Comprehensive Multi-Dimensional MRI for the Simultaneous Assessment of Cardiopulmonary Anatomy and Physiology

Time-Resolved 3D cardiopulmonary MRI reconstruction using spatial transformer network

The accurate visualization and assessment of the complex cardiac and pulmonary structures in 3D is critical for the diagnosis and treatment of cardiovascular and respiratory disorders. Conventional 3D cardiac magnetic resonance imaging (MRI) techniques suffer from long acquisition times, motion artifacts, and limited spatiotemporal resolution. This study proposes a novel time-resolved 3D cardiopulmonary MRI reconstruction method based on spatial transformer networks (STNs) to reconstruct the 3D cardiopulmonary MRI acquired using 3D center-out radial ultra-short echo time (UTE) sequences. The proposed reconstruction method employed an STN-based deep learning framework, which used a combination of data-processing, grid generator, and sampler. The reconstructed 3D images were compared against the start-of-the-art time-resolved reconstruction method. The results showed that the proposed time-resolved 3D cardiopulmonary MRI reconstruction using STNs offers a robust and efficient approach to obtain high-quality images. This method effectively overcomes the limitations of conventional 3D cardiac MRI techniques and has the potential to improve the diagnosis and treatment planning of cardiopulmonary disorders.

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

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

PURPOSE

To develop a free-breathing myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction.

METHODS

Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incompleteT 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurateT 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction.

RESULTS

In comparison to an IR spin-echo reference, phantom results confirm goodT 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improvesT 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardialT 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability.

CONCLUSION

Motion-resolved myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Highly Accelerated Compressed-Sensing 4D Flow for Intracardiac Flow Assessment

BACKGROUND

Four-dimensional (4D) flow MRI allows for the quantification of complex flow patterns; however, its clinical use is limited by its inherently long acquisition time. Compressed sensing (CS) is an acceleration technique that provides substantial reduction in acquisition time.

PURPOSE

To compare intracardiac flow measurements between conventional and CS-based highly accelerated 4D flow acquisitions.

STUDY TYPE

Prospective.

SUBJECTS

Fifty healthy volunteers (28.0 ± 7.1 years, 24 males).

FIELD STRENGTH/SEQUENCE

Whole heart time-resolved 3D gradient echo with three-directional velocity encoding (4D flow) with conventional parallel imaging (factor 3) as well as CS (factor 7.7) acceleration at 3 T.

ASSESSMENT

4D flow MRI data were postprocessed by applying a valve tracking algorithm. Acquisition times, flow volumes (mL/cycle) and diastolic function parameters (ratio of early to late diastolic left ventricular peak velocities [E/A] and ratio of early mitral inflow velocity to mitral annular early diastolic velocity [E/e']) were quantified by two readers.

STATISTICAL TESTS

Paired-samples t-test and Wilcoxon rank sum test to compare measurements. Pearson correlation coefficient (r), Bland-Altman-analysis (BA) and intraclass correlation coefficient (ICC) to evaluate agreement between techniques and readers. A P value < 0.05 was considered statistically significant.

RESULTS

A significant improvement in acquisition time was observed using CS vs. conventional accelerated acquisition (6.7 ± 1.3 vs. 12.0 ± 1.3 min). Net forward flow measurements for all valves showed good correlation (r > 0.81) and agreement (ICCs > 0.89) between conventional and CS acceleration, with 3.3%-8.3% underestimation by the CS technique. Evaluation of diastolic function showed 3.2%-17.6% error: E/A 2.2 [1.9-2.4] (conventional) vs. 2.3 [2.0-2.6] (CS), BA bias 0.08 [-0.81-0.96], ICC 0.82; and E/e' 4.6 [3.9-5.4] (conventional) vs. 3.8 [3.4-4.3] (CS), BA bias -0.90 [-2.31-0.50], ICC 0.89.

