Real-time Cardiac Imaging Research Articles

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

To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. 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. 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. NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-resolution spiral real-time cine imaging, including 2D balanced steady-state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U-Net-based image reconstruction network and 2D U-Net-based image segmentation network were proposed and evaluated. Low-rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal-to-noise ratio, normalized root-mean-square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL-based image reconstruction and quantification techniques enabled 1-min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high-resolution spiral real-time cine imaging with excellent performance.

Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery.

The increase in the use of ultrasound-guided interventional therapy for cardiovascular diseases has increased the importance of intraoperative real-time cardiac ultrasound image interpretation. We thus aimed to develop a deep learning-based model to accurately identify, localize, and track the critical cardiac structures and lesions (9 kinds in total) and to validate the algorithm's performance using independent data sets. This diagnostic study developed a deep learning-based model using data collected from Fuwai Hospital between January 2018 and June 2019. The model was validated with independent French and American data sets. In total, 17,114 cardiac structures and lesions were used to develop the algorithm. The model findings were compared with those of 15 specialized physicians in multiple centers. For external validation, 516,805 tags and 27,938 tags were used from 2 different data sets. Regarding structure identification, the area under the receiver operating characteristic curve (AUC) of each structure in the training data set, optimal performance in the test data set, and median AUC of each structure identification were 1 (95% CI: 1-1), 1 (95% CI: 1-1), and 1 (95% CI: 1-1), respectively. Regarding structure localization, the optimal average accuracy was 0.83. As for structure identification, the accuracy of the model significantly outperformed the median performance of the experts (P<0.01). The optimal identification accuracies of the model in 2 independent external data sets were 89.5% and 90%, respectively (P=0.626). The model outperformed most human experts and was comparable to the optimal performance of all human experts in cardiac structure identification and localization, and could be used in the external data sets.

Educational Impact of Targeted Neonatal Echocardiography and Hemodynamics Programs on Neonatal-Perinatal Medicine Fellows.

Targeted neonatal echocardiography (TNE) is a real-time cardiac imaging modality used by a hemodynamics program to aid in diagnosis, treatment, and monitoring of neonatal cardiovascular illness. This study aimed to describe trainees' perspectives on existing hemodynamics education and perceived impacts of TNE and hemodynamics services on their education. This was a mixed quantitative and qualitative study that surveyed neonatal-perinatal medicine (NPM) fellow trainees in Canada and the United States, at programs both with and without a hemodynamics service. Ninety-two fellows responded to the survey, of whom 24 (26%) were enrolled in a program with an active hemodynamics service, 64 (70%) were training at a program without a hemodynamics service, and 4 (4%) were unsure. Trainees at centers with a hemodynamics service were more satisfied with their cardiovascular physiology education (91 vs. 69%, p = 0.040). Twenty-five percent of all trainees felt they do not have sufficient hemodynamics training to prepare them for independent practice. Areas of knowledge gaps were identified. Bedside teaching combined with didactic teaching was identified as useful means of teaching. Most trainees believed that TNE and a hemodynamics service are valuable educational assets. Thoughtful curriculum design for real-time and consolidation learning, with specific emphasis on content gaps, should be considered. · NPM Fellows perceive TNE & Neonatal Hemodynamics service as a valuable educational opportunity.. · Incorporation of TNE/Hemodynamics teaching into NPM curriculum can enrich trainee experience.. · Combining bedside and classroom teaching is key to successful cardiovascular training..

Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network.

Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time-consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning-based reconstruction of undersampled spiral k-space data is presented to enable free breathing real-time cardiac MRI with good image quality and short reconstruction times. Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non-Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard. The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9s per image, which is considerably shorter than conventional model-based approaches. The VN furthermore performed better than a U-Net and not inferior to a low-rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data. The proposed VN method enables real-time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.

Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images

Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum heart rate. The algorithm was validated for exercise by comparing LV mass during exercise to rest. Respiratory sorting to end expiration and end inspiration (processing time 20 to 40 min) succeeded in all research participants. Bias ± SD for LV mass was 0 ± 5 g when comparing real-time CMR at rest, and 0 ± 7 g when comparing real-time CMR during exercise to ECG-gated at rest. This study presents a semi-automatic algorithm to retrospectively perform respiratory sorting in free breathing real-time CMR. This can facilitate implementation of exercise CMR with non-ECG-gated free breathing real-time imaging, without any additional physiological input.

A System for Real-Time, Online Mixed-Reality Visualization of Cardiac Magnetic Resonance Images.

Image-guided cardiovascular interventions are rapidly evolving procedures that necessitate imaging systems capable of rapid data acquisition and low-latency image reconstruction and visualization. Compared to alternative modalities, Magnetic Resonance Imaging (MRI) is attractive for guidance in complex interventional settings thanks to excellent soft tissue contrast and large fields-of-view without exposure to ionizing radiation. However, most clinically deployed MRI sequences and visualization pipelines exhibit poor latency characteristics, and spatial integration of complex anatomy and device orientation can be challenging on conventional 2D displays. This work demonstrates a proof-of-concept system linking real-time cardiac MR image acquisition, online low-latency reconstruction, and a stereoscopic display to support further development in real-time MR-guided intervention. Data are acquired using an undersampled, radial trajectory and reconstructed via parallelized through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) implemented on graphics processing units. Images are rendered for display in a stereoscopic mixed-reality head-mounted display. The system is successfully tested by imaging standard cardiac views in healthy volunteers. Datasets comprised of one slice (46 ms), two slices (92 ms), and three slices (138 ms) are collected, with the acquisition time of each listed in parentheses. Images are displayed with latencies of 42 ms/frame or less for all three conditions. Volumetric data are acquired at one volume per heartbeat with acquisition times of 467 ms and 588 ms when 8 and 12 partitions are acquired, respectively. Volumes are displayed with a latency of 286 ms or less. The faster-than-acquisition latencies for both planar and volumetric display enable real-time 3D visualization of the heart.

4 Educational Impact of Targeted Neonatal Echocardiography (TNE) on Neonatal-Perinatal Medicine (NPM) Subspecialty Residents

Abstract Primary Subject area Neonatal-Perinatal Medicine Background Targeted Neonatal Echocardiography (TNE) is a real-time cardiac imaging modality used by neonatologists who have completed a minimum of one-year training in cardiovascular physiology and imaging methodology, to optimize management of neonatal cardiorespiratory compromise. TNE is provided as a clinical service, to aid in diagnosis, treatment, and monitoring of neonatal cardiovascular illness. Potential impacts of TNE on trainee education are unknown. Objectives This study aimed to describe trainees’ perspectives on existing neonatal hemodynamics education and perceived impacts of TNE on their education. Design/Methods This was a mixed quantitative and qualitative study that surveyed NPM subspecialty residents in Canada and the United States of America (USA), at centres both with and without TNE. Survey questions sought to explore current perspectives on cardiac and hemodynamics curriculum in training programs. Results 92 residents responded to the survey, of whom 24 (26%) were enrolled in a program with an active TNE service, 64 (70%) were training at a non-TNE centre, and 4 (4%) were unsure. Trainees at TNE centres were more satisfied with their overall hemodynamics training (91% vs. 69%, p=0.040; Table 1). 25% of all trainees felt they do not have sufficient hemodynamics training to prepare them for independent practice. On analysis of curriculum content, cardiac development, myocardial functioning in prematurity, and cardiac mechanics emerged as areas of knowledge gaps among all residents (Table 2). TNE centre trainees reported that bedside TNE teaching offers immediate insights into how patients respond to management decisions, while didactic TNE teaching allows for learning at a slower pace, with appropriate knowledge consolidation. Only 9% of respondents at TNE sites reported a formal process of hemodynamics review between clinical and TNE teams. 90% of respondents believed that a TNE service would positively impact their hemodynamics education by improvement in knowledge of cardiovascular physiology and management decisions. Conclusion Most trainees believed that TNE may be a valuable educational tool, with current perceived satisfaction with overall hemodynamics training higher among residents training at TNE centres. 25% of all trainees felt unprepared for transitioning to independent practice. Hence, thoughtful curriculum design for real-time and consolidation learning, with specific emphasis on content gaps, as well as working toward a formal process of hemodynamics review, should be considered.

