Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging

Rational approximation of golden angles: Accelerated reconstructions for radial MRI

PURPOSE

To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio-based sampling schemes enables a flexible retrospective choice of temporal resolution, while preserving good coverage of k-space for each individual bin. Nevertheless, irrational increments prohibit precomputation of the point-spread function (PSF), can lead to numerical problems, and require more complex processing steps. To avoid these problems, a new sampling scheme based on a rational approximation of golden angles (RAGA) is developed.

METHODS

The theoretical properties of RAGA sampling are mathematically derived. Sidelobe-to-peak ratios (SPR) are numerically computed and compared to the corresponding golden ratio sampling schemes. The sampling scheme is implemented in the BART toolbox and in a radial gradient-echo sequence. Feasibility is shown for quantitative imaging in a phantom and a cardiac scan of a healthy volunteer.

RESULTS

RAGA sampling can accurately approximate golden ratio sampling and has almost identical PSF and SPR. In contrast to golden ratio sampling, each frame can be reconstructed with the same equidistant trajectory using different sampling masks, and the angle of each acquired spoke can be encoded as a small index, which simplifies processing of the acquired data.

CONCLUSION

RAGA sampling provides the advantages of golden ratio sampling while simplifying data processing, rendering it a valuable tool for dynamic and quantitative MRI.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma

PURPOSE

To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI.

METHODS

A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset.

RESULTS

For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions.

CONCLUSION

The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1- and T2-weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2*, and T1ρ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model

PURPOSE

To develop a new method for free-breathing 3D extracellular volume (ECV) mapping of the whole heart at 3 T.

METHODS

A free-breathing 3D cardiac ECV mapping method was developed at 3 T. T1 mapping was performed before and after contrast agent injection using a free-breathing electrocardiogram-gated inversion recovery sequence with spoiled gradient echo readout. A linear tangent space alignment model-based method was used to reconstruct high-frame-rate dynamic images from (k,t)-space data sparsely sampled along a random stack-of-stars trajectory. Joint T1 and transmit B1 estimation were performed voxel-by-voxel for pre- and post-contrast T1 mapping. To account for the time-varying T1 after contrast agent injection, a linearly time-varying T1 model was introduced for post-contrast T1 mapping. ECV maps were generated by aligning pre- and post-contrast T1 maps through affine transformation.

RESULTS

The feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3 T. We obtained 3D ECV maps at a spatial resolution of 1.9 × 1.9 × 4.5 mm3 and a FOV of 308 × 308 × 144 mm3, with a scan time of 10.1 ± 1.4 and 10.6 ± 1.6 min before and after contrast agent injection, respectively. The ECV maps and the pre- and post-contrast T1 maps obtained by the proposed method were in good agreement with the 2D MOLLI method both qualitatively and quantitatively.

CONCLUSION

The proposed method allows for free-breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method.

3D B1+ corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation

PURPOSE

To develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1ρ mapping at 3T.

METHODS

An electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1ρ preparation pulses for T1 and T1ρ sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1ρ quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique.

RESULTS

The proposed technique achieved strong correlation (T1: R2 = 0.99; T1ρ: R2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1ρ maps with spatial resolution of 2 × 2 × 4 mm were obtained with scan time of 5.4 ± 0.5 min, demonstrating comparable T1 (1236 ± 59 ms) and T1ρ (50.2 ± 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions.

CONCLUSION

The proposed technique achieved efficient 3D joint myocardial T1 and T1ρ mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking

PURPOSE

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework.

METHODS

A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach.

RESULTS

In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time.

CONCLUSION

The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Accelerated Cartesian cardiac T2 mapping based on a calibrationless locally low-rank tensor constraint

Background

Cardiac T2 mapping is a valuable tool for diagnosing myocardial edema, inflammation, and infiltration, yet its spatial resolution is limited by the single-shot balanced steady-state free precession acquisition and duration of the cardiac quiescent period, which may reduce sensitivity in detecting focal lesions in the myocardium. To improve spatial resolution without extending the acquisition window, this study examined a novel accelerated Cartesian cardiac T2 mapping technique.