DATA CONCLUSION

Analysis of intracardiac flow patterns and evaluation of diastolic function using a highly accelerated 4D flow sequence prototype is feasible, but it shows underestimation of flow measurements by approximately 10%.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

Ferumoxytol-enhanced 4D multiphase, steady-state imaging with magnetic resonance in congenital heart disease: ventricular volume and function across 2D and 3D software platforms

Background

Quantitative ventricular volumetry and function are important in the management of congenital heart disease (CHD). Ferumoxytol-enhanced (FE) 4D multiphase, steady state imaging with contrast enhancement (MUSIC) enables high-resolution, 3D cardiac phase-resolved magnetic resonance imaging (MRI) of the beating heart and extracardiac vessels in a single acquisition and without concerns about renal impairment. We aim to evaluate the semi-automatic quantification of ventricular volumetry and function of 4D MUSIC MRI using 2D and 3D software platforms.

Methods

This HIPAA-compliant and IRB-approved study prospectively recruited 50 children with CHD (3 days to 18 years) who underwent 4D MUSIC MRI at 3.0T between 2013-2017 for clinical indications. Each patient was either intubated in the neonatal intensive care unit (NICU) or underwent general anesthesia at MRI suite. For 2D analysis, we reformatted MUSIC images in Digital Imaging and Communications in Medicine (DICOM) format into ventricular short-axis slices with zero interslice gap. For 3D analysis, we imported DICOMs into a commercially available 3D software platform. Using semi-automatic thresholding, we quantified biventricular volume and ejection fraction (EF). We assessed the bias between MUSIC-derived 2D vs. 3D measurements and correlation between MUSIC vs. conventional 2D balanced steady-state free precession (bSSFP) cine images. We evaluated intra- and inter-observer agreement.

Results

There was a high degree of correlation between MUSIC-derived volumetric and functional measurements using 2D vs. 3D software (r=0.99, P<0.001). Volumes derived using 3D software platforms were larger than 2D by 0.2 to 2.0 mL/m2 whereas EF measurements were higher by 1.2-3.0%. MUSIC volumetric and functional measures derived from 2D and 3D software platforms corresponded highly with those derived from multi-slice SSFP cine images (r=0.99, P<0.001). The mean difference in volume for reformatted 4D MUSIC relative to bSSFP cine was 1.5 to 3.9 mL/m2. Intra- and inter-observer reliability was excellent.

Conclusions

Accurate and reliable ventricular volumetry and function can be derived from FE 4D MUSIC MRI studies using commercially available 2D and 3D software platforms. If fully validated in multicenter studies, the FE 4D-MUSIC pulse sequence may supercede conventional multislice 2D cine cardiovascular MRI acquisition protocols for functional evaluation of children with complex CHD.

Pilot tone navigation for respiratory and cardiac motion-resolved free-running 5D flow MRI

Purpose

In this work, we integrated the pilot tone (PT) navigation system into a reconstruction framework for respiratory and cardiac motion‐resolved 5D flow. We tested the hypotheses that PT would provide equivalent respiratory curves, cardiac triggers, and corresponding flow measurements to a previously established self‐gating (SG) technique while being independent from changes to the acquisition parameters.

Methods

Fifteen volunteers and 9 patients were scanned with a free‐running 5D flow sequence, with PT integrated. Respiratory curves and cardiac triggers from PT and SG were compared across all subjects. Flow measurements from 5D flow reconstructions using both PT and SG were compared to each other and to a reference electrocardiogram‐gated and respiratory triggered 4D flow acquisition. Radial trajectories with variable readouts per interleave were also tested in 1 subject to compare cardiac trigger quality between PT and SG.

Results

The correlation between PT and SG respiratory curves were 0.95 ± 0.06 for volunteers and 0.95 ± 0.04 for patients. Heartbeat duration measurements in volunteers and patients showed a bias to electrocardiogram measurements of, respectively, 0.16 ± 64.94 ms and 0.01 ± 39.29 ms for PT versus electrocardiogram and of 0.24 ± 63.68 ms and 0.09 ± 32.79 ms for SG versus electrocardiogram. No significant differences were reported for the flow measurements between 5D flow PT and from 5D flow SG. A decrease in the cardiac triggering quality of SG was observed for increasing readouts per interleave, whereas PT quality remained constant.

