Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction

Single-shot diffusion trace spectroscopic imaging using radial echo planar trajectories

PURPOSE

Demonstrate the feasibility and evaluate the performance of single-shot diffusion trace-weighted radial echo planar spectroscopic imaging (Trace DW-REPSI) for quantifying the trace ADC in phantom and in vivo using a 3T clinical scanner.

THEORY AND METHODS

Trace DW-REPSI datasets were acquired in 10 phantom and 10 healthy volunteers, with a maximum b-value of 1601 s/mm2 and diffusion time of 10.75 ms. The self-navigation properties of radial acquisitions were used for corrections of shot-to-shot phase and frequency shift fluctuations of the raw data. In vivo trace ADCs of total NAA (tNAA), total creatine (tCr), and total choline (tCho) extrapolated to pure gray and white matter fractions were compared, as well as trace ADCs estimated in voxels within white or gray matter-dominant regions.

RESULTS

Trace ADCs in phantom show excellent agreement with reported values, and in vivo ADCs agree well with the expected differences between gray and white matter. For tNAA, tCr, and tCho, the trace ADCs extrapolated to pure gray and white matter ranged from 0.18-0.27 and 0.26-0.38 μm2/ms, respectively. In sets of gray and white matter-dominant voxels, the values ranged from 0.21 to 0.27 and 0.24 to 0.31 μm2/ms, respectively. The overestimated trace ADCs from this sequence can be attributed to the short diffusion time.

CONCLUSION

This study presents the first demonstration of the single-shot diffusion trace-weighted spectroscopic imaging sequence using radial echo planar trajectories. The Trace DW-REPSI sequence could provide an estimate of the trace ADC in a much shorter scan time compared to conventional approaches that require three separate measurements.

Respiratory motion-corrected T1 mapping of the abdomen

OBJECTIVE

The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency.

MATERIALS AND METHODS

Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times.

RESULTS

For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s.

DISCUSSION

The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI

BACKGROUND

Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality.

PURPOSE

This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique.

METHODS

In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images.

RESULTS

SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns.

CONCLUSIONS

The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Reduction of ringing artifacts induced by diaphragm drifting in free-breathing dynamic pulmonary MRI using 3D koosh-ball acquisition

PURPOSE

To reduce the ringing artifacts of the motion-resolved images in free-breathing dynamic pulmonary MRI.

METHODS

A golden-step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior-inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four-dimensional images were reconstructed from the motion-resolved k-space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi-density-compensated NUFFT, extra-dimensional UTE, and motion-state weighted motion-compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects.

RESULTS

The quantified ringing artifacts level in the motion-resolved image is positively correlated with the quantified nonuniformity level of the corresponding k-space. The nonuniformity levels of the end-expiratory and end-inspiratory k-space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique.

CONCLUSION

Ringing artifacts in the motion-resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free-breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free-breathing liver and kidney MRI based on full-echo interleaved 3D radial acquisition.

Quiescent frame, contrast-enhanced coronary magnetic resonance angiography reconstructed using limited number of physiologic frames from 5D free-running acquisitions

BACKGROUND

5D, free-running imaging resolves sets of 3D whole-heart images in both cardiac and respiratory dimensions. In an application such as coronary imaging when a single, static image is of interest, computationally expensive offline iterative reconstruction is still needed to compute the multiple 3D datasets.

PURPOSE

Evaluate how the number of physiologic bins included in the reconstruction affects the computational cost and resulting image quality of a single, static volume reconstruction.

STUDY TYPE

Retrospective.

SUBJECTS

15 pediatric patients following Ferumoxytol infusion (4 mg/kg).

FIELD STRENGTH/SEQUENCE

1.5 T/Ungated 5D free-running GRE sequence.

ASSESSMENT

The raw data of each subject were binned and reconstructed into a 5D (x-y-z-cardiac-respiratory) images. 1, 3, 5, 7, and 9 bins adjacent to both sides of the retrospectively determined cardiac resting phase and 1, 3 bins adjacent to the end-expiration phase are used for limited frame reconstructions. The static volume within each limited reconstruction was compared with the corresponding full 5D reconstruction using the structural similarity index measure (SSIM). A non-linear regression model was used to fit SSIM with the percentage of data used compared to full reconstruction (% data). A linear regression model was used to fit computation time with % raw data used. Coronary artery sharpness is measured on each limited reconstructed images to determine the minimal number of cardiac and respiratory bins needed to preserve image quality.

STATISTICAL TESTS

The coefficient of determination (R2) is computed for each regression model.

RESULTS

The % of data used in the reconstruction was linearly related to the computational time (R2 = 0.99). The SSIM of the static image from the limited reconstructions is non-linearly related with the % of data used (R2 = 0.80). Over the 15 patients, the model showed SSIM of 0.9 with 18% of data, and SSIM of 0.96 with 30% of data. The coronary artery sharpness of images reconstructed using no less than 5 cardiac and all respiratory phases is not significantly different from the full reconstructed images using all cardiac and respiratory bins.

DATA CONCLUSION

Reconstruction using only a limited number of acquired physiological states can linearly reduce the computational cost while preserving similarity to the full reconstruction image. It is suggested to use no less than 5 cardiac and all respiratory phases in the limited reconstruction to best preserve the original quality seen on the full reconstructed images.

Stop moving: MR motion correction as an opportunity for artificial intelligence

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation

BACKGROUND

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

METHODS

Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis.

RESULTS

These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.

CONCLUSIONS

Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.

Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging

BACKGROUND

Free-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR.

METHODS

Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers.

RESULTS

Intra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52).

CONCLUSION

The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest.

Combined free-running four-dimensional anatomical and flow magnetic resonance imaging with native contrast using Synchronization of Neighboring Acquisitions by Physiological Signals

BACKGROUND

Four-dimensional (4D) flow magnetic resonance imaging (MRI) often relies on the injection of gadolinium- or iron-oxide-based contrast agents to improve vessel delineation. In this work, a novel technique is developed to acquire and reconstruct 4D flow data with excellent dynamic visualization of blood vessels but without the need for contrast injection. Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS) uses pilot tone (PT) navigation to retrospectively synchronize the reconstruction of two free-running three-dimensional radial acquisitions, to create co-registered anatomy and flow images.

METHODS

Thirteen volunteers and two Marfan syndrome patients were scanned without contrast agent using one free-running fast interrupted steady-state (FISS) sequence and one free-running phase-contrast MRI (PC-MRI) sequence. PT signals spanning the two sequences were recorded for retrospective respiratory motion correction and cardiac binning. The magnitude and phase images reconstructed, respectively, from FISS and PC-MRI, were synchronized to create SyNAPS 4D flow datasets. Conventional two-dimensional (2D) flow data were acquired for reference in ascending (AAo) and descending aorta (DAo). The blood-to-myocardium contrast ratio, dynamic vessel area, net volume, and peak flow were used to compare SyNAPS 4D flow with Native 4D flow (without FISS information) and 2D flow. A score of 0-4 was given to each dataset by two blinded experts regarding the feasibility of performing vessel delineation.

