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To evaluate a novel 2D simultaneous multi-slice (SMS) myocardial perfusion acquisition and compare directly to a published quantitative 3D stack-of-stars (SoS) acquisition.A hybrid saturation recovery radial 2D SMS sequence following a single saturation was created for the quantification of myocardial blood flow (MBF). This sequence acquired three slices simultaneously and generated an arterial input function (AIF) using the first 24 rays. Validation was done in a novel way by alternating heartbeats between the hybrid 2D SMS and the 3D SoS acquisitions. Initial studies were done to study the effects of using only every other beat for the 2D SMS in two subjects, and for the 3D SoS in four subjects. The proposed alternating acquisitions were then performed in ten dog studies at rest, four dog studies at adenosine stress, and two human resting studies. Quantitative MBF analysis was performed for 2D SMS and 3D SoS separately, using a compartment model.Acquiring every-other-beat data resulted in 6 ± 5% ("ideal") and 11 ± 8% ("practical") perfusion changes for both 2D SMS and 3D SoS methods. For alternating acquisitions, 2D SMS and 3D SoS quantitative perfusion values were comparable for both the twelve rest studies (2D SMS: 0.69 ± 0.16 vs 3D: 0.69 ± 0.15 ml/g/min, p = 0.55) and the four stress studies (2D SMS: 1.28 ± 0.22 vs 3D: 1.30 ± 0.24 ml/g/min, p = 0.61).Every-other-beat acquisition changed estimated perfusion values relatively little for both sequences. The quantitative hybrid radial 2D SMS myocardial first-pass perfusion imaging sequence gave results similar to 3D perfusion when compared directly with an alternating beat acquisition.

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.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.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 10Our 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 (

Purpose To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method. Methods Dynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH sequence. Input to the networks consisted of complex coil combined images reconstructed using the inverse Fourier transform of undersampled radial SMS k-space data. Ground truth images were reconstructed using the PT-STCR pipeline. The performance of the residual booster 3D U-Net was tested by comparing it to state-of-the-art network architectures including MoDL, CRNN-MRI, and other U-Net variants. Results Results demonstrate significant improvements in speed requiring approximately 8 seconds to reconstruct one radial SMS dataset which is approximately 200 times faster than the PT-STCR method. Images reconstructed with the residual booster 3D U-Net retain quality of ground truth PT-STCR images (0.963 SSIM/40.238 PSNR/0.147 NRMSE). The residual booster 3D U-Net has superior performance compared to existing network architectures in terms of image quality, temporal dynamics, and reconstruction time. Conclusion Residual and booster learning combined with the 3D U-Net architecture was shown to be an effective network for reconstructing high-quality images from undersampled radial SMS datasets while bypassing the reconstruction time of the PT-STCR method.

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.

PurposeTo evaluate a myocardial perfusion acquisition that alternates 2D simultaneous multi-slice (SMS) and 3D stack-of-stars (SoS) acquisitions each heartbeat. MethodsA hybrid saturation recovery radial 2D SMS and a saturation recovery 3D SoS sequence were created for the quantification of myocardial blood flow (MBF). Initial studies were done to study the effects of using only every other beat for the 2D SMS in two subjects, and for the 3D SoS in two subjects. Alternating heartbeat 2D SMS and 3D SoS were then performed in ten dog studies at rest, four dog studies at adenosine stress, and two human resting studies. 2D SMS acquisition acquired three slices and 3D SoS acquired six slices. An arterial input function (AIF) for 2D SMS was obtained using the first 24 rays. For 3D, the AIF was obtained in a 2D slice prior to each 3D SoS readout. Quantitative MBF analysis was performed for 2D SMS and 3D SoS separately, using a two-compartment model. ResultsAcquiring every-other-beat data resulted in 5-20% perfusion changes at rest for both 2D SMS and 3D SoS methods. For alternating acquisitions, 2D SMS and 3D SoS quantitative perfusion values were comparable for both the twelve rest studies (2D SMS: 0.68±0.15 vs 3D: 0.69±0.15 ml/g/min, p=0.85) and the four stress studies (2D SMS: 1.28±0.22 vs 3D: 1.30±0.24 ml/g/min, p=0.66).ConclusionEvery-other-beat acquisition changed estimated perfusion values relatively little for both sequences. 2D SMS and 3D SoS gave similar quantitative perfusion estimates when used in an alternating every-other-heartbeat acquisition. Such an approach allows consideration of more diverse perfusion acquisitions that could have complementary features, although testing in a cardiac disease population is needed.