Conclusion

PT has been successfully integrated in 5D flow MRI and has shown equivalent results to the previously described 5D flow SG technique, while being completely acquisition‐independent.

Using 5D flow MRI to decode the effects of rhythm on left atrial 3D flow dynamics in patients with atrial fibrillation

PURPOSE

This study used a 5D flow framework to explore the influence of arrhythmia on thrombogenic hemodynamic parameters in patients with atrial fibrillation (AF).

METHODS

A fully self-gated, 3D radial, highly accelerated free-running 5D flow sequence with interleaved four-point velocity-encoding was acquired using an in vitro arrhythmic flow phantom and in 25 patients with a history of AF (68 ± 8 y, 6 female). Self-gating signals were used to calculate AF burden, bin data, and tag each k-space line with its RRLength . Data were binned as an RR-resolved dataset with four RR-interval bins (RR1-RR4, short-to-long) for compressed sensing reconstruction. AF burden was calculated as interquartile range of all intrascan RR-intervals divided by median RR-interval, and left atrial (LA) stasis as the percent of the cardiac cycle where the velocity was <0.1 m/s.

RESULTS

In vitro results demonstrated successful recovery of RR-binned flow curves using RR-resolved 5D flow compared to a real-time PC reference standard. In vivo, 5D flow was acquired in 8:48 minutes. AF burden was significantly correlated with 5D flow-derived peak (PV) and mean (MV) velocity and stasis (|ρ| = 0.54-0.75, P < .001). Sensitivity analyses determined a threshold for low versus high AF burden at 9.7%. High burden patients had increased LA mean stasis (up to +42%, P < .01), and lower MV and PV (-30%, -40.6%, respectively, P < .01). RR4 deviated furthest from respiratory-resolved reconstruction (end-expiration) with increased mean stasis (7.6% ± 14.0%, P = .10) and decreased PV (-12.7 ± 14.2%, P = .09).

CONCLUSIONS

RR-resolved 5D flow can capture temporal and RR-resolved 3D hemodynamics in <10 minutes and offers a novel approach to investigate arrhythmias.

Retrospective Camera-Based Respiratory Gating in Clinical Whole-Heart 4D Flow MRI

Background

Respiratory gating is generally recommended in 4D flow MRI of the heart to avoid blurring and motion artifacts. Recently, a novel automated contact‐less camera‐based respiratory motion sensor has been introduced.

Purpose

To compare camera‐based respiratory gating (CAM) with liver‐lung‐navigator‐based gating (NAV) and no gating (NO) for whole‐heart 4D flow MRI.

Study Type

Retrospective.

Subjects

Thirty two patients with a spectrum of cardiovascular diseases.

Field Strength/Sequence

A 3T, 3D‐cine spoiled‐gradient‐echo‐T1‐weighted‐sequence with flow‐encoding in three spatial directions.

Assessment

Respiratory phases were derived and compared against each other by cross‐correlation. Three radiologists/cardiologist scored images reconstructed with camera‐based, navigator‐based, and no respiratory gating with a 4‐point Likert scale (qualitative analysis). Quantitative image quality analysis, in form of signal‐to‐noise ratio (SNR) and liver‐lung‐edge (LLE) for sharpness and quantitative flow analysis of the valves were performed semi‐automatically.

Statistical Tests

One‐way repeated measured analysis of variance (ANOVA) with Wilks's lambda testing and follow‐up pairwise comparisons. Significance level of P ≤ 0.05. Krippendorff's‐alpha‐test for inter‐rater reliability.

Results

The respiratory signal analysis revealed that CAM and NAV phases were highly correlated (C = 0.93 ± 0.09, P < 0.01). Image scoring showed poor inter‐rater reliability and no significant differences were observed (P ≥ 0.16). The image quality comparison showed that NAV and CAM were superior to NO with higher SNR (P = 0.02) and smaller LLE (P < 0.01). The quantitative flow analysis showed significant differences between the three respiratory‐gated reconstructions in the tricuspid and pulmonary valves (P ≤ 0.05), but not in the mitral and aortic valves (P > 0.05). Pairwise comparisons showed that reconstructions without respiratory gating were different in flow measurements to either CAM or NAV or both, but no differences were found between CAM and NAV reconstructions.