RESULTS

Blood-to-myocardium contrast ratio for SyNAPS 4D flow magnitude images (1.5 ± 0.3) was significantly higher than for Native 4D flow (0.7 ± 0.1, p < 0.01) and was comparable to 2D flow (2.3 ± 0.9, p = 0.02). Image quality scores of SyNAPS 4D flow from the experts (M.P.: 1.9 ± 0.3, E.T.: 2.5 ± 0.5) were overall significantly higher than the scores from Native 4D flow (M.P.: 1.6 ± 0.6, p = 0.03, E.T.: 0.8 ± 0.4, p < 0.01) but still significantly lower than the scores from the reference 2D flow datasets (M.P.: 2.8 ± 0.4, p < 0.01, E.T.: 3.5 ± 0.7, p < 0.01). The Pearson correlation coefficient between the dynamic vessel area measured on SyNAPS 4D flow and that from 2D flow was 0.69 ± 0.24 for the AAo and 0.83 ± 0.10 for the DAo, whereas the Pearson correlation between Native 4D flow and 2D flow measurements was 0.12 ± 0.48 for the AAo and 0.08 ± 0.39 for the DAo. Linear correlations between SyNAPS 4D flow and 2D flow measurements of net volume (r2 = 0.83) and peak flow (r2 = 0.87) were larger than the correlations between Native 4D flow and 2D flow measurements of net volume (r2 = 0.79) and peak flow (r2 = 0.76).

CONCLUSION

The feasibility and utility of SyNAPS were demonstrated for joint whole-heart anatomical and flow MRI without requiring electrocardiography gating, respiratory navigators, or contrast agents. Using SyNAPS, a high-contrast anatomical imaging sequence can be used to improve 4D flow measurements that often suffer from poor delineation of vessel boundaries in the absence of contrast agents.

Three-dimensional sector-wise golden angle-improved k-space uniformity after electrocardiogram binning

PURPOSE

To develop and evaluate a 3D sector-wise golden-angle (3D-SWIG) profile ordering scheme for cardiovascular MR cine imaging that maintains high k-space uniformity after electrocardiogram (ECG) binning.

METHOD

Cardiovascular MR (CMR) was performed at 1.5 T. A balanced SSFP pulse sequence was implemented with a novel 3D-SWIG radial ordering, where k-space was divided into wedges, and each wedge was acquired in a separate heartbeat. The high uniformity of k-space coverage after physiological binning can be used to perform functional imaging using a very short acquisition. The 3D-SWIG was compared with two commonly used 3D radial trajectories for CMR (i.e., double golden angle and spiral phyllotaxis) in numerical simulations. Free-breathing 3D-SWIG and conventional breath-held 2D cine were compared in patients (n = 17) referred clinically for CMR. Quantitative comparison was performed based on left ventricular segmentation.

RESULTS

Numerical simulations showed that 3D-SWIG both required smaller steps between successive readouts and achieved better k-space sampling uniformity after binning than either the double golden angle or spiral phyllotaxis trajectories. In vivo evaluation showed that measurements of left ventricular ejection fraction calculated from a 48 heart-beat free-breathing 3D-SWIG acquisition were highly reproducible and agreed with breath-held 2D-Cartesian cine (mean ± SD difference of -3.1 ± 3.5% points).

CONCLUSIONS

The 3D-SWIG acquisition offers a simple solution for highly improved k-space uniformity after physiological binning. The feasibility of the 3D-SWIG method is demonstrated in this study through whole-heart cine imaging during free breathing with an acquisition time of less than 1 min.

Kz-accelerated variable-density stack-of-stars MRI

This work aimed to develop a modified stack-of-stars golden-angle radial sampling scheme with variable-density acceleration along the slice (kz) dimension (referred to as VD-stack-of-stars) and to test this new sampling trajectory with multi-coil compressed sensing reconstruction for rapid motion-robust 3D liver MRI. VD-stack-of-stars sampling implements additional variable-density undersampling along the kz dimension, so that slice resolution (or volumetric coverage) can be increased without prolonging scan time. The new sampling trajectory (with increased slice resolution) was compared with standard stack-of-stars sampling with fully sampled kz (with standard slice resolution) in both non-contrast-enhanced free-breathing liver MRI and dynamic contrast-enhanced MRI (DCE-MRI) of the liver in volunteers. For both sampling trajectories, respiratory motion was extracted from the acquired radial data, and images were reconstructed using motion-compensated (respiratory-resolved or respiratory-weighted) dynamic radial compressed sensing reconstruction techniques. Qualitative image quality assessment (visual assessment by experienced radiologists) and quantitative analysis (as a metric of image sharpness) were performed to compare images acquired using the new and standard stack-of-stars sampling trajectories. Compared to standard stack-of-stars sampling, both non-contrast-enhanced and DCE liver MR images acquired with VD-stack-of-stars sampling presented improved overall image quality, sharper liver edges and increased hepatic vessel clarity in all image planes. The results have suggested that the proposed VD-stack-of-stars sampling scheme can achieve improved performance (increased slice resolution or volumetric coverage with better image quality) over standard stack-of-stars sampling in free-breathing DCE-MRI without increasing scan time. The reformatted coronal and sagittal images with better slice resolution may provide added clinical value.

4D Golden-Angle Radial MRI at Subsecond Temporal Resolution

Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.

Non-contrast free-breathing whole-heart 3D cine cardiovascular magnetic resonance with a novel 3D radial leaf trajectory

PURPOSE

To develop and validate a non-contrast free-breathing whole-heart 3D cine steady-state free precession (SSFP) sequence with a novel 3D radial leaf trajectory.

METHODS

We used a respiratory navigator to trigger acquisition of 3D cine data at end-expiration to minimize respiratory motion in our 3D cine SSFP sequence. We developed a novel 3D radial leaf trajectory to reduce gradient jumps and associated eddy-current artifacts. We then reconstructed the 3D cine images with a resolution of 2.0mm³ using an iterative nonlinear optimization algorithm. Prospective validation was performed by comparing ventricular volumetric measurements from a conventional breath-hold 2D cine ventricular short-axis stack against the non-contrast free-breathing whole-heart 3D cine dataset in each patient (n = 13).

RESULTS

All 3D cine SSFP acquisitions were successful and mean scan time was 07:09 ± 01:31 min. End-diastolic ventricular volumes for left ventricle (LV) and right ventricle (RV) measured from the 3D datasets were smaller than those from 2D (LV: 159.99 ± 42.99 vs. 173.16 ± 47.42; RV: 180.35 ± 46.08 vs. 193.13 ± 49.38; p-value≤0.044; bias<8%), whereas ventricular end-systolic volumes were more comparable (LV: 79.12 ± 26.78 vs. 78.46 ± 25.35; RV: 97.18 ± 32.35 vs. 102.42 ± 32.53; p-value≥0.190, bias<6%). The 3D cine data had a lower subjective image quality score.

CONCLUSION

Our non-contrast free-breathing whole-heart 3D cine sequence with novel leaf trajectory was robust and yielded smaller ventricular end-diastolic volumes compared to 2D cine imaging. It has the potential to make examinations easier and more comfortable for patients.

Accelerated 3D free-breathing high-resolution myocardial T1ρ mapping at 3 Tesla

PURPOSE

To develop a fast free-breathing whole-heart high-resolution myocardial T_1ρ mapping technique with robust spin-lock preparation that can be performed at 3 Tesla.