This paper addresses the contributions of low-frequency postural laryngeal kinematics to frequency and amplitude modulations of the vocal output signal. A one-dimensional Navier–Stokes solution for fluid transport and wave propagation is used along with a laryngeal source model to simulate voice production. Vocal fold adduction and vertical laryngeal posture are modulated sinusoidally at 2–10 Hz with various amplitudes to approximate kinematic phenomena observed with MRI during production of vocal vibrato and voice tremor. The acoustic modulations are quantified in terms of formant (resonance) and fundamental frequency shifts in the radiated output signal from the mouth. Postural oscillation of the larynx is a common kinematic component of vocal tremor, although less documented during production of vibrato.

Objective: Myocardial first-pass perfusion imaging with MRI is well-established clinically. However, it is potentially weakened by limited myocardial coverage compared to nuclear medicine. Clinical evaluations of whole-heart MRI perfusion by 3D methods, while promising, have to date had the limit of breathhold requirements at stress. This work aims to develop a new free-breathing 3D myocardial perfusion method, and to test its performance in a small patient population. Methods: This work required tolerance to respiratory motion for stress investigations, and therefore employed a “stack-of-stars” hybrid Cartesian-radial MRI acquisition method. The MRI sequence was highly optimised for rapid acquisition and combined with a compressed sensing reconstruction. Stress and rest datasets were acquired in four healthy volunteers, and in six patients with coronary artery disease (CAD), which were compared against clinical reference information.Results: This free-breathing method produced datasets that appeared consistent with clinical reference data in detecting moderate-to-strong induced perfusion abnormalities. However, the majority of the mild defects identified clinically were not detected by the method, potentially due to the presence of transient myocardial artefacts present in the images. Discussion: The feasibility of detecting CAD using this 3D first-pass perfusion sequence during free-breathing is demonstrated. Good agreement on typical moderate-to-strong CAD cases is promising, however, questions still remain on the sensitivity of the technique to milder cases.

Dynamic contrast-enhanced (DCE)-MRI has evolved into a mature imaging modality that is used to study the physiological processes and to reveal a variety of diseases, tumor stages, and organ functionalities. Quantitative DCE-MRI requires high spatiotemporal resolution to trace the contrast agent changes over time to obtain perfusion maps in the area of interest, where specific imaging challenges emerge. This chapter gives an overview of the basic principles and techniques involved in quantitative DCE-MRI. A review of the contrast agent mechanism is provided. An imaging protocol with advanced acquisition and reconstruction methods, including data undersampling, non-Cartesian sampling, parallel imaging, and constrained reconstruction, are also discussed. These techniques can achieve a higher spatiotemporal resolution for quantitative analysis. Available options for postprocessing of DCE-MRI data are also discussed, involving motion compensation, area- and pixel-wise methods, and tracer kinetic models.

Background: Quantifying myocardial perfusion is complicated by the complexity of pharmacokinetic model being used and the reliability of perfusion parameter estimates. More complex modeling provides more information about the underlying physiology, but too many parameters in complex models introduce a new problem of reliable estimation. To overcome the problem of multiple parameters, we have developed a technique that combines knowledge from two different cardiac magnetic resonance (MR) imaging techniques: dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and T1 mapping. Using extracellular volume (ECV) estimates from T1 mapping may allow more robust model parameter estimates. Methods: Simulations and human scans were performed. The myocardial perfusion scans used an ungated saturation recovery prepared TurboFLASH pulse sequence. Four short-axis (SA) slices were acquired after a single saturation pulse with a saturation recovery time of ~25 ms before the first slice. Gadoteridol was injected and ~240 frames were acquired over a minute with shallow breathing and no electrocardiograph (ECG) gating. This was followed 20±5 minutes later by an injection of regadenoson to induce hyperemia. The data were acquired using an under-sampled golden angle radial acquisition. Modified look-locker inversion recovery (MOLLI) T1 mapping was performed in 3 slices pre- and post-contrast. The pre- and post-contrast T1 maps were used for ECV estimation. Quantification of perfusion was done using a 4-parameter model with additional information about ECV supplied during model fitting. Phase contrast scans of the coronary sinus (CS) were acquired at rest and immediately after the stress perfusion acquisition to estimate global flow. Results: Without ECV information, the 5-parameter model fails to converge to a unique solution and often gives incorrect estimates for the perfusion parameters. The myocardial blood flow (MBF) estimates during rest and stress were 0.9±0.1 and 2.3±0.6 mL/min/g, respectively. The extraction fraction estimates were 0.49±0.04 and 0.34±0.05 during rest and stress, respectively. Conclusions: These results show that it is possible to successfully fit a dynamic perfusion model with an extraction fraction parameter by using information from T1 mapping scans. This hybrid approach is especially important when the 5-parameter model alone fails to converge on a unique solution. This work is a good example of exploiting information overlaps between various cardiac MR imaging techniques.