Data Conclusion

Camera‐based respiratory gating performed as well as conventional liver‐lung‐navigator‐based respiratory gating. Quantitative image quality analysis showed that both techniques were equivalent and superior to no‐gating‐reconstructions. Quantitative flow analysis revealed local flow differences (tricuspid/pulmonary valves) in images of no‐gating‐reconstructions, but no differences were found between images reconstructed with camera‐based and navigator‐based respiratory gating.

Level of Evidence

3

Technical Efficacy

Stage 2

Cardiac Magnetic Resonance Quantification of Structure-Function Relationships in Heart Failure

Classification of heart failure is based on the left ventricular ejection fraction (EF): preserved EF, midrange EF, and reduced EF. There remains an unmet need for further heart failure phenotyping of ventricular structure-function relationships. Because of high spatiotemporal resolution, cardiac magnetic resonance (CMR) remains the reference modality for quantification of ventricular contractile function. The authors aim to highlight novel frameworks, including theranostic use of ferumoxytol, to enable more efficient evaluation of ventricular function in heart failure patients who are also frequently anemic, and to discuss emerging quantitative CMR approaches for evaluation of ventricular structure-function relationships in heart failure.

Cardiac and Respiratory Self-Gating in Radial MRI Using an Adapted Singular Spectrum Analysis (SSA-FARY)

Cardiac Magnetic Resonance Imaging (MRI) is time-consuming and error-prone. To ease the patient's burden and to increase the efficiency and robustness of cardiac exams, interest in methods based on continuous steady-state acquisition and self-gating has been growing in recent years. Self-gating methods extract the cardiac and respiratory signals from the measurement data and then retrospectively sort the data into cardiac and respiratory phases. Repeated breathholds and synchronization with the heart beat using some external device as required in conventional MRI are then not necessary. In this work, we introduce a novel self-gating method for radially acquired data based on a dimensionality reduction technique for time-series analysis (SSA-FARY). Building on Singular Spectrum Analysis, a zero-padded, time-delayed embedding of the auto-calibration data is analyzed using Principle Component Analysis. We demonstrate the basic functionality of SSA-FARY using numerical simulations and apply it to in-vivo cardiac radial single-slice bSSFP and Simultaneous Multi-Slice radiofrequency-spoiled gradient-echo measurements, as well as to Stack-of-Stars bSSFP measurements. SSA-FARY reliably detects the cardiac and respiratory motion and separates it from noise. We utilize the generated signals for high-dimensional image reconstruction using parallel imaging and compressed sensing with in-plane wavelet and (spatio-)temporal total-variation regularization.

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.

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions

PURPOSE

To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging.

THEORY AND METHODS

The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging.

RESULTS

The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features.

CONCLUSIONS

We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.

Pseudo-spiral sampling and compressed sensing reconstruction provides flexibility of temporal resolution in accelerated aortic 4D flow MRI: A comparison with k-t principal component analysis

INTRODUCTION

Time-resolved three-dimensional phase contrast MRI (4D flow) of aortic blood flow requires acceleration to reduce scan time. Two established techniques for highly accelerated 4D flow MRI are k-t principal component analysis (k-t PCA) and compressed sensing (CS), which employ either regular or random k-space undersampling. The goal of this study was to gain insights into the quantitative differences between k-t PCA- and CS-derived aortic blood flow, especially for high temporal resolution CS 4D flow MRI.

METHODS

The scan protocol consisted of both k-t PCA and CS accelerated 4D flow MRI, as well as a 2D flow reference scan through the ascending aorta acquired in 15 subjects. 4D flow scans were accelerated with factor R = 8. For CS accelerated scans, we used a pseudo-spiral Cartesian sampling scheme, which could additionally be reconstructed at higher temporal resolution, resulting in R = 13. 4D flow data were compared with the 2D flow scan in terms of flow, peak flow and stroke volume. A 3D peak systolic voxel-wise velocity and wall shear stress (WSS) comparison between k-t PCA and CS 4D flow was also performed.