Real-time exercise stress cardiac MRI with Fourier-series reconstruction from golden-angle radial data

Magnetic resonance imaging (MRI) can measure cardiac response to exercise stress for evaluating and managing heart patients in the practice of clinical cardiology. However, exercise stress cardiac MRI have been clinically limited by the ability of available MRI techniques to quantitatively measure fast and unstable cardiac dynamics during exercise. The presented work is to develop a new real-time MRI technique for improved quantitative performance of exercise stress cardiac MRI. This technique seeks to represent real-time cardiac images as a sparse Fourier-series along the time. With golden-angle radial acquisition, parallel imaging and compressed sensing can be integrated into a linear system of equations for resolving Fourier coefficients that are in turn used to generate real-time cardiac images from the Fourier-series representation. Fourier-series reconstruction from golden-angle radial data can effectively address data insufficiency due to MRI speed limitation, providing a real-time approach to exercise stress cardiac MRI. To demonstrate the feasibility, an exercise stress cardiac MRI experiment was run to investigate biventricular response to in-scanner biking exercise in a cohort of sixteen healthy volunteers. It was found that Fourier-series reconstruction from golden-angle radial data effectively detected exercise-induced increase in stroke volume and ejection fraction in a healthy heart. The presented work will improve the applications of exercise stress cardiac MRI in the practice of clinical cardiology.

AUTOMATIC EXTRACTION AND SIGN DETERMINATION OF RESPIRATORY SIGNAL IN REAL-TIME CARDIAC MAGNETIC RESONANCE IMAGING.

In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected sequentially under free-breathing conditions. A complete heartbeat from each slice is then used for cardiac function quantification. The inter-slice respiratory mismatch can compromise accurate quantification of cardiac function. Methods based on principal components analysis (PCA) have been proposed to extract the respiratory signal from RT cardiac cine, but these methods cannot resolve the inter-slice sign ambiguity of the respiratory signal. In this work, we propose a fully automatic sign correction procedure based on the similarity of neighboring slices and correlation to the center-of-mass curve. The proposed method is evaluated in eleven volunteers, with ten slices per volunteer. The motion in a manually selected region-of-interest (ROI) is used as a reference. The results show that the extracted respiratory signal has a high, positive correlation with the reference in all cases. The qualitative assessment of images also shows that the proposed approach can accurately identify heartbeats, one from each slice, belonging to the same respiratory phase. This approach can improve cardiac function quantification for RT cine without manual intervention.

Feasibility of real-time cardiac MRI in mice using tiny golden angle radial sparse.

Cardiovascular magnetic resonance imaging has proven valuable for the assessment of structural and functional cardiac abnormalities. Even although it is an established imaging method in small animals, the long acquisition times of gated or self-gated techniques still limit its widespread application. In this study, the application of tiny golden angle radial sparse MRI (tyGRASP) for real-time cardiac imaging was tested in 12 constitutive nexilin (Nexn) knock-out (KO) mice, both heterozygous (Het, N = 6) and wild-type (WT, N = 6), and the resulting functional parameters were compared with a well-established self-gating approach. Real-time images were reconstructed for different temporal resolutions of between 16.8 and 79.8 ms per image. The suggested approach was additionally tested for dobutamine stress and qualitative first-pass perfusion imaging. Measurements were repeated twice within 2 weeks for reproducibility assessment. In direct comparison with the high-quality, self-gated technique, the real-time approach did not show any significant differences in global function parameters for acquisition times below 50 ms (rest) and 31.5 ms (stress). Compared with WT, the end-diastolic volume (EDV) and end-systolic volume (ESV) were markedly higher (P < 0.05) and the ejection fraction (EF) was significantly lower in the Het Nexn-KO mice at rest (P < 0.001). For the stress investigation, a clear decrease of EDV and ESV, and an increase in EF, but maintained stroke volume, could be observed in both groups. Combined with ECG-triggering, tyGRASP provided first-pass perfusion data with a temporal resolution of one image per heartbeat, allowing the quantitative assessment of upslope curves in the blood-pool and myocardium. Excellent inter-study reproducibility was achieved in all the functional parameters. The tyGRASP is a valuable real-time MRI technique for mice, which significantly reduces the scan time in preclinical cardiac functional imaging, providing sufficient image quality for deriving accurate functional parameters, and has the potential to investigate real-time and beat-to-beat changes.