METHODS

An adiabatically excited continuous-wave spin-lock module, insensitive to field inhomogeneities, was implemented with an electrocardiogram-triggered low-flip angle spoiled gradient echo sequence with variable-density 3D Cartesian undersampling at a 3 Tesla whole-body scanner. A saturation pulse was performed at the beginning of each cardiac cycle to null the magnetization before T_1ρ preparation. Multiple T_1ρ -weighted images were acquired with T_1ρ preparations with different spin-lock times in an interleaved fashion. Respiratory self-gating approach was adopted along with localized autofocus to enable 3D translational motion correction of the data acquired in each heartbeat. After motion correction, multi-contrast locally low-rank reconstruction was performed to reduce undersampling artifacts. The accuracy and feasibility of the 3D T_1ρ mapping technique was investigated in phantoms and in vivo in 10 healthy subjects compared with the 2D T_1ρ mapping.

RESULTS

The 3D T_1ρ mapping technique provided similar phantom T_1ρ measurements in the range of 25-120 ms to the 2D T_1ρ mapping reference over a wide range of simulated heart rates. With the robust adiabatically excited continuous-wave spin-lock preparation, good quality 2D and 3D in vivo T_1ρ -weighted images and T_1ρ maps were obtained. Myocardial T_1ρ values with the 3D T_1ρ mapping were slightly longer than 2D breath-hold measurements (septal T_1ρ : 52.7 ± 1.4 ms vs. 50.2 ± 1.8 ms, P < 0.01).

CONCLUSION

A fast 3D free-breathing whole-heart T_1ρ mapping technique was proposed for T_1ρ quantification at 3 T with isotropic spatial resolution (2 mm³ ) and short scan time of ∼4.5 min.

Golden-Angle Radial MRI: Basics, Advances, and Applications

In recent years, golden‐angle radial sampling has received substantial attention and interest in the magnetic resonance imaging (MRI) community, and it has become a popular sampling trajectory for both research and clinical use. However, although the number of relevant techniques and publications has grown rapidly, there is still a lack of a review paper that provides a comprehensive overview and summary of the basics of golden‐angle rotation, the advantages and challenges/limitations of golden‐angle radial sampling, and recommendations in using different types of golden‐angle radial trajectories for MRI applications. Such a review paper is expected to be helpful both for clinicians who are interested in learning the potential benefits of golden‐angle radial sampling and for MRI physicists who are interested in exploring this research direction. The main purpose of this review paper is thus to present an overview and summary about golden‐angle radial MRI sampling. The review consists of three sections. The first section aims to answer basic questions such as: what is a golden angle; how is the golden angle calculated; why is golden‐angle radial sampling useful, and what are its limitations. The second section aims to review more advanced trajectories of golden‐angle radial sampling, including tiny golden‐angle rotation, stack‐of‐stars golden‐angle radial sampling, and three‐dimensional (3D) kooshball golden‐angle radial sampling. Their respective advantages and limitations and potential solutions to address these limitations are also discussed. Finally, the third section reviews MRI applications that can benefit from golden‐angle radial sampling and provides recommendations to readers who are interested in implementing golden‐angle radial trajectories in their MRI studies.

Impact of Respiratory Gating on Hemodynamic Parameters from 4D Flow MRI

The hemodynamic parameters from 4D flow datasets have shown promising diagnostic value in different cardiovascular pathologies. However, the behavior of these parameters can be affected when the 4D flow data are corrupted by respiratory motion. The purpose of this work was to perform a quantitative comparison between hemodynamic parameters computed from 4D flow cardiac MRI both with and without respiratory self-gating. We considered 4D flow MRI data from 15 healthy volunteers (10 men and 5 women, 30.40 ± 6.23 years of age) that were acquired at 3T. Using a semiautomatic segmentation process of the aorta, we obtained the hemodynamic parameters from the 4D flow MRI, with and without respiratory self-gating. A statistical analysis, using the Wilcoxon signed-rank test and Bland–Altman, was performed to compare the hemodynamic parameters from both acquisitions. We found that the calculations of the hemodynamic parameters from 4D flow data that were acquired without respiratory self-gating showed underestimated values in the aortic arch, and the descending and diaphragmatic aorta. We also found a significant variability of the hemodynamic parameters in the ascending aorta of healthy volunteers when comparing both methods. The 4D flow MRI requires respiratory compensation to provide reliable calculations of hemodynamic parameters.

Continuous cardiac thermometry via simultaneous catheter tracking and undersampled radial golden angle acquisition for radiofrequency ablation monitoring

The complexity of the MRI protocol is one of the factors limiting the clinical adoption of MR temperature mapping for real-time monitoring of cardiac ablation procedures and a push-button solution would ease its use. Continuous gradient echo golden angle radial acquisition combined with intra-scan motion correction and undersampled temperature determination could be a robust and more user-friendly alternative than the ultrafast GRE-EPI sequence which suffers from sensitivity to magnetic field susceptibility artifacts and requires ECG-gating. The goal of this proof-of-concept work is to establish the temperature uncertainty as well as the spatial and temporal resolutions achievable in an Agar-gel phantom and in vivo using this method. GRE radial golden angle acquisitions were used to monitor RF ablations in a phantom and in vivo in two sheep hearts with different slice orientations. In each case, 2D rigid motion correction based on catheter micro-coil signal, tracking its motion, was performed and its impact on the temperature imaging was assessed. The temperature uncertainty was determined for three spatial resolutions (1 × 1 × 3 mm³, 2 × 2 × 3 mm³, and 3 × 3 × 3 mm³) and three temporal resolutions (0.48, 0.72, and 0.97 s) with undersampling acceleration factors ranging from 2 to 17. The combination of radial golden angle GRE acquisition, simultaneous catheter tracking, intra-scan 2D motion correction, and undersampled thermometry enabled temperature monitoring in the myocardium in vivo during RF ablations with high temporal (< 1 s) and high spatial resolution. The temperature uncertainty ranged from 0.2 ± 0.1 to 1.8 ± 0.2 °C for the various temporal and spatial resolutions and, on average, remained superior to the uncertainty of an EPI acquisition while still allowing clinical monitoring of the RF ablation process. The proposed method is a robust and promising alternative to EPI acquisition to monitor in vivo RF cardiac ablations. Further studies remain required to improve the temperature uncertainty and establish its clinical applicability.

Cardiac MR: From Theory to Practice

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3 min

BACKGROUND

The application of cardiovascular magnetic resonance angiography (CMRA) for the assessment of thoracic aortic disease is often associated with prolonged and unpredictable acquisition times and residual motion artefacts. To overcome these limitations, we have integrated undersampled acquisition with image-based navigators and inline non-rigid motion correction to enable a free-breathing, contrast-free Cartesian CMRA framework for the visualization of the thoracic aorta in a short and predictable scan of 3 min.

METHODS

35 patients with thoracic aortic disease (36 ± 13y, 14 female) were prospectively enrolled in this single-center study. The proposed 3D T2-prepared balanced steady state free precession (bSSFP) sequence with image-based navigator (iNAV) was compared to the clinical 3D T2-prepared bSSFP with diaphragmatic-navigator gating (dNAV), in terms of image acquisition time. Three cardiologists blinded to iNAV vs. dNAV acquisition, recorded image quality scores across four aortic segments and their overall diagnostic confidence. Contrast ratio (CR) and relative standard deviation (RSD) of signal intensity (SI) in the corresponding segments were estimated. Co-axial aortic dimensions in six landmarks were measured by two readers to evaluate the agreement between the two methods, along with inter-observer and intra-observer agreement. Kolmogorov-Smirnov test, Mann-Whitney U (MWU), Bland-Altman analysis (BAA), intraclass correlation coefficient (ICC) were used for statistical analysis.

