Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.

PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q-space data. METHODS: We implement three DL approaches to jointly estimate parametric maps of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and multi-compartment spherical mean technique (SMT). A per-voxel q-space deep learning (1D-qDL), a per-slice convolutional neural network (2D-CNN), and a 3D-patch-based microstructure estimation with sparse coding using a separable dictionary (MESC-SD) network are considered. RESULTS: The accuracy of estimated diffusion maps depends on the q-space undersampling, the selected network architecture, and the region and the parameter of interest. The smallest errors are observed for the MESC-SD network architecture (less than 10 % normalized RMSE in most brain regions). CONCLUSION: Our experiments show that DL methods are very efficient tools to simultaneously estimate several diffusion maps from undersampled q-space data. These methods can significantly reduce both the scan ( ∼ 6-fold) and processing times ( ∼ 25-fold) for estimating advanced parametric diffusion maps while achieving a reasonable accuracy.

Whole-heart, ungated, free-breathing, cardiac-phase-resolved myocardial perfusion MRI by using Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state (CRIMP).

PURPOSE: To develop a whole-heart, free-breathing, non-electrocardiograph (ECG)-gated, cardiac-phase-resolved myocardial perfusion MRI framework (CRIMP; Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state) and test its quantification feasibility. METHODS: CRIMP used interleaved radial simultaneous multi-slice (SMS) slice groups to cover the whole heart in 9 or 12 short-axis slices. The sequence continuously acquired data without magnetization preparation, ECG gating or breath-holding, and captured multiple cardiac phases. Images were reconstructed by a motion-compensated patch-based locally low-rank reconstruction. Bloch simulations were performed to study the signal-to-noise ratio/contrast-to-noise ratio (SNR/CNR) for CRIMP and to study the steady-state signal under motion. Seven patients were scanned with CRIMP at stress and rest to develop the sequence. One human and two dogs were scanned at rest with a dual-bolus method to test the quantification feasibility of CRIMP. The dual-bolus scans were performed using both CRIMP and an ungated radial SMS saturation recovery (SMS-SR) sequence with injection dose = 0.075 mmol/kg to compare the sequences in terms of SNR, cardiac phase resolution and quantitative myocardial blood flow (MBF). RESULTS: Perfusion images with multiple cardiac phases in all image slices with a temporal resolution of 72 ms/frame were obtained. Simulations and in-vivo acquisitions showed CRIMP kept the inner slices in steady-state regardless of motion. CRIMP outperformed SMS-SR in slice coverage (9 over 6), SNR (mean 20% improvement), and provided cardiac phase resolution. CRIMP and SMS-SR sequences provided comparable MBF values (rest systolic CRIMP = 0.58 ± 0.07, SMS-SR = 0.61 ± 0.16). CONCLUSION: CRIMP allows for whole-heart, cardiac-phase-resolved myocardial perfusion images without ECG-gating or breath-holding. The sequence can provide MBF if an accurate arterial input function is obtained separately.

Quantitative 3D myocardial perfusion with an efficient arterial input function.

PURPOSE: The purpose of this study was to further develop and combine several innovative sequence designs to achieve quantitative 3D myocardial perfusion. These developments include an optimized 3D stack-of-stars readout (150 ms per beat), efficient acquisition of a 2D arterial input function, tailored saturation pulse design, and potential whole heart coverage during quantitative stress perfusion. THEORY AND METHODS: All studies were performed free-breathing on a Prisma 3T MRI scanner. Phantom validation was used to verify sequence accuracy. A total of 21 subjects (3 patients with known disease) were scanned, 12 with a rest only protocol and 9 with both stress (regadenoson) and rest protocols. First pass quantitative perfusion was performed with gadoteridol (0.075 mmol/kg). RESULTS: Implementation and quantitative perfusion results are shown for healthy subjects and subjects with known coronary disease. Average rest perfusion for the 15 included healthy subjects was 0.79 ± 0.19 mL/g/min, the average stress perfusion for 6 healthy subject studies was 2.44 ± 0.61 mL/g/min, and the average global myocardial perfusion reserve ratio for 6 healthy subjects was 3.10 ± 0.24. Perfusion deficits for 3 patients with ischemia are shown. Average resting heart rate was 59 ± 7 bpm and the average stress heart rate was 81 ± 10 bpm. CONCLUSION: This work demonstrates that a quantitative 3D myocardial perfusion sequence with the acquisition of a 2D arterial input function is feasible at high stress heart rates such as during stress. T1 values and gadolinium concentrations of the sequence match the reference standard well in a phantom, and myocardial rest and stress perfusion and myocardial perfusion reserve values are consistent with those published in literature.