Sliding window reduced FOV reconstruction for real-time cardiac imaging

BackgroundCurrent functional cardiovascular imaging protocols mostly rely on electrocardiogram (ECG) gating and breathholding. The resulting image quality can substantially suffer from insufficient patient cooperation or severe arrhythmia. Real-time imaging can mitigate these effects but requires highly accelerated techniques, usually relying on non-cartesian trajectories and Compressed Sensing (CS). MethodsWe investigate a sliding window reduced field of view (FOV) Echo Planar Imaging (EPI) technique for real-time cardiac MRI. Segmented EPI has been combined with a subtraction technique for reducing the FOV in cardiac applications to the region of the beating heart. Residual respiratory motion, potentially impairing the image quality, has been addressed by continuous update of the static image fraction, which is derived from a low-temporal resolution sliding window reconstruction. For further acceleration, the proposed technique was combined with parallel imaging. ResultsThe sliding window reduced FOV technique was proven feasible to reconstruct images of diagnostic image quality at a temporal resolution of 36.5ms per image. Semi-quantitative evaluation of image quality showed significant improvement over the existing rFOV method (p=0.039). Derived functional parameters show comparable results as with the BH-CINE reference. However, a trend to a slight underestimation of the largest and smallest in-plane volumes is observed. ConclusionThe proposed technique is feasible of providing real-time cardiac MRI with a temporal resolution better than 40ms without the need of computably complex reconstruction techniques.

Parallel imaging and convolutional neural network combined fast MR image reconstruction: Applications in low-latency accelerated real-time imaging.

To develop and evaluate a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high-quality accelerated real-time MR imaging. Conventional Parallel Imaging reconstruction resolved as gradient descent steps was compacted as network layers and interleaved with convolutional layers in a general convolutional neural network. All parameters of the network were determined during the offline training process, and applied to unseen data once learned. The proposed network was first evaluated for real-time cardiac imaging at 1.5T and real-time abdominal imaging at 0.35T, using threefold to fivefold retrospective undersampling for cardiac imaging and threefold retrospective undersampling for abdominal imaging. Then, prospective undersampling with fourfold acceleration was performed on cardiac imaging to compare the proposed method with standard clinically available GRAPPA method and the state-of-the-art L1-ESPIRiT method. Both retrospective and prospective evaluations confirmed that the proposed network was able to images with a lower noise level and reduced aliasing artifacts in comparison with the single-coil based and L1-ESPIRiT reconstructions for cardiac imaging at 1.5T, and the GRAPPA and L1-ESPIRiT reconstructions for abdominal imaging at 0.35T. Using the proposed method, each frame can be reconstructed in less than 100ms, suggesting its clinical compatibility. The proposed Parallel Imaging and convolutional neural network combined reconstruction framework is a promising technique that allows low-latency and high-quality real-time MR imaging.

Stress echocardiography: An overview for use in pediatric and congenital cardiology.