RESULTS

The mean difference in flow/peak flow/stroke volume between the 2D flow scan and the 4D flow CS with R = 8 and R = 13 was 4.2%/9.1%/3.0% and 5.3%/7.1%/1.9%, respectively, whereas for k-t PCA with R = 8 the difference was 9.7%/25.8%/10.4%. In the voxel-by-voxel 4D flow comparison we found 13.6% and 3.5% lower velocity and WSS values of k-t PCA compared with CS with R = 8, and 15.9% and 5.5% lower velocity and WSS values of k-t PCA compared with CS with R = 13.

CONCLUSION

Pseudo-spiral accelerated 4D flow acquisitions in combination with CS reconstruction provides a flexible choice of temporal resolution. We showed that our proposed strategy achieves better agreement in flow values with 2D reference scans compared with using k-t PCA accelerated acquisitions.

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.

Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking

High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical and physiological dynamics. The low speed of MRI necessitates acceleration methods such as deep learning reconstruction from under-sampled data. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. In this work, we show that by applying deep neural networks inside a priori calculated temporal feature spaces, we enable deep learning reconstruction with global temporal modeling even for image sequences with >40,000 frames. One proposed variation of our approach using dilated multi-level Densely Connected Network (mDCN) speeds up feature space coordinate calculation by 3000x compared to conventional iterative methods, from 20 minutes to 0.39 seconds. Thus, the combination of low-rank tensor and deep learning models not only makes large-scale dynamic MRI feasible but also practical for routine clinical application.

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.

GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation

PURPOSE

To propose a highly accelerated, high-resolution dynamic contrast-enhanced MRI (DCE-MRI) technique called GRASP-Pro (golden-angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self-calibrating subspace constraint with automated selection of contrast phases.

METHODS

GRASP-Pro reconstruction enforces a combination of an explicit low-rank subspace-constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low-resolution portion of radial k-space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP-Pro was demonstrated for high spatiotemporal resolution DCE-MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level.

RESULTS

Compared to GRASP, GRASP-Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection.

CONCLUSION

GRASP-Pro is a promising method for rapid and continuous DCE-MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow.

Multipoint 5D flow cardiovascular magnetic resonance - accelerated cardiac- and respiratory-motion resolved mapping of mean and turbulent velocities

BACKGROUND

Volumetric quantification of mean and fluctuating velocity components of transient and turbulent flows promises a comprehensive characterization of valvular and aortic flow characteristics. Data acquisition using standard navigator-gated 4D Flow cardiovascular magnetic resonance (CMR) is time-consuming and actual scan times depend on the breathing pattern of the subject, limiting the applicability of the method in a clinical setting. We sought to develop a 5D Flow CMR framework which combines undersampled data acquisition including multipoint velocity encoding with low-rank image reconstruction to provide cardiac- and respiratory-motion resolved assessment of velocity maps and turbulent kinetic energy in fixed scan times.

METHODS

Data acquisition and data-driven motion state detection was performed using an undersampled Cartesian tiny Golden angle approach. Locally low-rank (LLR) reconstruction was implemented to exploit correlations among heart phases and respiratory motion states. To ensure accurate quantification of mean and turbulent velocities, a multipoint encoding scheme with two velocity encodings per direction was incorporated. Velocity-vector fields and turbulent kinetic energy (TKE) were obtained using a Bayesian approach maximizing the posterior probability given the measured data. The scan time of 5D Flow CMR was set to 4 min. 5D Flow CMR with acceleration factors of 19 .0 ± 0.21 (mean ± std) and velocity encodings (VENC) of 0.5 m/s and 1.5 m/s per axis was compared to navigator-gated 2x SENSE accelerated 4D Flow CMR with VENC = 1.5 m/s in 9 subjects. Peak velocities and peak flow were compared and magnitude images, velocity and TKE maps were assessed.

RESULTS

While net scan time of 5D Flow CMR was 4 min independent of individual breathing patterns, the scan times of the standard 4D Flow CMR protocol varied depending on the actual navigator gating efficiency and were 17.8 ± 3.9 min on average. Velocity vector fields derived from 5D Flow CMR in the end-expiratory state agreed well with data obtained from the navigated 4D protocol (normalized root-mean-square error 8.9 ± 2.1%). On average, peak velocities assessed with 5D Flow CMR were higher than for the 4D protocol (3.1 ± 4.4%).