Reproducibility of clinical late gadolinium enhancement magnetic resonance imaging in detecting left atrial scar after atrial fibrillation ablation.

INTRODUCTION: Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) can be used to detect postablation atrial scar (PAAS) but its reproducibility and reliability in clinical scans across different magnetic flux densities and scar detection methods are unknown. METHODS: Patients (n = 45) having undergone two consecutive MRIs (3 months apart) on 3T and 1.5T scanners were studied. We compared PAAS detection reproducibility using four methods of thresholding: simple thresholding, Otsu thresholding, 3.3 standard deviations (SD) above blood pool (BP) mean intensity, and image intensity ratio (IIR). We performed a texture study by dividing the left atrial wall intensity histogram into deciles and evaluated the correlation of the same decile of the two scans as well as to a randomized distribution of intensities, quantified using Dice Similarity Coefficient (DSC). RESULTS: The choice of scanner did not significantly affect the reproducibility. The scar detection performed by Otsu thresholding (DSC of 71.26 ± 8.34) resulted in a better correlation of the two scans compared with the methods of 3.3 SD above BP mean intensity (DSC of 57.78 ± 21.2, p < .001) and IIR above 1.61 (DSC of 45.76 ± 29.55, p <.001). Texture analysis showed that correlation only for voxels with intensities in deciles above the 70th percentile of wall intensity histogram was better than random distribution (p < .001). CONCLUSIONS: Our results demonstrate that clinical LGE-MRI can be reliably used for visualizing PAAS across different magnetic flux densities if the threshold is greater than 70th percentile of the wall intensity distribution. Also, atrial wall-based thresholding is better than BP-based thresholding for reproducible PAAS detection.

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

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.

Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding.

PURPOSE: Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. METHODS: GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. RESULTS: GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. CONCLUSION: The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Validation of highly accelerated real-time cardiac cine MRI with radial k-space sampling and compressed sensing in patients at 1.5T and 3T.

PURPOSE: To validate an optimal 12-fold accelerated real-time cine MRI pulse sequence with radial k-space sampling and compressed sensing (CS) in patients at 1.5T and 3T. METHODS: We used two strategies to reduce image artifacts arising from gradient delays and eddy currents in radial k-space sampling with balanced steady-state free precession readout. We validated this pulse sequence against a standard breath-hold cine sequence in two patient cohorts: a myocardial infarction (n = 16) group at 1.5T and chronic kidney disease group (n = 18) at 3T. Two readers independently performed visual analysis of 68 cine sets in four categories (myocardial definition, temporal fidelity, artifact, noise) on a 5-point Likert scale (1 = nondiagnostic, 2 = poor, 3 = adequate or moderate, 4 = good, 5 = excellent). Another reader calculated left ventricular (LV) functional parameters, including ejection fraction. RESULTS: Compared with standard cine, real-time cine produced nonsignificantly different visually assessed scores, except for the following categories: 1) temporal fidelity scores were significantly lower (P = 0.013) for real-time cine at both field strengths, 2) artifacts scores were significantly higher (P = 0.013) for real-time cine at both field strengths, and 3) noise scores were significantly (P = 0.013) higher for real-time cine at 1.5T. Standard and real-time cine pulse sequences produced LV functional parameters that were in good agreement (e.g., absolute mean difference in ejection fraction <4%). CONCLUSION: This study demonstrates that an optimal 12-fold, accelerated, real-time cine MRI pulse sequence using radial k-space sampling and CS produces good to excellent visual scores and relatively accurate LV functional parameters in patients at 1.5T and 3T. Magn Reson Med 79:2745-2751, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Accelerated dynamic MRI using patch regularization for implicit motion compensation.