Currently, the role of stress echocardiography primarily resides in diagnosing acquired coronary artery disease (CAD) in adults. Besides an increasing concern for traditional CAD in young patients due to obesity and other chronic pediatric diseases, there is also a growing population of adolescents and young adults with "at risk" coronary arteries due to: reimplanted coronaries in congenital heart disease, anomalous origin of the native coronary arteries, coronary abnormalities in Kawasaki's disease, and posttransplant coronary vasculopathy. Stress echocardiography is well suited for routine screening and monitoring in these patients. Also, due to the ability of stress echocardiography to provide real-time cardiac imaging, it is useful in areas beyond coronary ischemia. Utilizing Doppler derived velocities and pressure gradients, one is able to further evaluate and risk-stratify patients with valvular heart disease. In addition, stress echocardiography is useful in evaluating other areas including ventricular and coronary reserve. The benefits of stress echocardiography are it is: readily available, portable, and relatively cheap. It can be performed without sedation or radiation exposure which becomes very important in younger patients that require periodic monitoring. Stress echocardiography can also evaluate functional abnormalities instead of relative perfusion defects. Overall, stress echocardiography is currently an underutilized imaging modality that has a wide, and expanding, range of application in the practice of pediatric and congenital cardiology.

Real-time cardiac magnetic resonance cine imaging with sparse sampling and iterative reconstruction for left-ventricular measures: Comparison with gold-standard segmented steady-state free precession

BackgroundSegmented cine imaging with a steady-state free-precession sequence (Cine-SSFP) is currently the gold standard technique for measuring ventricular volumes and mass, but due to multi breath-hold (BH) requirements, it is prone to misalignment of consecutive slices, time consuming and dependent on respiratory capacity. Real-time cine avoids those limitations, but poor spatial and temporal resolution of conventional sequences has prevented its routine application. We sought to examine the accuracy and feasibility of a newly developed real-time sequence with aggressive under-sampling of k-space using sparse sampling and iterative reconstruction (Cine-RT). MethodsStacks of short-axis cines were acquired covering both ventricles in a 1.5T system using gold standard Cine-SSFP and Cine-RT. Acquisition parameters for Cine-SSFP were: acquisition matrix of 224×196, temporal resolution of 39ms, retrospective gating, with an average of 8 heartbeats per slice and 1–2 slices/BH. For Cine-RT: acquisition matrix of 224×196, sparse sampling net acceleration factor of 11.3, temporal resolution of 41ms, prospective gating, real-time acquisition of 1 heart-beat/slice and all slices in one BH. LV contours were drawn at end diastole and systole to derive LV volumes and mass. ResultsForty-one consecutive patients (15 male; 41±17years) in sinus rhythm were successfully included. All images from Cine-SSFP and Cine-RT were considered to have excellent quality. Cine-RT-derived LV volumes and mass were slightly underestimated but strongly correlated with gold standard Cine-SSFP. Inter- and intra-observer analysis presented similar results between both sequences. ConclusionsCine-RT featuring sparse sampling and iterative reconstruction can achieve spatial and temporal resolution equivalent to Cine-SSFP, providing excellent image quality, with similar precision measurements and highly correlated and only slightly underestimated volume and mass values.

Real-time SPARSE-SENSE cardiac cine MR imaging: optimization of image reconstruction and sequence validation