Methods

We introduce a novel improved-resolution cardiac T2 mapping approach leveraging a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT)-constrained reconstruction algorithm in conjunction with Cartesian undersampling trajectory. The method was validated with phantom imaging and in vivo imaging that involved 13 healthy participants and 20 patients. The SCC-LLRT algorithm was compared with a conventional locally low-rank (LLR)-constrained algorithm and a nonlinear inversion (NLINV) reconstruction algorithm. The improved-resolution T2 mapping (1.4 mm × 1.4 mm) was compared globally and regionally with the regular-resolution T2 mapping (2.3 mm × 1.9 mm) according to the 16-segment model of the American Heart Association. The agreement between the improved-resolution and regular-resolution T2 mappings was evaluated by linear regression and Bland-Altman analyses. Image quality was scored by two experienced reviewers on a five-point scale (1, worst; 5, best).

Results

In healthy participants, SCC-LLRT significantly reduced artifacts (4.50±0.39) compared with LLR (2.31±0.60; P<0.001) and NLINV (3.65±0.56; P<0.01), suppressed noise (4.12±0.35) compared with NLINV (2.65±0.50; P<0.001), and improved the overall image quality (4.38±0.40) compared with LLR (2.54±0.41; P<0.001) and NLINV (3.04±0.50; P<0.001). Compared with the regular-resolution T2 mapping, the proposed method significantly improved the sharpness of myocardial boundaries (4.46±0.60 vs. 3.04±0.50; P<0.001) and the conspicuity of papillary muscles and fine structures (4.46±0.63 vs. 2.65±0.30; P<0.001). Myocardial T2 values obtained with the proposed method correlated significantly with those from regular-resolution T2 mapping in both healthy participants (r=0.79; P<0.01) and patients (r=0.94; P<0.001).

Conclusions

The proposed SCC-LLRT-constrained reconstruction algorithm in conjunction with Cartesian undersampling pattern achieved improved-resolution cardiac T2 mapping of comparable accuracy, precision, and scan-rescan reproducibility compared with the regular-resolution T2 mapping. The higher resolution improved the sharpness of myocardial borders and the conspicuity of image fine details, which may increase diagnostic confidence in cardiac T2 mapping for detecting small lesions.

Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior

OBJECTIVE

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications.

METHODS

A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data.

RESULTS

In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images.

CONCLUSION AND SIGNIFICANCE

The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

Comprehensive review of artifacts in cardiac MRI and their mitigation

Cardiac magnetic resonance imaging (CMR) is an important clinical tool that obtains high-quality images for assessment of cardiac morphology, function, and tissue characteristics. However, the technique may be prone to artifacts that may limit the diagnostic interpretation of images. This article reviews common artifacts which may appear in CMR exams by describing their appearance, the challenges they mitigate true pathology, and offering possible solutions to reduce their impact. Additionally, this article acts as an update to previous CMR artifacts reports by including discussion about new CMR innovations.

Highly accelerated non-contrast-enhanced time-resolved 4D MRA using stack-of-stars golden-angle radial acquisition with a self-calibrated low-rank subspace reconstruction

PURPOSE

To develop a highly accelerated non-contrast-enhanced 4D-MRA technique by combining stack-of-stars golden-angle radial acquisition with a modified self-calibrated low-rank subspace reconstruction.

METHODS

A low-rank subspace reconstruction framework was introduced in radial 4D MRA (SUPER 4D MRA) by combining stack-of-stars golden-angle radial acquisition with control-label k-space subtraction-based low-rank subspace modeling. Radial 4D MRA data were acquired and reconstructed using the proposed technique on 12 healthy volunteers and 1 patient with steno-occlusive disease. The performance of SUPER 4D MRA was compared with two temporally constrained reconstruction methods (golden-angle radial sparse parallel [GRASP] and GRASP-Pro) at different acceleration rates in terms of image quality and delineation of blood dynamics.

RESULTS

SUPER 4D MRA outperformed the other two reconstruction methods, offering superior image quality with a clear background and detailed delineation of cerebrovascular structures as well as great temporal fidelity in blood flow dynamics. SUPER 4D MRA maintained excellent performance even at higher acceleration rates.

CONCLUSIONS

SUPER 4D MRA is a promising technique for highly accelerating 4D MRA acquisition without comprising both temporal fidelity and image quality.

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation

PURPOSE

To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts.

THEORY

We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps.

METHODS

We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as thel 1 $$ {l}_1 $$ -norm minimization, PICS and other model-based approaches such as GraSP, MOBA.

RESULTS

Compared to conventional compressed sensing approaches such as thel 1 $$ {l}_1 $$ -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors inT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance inT 1 $$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches.