RESULTS

The scan time for the iNAV-based approach was significantly shorter (3.1 ± 0.5 min vs. 12.0 ± 3.0 min for dNAV, P = 0.005). Reconstruction was performed inline in 3.0 ± 0.3 min. Diagnostic confidence was similar for the proposed iNAV versus dNAV for all three reviewers (Reviewer 1: 3.9 ± 0.3 vs. 3.8 ± 0.4, P = 0.7; Reviewer 2: 4.0 ± 0.2 vs. 3.9 ± 0.3, P = 0.4; Reviewer 3: 3.8 ± 0.4 vs. 3.7 ± 0.6, P = 0.3). The proposed method yielded higher image quality scores in terms of artefacts from respiratory motion, and non-diagnostic images due to signal inhomogeneity were observed less frequently. While the dNAV approach outperformed the iNAV method in the CR assessment, the iNAV sequence showed improved signal homogeneity along the entire thoracic aorta [RSD SI 5.1 (4.4, 6.5) vs. 6.5 (4.6, 8.6), P = 0.002]. BAA showed a mean difference of < 0.05 cm across the 6 landmarks between the two datasets. ICC showed excellent inter- and intra-observer reproducibility.

CONCLUSIONS

Thoracic aortic iNAV-based CMRA with fast acquisition (~ 3 min) and inline reconstruction (3 min) is proposed, resulting in high diagnostic confidence and reproducible aortic measurements.

Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV

PURPOSE

Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction.

METHODS

Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC.

RESULTS

Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant.

CONCLUSION

The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.

Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography

PURPOSE

To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction.

METHODS

Undersampled and respiratory motion corrected CMRA with overall short scans of 5 to 10 min have been recently proposed. However, image reconstruction with this approach remains lengthy, since it relies on several non-rigid image registrations to estimate the respiratory motion and on a subsequent iterative optimization to correct for motion during the undersampled reconstruction. Here we introduce a self-supervised diffeomorphic non-rigid respiratory motion estimation network, DiRespME-net, to speed up respiratory motion estimation. We couple this with an efficient GPU-based implementation of the subsequent motion-corrected iterative reconstruction. DiRespME-net is based on a U-Net architecture, and is trained in a self-supervised fashion, with a loss enforcing image similarity and spatial smoothness of the motion fields. Motion predicted by DiRespME-net was used for GPU-based motion-corrected CMRA in 12 test subjects and final images were compared to those produced by state-of-the-art reconstruction. Vessel sharpness and visible length of the right coronary artery (RCA) and the left anterior descending (LAD) coronary artery were used as metrics of image quality for comparison.

RESULTS

No statistically significant difference in image quality was found between images reconstructed with the proposed approach (MC:DiRespME-net) and a motion-corrected reconstruction using cubic B-splines (MC:Nifty-reg). Visible vessel length was not significantly different between methods (RCA: MC:Nifty-reg 5.7 ± 1.7 cm vs MC:DiRespME-net 5.8 ± 1.7 cm, P = 0.32; LAD: MC:Nifty-reg 7.0 ± 2.6 cm vs MC:DiRespME-net 6.9 ± 2.7 cm, P = 0.81). Similarly, no statistically significant difference between methods was observed in terms of vessel sharpness (RCA: MC:Nifty-reg 60.3 ± 7.2% vs MC:DiRespME-net 61.0 ± 6.8%, P = 0.19; LAD: MC:Nifty-reg 57.4 ± 7.9% vs MC:DiRespME-net 58.1 ± 7.5%, P = 0.27). The proposed approach achieved a 50-fold reduction in computation time, resulting in a total reconstruction time of approximately 20 s.

CONCLUSIONS

The proposed self-supervised learning-based motion corrected reconstruction enables fast motion-corrected CMRA image reconstruction, holding promise for integration in clinical routine.

Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute

PURPOSE

To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.

METHODS

Undersampled motion-corrected reconstructions have enabled free-breathing isotropic 3D CMRA in ~5-10 min acquisition times. In this work, we propose a deep-learning-based SR framework, combined with non-rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16-fold increase in spatial resolution is achieved by reconstructing a high-resolution (HR) isotropic CMRA (0.9 mm3 or 1.2 mm3 ) from a low-resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm3 or 1.2 × 4.8 × 4.8 mm3 ). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch-level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR-CMRA in ~50 s under free-breathing. Vessel sharpness and length of the coronary arteries from the SR-CMRA is compared against the HR-CMRA.

RESULTS

SR-CMRA showed statistically significant (P < .001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR-CMRA. Good generalization to input resolution and image/patch-level processing was found. SR-CMRA enabled recovery of coronary stenosis similar to HR-CMRA with comparable qualitative performance.

CONCLUSION

The proposed SR-CMRA provides a 16-fold increase in spatial resolution with comparable image quality to HR-CMRA while reducing the predictable scan time to <1 min.

End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA

PURPOSE

To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA).

METHODS

A novel deep learning framework was developed consisting of a diffeomorphic registration network and a motion-informed model-based deep learning (MoDL) reconstruction network. The registration network receives as input highly undersampled (~22×) respiratory-resolved images and outputs 3D nonrigid respiratory motion fields between the images. The motion-informed MoDL performs MoCo reconstruction from undersampled data using the predicted motion fields. The whole deep learning framework, termed as MoCo-MoDL, was trained end-to-end in a supervised manner for simultaneous 3D nonrigid motion estimation and MoCo reconstruction. MoCo-MoDL was compared with a state-of-the-art nonrigid MoCo CMRA reconstruction technique in 15 retrospectively undersampled datasets and 9 prospectively undersampled acquisitions.

RESULTS

The acquisition time for ninefold accelerated CMRA was ~2.5 min. The reconstruction time was ~22 s for the proposed MoCo-MoDL and ~35 min for the conventional approach. MoCo-MoDL achieved higher peak SNR (27.86 ± 3.00 vs. 26.71 ± 2.79; P < .05) and structural similarity (0.78 ± 0.06 vs. 0.75 ± 0.06; P < .05) than the conventional approach. Similar vessel length and visual image quality score were obtained with the 2 methods, whereas improved vessel sharpness was observed with MoCo-MoDL.

CONCLUSION

An end-to-end deep learning approach was introduced for simultaneous nonrigid motion estimation and MoCo reconstruction of highly undersampled free-breathing whole-heart CMRA. The rapid free-breathing CMRA acquisition together with the fast reconstruction of the proposed approach promises easy integration into clinical workflow.

Current status of deep learning in abdominal image reconstruction

Abdominal magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for disease screening, diagnosis, and treatment guidance. However, abdominal MRI has disadvantages including slow speed and vulnerability to motions, while CT suffers from problems of radiation. It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality. Recently, deep learning-based image reconstruction has become a hot topic in the field of medical imaging. This study reviews the latest research on deep learning reconstruction in abdominal imaging, including the widely used convolutional neural network, generative adversarial network, and recurrent neural network.

Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes

Cardiovascular disease is the leading cause of mortality worldwide, with atherosclerotic coronary artery disease (CAD) accounting for the majority of cases. X-ray coronary angiography and computed tomography coronary angiography (CCTA) are the imaging modalities of choice for the assessment of CAD. However, the use of ionising radiation and iodinated contrast agents remain drawbacks. There is therefore a clinical need for an alternative modality for the early identification and longitudinal monitoring of CAD without these associated drawbacks. Coronary magnetic resonance angiography (CMRA) could be a potential alternative for the detection and monitoring of coronary arterial stenosis, without exposing patients to ionising radiation or iodinated contrast agents. Further advantages include its versatility, excellent soft tissue characterisation and suitability for repeat imaging. Despite the early promise of CMRA, widespread clinical utilisation remains limited due to long and unpredictable scan times, onerous scan planning, lower spatial resolution, as well as motion related image quality degradation. The past decade has brought about a resurgence in CMRA technology, with significant leaps in image acceleration, respiratory and cardiac motion estimation and advanced motion corrected or motion-resolved image reconstruction. With the advent of artificial intelligence, great advances are also seen in deep learning-based motion estimation, undersampled and super-resolution reconstruction promising further improvements of CMRA. This has enabled high spatial resolution (1 mm isotropic), 3D whole heart CMRA in a clinically feasible and reliable acquisition time of under 10 min. Furthermore, latest super-resolution image reconstruction approaches which are currently under evaluation promise acquisitions as short as 1 min. In this review, we will explore the recent technological advances that are designed to bring CMRA closer to clinical reality.

Radial self-navigated native magnetic resonance angiography in comparison to navigator-gated contrast-enhanced MRA of the entire thoracic aorta in an aortic patient collective

BACKGROUND

The native balanced steady state with free precession (bSSFP) magnetic resonance angiography (MRA) technique has been shown to provide high diagnostic image quality for thoracic aortic disease. This study compares a 3D radial respiratory self-navigated native MRA (native-SN-MRA) based on a bSSFP sequence with conventional Cartesian, 3D, contrast-enhanced MRA (CE-MRA) with navigator-gated respiration control for image quality of the entire thoracic aorta.

METHODS

Thirty-one aortic native-SN-MRA were compared retrospectively (63.9 ± 10.3 years) to 61 CE-MRA (63.1 ± 11.7 years) serving as a reference standard. Image quality was evaluated at the aortic root/ascending aorta, aortic arch and descending aorta. Scan time was recorded. In 10 patients with both MRA sequences, aortic pathologies were evaluated and normal and pathologic aortic diameters were measured. The influence of artifacts on image quality was analyzed.

RESULTS

Compared to the overall image quality of CE-MRA, the overall image quality of native-SN-MRA was superior for all segments analyzed (aortic root/ascending, p < 0.001; arch, p < 0.001, and descending, p = 0.005). Regarding artifacts, the image quality of native-SN-MRA remained superior at the aortic root/ascending aorta and aortic arch before and after correction for confounders of surgical material (i.e., susceptibility-related artifacts) (p = 0.008 both) suggesting a benefit in terms of motion artifacts. Native-SN-MRA showed a trend towards superior intraindividual image quality, but without statistical significance. Intraindividually, the sensitivity and specificity for the detection of aortic disease were 100% for native-SN-MRA. Aortic diameters did not show a significant difference (p = 0.899). The scan time of the native-SN-MRA was significantly reduced, with a mean of 05:56 ± 01:32 min vs. 08:51 ± 02:57 min in the CE-MRA (p < 0.001).

CONCLUSIONS

Superior image quality of the entire thoracic aorta, also regarding artifacts, can be achieved with native-SN-MRA, especially in motion prone segments, in addition to a shorter acquisition time.

Accelerated free-breathing 3D whole-heart magnetic resonance angiography with a radial phyllotaxis trajectory, compressed sensing, and curvelet transform

PURPOSE

To develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform.

METHOD

A 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness.

RESULTS

Fifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our proposed radial phyllotaxis trajectory. Scan time for radial phyllotaxis was significantly shorter than Cartesian (4.88 ± 0.86 min. vs. 6.84 ± 1.79 min., P-value = 0.004). Radial CS curvelet border sharpness was slightly lower than Cartesian and, for the majority of vessels, was significantly better than radial CS wavelet (P-value < 0.050).

CONCLUSION

The proposed technique of 3D whole-heart MRA acquisition with a radial CS curvelet has a shorter scan time and slightly lower vessel sharpness compared to the Cartesian acquisition with radial profile ordering, and has slightly better sharpness than radial CS wavelet. Future work on this technique includes additional clinical trials and extending this technique to 3D cine imaging.

2D cine vs. 3D self-navigated free-breathing high-resolution whole heart cardiovascular magnetic resonance for aortic root measurements in congenital heart disease

BACKGROUND

Cardiovascular magnetic resonance (CMR) is considered the method of choice for evaluation of aortic root dilatation in congenital heart disease. Usually, a cross-sectional 2D cine stack is acquired perpendicular to the vessel's axis. However, this method requires a considerable patient collaboration and precise planning of image planes. The present study compares a recently introduced 3D self-navigated free-breathing high-resolution whole heart CMR sequence (3D self nav) allowing a multiplanar retrospective reconstruction of the aortic root as an alternative to the 2D cine technique for determination of aortic root diameters.

METHODS

A total of 6 cusp-commissure (CuCo) and cusp-cusp (CuCu) enddiastolic diameters were measured by two observers on 2D cine and 3D self nav cross-sectional planes of the aortic root acquired on a 1.5 T CMR scanner. Asymmetry of the aortic root was evaluated by the ratio of the minimal to the maximum 3D self nav CuCu diameter. CuCu diameters were compared to standard transthoracic echocardiographic (TTE) aortic root diameters.

RESULTS

Sixty-five exams in 58 patients (32 ± 15 years) were included. Typically, 2D cine and 3D self nav spatial resolution was 1.1-1.52 × 4.5-7 mm and 0.9-1.153 mm, respectively. 3D self nav yielded larger maximum diameters than 2D cine: CuCo 37.2 ± 6.4 vs. 36.2 ± 7.0 mm (p = 0.006), CuCu 39.7 ± 6.3 vs. 38.5 ± 6.5 mm (p < 0.001). CuCu diameters were significantly larger (2.3-3.9 mm, p < 0.001) than CuCo and TTE diameters on both 2D cine and 3D self nav. Intra- and interobserver variabilities were excellent for both techniques with bias of -0.5 to 1.0 mm. Intra-observer variability of the more experienced observer was better for 3D self nav (F-test p < 0.05). Aortic root asymmetry was more pronounced in patients with bicuspid aortic valve (BAV: 0.73 (interquartile (IQ) 0.69; 0.78) vs. 0.93 (IQ 0.9; 0.96), p < 0.001), which was associated to a larger difference of maximum CuCu to TTE diameters: 5.5 ± 3.3 vs. 3.3 ± 3.8 mm, p = 0.033.

CONCLUSION

Both, the 3D self nav and 2D cine CMR techniques allow reliable determination of aortic root diameters. However, we propose to privilege the 3D self nav technique and measurement of CuCu diameters to avoid underestimation of the maximum diameter, particularly in patients with asymmetric aortic roots and/or BAV.

Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV)

BACKGROUND

Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV).

METHODS

We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0-4 in a subset of patients by two reviewers in consensus.

RESULTS

The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8).

CONCLUSION

fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility.

Similarity-driven multi-dimensional binning algorithm (SIMBA) for free-running motion-suppressed whole-heart MRA

PURPOSE

Whole-heart MRA techniques typically target predetermined motion states, address cardiac and respiratory dynamics independently, and require either complex planning or computationally demanding reconstructions. In contrast, we developed a fast data-driven reconstruction algorithm with minimal physiological assumptions and compatibility with ungated free-running sequences.