CONCLUSIONS

Respiratory-motion resolved multipoint 5D Flow CMR allows mapping of mean and turbulent velocities in the aorta in 4 min.

Comparison of two accelerated 4D-flow sequences for aortic flow quantification

To compare two broadly used 4D-flow- with a 2D-flow-sequence in healthy volunteers, regarding absolute flow parameters, image quality (IQ), and eddy current correction (ECC). Forty volunteers (42 ± 11.8 years, 22 females) were examined with a 3T scanner. Thoracic aortic flow was assessed using a 3D-T2w-SPACE-STIR-sequence for morphology and two accelerated 4D-flow sequences for comparison, one with k-t undersampling and one with standard GRAPPA parallel-imaging. 2D-flow was used as reference standard. The custom-made software tool Bloodline enabled flow measurements for all analyses at the same location. Quantitative flow analyses were performed with and without ECC. One reader assessed pathline IQ (IQ-PATH) and occurrence of motion artefacts (IQ-ART) on a 3-point grading scale, the higher the better. k-t GRAPPA allowed a significant mean scan time reduction of 46% (17:56 ± 5:26 min vs. 10:40 ± 3:15 min) and provided significantly fewer motion artefacts than standard GRAPPA (IQ-ART 1.57 ± 0.55 vs. 0.84 ± 0.48; p < 0.001). Neither 4D-flow sequence significantly differed in flow volume nor peak velocity results with or without ECC. Nevertheless, the correlation between both 4D-flow sequences and 2D-flow was better with ECC; the k-t GRAPPA sequence performed best (R = 0.96 vs. 0.90). k-t GRAPPA 4D-flow was not inferior to a standard GRAPPA-sequence, showed fewer artefacts, comparable IQ and was almost two-fold faster.

High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI

PURPOSE

To develop a new high-dimensionality undersampled patch-based reconstruction (HD-PROST) for highly accelerated 2D and 3D multi-contrast MRI.

METHODS

HD-PROST jointly reconstructs multi-contrast MR images by exploiting the highly redundant information, on a local and non-local scale, and the strong correlation shared between the multiple contrast images. This is achieved by enforcing multi-dimensional low-rank in the undersampled images. 2D magnetic resonance fingerprinting (MRF) phantom and in vivo brain acquisitions were performed to evaluate the performance of HD-PROST for highly accelerated simultaneous T1 and T2 mapping. Additional in vivo experiments for reconstructing multiple undersampled 3D magnetization transfer (MT)-weighted images were conducted to illustrate the impact of HD-PROST for high-resolution multi-contrast 3D imaging.

RESULTS

In the 2D MRF phantom study, HD-PROST provided accurate and precise estimation of the T1 and T2 values in comparison to gold standard spin echo acquisitions. HD-PROST achieved good quality maps for the in vivo 2D MRF experiments in comparison to conventional low-rank inversion reconstruction. T1 and T2 values of white matter and gray matter were in good agreement with those reported in the literature for MRF acquisitions with reduced number of time point images (500 time point images, ~2.5 s scan time). For in vivo MT-weighted 3D acquisitions (6 different contrasts), HD-PROST achieved similar image quality than the fully sampled reference image for an undersampling factor of 6.5-fold.

CONCLUSION

HD-PROST enables multi-contrast 2D and 3D MR images in a short acquisition time without compromising image quality. Ultimately, this technique may increase the potential of conventional parameter mapping.

Volumetric abdominal perfusion measurement using a pseudo-randomly sampled 3D fast-spin-echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction

PURPOSE

To improve image quality and spatial coverage for abdominal perfusion imaging by implementing an arterial spin labeling (ASL) sequence that combines variable-density 3D fast-spin-echo (FSE) with Cartesian trajectory and compressed-sensing (CS) reconstruction.