Objective: To develop a kernel optimization method called coil-combined split slice-GRAPPA (CC-SSG) to improve the accuracy of the reconstructed coil-combined images for simultaneous multi-slice (SMS) diffusion weighted imaging (DWI) data. Methods: The CC-SSG method optimizes the tuning parameters in the k-space SSG kernels to achieve an optimal trade-off between the intra-slice artifact and inter-slice leakage after the root-sum-of-squares (rSOS) coil combining of the de-aliased SMS DWI data. A detailed analysis is conducted to evaluate the contributions of the intra-slice artifact and inter-slice leakage to the total reconstruction error after coil combining. Results: Comparisons of the proposed CC-SSG method with the slice-GRAPPA (SG) and split slice-GRAPPA (SSG) methods are provided using two in-vivo readout-segmented (RS) EPI datasets collected from stroke patients. The CC-SSG method demonstrates improved accuracy of the reconstructed coil-combined images and the estimated diffusion tensor imaging (DTI) maps. Conclusion: CC-SSG strikes a good balance between the intra-slice artifact and inter-slice leakage for rSOS coil combining, and so can yield better reconstruction performance compared to SG and SSG for rSOS reconstruction. The optimal trade-off between the two artifacts is robust to the contrast of SMS data and the choice of the coil combining method.

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.

Purpose Dynamic contrast enhanced MRI of the heart typically acquires 2-4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework. Methods 12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio. Results Full coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions. Conclusion Acquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.

The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single-band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by ∼ 5% and increases the structural similarity index (SSIM) by ∼ 4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dispersion index (ODI) by ∼ 5% and the mean value of intracellular volume fraction by ∼ 7% in regions of interest in brain white matter compared to SSG.

Current late gadolinium enhancement (LGE) imaging of left atrial (LA) scar or fibrosis is relatively slow and requires 5-15min to acquire an undersampled (R=1.7) 3D navigated dataset. The GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) based parallel imaging method is the current clinical standard for accelerating 3D LGE imaging of the LA and permits an acceleration factor ~R=1.7. Two compressed sensing (CS) methods have been developed to achieve higher acceleration factors: a patch based collaborative filtering technique tested with acceleration factor R~3, and a technique that uses a 3D radial stack-of-stars acquisition pattern (R~1.8) with a 3D total variation constraint. The long reconstruction time of these CS methods makes them unwieldy to use, especially the patch based collaborative filtering technique. In addition, the effect of CS techniques on the quantification of percentage of scar/fibrosis is not known. We sought to develop a practical compressed sensing method for imaging the LA at high acceleration factors. In order to develop a clinically viable method with short reconstruction time, a Split Bregman (SB) reconstruction method with 3D total variation (TV) constraints was developed and implemented. The method was tested on 8 atrial fibrillation patients (4 pre-ablation and 4 post-ablation datasets). Blur metric, normalized mean squared error and peak signal to noise ratio were used as metrics to analyze the quality of the reconstructed images, Quantification of the extent of LGE was performed on the undersampled images and compared with the fully sampled images. Quantification of scar from post-ablation datasets and quantification of fibrosis from pre-ablation datasets showed that acceleration factors up to R~3.5 gave good 3D LGE images of the LA wall, using a 3D TV constraint and constrained SB methods. This corresponds to reducing the scan time by half, compared to currently used GRAPPA methods. Reconstruction of 3D LGE images using the SB method was over 20 times faster than standard gradient descent methods.

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.

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 2015.

Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.