THEORY AND METHODS

We propose a similarity-driven multi-dimensional binning algorithm (SIMBA) that clusters continuously acquired k-space data to find a motion-consistent subset for whole-heart MRA reconstruction. Free-running 3D radial data sets from 12 non-contrast-enhanced scans of healthy volunteers and six ferumoxytol-enhanced scans of pediatric cardiac patients were reconstructed with non-motion-suppressed regridding of all the acquired data ("All Data"), with SIMBA, and with a previously published free-running framework (FRF) that uses cardiac and respiratory self-gating and compressed sensing. Images were compared for blood-myocardium sharpness and contrast ratio, visibility of coronary artery ostia, and right coronary artery sharpness.

RESULTS

Both the 20-second SIMBA reconstruction and FRF provided significantly higher blood-myocardium sharpness than All Data in both patients and volunteers (P < .05). The SIMBA reconstruction provided significantly sharper blood-myocardium interfaces than FRF in volunteers (P < .001) and higher blood-myocardium contrast ratio than All Data and FRF, both in volunteers and patients (P < .05). Significantly more ostia could be visualized with both SIMBA (31 of 36) and FRF (34 of 36) than with All Data (4 of 36) (P < .001). Inferior right coronary artery sharpness using SIMBA versus FRF was observed (volunteers: SIMBA 36.1 ± 8.1%, FRF 40.4 ± 8.9%; patients: SIMBA 35.9 ± 7.7%, FRF 40.3 ± 6.1%, P = not significant).

CONCLUSION

The SIMBA technique enabled a fast, data-driven reconstruction of free-running whole-heart MRA with image quality superior to All Data and similar to the more time-consuming FRF reconstruction.

Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning

The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion-related image artifacts without requiring paired data.

Noncontrast Magnetic Resonance Angiography in the Era of Nephrogenic Systemic Fibrosis and Gadolinium Deposition

ABSTRACT

Gadolinium-based contrast agents for clinical magnetic resonance imaging are overall safe. However, the discovery of nephrogenic systemic fibrosis in patients with severe renal impairment and gadolinium deposition in patients receiving contrast have generated developments in contrast-free imaging of the vasculature, that is, noncontrast magnetic resonance angiography. This article presents an update on noncontrast magnetic resonance angiography techniques, with comparison to other imaging alternatives. Potential benefits and challenges to implementation, and evidence to date for various clinical applications are discussed.

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning

Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation of non-rigid motion fields from undersampled images at different respiratory positions (or bins). However, state-of-the-art registration methods can be time-consuming. This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained patch-wise to circumvent the challenges of training a 3D network volume-wise which requires large amounts of GPU memory and 3D datasets. We perform 5-fold cross-validation with 45 CMRA datasets and demonstrate that RespME-net can predict 3D non-rigid motion fields with subpixel accuracy (0.44 ± 0.38 mm) within ~10 seconds, being ~20 times faster than a GPU-implemented state-of-the-art non-rigid registration method. Moreover, we perform non-rigid motion-compensated CMRA reconstruction for 9 additional patients. The proposed RespME-net has achieved similar motion-corrected CMRA image quality to the conventional registration method regarding coronary artery length and sharpness.

Coronary Magnetic Resonance Angiography: Technical Innovations Leading Us to the Promised Land?

Coronary artery disease remains the leading cause of cardiovascular morbidity and mortality. Invasive X-ray angiography and coronary computed tomography angiography are established gold standards for coronary luminography. However, they expose patients to invasive complications, ionizing radiation, and iodinated contrast agents. Among a number of imaging modalities, coronary cardiovascular magnetic resonance (CMR) angiography may be used in some cases as an alternative for the detection and monitoring of coronary arterial stenosis, with advantages including its versatility, excellent soft tissue characterization, and avoidance of ionizing radiation and iodinated contrast agents. In this review, we explore the recent advances in motion correction, image acceleration, and reconstruction technologies that are bringing coronary CMR angiography closer to widespread clinical implementation.

Radial-based acquisition strategies for pre-procedural non-contrast cardiovascular magnetic resonance angiography of the pulmonary veins

BACKGROUND

Computed tomography angiography (CTA) or contrast-enhanced (CE) cardiovascular magnetic resonance angiography (CMRA) is often obtained in patients with atrial fibrillation undergoing evaluation prior to pulmonary vein (PV) isolation. Drawbacks of CTA include radiation exposure and potential risks from iodinated contrast agent administration. Free-breathing 3D balanced steady-state free precession (bSSFP) Non-contrast CMRA is a potential imaging option, but vascular detail can be suboptimal due to ghost artifacts and blurring that tend to occur with a Cartesian k-space trajectory or, in some cases, inconsistent respiratory gating. We therefore explored the potential utility of both breath-holding and free-breathing non-contrast CMRA, using radial k-space trajectories that are known to be less sensitive to flow and motion artifacts than Cartesian.

MAIN BODY

Free-breathing 3D Cartesian and radial stack-of-stars acquisitions were compared in 6 healthy subjects. In addition, 27 patients underwent CTA and non-contrast CMRA for PV mapping. Three radial CMR acquisition strategies were tested: (1) breath-hold (BH) 2D radial bSSFP (BH-2D); (2) breath-hold, multiple thin-slab 3D stack-of-stars bSSFP (BH-SOS); and (3) navigator-gated free-breathing (FB) 3D stack-of-star bSSFP using a spatially non-selective RF excitation (FB-NS-SOS). A non-rigid registration algorithm was used to compensate for variations in breath-hold depth. In healthy subjects, image quality and vessel sharpness using a free-breathing 3D SOS acquisition was significantly better than free-breathing (FB) Cartesian 3D. In patients, diagnostic image quality was obtained using all three radial CMRA techniques, with BH-SOS and FB-NS-SOS outperforming BH-2D. There was overall good correlation for PV maximal diameter between BH-2D and CTA (ICC = 0.87/0.83 for the two readers), excellent correlation between BH-SOS and CTA (ICC = 0.90/0.91), and good to excellent correlation between FB-NS-SOS and CTA (ICC = 0.87/0.94). For PV area, there was overall good correlation between BH-2D and CTA (ICC = 0.79/0.83), good to excellent correlation between BH-SOS and CTA (ICC = 0.88/0.91) and excellent correlation between FB-NS-SOS and CTA (ICC = 0.90/0.95). CNR was significantly higher with BH-SOS (mean = 11.04) by comparison to BH-2D (mean = 6.02; P = 0.007) and FB-NS-SOS (mean = 5.29; P = 0.002).

CONCLUSION

Our results suggest that a free-breathing stack-of-stars bSSFP technique is advantageous in providing accurate depiction of PV anatomy and ostial measurements without significant degradation from off-resonance artifacts, and with better image quality than Cartesian 3D. For patients in whom respiratory gating is unsuccessful, a breath-hold thin-slab stack-of-stars technique with retrospective motion correction may be a useful alternative.

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media. However, technical limitations including reduced spatial resolution, long acquisition times, and low signal-to-noise ratios prevent it from clinical routine utilization. Nonetheless, each of these limitations can be specifically addressed by a combination of novel technologies including super-resolution imaging, compressed sensing, and deep-learning reconstruction. In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments that hold unique potential to optimize coronary MRA image acquisition and post-processing. In the final section, we examine the various research-based coronary MRA methods and metrics that can be leveraged to assess coronary stenosis severity, physiological function, and atherosclerotic plaque characterization. We specifically discuss how such technologies may contribute to the clinical translation of coronary MRA into a robust modality for routine clinical use.