PURPOSE: To introduce a fast algorithm for motion-compensated accelerated dynamic MRI. METHODS: An efficient patch smoothness regularization scheme, which implicitly compensates for inter-frame motion, is introduced to recover dynamic MRI data from highly undersampled measurements. The regularization prior is a sum of distances between each rectangular patch in the dataset with other patches in the dataset using a saturating distance metric. Unlike current motion estimation and motion compensation (ME-MC) methods, the proposed scheme does not require reference frames or complex motion models. The proposed algorithm, which alternates between inter-patch shrinkage step and conjugate gradient algorithm, is considerably more computationally efficient than ME-MC methods. The reconstructions obtained using the proposed algorithm is compared against state-of-the-art methods. RESULTS: The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts. In comparison to the existing state-of-the-art ME-MC methods, PRICE provides comparable or even better image quality with faster reconstruction times (approximately nine times faster). CONCLUSION: The presented scheme enables computationally efficient and effective motion-compensated reconstruction in a variety of applications with large inter-frame motion and contrast changes. This algorithm could be seen as an alternative over the current state-of-the-art ME-MC schemes that are computationally expensive. Magn Reson Med 77:1238-1248, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Interstudy repeatability of self-gated quantitative myocardial perfusion MRI.

PURPOSE: To evaluate the interstudy repeatability of multislice quantitative cardiovascular magnetic resonance myocardial blood flow (MBF), myocardial perfusion reserve (MPR), and extracellular volume (ECV). A unique saturation recovery self-gated acquisition was used for the perfusion scans. MATERIALS AND METHODS: An ungated golden angle radial turboFLASH pulse sequence was used to scan 10 subjects on two separate days on a 3T scanner. A single saturation pulse was followed by a set of four slices. Rest and hyperemia scans were acquired during free breathing. The images were reconstructed using an iterative algorithm with spatiotemporal constraints. The ungated images were retrospectively binned (self-gated) into near-systole and near-diastole. Deformable registration was performed to adjust for respiratory and residual cardiac motion, and the data were fit with a Fermi model to estimate the interstudy repeatability of quantitative self-gated MBF and MPR. RESULTS: The coefficient of variation (CoV) of the territorial MPR using the self-gated near-systole data was 18.6%. The self-gated near-diastole data gave less good CoV of MPR, equal to 46.2%. For MBFs, and using smaller (segmental) regions, the CoVs were 20.1% and 22.7% for the estimation of myocardial blood flow at stress and rest, respectively, using the self-gated near-systole data. The self-gated near-diastole data gave CoV = 48.6% and 44.9% for stress and rest. CONCLUSION: The self-gated free-breathing technique for quantification of myocardial blood flow showed good repeatability for near-systole, with results comparable to published studies on interstudy repeatability of quantitative myocardial perfusion MRI using ECG-gating and breath-holds. Self-gated near-diastole data results were less repeatable. J. Magn. Reson. Imaging 2016;43:1369-1378.

Comparison of centric and reverse-centric trajectories for highly accelerated three-dimensional saturation recovery cardiac perfusion imaging.