In cardiac perfusion imaging, choice of flip angle is an important factor for steady state acquisition. This work focuses on presenting an analytical framework for understanding how non-ideal slice excitation profiles affect contrast in ungated 2D steady state cardiac perfusion studies, and to study a technique for estimating flip angle that maximizes enhanced/unenhanced myocardial contrast-to-noise ratio (CNR) in single slice and multi-slice acquisitions. A numerical simulation of ungated 2D golden ratio radial spoiled gradient echo (SPGR) was created that takes into consideration the actual (Bloch simulated) slice excitation profile. The effect of slice excitation profile on myocardial CNR as a function of flip angle was assessed in phantoms and in vivo. For fast RF pulses, the flip angle that yields maximum CNR (considering the actual slice excitation profile) was considerably higher than expected, assuming an ideal excitation. The simulation framework presented accurately predicts the flip angle yielding maximum CNR when the actual slice excitation profile is taken into consideration. The prescribed flip angle for optimal contrast in ungated 2D steady-state SPGR cardiac perfusion studies can vary significantly from that calculated when an ideal slice excitation profile is assumed. Consideration of the actual slice excitation can yield a more optimal flip angle estimate in both the single slice and multi-slice cases.

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

Cardiovascular magnetic resonance (CMR) perfusion has been established as a useful imaging modality for the detection of coronary artery disease (CAD). However, there are several limitations when applying standard, ECG-gated stress/rest perfusion CMR to patients with atrial fibrillation (AF). In this study we investigate an approach with no ECG gating and a rapid rest/stress perfusion protocol to determine its accuracy for detection of CAD in patients with AF. 26 patients with AF underwent a rapid rest/regadenoson stress CMR perfusion imaging protocol, and all patients had X-ray coronary angiography. An ungated radial myocardial perfusion sequence was used. Imaging protocol included: rest perfusion image acquisition, followed nearly immediately by administration of regadenoson to induce hyperemia, 60 s wait, and stress image acquisition. CMR perfusion images were interpreted by three blinded readers as normal or abnormal. Diagnostic accuracy was evaluated by comparison to X-ray angiography. 21 of the CMR rest/stress perfusion scans were negative, and 5 were positive by angiography criteria. Majority results of the ungated datasets from all of the readers showed a sensitivity, specificity and accuracy of 80, 100 and 96%, respectively, for detection of CAD. An ungated, rapid rest/stress regadenoson perfusion CMR protocol appears to be useful for the diagnosis of obstructive CAD in patients with AF.

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. Magn Reson Med 74:1070–1076, 2015. © 2014 Wiley Periodicals, Inc.

Purpose To investigate algebraic reconstruction technique (ART) for parallel imaging reconstruction of radial data, applied to accelerated cardiac cine. Methods A graphics processing unit (GPU)-accelerated ART reconstruction was implemented and applied to simulations, point spread functions and in 12 subjects imaged with radial cardiac cine acquisitions. Cine images were reconstructed with radial ART at multiple undersampling levels (192 Nr × Np = 96 to 16). Images were qualitatively and quantitatively analyzed for sharpness and artifacts, and compared to filtered back-projection, and conjugate gradient SENSE. Results Radial ART provided reduced artifacts and mainly preserved spatial resolution, for both simulations and in vivo data. Artifacts were qualitatively and quantitatively less with ART than filtered back-projection using 48, 32, and 24 Np, although filtered back-projection provided quantitatively sharper images at undersampling levels of 48-24 Np (all P

Purpose To evaluate the use of three different pre-reconstruction interpolation methods to convert non-Cartesian k-space data to Cartesian samples such that iterative reconstructions can be performed more simply and more rapidly. Methods Phantom as well as cardiac perfusion radial datasets were reconstructed by four different methods. Three of the methods used pre-reconstruction interpolation once followed by a fast Fourier transform (FFT) at each iteration. The methods were: bilinear interpolation of nearest-neighbor points (BINN), 3-point interpolation, and a multi-coil interpolator called GRAPPA Operator Gridding (GROG). The fourth method performed a full non-Uniform FFT (NUFFT) at each iteration. An iterative reconstruction with spatiotemporal total variation constraints was used with each method. Differences in the images were quantified and compared. Results The GROG multi-coil interpolation, the 3-point interpolation, and the NUFFT-at-each-iteration approaches produced high quality images compared to BINN, with the GROG-derived images having the fewest streaks among the three pre-interpolation approaches. However, all reconstruction methods produced approximately equal results when applied to perfusion quantitation tasks. Pre-reconstruction interpolation gave approximately an 83% reduction in reconstruction time. Conclusion Image quality suffers little from using a pre-reconstruction interpolation approach compared to the more accurate NUFFT-based approach. GROG-based pre-reconstruction interpolation appears to offer the best compromise by using multi-coil information to perform the interpolation to Cartesian sample points prior to image reconstruction. Speed gains depend on the implementation and relatively standard optimizations on a MATLAB platform result in pre-interpolation speedups of ~6 compared to using NUFFT at every iteration, reducing the reconstruction time from around 42 minutes to 7 minutes. This article is protected by copyright. All rights reserved.