METHODS

A volumetric FSE sequence was modified to include background-suppressed pseudo-continuous ASL labeling and to support variable-density (VD) Poisson-disk sampling for acceleration. We additionally explored the benefits of center oversampling and variable outer k-space sampling. Fourteen healthy volunteers were scanned on a 3T scanner to test acceleration factors as well as the various sampling schemes described under synchronized-breathing to limit motion issues. A CS reconstruction was implemented using the BART toolbox to reconstruct perfusion-weighted ASL volumes, assessing the impact of acceleration, different reconstruction, and sampling strategies on image quality.

RESULTS

CS acceleration is feasible with ASL, and a strong renal perfusion signal could be observed even at very high acceleration rates (≈15). We have shown that ASL k-space complex subtraction was desirable before CS reconstruction. Although averaging of multiple highly accelerated images helped to reduce artifacts from physiologic fluctuations, superior image quality was achieved by interleaving of different highly undersampled pseudo-random spatial sampling patterns and using 4D-CS reconstruction. Combination of these enhancements produces high-quality ASL volumes in under 5 min.

CONCLUSIONS

High-quality isotropic ASL abdominal perfusion volumes can be obtained in healthy volunteers with a VD-FSE and CS reconstruction. This lays the groundwork for future developments toward whole abdomen free-breathing non-contrast perfusion imaging.

Aortic 4D flow MRI in 2 minutes using compressed sensing, respiratory controlled adaptive k-space reordering, and inline reconstruction

PURPOSE

To evaluate the accuracy and feasibility of a free-breathing 4D flow technique using compressed sensing (CS), where 4D flow imaging of the thoracic aorta is performed in 2 min with inline image reconstruction on the MRI scanner in less than 5 min.

METHODS

The 10 in vitro 4D flow MRI scans were performed with different acceleration rates on a pulsatile flow phantom (9 CS acceleration factors [R = 5.4-14.1], 1 generalized autocalibrating partially parallel acquisition [GRAPPA] R = 2). Based on in vitro results, CS-accelerated 4D flow of the thoracic aorta was acquired in 20 healthy volunteers (38.3 ± 15.2 years old) and 11 patients with aortic disease (61.3 ± 15.1 years) with R = 7.7. A conventional 4D flow scan was acquired with matched spatial coverage and temporal resolution.

RESULTS

CS depicted similar hemodynamics to conventional 4D flow in vitro, and in vivo, with >70% reduction in scan time (volunteers: 1:52 ± 0:25 versus 7:25 ± 2:35 min). Net flow values were within 3.5% in healthy volunteers, and voxel-by-voxel comparison demonstrated good agreement. CS significantly underestimated peak velocities (v_max ) and peak flow (Q_max ) in both volunteers and patients (volunteers: v_max , -16.2% to -9.4%, Q_max : -11.6% to -2.9%, patients: v_max , -11.2% to -4.0%; Q_max , -10.2% to -5.8%).

CONCLUSION

Aortic 4D flow with CS is feasible in a two minute scan with less than 5 min for inline reconstruction. While net flow agreement was excellent, CS with R = 7.7 produced underestimation of Q_max and v_max ; however, these were generally within 13% of conventional 4D flow-derived values. This approach allows 4D flow to be feasible in clinical practice for comprehensive assessment of hemodynamics.

Motion-compensated reconstruction of magnetic resonance images from undersampled data

Magnetic resonance imaging of patients who find difficulty lying still or holding their breath can be challenging. Unresolved intra-frame motion yields blurring artifacts and limits spatial resolution. To correct for intra-frame non-rigid motion, such as in pediatric body imaging, this paper describes a multi-scale technique for joint estimation of the motion occurring during the acquisition and of the desired uncorrupted image. This technique regularizes the motion coefficients to enforce invertibility and minimize numerical instability. This multi-scale approach takes advantage of variable-density sampling patterns used in accelerated imaging to resolve large motion from a coarse scale. The resulting method improves image quality for a set of two-dimensional reconstructions from data simulated with independently generated deformations, with statistically significant increases in both peak signal to error ratio and structural similarity index. These improvements are consistent across varying undersampling factors and severities of motion and take advantage of the variable density sampling pattern.