PURPOSE: Highly undersampled three-dimensional (3D) saturation-recovery sequences are affected by k-space trajectory since the magnetization does not reach steady state during the acquisition and the slab excitation profile yields different flip angles in different slices. This study compares centric and reverse-centric 3D cardiac perfusion imaging. METHODS: An undersampled (98 phase encodes) 3D ECG-gated saturation-recovery sequence that alternates centric and reverse-centric acquisitions each time frame was used to image phantoms and in vivo subjects. Flip angle variation across the slices was measured, and contrast with each trajectory was analyzed via Bloch simulation. RESULTS: Significant variations in flip angle were observed across slices, leading to larger signal variation across slices for the centric acquisition. In simulation, severe transient artifacts were observed when using the centric trajectory with higher flip angles, placing practical limits on the maximum flip angle used. The reverse-centric trajectory provided less contrast, but was more robust to flip angle variations. CONCLUSION: Both of the k-space trajectories can provide reasonable image quality. The centric trajectory can have higher CNR, but is more sensitive to flip angle variation. The reverse-centric trajectory is more robust to flip angle variation.

Quantification of myocardial perfusion with self-gated cardiovascular magnetic resonance.

BACKGROUND: Current myocardial perfusion measurements make use of an ECG-gated pulse sequence to track the uptake and washout of a gadolinium-based contrast agent. The use of a gated acquisition is a problem in situations with a poor ECG signal. Recently, an ungated perfusion acquisition was proposed but it is not known how accurately quantitative perfusion estimates can be made from such datasets that are acquired without any triggering signal. METHODS: An undersampled saturation recovery radial turboFLASH pulse sequence was used in 7 subjects to acquire dynamic contrast-enhanced images during free-breathing. A single saturation pulse was followed by acquisition of 4-5 slices after a delay of ~40 msec. This was repeated without pause and without any type of gating. The same pulse sequence, with ECG-gating, was used to acquire gated data as a ground truth. An iterative spatio-temporal constrained reconstruction was used to reconstruct the undersampled images. After reconstruction, the ungated images were retrospectively binned ("self-gated") into two cardiac phases using a region of interest based technique and deformably registered into near-systole and near-diastole. The gated and the self-gated datasets were then quantified with standard methods. RESULTS: Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar. Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different. CONCLUSION: The self-gated technique for quantification of regional myocardial perfusion matched ECG-gated perfusion measurements well in normal subjects at rest. Self-gated systolic perfusion values matched ECG-gated perfusion values better than did diastolic values.

A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance.

A comprehensive review is undertaken of the methods available for 3D whole-heart first-pass perfusion (FPP) and their application to date, with particular focus on possible acceleration techniques. Following a summary of the parameters typically desired of 3D FPP methods, the review explains the mechanisms of key acceleration techniques and their potential use in FPP for attaining 3D acquisitions. The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing. An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol. Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature. Although many 3D FPP methods are too early in development for the type of clinical trials required to show any clear benefit over current 2D FPP methods, the review includes the small but growing quantity of clinical research work already using 3D FPP, alongside the more technical work. Broader challenges concerning FPP such as quantitative analysis are not covered, but challenges with particular impact on 3D FPP methods, particularly with regards to motion effects, are discussed along with anticipated future work in the field.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

PURPOSE: To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. METHODS: Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. RESULTS: Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. CONCLUSION: The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods.

Evaluation of highly accelerated real-time cardiac cine MRI in tachycardia.