Objective Simultaneous multi-slice (SMS) imaging is a slice acceleration technique that acquires multiple slices in the same time as a single slice. Radial controlled aliasing in parallel imaging results in higher acceleration (radial CAIPIRINHA or CAIPI) is a promising SMS method with less severe slice aliasing artifacts as compared to its Cartesian counterpart. Here we use radial CAIPI with data undersampling and constrained reconstruction to improve the utility of ungated cardiac perfusion acquisitions. We test the proposed framework with a traditional saturation recovery fast low-angle shot (turboFLASH) sequence and also without saturation recovery as a steady-state spoiled gradient echo (SPGR) sequence on animal and human studies. Methods Simulations and phantom studies were performed for both the turboFLASH and the SPGR radial CAIPI methods. Ungated undersampled golden ratio radial CAIPI data with saturation recovery were acquired in 8 dogs and 2 human subjects. The CAIPI data without saturation pulses were acquired in 4 human subjects. For both methods, slice acceleration factors of two and three were used. A new spatio-temporal reconstruction using total variation and patch-based low rank constraints was used to jointly reconstruct the multi-slice multi-coil images. Results Phantom scans and computer simulations showed that ungated SPGR generally provides better contrast to noise ratio (CNR) than the saturation recovery sequence if the saturation recovery time is less than 100 ms. Both of the ungated radial CAIPI methods demonstrated promising image quality in terms of preserving dynamics of the contrast agent and maintaining anatomical structures, even with three slices acquired simultaneously. Conclusion Ungated simultaneous multi-slice acquisitions with either a saturation recovery turboFLASH sequence or a steady-state gradient echo SPGR sequence are feasible and provide increased slice coverage without loss of temporal resolution. Compared with a sensitivity encoding (SENSE) SMS reconstruction, the constrained reconstruction method provides better image quality for undersampled radial CAIPI data.

The practical utility of diffusion tensor imaging (DTI), 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 employs 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 (FA) and mean diffusivity (MD) mapping were compared to 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 to 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 provide more accurate fiber orientation, FA and MD measurements compared to the alternative methods. Moreover, depending on the degree of undersampling used and the DTI parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled DTI datasets that consist of 33% to 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 DTI.

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

Purpose: To determine the feasibility of three-dimensional (3D) hybrid radial (stack-of-stars) MRI with spatiotemporal total variation (TV) constrained reconstruction for dynamic contrast enhanced myocardial perfusion imaging. Methods: An ECG-triggered saturation recovery turboFLASH sequence with undersampled stack-of-stars sampling with spatiotemporal TV constrained reconstruction was developed for dynamic contrast enhanced myocardial perfusion imaging. Simulations were performed to study the dependence of the approach to steady state on flip angle and saturation recovery time for this stack-of-stars acquisition. Phantom studies were used to show the effect of the flip angle selection and imperfect spoiling on image qualities. Studies were done in three humans to test the feasibility of the approach for myocardial perfusion imaging. Results: The simulation and phantom studies showed that imperfect spoiling and magnetization changes during the readout were a function of flip angle and nonoptimized selection of flip angle could degrade the images. Low flip angle acquisitions in the human subjects result in images with good quality similar to multislice radial 2D images. Conclusions: 3D stack-of-stars sampling with spatiotemporal TV constrained reconstruction provides a promising alternative for myocardial perfusion imaging.