3D free-breathing cardiac magnetic resonance fingerprinting

PURPOSE

To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T₁ and T₂ mapping from a free-breathing scan.

METHODS

Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T₁ and T₂ mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T₁ and T₂ tissue characterization from a free-breathing scan. Variable inversion recovery and T₂ preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T₁ /T₂ phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T₂ -GraSE mapping techniques at 1.5 T.

RESULTS

3D cMRF T₁ and T₂ measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T₁ and T₂ (T₂ < 100 ms), respectively. in vivo left ventricle (LV) myocardial T₁ values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T₂ values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T₁ , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T₂ .

CONCLUSION

The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T₁ and T₂ maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.

Fully self-gated whole-heart 4D flow imaging from a 5-minute scan

PURPOSE

To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed 5-minute scan.

THEORY AND METHODS

The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from 8 healthy volunteers and 2 patients and compared with compressed sensing technique, L1-SENSE.

RESULTS

Healthy subjects-Compared with 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared with traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All 3 indices are significantly and more markedly underestimated by L1-SENSE. Patients-Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity.

CONCLUSIONS

The combination of highly accelerated 5-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.

Variable anisotropic FOV for 3D radial imaging with spiral phyllotaxis (VASP)

PURPOSE

To develop a new 3D radial trajectory based on the natural spiral phyllotaxis (SP), with variable anisotropic FOV.

THEORY & METHODS

A 3D radial trajectory based on the SP with favorable interleaving properties for cardiac imaging has been proposed by Piccini et al (Magn Reson Med. 2011;66:1049-1056), which supports a FOV with a fixed anisotropy. However, a fixed anisotropy can be inefficient when sampling objects with different anisotropic dimensions. We extend Larson's 3D radial method to provide variable anisotropic FOV for spiral phyllotaxis (VASP). Simulations were performed to measure distance between successive projections, analyze point spread functions, and compare aliasing artifacts for both VASP and conventional SP. VASP was fully implemented on a whole-body clinical MR scanner. Phantom and in vivo cardiac images were acquired at 1.5 tesla.

RESULTS

Simulations, phantom, and in vivo experiments confirmed that VASP can achieve variable anisotropic FOV while maintaining the favorable interleaving properties of SP. For an anisotropic FOV with 100:100:35 ratio, VASP required ~65% fewer radial projections than the conventional SP to satisfy Nyquist criteria. Alternatively, when the same number of radial projections were used as in conventional SP, VASP produced fewer aliasing artifacts for anisotropic objects within the excited imaging volumes.

CONCLUSION

We have developed a new method (VASP), which enables variable anisotropic FOV for 3D radial trajectory with SP. For anisotropic objects within the excited imaging volumes, VASP can reduce scan times and/or reduce aliasing artifacts.

Rapid whole-heart CMR with single volume super-resolution

BACKGROUND

Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using a deep-learning single volume super-resolution reconstruction, to recover high-resolution features from rapidly acquired low-resolution WH-bSSFP images.

METHODS

A 3D residual U-Net was trained using synthetic data, created from a library of 500 high-resolution WH-bSSFP images by simulating 50% slice resolution and 50% phase resolution. The trained network was validated with 25 synthetic test data sets. Additionally, prospective low-resolution data and high-resolution data were acquired in 40 patients. In the prospective data, vessel diameters, quantitative and qualitative image quality, and diagnostic scoring was compared between the low-resolution, super-resolution and reference high-resolution WH-bSSFP data.

RESULTS

The synthetic test data showed a significant increase in image quality of the low-resolution images after super-resolution reconstruction. Prospectively acquired low-resolution data was acquired ~× 3 faster than the prospective high-resolution data (173 s vs 488 s). Super-resolution reconstruction of the low-resolution data took < 1 s per volume. Qualitative image scores showed super-resolved images had better edge sharpness, fewer residual artefacts and less image distortion than low-resolution images, with similar scores to high-resolution data. Quantitative image scores showed super-resolved images had significantly better edge sharpness than low-resolution or high-resolution images, with significantly better signal-to-noise ratio than high-resolution data. Vessel diameters measurements showed over-estimation in the low-resolution measurements, compared to the high-resolution data. No significant differences and no bias was found in the super-resolution measurements in any of the great vessels. However, a small but significant for the underestimation was found in the proximal left coronary artery diameter measurement from super-resolution data. Diagnostic scoring showed that although super-resolution did not improve accuracy of diagnosis, it did improve diagnostic confidence compared to low-resolution imaging.

CONCLUSION

This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting. We were able to train the network using synthetic training data from retrospective high-resolution whole heart data. The resulting network can be applied very quickly, making these techniques particularly appealing within busy clinical workflow. Thus, we believe that this technique may help speed up whole heart CMR in clinical practice.

PET/MRI of atherosclerosis

Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.

Cardiovascular magnetic resonance imaging for structural heart disease

Cardiovascular magnetic resonance (CMR) has increasingly become a powerful imaging technique over the past few decades due to increasing knowledge about clinical applications, operator experience and technological advances, including the introduction of high field strength magnets, leading to improved signal-to-noise ratio. Its success is attributed to the free choice of imaging planes, the wide variety of imaging techniques, and the lack of harmful radiation. Developments in CMR have led to the accurate evaluation of cardiac structure, function and tissues characterisation, so this non-invasive technique has become a powerful tool for a broad range of cardiac pathologies. This review will provide an introduction of magnetic resonance imaging (MRI) physics, an overview of the current techniques and clinical application of CMR in structural heart disease, and illustrated examples of its use in clinical practice.

A quantitative comparison between a navigated Cartesian and a self-navigated radial protocol from clinical studies for free-breathing 3D whole-heart bSSFP coronary MRA

PURPOSE

Navigator-gated 3D bSSFP whole-heart coronary MRA has been evaluated in several large studies including a multi-center trial. Patient studies have also been performed with more recent self-navigated techniques. In this study, these two approaches are compared side-by-side using a Cartesian navigator-gated and corrected (CNG) and a 3D radial self-navigated (RSN) protocol from published patient studies.

METHODS

Sixteen healthy subjects were examined with both sequences on a 1.5T scanner. Assessment of the visibility of coronary ostia and quantitative comparisons of acquisition times, blood pool homogeneity, and visible length and sharpness of the right coronary artery (RCA) and the combined left main (LM)+left anterior descending (LAD) coronary arteries were performed. Paired sample t-tests with P < .05 considered statistically significant were used for all comparisons.

RESULTS

The acquisition time was 5:40 ± 0:28 min (mean ± SD) for RSN, being significantly shorter than the 16:59 ± 5:05 min of CNG (P < .001). RSN images showed higher blood pool homogeneity (P < .001). All coronary ostia were visible with both techniques. CNG provided significantly higher vessel sharpness in the RCA (CNG: 50.0 ± 8.6%, RSN: 34.2 ± 6.9%, P < .001) and the LM+LAD (CNG: 48.7 ± 6.7%, RSN: 32.3 ± 7.1%, P < .001). The visible vessel length was significantly longer in the LM+LAD using CNG (CNG: 9.8 ± 2.7 cm, RSN: 8.5 ± 2.6 cm, P < .05) but not in the RCA (CNG: 9.7 ± 2.3 cm, RSN: 9.3 ± 2.9 cm, P = .29).

