective border sharpness, significantly lower subjective image quality scores at all four locations (all P < 0.01), and shorter scan times (P < 0.05). Compared with SENSE rate 6, CS rate 6 had similar objective border sharpness at all four locations, significantly better subjective image quality scores at three of four locations (all P < 0.01), and similar scan times (P = 0.24). CONCLUSION: Compared with SENSE with a comparable acceleration rate, a variable-density Poisson-disc undersampling pattern and CS reconstruction achieved better subjective image quality and similar border sharpness. Magn Reson Med 79:761-769, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Simultaneous multi-slice MRI using cartesian and radial FLASH and regularized nonlinear inversion: SMS-NLINV.

PURPOSE: The development of a calibrationless parallel imaging method for accelerated simultaneous multi-slice (SMS) MRI based on Regularized Nonlinear Inversion (NLINV), evaluated using Cartesian and radial fast low-angle shot (FLASH). THEORY AND METHODS: NLINV is a parallel imaging method that jointly estimates image content and coil sensitivities using a Newton-type method with regularization. Here, NLINV is extended to SMS-NLINV for reconstruction and separation of all simultaneously acquired slices. The performance of the extended method is evaluated for different sampling schemes using phantom and in vivo experiments based on Cartesian and radial SMS-FLASH sequences. RESULTS: The basic algorithm was validated in Cartesian experiments by comparison with ESPIRiT. For Cartesian and radial sampling, improved results are demonstrated compared to single-slice experiments, and it is further shown that sampling schemes using complementary samples outperform schemes with the same samples in each partition. CONCLUSION: The extension of the NLINV algorithm for SMS data was implemented and successfully demonstrated in combination with a Cartesian and radial SMS-FLASH sequence. Magn Reson Med 79:2057-2066, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH.

PURPOSE: To develop a model-based reconstruction technique for single-shot T1 mapping with high spatial resolution, accuracy, and precision using an inversion-recovery (IR) fast low-angle shot (FLASH) acquisition with radial encoding. METHODS: The proposed model-based reconstruction jointly estimates all model parameters, that is, the equilibrium magnetization, steady-state magnetization, 1/ T1*, and all coil sensitivities from the data of a single-shot IR FLASH acquisition with a small golden-angle radial trajectory. Joint sparsity constraints on the parameter maps are exploited to improve the performance of the iteratively regularized Gauss-Newton method chosen for solving the nonlinear inverse problem. Validations include both a numerical and experimental T1 phantom, as well as in vivo studies of the human brain and liver at 3 T. RESULTS: In comparison to previous reconstruction methods for single-shot T1 mapping, which are based on real-time MRI with pixel-wise fitting and a model-based approach with a predetermination of coil sensitivities, the proposed method presents with improved robustness against phase errors and numerical precision in both phantom and in vivo studies. CONCLUSION: The comprehensive model-based reconstruction with L1 regularization offers rapid and robust T1 mapping with high accuracy and precision. The method warrants accelerated computing and online implementation for extended clinical trials. Magn Reson Med 79:730-740, 2018. © 2017 International Society for Magnetic Resonance in Medicine.