Electrocardiogram (ECG)-gated breath-hold cine MRI is considered to be the gold standard test for the assessment of cardiac function. However, it may fail in patients with arrhythmia, impaired breath-hold capacity and poor ECG gating. Although ungated real-time cine MRI may mitigate these problems, commercially available real-time cine MRI pulse sequences using parallel imaging typically yield relatively poor spatiotemporal resolution because of their low image acquisition efficiency. As an extension of our previous work, the purpose of this study was to evaluate the diagnostic quality and accuracy of eight-fold-accelerated real-time cine MRI with compressed sensing (CS) for the quantification of cardiac function in tachycardia, where it is challenging for real-time cine MRI to provide sufficient spatiotemporal resolution. We evaluated the performances of eight-fold-accelerated cine MRI with CS, three-fold-accelerated real-time cine MRI with temporal generalized autocalibrating partially parallel acquisitions (TGRAPPA) and ECG-gated breath-hold cine MRI in 21 large animals with tachycardia (mean heart rate, 104 beats per minute) at 3T. For each cine MRI method, two expert readers evaluated the diagnostic quality in four categories (image quality, temporal fidelity of wall motion, artifacts and apparent noise) using a Likert scale (1-5, worst to best). One reader evaluated the left ventricular functional parameters. The diagnostic quality scores were significantly different between the three cine pulse sequences, except for the artifact level between CS and TGRAPPA real-time cine MRI. Both ECG-gated breath-hold cine MRI and eight-fold accelerated real-time cine MRI yielded all four scores of ≥ 3.0 (acceptable), whereas three-fold-accelerated real-time cine MRI yielded all scores below 3.0, except for artifact (3.0). The left ventricular ejection fraction (LVEF) measurements agreed better between ECG-gated cine MRI and eight-fold-accelerated real-time cine MRI (mean difference, -1.6%) than between ECG-gated cine MRI and three-fold-accelerated real-time cine MRI (mean difference, -5.7%). Eight-fold-accelerated real-time cine MRI with CS yields acceptable diagnostic quality and relatively accurate LVEF measurements in the challenging setting of tachycardia.

Alterations in atrial perfusion during atrial fibrillation.

Left atrial (LA) perfusion during disease states has been a topic of much interest, because the clinical implications and detrimental effects of lack of blood flow to the atria are numerous. In the chronic setting, changes in perfusion may lead to LA ischaemia and structural remodelling, a factor implicated in the self-perpetuation of chronic atrial fibrillation (AF). The association between AF and altered LA perfusion has been studied, but a direct causal association between perfusion changes and AF has not been established. A comprehensive literature search of Medline, Embase and Google Scholar databases was conducted from 1960 to February 2014. We systematically analysed reference lists of physiological articles and reviews for other possibly relevant studies. The aim of this review is to provide a comprehensive discussion of the AF-mediated changes in LA perfusion and the potential mechanisms underlying the alterations in coronary flow to the LA in this setting. In addition, we discuss the clinical contexts in which changes in LA perfusion may be relevant. Finally, this article highlights the need for longitudinal studies of AF that would elucidate the changes in LA perfusion resulting from chronic AF and lead to advancements in effective treatments to prevent progression of this disease.

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Ablation-induced left atrial mechanical dysfunction recovers in weeks after ablation.

BACKGROUND: The immediate impact of catheter ablation on left atrial mechanical function and the timeline for its recovery in patients undergoing ablation for atrial fibrillation (AF) remain uncertain. The mechanical function response to catheter ablation in patients with different AF types is poorly understood. METHODS: A total of 113 AF patients were included in this retrospective study. Each patient had three magnetic resonance imaging (MRI) studies in sinus rhythm: one pre-ablation, one immediate post-ablation (within 2 days after ablation), and one post-ablation follow-up MRI (≤ 3 months). We used feature tracking in the MRI cine images to determine peak longitudinal atrial strain (PLAS). We evaluated the change in strain from pre-ablation, immediately after ablation to post-ablation follow-up in a short-term study (< 50 days) and a 3-month study (3 months after ablation). RESULTS: The PLAS exhibited a notable reduction immediately after ablation, compared to both pre-ablation levels and those observed in follow-up studies conducted at short-term (11.1 ± 9.0 days) and 3-month (69.6 ± 39.6 days) intervals. However, there was no difference between follow-up and pre-ablation PLAS. The PLAS returned to 95% pre-ablation level within 10 days. Paroxysmal AF patients had significantly higher pre-ablation PLAS than persistent AF patients in pre-ablation MRIs. Both type AF patients had significantly lower immediate post-ablation PLAS compared with pre-ablation and post-ablation PLAS. CONCLUSION: The present study suggested a significant drop in PLAS immediately after ablation. Left atrial mechanical function recovered within 10 days after ablation. The drop in PLAS did not show a substantial difference between paroxysmal and persistent AF patients.

Model-based reconstruction of undersampled diffusion tensor k-space data.