CONCLUSION

CNG provided superior vessel sharpness and might hence be the better option for examining coronary lumina. However, its blood pool inhomogeneity and prolonged and unpredictable acquisition times compared to RSN may make clinical adoption more challenging.

Self-calibrated interpolation of non-Cartesian data with GRAPPA in parallel imaging

PURPOSE

To develop a non-Cartesian k-space reconstruction method using self-calibrated region-specific interpolation kernels for highly accelerated acquisitions.

METHODS

In conventional non-Cartesian GRAPPA with through-time GRAPPA (TT-GRAPPA), the use of region-specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT-GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self-calibrated Interpolation of Non-Cartesian data with GRAPPA (SING) to self-calibrate region-specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region-specific interpolation kernel geometry, and uses a novel local k-space regularization through an extension of TT-GRAPPA. This calibration approach is used to reconstruct non-Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate-gradient SENSE and TT-GRAPPA in numerical phantoms and in vivo cine data sets.

RESULTS

In both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT-GRAPPA, and provides improved reconstruction quality over conjugate-gradient SENSE. Furthermore, temporal fidelity in SING and TT-GRAPPA is similar for the same acceleration rates. G-factor evaluation over the heart shows that SING and TT-GRAPPA provide similar noise amplification at moderate and high rates.

CONCLUSION

The proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT-GRAPPA.

3D Whole-heart free-breathing qBOOST-T2 mapping

PURPOSE

To develop an accelerated motion corrected 3D whole-heart imaging approach (qBOOST-T2) for simultaneous high-resolution bright- and black-blood cardiac MR imaging and quantitative myocardial T2 characterization.

METHODS

Three undersampled interleaved balanced steady-state free precession cardiac MR volumes were acquired with a variable density Cartesian trajectory and different magnetization preparations: (1) T2-prepared inversion recovery (T2prep-IR), (2) T2-preparation, and (3) no preparation. Image navigators were acquired prior the acquisition to correct for 2D translational respiratory motion. Each 3D volume was reconstructed with a low-rank patch-based reconstruction. The T2prep-IR volume provides bright-blood anatomy visualization, the black-blood volume is obtained by means of phase sensitive reconstruction between first and third datasets, and T2 maps are generated by matching the signal evolution to a simulated dictionary. The proposed sequence has been evaluated in simulations, phantom experiments, 11 healthy subjects and compared with 3D bright-blood cardiac MR and standard 2D breath-hold balanced steady-state free precession T2 mapping. The feasibility of the proposed approach was tested on 4 patients with suspected cardiovascular disease.

RESULTS

High linear correlation (y = 1.09 × -0.83, R² = 0.99) was found between the proposed qBOOST-T2 and T2 spin echo measurements in phantom experiment. Good image quality was observed in vivo with the proposed 4x undersampled qBOOST-T2. Mean T2 values of 53.1 ± 2.1 ms and 55.8 ± 2.7 ms were measured in vivo for 2D balanced steady-state free precession T2 mapping and qBOOST-T2, respectively, with linear correlation of y = 1.02x+1.46 (R² = 0.61) and T2 bias = 2.7 ms.

CONCLUSION

The proposed qBOOST-T2 sequence allows the acquisition of 3D high-resolution co-registered bright- and black-blood volumes and T2 maps in a single scan of ~11 min, showing promising results in terms of T2 quantification.

Motion robust respiratory-resolved 3D radial flow MRI and its application in neonatal congenital heart disease

PURPOSE

To test and implement a motion-robust and respiratory-resolved 3D Radial Flow framework that addresses the need for rapid, high resolution imaging in neonatal patients with congenital heart disease.

METHODS

A 4-point velocity encoding and 3D radial trajectory with double-golden angle ordering was combined with bulk motion correction (from projection center of mass) and respiration phase detection (from principal component analysis of heartbeat-averaged data) to create motion-robust 3D velocity cardiac time-averaged data. This framework was tested in a whole-chest digital phantom with simulated bulk and realistic physiological motion. In vivo imaging was performed in 20 congenital heart disease infants under feed-and-sleep with submillimeter isotropic resolution in ~3 min. Flows were validated against clinical 2D PCMRI and whole-heart visualizations of blood flow were performed.

RESULTS

The proposed framework resolved all simulated digital phantom motion states (mean ± standard error: rotation - azimuthal = 0.29 ± 0.02°; translation - T_y = 1.29 ± 0.12 mm, T_z = -0.27 ± 0.13 mm; rotation+translation - polar = 0.49 ± 0.16°, T_x = -2.47 ± 0.51 mm, T_z = 5.78 ± 1.33 mm). Measured timing errors of peak expiration across all signal-to-noise ratio values were 22% of the true respiratory period (range = [404-489 ± 298-334] ms). For in vivo imaging, motion correction improved 3D Radial Flow measurements (no correction: R² = 0.62, root mean square error = 0.80 L/min/m² , Bland-Altman bias [limits of agreement] = -0.21 [-1.40, 0.94] L/min/m² ; motion corrected, expiration: R² = 0.90, root mean square error = 0.46 L/min/m² , bias [limits of agreement] = 0.06 [-0.49, 0.62] L/min/m² ). Respiratory-resolved 3D velocity visualizations were achieved in various neonatal pathologies pre- and postsurgical correction.

CONCLUSION

3D cardiac flow may be visualized and accurately quantified in neonatal subjects using the proposed framework. This technique may enable more comprehensive hemodynamic studies in small infants.

Correcting versus resolving respiratory motion in free-breathing whole-heart MRA: a comparison in patients with thoracic aortic disease

Background

Whole-heart magnetic resonance angiography (MRA) requires sophisticated methods accounting for respiratory motion. Our purpose was to evaluate the image quality of compressed sensing-based respiratory motion-resolved three-dimensional (3D) whole-heart MRA compared with self-navigated motion-corrected whole-heart MRA in patients with known thoracic aorta dilation.

Methods

Twenty-five patients were prospectively enrolled in this ethically approved study. Whole-heart 1.5-T MRA was acquired using a prototype 3D radial steady-state free-precession free-breathing sequence. The same data were reconstructed with a one-dimensional motion-correction algorithm (1D-MCA) and an extradimensional golden-angle radial sparse parallel reconstruction (XD-GRASP). Subjective image quality was scored and objective image quality was quantified (signal intensity ratio, SIR; vessel sharpness). Wilcoxon, McNemar, and paired t tests were used.

Results

Subjective image quality was significantly higher using XD-GRASP compared to 1D-MCA (median 4.5, interquartile range 4.5–5.0 versus 4.0 [2.25–4.75]; p < 0.001), as well as signal homogeneity (3.0 [3.0–3.0] versus 2.0 [2.0–3.0]; p = 0.003), and image sharpness (3.0 [2.0–3.0] vs 2.0 [1.25–3.0]; p < 0.001). SIR with the 1D-MCA and XD-GRASP was 6.1 ± 3.9 versus 7.4 ± 2.5, respectively (p < 0.001); while signal homogeneity was 274.2 ± 265.0 versus 199.8 ± 67.2 (p = 0.129). XD-GRASP provided a higher vessel sharpness (45.3 ± 10.7 versus 40.6 ± 101, p = 0.025).

Conclusions

XD-GRASP-based motion-resolved reconstruction of free-breathing 3D whole-heart MRA datasets provides improved image contrast, sharpness, and signal homogeneity and seems to be a promising technique that overcomes some of the limitations of motion correction or respiratory navigator gating.