Improved real-time cardiac magnetic resonance (CMR) sequences have currently been introduced, but so far only limited practical experience exists. This study aimed at image reconstruction optimization and clinical validation of a new highly accelerated real-time cine SPARSE-SENSE sequence. Left ventricular (LV) short-axis stacks of a real-time free-breathing SPARSE-SENSE sequence with high spatiotemporal resolution and of a standard segmented cine SSFP sequence were acquired at 1.5T in 11 volunteers and 15 patients. To determine the optimal iterations, all volunteers' SPARSE-SENSE images were reconstructed using 10-200 iterations, and contrast ratios, image entropies, and reconstruction times were assessed. Subsequently, the patients' SPARSE-SENSE images were reconstructed with the clinically optimal iterations. LV volumetric values were evaluated and compared between both sequences. Sufficient image quality and acceptable reconstruction times were achieved when using 80 iterations. Bland-Altman plots and Passing-Bablok regression showed good agreement for all volumetric parameters. 80 iterations are recommended for iterative SPARSE-SENSE image reconstruction in clinical routine. Real-time cine SPARSE-SENSE yielded comparable volumetric results as the current standard SSFP sequence. Due to its intrinsic low image acquisition times, real-time cine SPARSE-SENSE imaging with iterative image reconstruction seems to be an attractive alternative for LV function analysis. • A highly accelerated real-time CMR sequence using SPARSE-SENSE was evaluated. • SPARSE-SENSE allows free breathing in real-time cardiac cine imaging. • For clinically optimal SPARSE-SENSE image reconstruction, 80 iterations are recommended. • Real-time SPARSE-SENSE imaging yielded comparable volumetric results as the reference SSFP sequence. • The fast SPARSE-SENSE sequence is an attractive alternative to standard SSFP sequences.

Dynamic MRI Using SmooThness Regularization on Manifolds (SToRM).

We introduce a novel algorithm to recover real time dynamic MR images from highly under-sampled k- t space measurements. The proposed scheme models the images in the dynamic dataset as points on a smooth, low dimensional manifold in high dimensional space. We propose to exploit the non-linear and non-local redundancies in the dataset by posing its recovery as a manifold smoothness regularized optimization problem. A navigator acquisition scheme is used to determine the structure of the manifold, or equivalently the associated graph Laplacian matrix. The estimated Laplacian matrix is used to recover the dataset from undersampled measurements. The utility of the proposed scheme is demonstrated by comparisons with state of the art methods in multi-slice real-time cardiac and speech imaging applications.

Non-Cartesian balanced steady-state free precession pulse sequences for real-time cardiac MRI.

To develop a new spiral-in/out balanced steady-state free precession (bSSFP) pulse sequence for real-time cardiac MRI and compare it with radial and spiral-out techniques. Non-Cartesian sampling strategies are efficient and robust to motion and thus have important advantages for real-time bSSFP cine imaging. This study describes a new symmetric spiral-in/out sequence with intrinsic gradient moment compensation and SSFP refocusing at TE = TR/2. In vivo real-time cardiac imaging studies were performed to compare radial, spiral-out, and spiral-in/out bSSFP pulse sequences. Furthermore, phase-based fat/water separation taking advantage of the refocusing mechanism of the spiral-in/out bSSFP sequence was also studied. The image quality of the spiral-out and spiral-in/out bSSFP sequences was improved with off-resonance and k-space trajectory correction. The spiral-in/out bSSFP sequence had the highest signal-to-noise ratio (SNR), contrast-to-noise ratio, and image quality ratings, with spiral-out bSSFP sequence second in each category and the radial bSSFP sequence third. The spiral-in/out bSSFP sequence provides separated fat and water images with no additional scan time. In this study, a new spiral-in/out bSSFP sequence was developed and tested. The superiority of spiral bSSFP sequences over the radial bSSFP sequence in terms of SNR and reduced artifacts was demonstrated in real-time MRI of cardiac function without image acceleration.

Comparison of a novel free breathing SSFP sequence with standard breath held SSFP- a pilot study

Background Despite advances in magnetic resonance imaging hardware and software, the acquisition of optimal cardiac magnetic resonance (CMR) images may be hampered by several factors. Most notably, scan length, the requirement for multiple breath holds and cardiac gating can all impact upon image quality. Real time cardiac imaging holds promise for reducing scan times via rapid acquisitions of images that are not reliant on breath holding or cardiac gating. Recently, a novel free breathing SSFP sequence was made available as part of a pre-release version of the HeartVista Cardiac Package, Version 1.0.0 (HeartVista, Inc.). Our goal was to compare the assessment of LV function between the HeartVista free breathing SSFP (FB-SSFP) sequence and standard FIESTA breath held SSFP.

Why is a new journal dedicated to echocardiography required?

Why is a new journal dedicated to echocardiography required?