The monitoring of thermal ablation procedures would benefit from an acceleration in the rate at which MRI temperature maps are acquired. Constrained reconstruction techniques have been shown to be capable of generating high quality images using only a fraction of the k-space data. Here, we present a temporally constrained reconstruction (TCR) algorithm applied to proton resonance frequency shift (PRF) data. The algorithm generates images from undersampled data by iteratively minimizing a cost function. The unique challenges of using an iterative constrained reconstruction technique to generate real-time images were addressed. For a set of eight heating experiments on ex vivo porcine tissue, a maximum reduction factor of 4 was achieved while keeping the root mean square error (RMSE) of the temperature below 0.5 degrees C. For a set of three heating experiments on in vivo canine muscle tissue, the maximum reduction factor achieved was 3 while keeping the temperature RMSE below 1.0 degrees C. At these reduction factors, the TCR algorithm underpredicted the thermal dose by an average of 6% for the ex vivo data and 28% for the in vivo data. Compared with sliding window and low resolution reconstructions, the RMSE of the TCR algorithm was significantly lower (P < 0.05 in all cases).

Dynamic contrast-enhanced (DCE) MRI is a powerful technique to probe an area of interest in the body. Here a temporally constrained reconstruction (TCR) technique that requires less k-space data over time to obtain good-quality reconstructed images is proposed. This approach can be used to improve the spatial or temporal resolution, or increase the coverage of the object of interest. The method jointly reconstructs the space-time data iteratively with a temporal constraint in order to resolve aliasing. The method was implemented and its feasibility tested on DCE myocardial perfusion data with little or no motion. The results obtained from sparse k-space data using the TCR method were compared with results obtained with a sliding-window (SW) method and from full data using the standard inverse Fourier transform (IFT) reconstruction. Acceleration factors of 5 (R = 5) were achieved without a significant loss in image quality. Mean improvements of 28 +/- 4% in the signal-to-noise ratio (SNR) and 14 +/- 4% in the contrast-to-noise ratio (CNR) were observed in the images reconstructed using the TCR method on sparse data (R = 5) compared to the standard IFT reconstructions from full data for the perfusion datasets. The method has the potential to improve dynamic myocardial perfusion imaging and also to reconstruct other sparse dynamic MR acquisitions.

Introduction: In patients with atrial fibrillation, the diagnostic accuracy of standard, gated stress/rest perfusion cardiovascular magnetic resonance (CMR) for detection of coronary artery (CAD) disease remains uncertain. Here we investigated using instead an ungated approach with no ECG gating with a rapid rest/stress perfusion protocol to determine its accuracy for detection of CAD in this patient population. To overcome the potential issue of peri-infarct ischemia with a rest-first sequence, we gave very little time between rest and stress perfusion scans. Methods: 23 patients with atrial fibrillation (68 ± 13 years, 13 males and 10 females) underwent a very rapid rest/regadenoson stress perfusion imaging protocol and X-Ray angiography in this study. An ungated radial myocardial perfusion sequence was used (TR/TE = 2.2/1.2 msec, 3T, 20 rays/slice, 5 slices after each saturation pulse, ~2х2х8 mm). Rest/stress protocol was performed in the following order: rest image acquisition (0.05 mmol/kg gadoteridol), administration of regadenoson 0.4 mg intravenously (0.4 mg/5 mL) to induce hyperemia, 70 second wait, then stress image acquisition (0.075 mmol/kg gadoteridol). CMR perfusion images were interpreted by a blinded reader as normal or abnormal, without viewing cine or late gadolinium enhanced images, and also evaluated for quality (1 to 5, lowest to highest). Diagnostic accuracy was evaluated by comparison to X-Ray angiography: significant lesions were defined as stenosis >70 % stenosis or functional flow reserve Results: 18 of the scans were negative and 5 were positive by angiography criteria. Sensitivity, specificity and accuracy for detection of CAD were 100%, 89% and 91%, respectively. Average scan time for imaging acquisition was 6.5 ± 4.0 minutes. Average image quality for both rest and stress perfusion images was 3.2 ± 0.9. Conclusions: An ungated myocardial perfusion sequence with rapid rest/regadenoson stress perfusion protocol addresses the concern of lingering hyperemia with regadenoson and problematic gating. This study suggests that assessment of myocardial perfusion with an ungated technique is an approach with high accuracy for detection of significant CAD in patients with atrial fibrillation.

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.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.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.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.