The practical utility of diffusion tensor imaging, especially for 3D high-resolution spin warp experiments of ex vivo specimens, has been hampered by long acquisition times. To accelerate the acquisition, a compressed sensing framework that uses a model-based formulation to reconstruct diffusion tensor fields from undersampled k-space data was presented and evaluated. Accuracies in brain specimen white matter fiber orientation, fractional anisotropy, and mean diffusivity mapping were compared with alternative methods achievable using the same scan time via reduced image resolution, fewer diffusion encoding directions, standard compressed sensing, or asymmetrical sampling reconstruction. The efficiency of the proposed approach was also compared with fully sampled cases across a range of the number of diffusion encoding directions. In general, the proposed approach was found to reduce the image blurring and noise and to provide more accurate fiber orientation, fractional anisotropy, and mean diffusivity measurements compared with the alternative methods. Moreover, depending on the degree of undersampling used and the diffusion tensor imaging parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled diffusion tensor imaging datasets that consist of 33-67% more encoding directions and require proportionally longer scan times. The findings show model-based compressed sensing to be promising for improving the resolution, accuracy, or scan time of diffusion tensor imaging.

Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE.

For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm(2)) and temporal resolution (∼40 ms), to produce high-quality real-time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real-time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE). Compared with reference, breath-hold cine MRI, our eightfold accelerated real-time cine MRI produced significantly worse qualitative grades (1-5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real-time cine and breath-hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation <10% for left ventricular volumes. Our proposed eightfold accelerated real-time cine MRI with k-t SPARSE-SENSE is a promising modality for rapid imaging of myocardial function.

Rapid ungated myocardial perfusion cardiovascular magnetic resonance: preliminary diagnostic accuracy.

BACKGROUND: Myocardial perfusion cardiovascular magnetic resonance (CMR) is a well-established method for detection of ischemic heart disease. However, ECG gating problems can result in image degradation and non-diagnostic scans, particularly in patients with arrhythmias. METHODS: A turboFLASH saturation recovery pulse sequence was used without any ECG triggering. One saturation pulse followed by 4-5 slices of undersampled radial k-space images was acquired rapidly, on the order of 40-50 msec per image. The acquisition of the set of 4-5 slices was continuously repeated approximately 4 times per second. An iterative constrained reconstruction method was used to reconstruct the ungated images. The ungated perfusion images were post-processed into three different sets of images (ungated, self-gated to near systole, and self-gated to near diastole). To test the ungated approach and compare the different processing methods, 8 patients scheduled for coronary angiography underwent stress and rest perfusion imaging with the ungated acquisition. Six patients had a history of atrial fibrillation (AF). Three blinded readers assessed image quality and presence/absence of disease. RESULTS: All 8 subjects successfully completed the perfusion CMR protocol and 7/8 underwent coronary angiography. Three patients were in atrial fibrillation during CMR. Overall, the CMR images were of high quality as assessed by the three readers. There was little difference in image quality between patients in AF compared to those in sinus rhythm (3.6±0.7 vs. 3.3±0.5). Stress/rest perfusion imaging showed normal perfusion in 4 patients, fixed perfusion defects in 2 patients, and reversible perfusion defects in 2 patients, corresponding with angiographic results. Pooled results from the independent readers gave a sensitivity of 0.92 (CI 0.65-0.99) and specificity of 0.92 (CI 0.65-0.99) for the detection of coronary artery disease using ungated perfusion imaging. The same sensitivity, and a specificity of 1 (CI 0.76-1), was achieved when the images were self-gated after acquisition into near systole or near diastole. CONCLUSIONS: Ungated radial dynamic perfusion CMR can give high quality imaging in patients in sinus rhythm and during atrial fibrillation. In this small cohort, high diagnostic accuracy was possible with this rapid perfusion imaging sequence. An ungated approach simplifies the acquisition and could expand the role of perfusion CMR to include patients with arrhythmia and those with gating problems.