CONCLUSION

AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

Magnetization transfer explains most of the T 1 variability in the MRI literature

Purpose

To identify the predominant source of theT 1 variability described in the literature, which ranges from 0.6-1.1s for brain white matter at 3T.

Methods

25T 1 -mapping methods from the literature were simulated with a mono-exponential and magnetization-transfer (MT) models, each followed by mono-exponential fitting. A single set of model parameters was assumed for the simulation of all methods, and these parameters were estimated by fitting the simulation-based to the corresponding literatureT 1 values of white matter at 3T.

Results

Mono-exponential simulations suggest good inter-method reproducibility and fail to explain the highly variableT 1 estimates in the literature. In contrast, MT simulations suggest that a mono-exponential fit results in a variableT 1 and explain up to 62% of the literature's variability.

Conclusion

The results suggest that a mono-exponential model does not adequately describe longitudinal relaxation in biological tissue. Therefore,T 1 in biological tissue should be considered only a semi-quantitative metric that is inherently contingent upon the imaging methodology; and comparisons between differentT 1 -mapping methods and the use of simplistic spin systems-such as doped-water phantoms-for validation should be viewed with caution.

Free-Running Cardiac and Respiratory Motion-Resolved Imaging: A Paradigm Shift for Managing Motion in Cardiac MRI?

Cardiac magnetic resonance imaging (MRI) is widely used for non-invasive assessment of cardiac morphology, function, and tissue characteristics due to its exquisite soft-tissue contrast. However, it remains time-consuming and requires proficiency, making it costly and limiting its widespread use. Traditional cardiac MRI is inefficient as signal acquisition is often limited to specific cardiac phases and requires complex view planning, parameter adjustments, and management of both respiratory and cardiac motion. Recent efforts have aimed to make cardiac MRI more efficient and accessible. Among these innovations, the free-running framework enables 5D whole-heart imaging without the need for an electrocardiogram signal, respiratory breath-holding, or complex planning. It uses a fully self-gated approach to extract cardiac and respiratory signals directly from the acquired image data, allowing for more efficient coverage in time and space without the need for electrocardiogram gating, triggering, navigators, or breath-holds. This review provides a comprehensive overview of the free-running framework, detailing its history, concepts, recent improvements, and clinical applications.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence

PURPOSE

This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion.

METHODS

MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine.

RESULTS

In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine.

CONCLUSION

MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

Ferumoxytol-Enhanced 5D Multiphase Steady-State Imaging Using Rotating Cartesian K-Space With Low-Rank Reconstruction for Pediatric Congenital Heart Disease

BACKGROUND

The rotating Cartesian k-space multiphase steady-state imaging with contrast (ROCK-MUSIC) pulse sequence enables acquisition of whole-heart, cardiac phase-resolved images in pediatric congenital heart disease (CHD) without reliance on the ventilator gating signal. Multidimensional reconstruction with low rank tensor (LRT) has shown promise for resolving complex cardiorespiratory motion.

PURPOSE

To enhance ROCK-MUSIC by resolving cardiorespiratory phases using LRT reconstruction and to enable semi-automatic hyperparameter tuning by developing an image quality scoring model.

STUDY TYPE

Retrospective.

POPULATION

Thirty patients (45% female, age 2 days to 6.7 years) with CHD.

FIELD STRENGTH/SEQUENCE

3-T, four-dimensional (4D) spoiled gradient recalled echo sequence.

ASSESSMENT

Eigenvector-based iTerative Self-consistent Parallel Imaging Reconstruction (ESPIRiT) served as the reference comparison for LRT reconstruction. A 4-point Likert scale was used for cardiac and vascular image quality scoring based on cardiac chamber definition, lumen signal uniformity, vascular margin clarity, and motion artifact. Ejection fraction and ventricular volumes were assessed in 16 patients. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness were computed.

STATISTICAL TESTS

Intraclass correlation coefficients, Wilcoxon signed-rank test, Bland-Altman. A P-value <0.05 was considered statistically significant.

RESULTS

Relative to ESPIRiT, LRT images received significantly higher cardiac (2.81 ± 0.57 vs. 3.19 ± 0.54) and vascular (2.81 ± 0.60 vs. 3.36 ± 0.53) image quality scores. Image quality scoring with semi-automated hyperparameter tuning showed strong correlations (R2 = 0.748) among image quality, SNR, and septal sharpness. Comparison of ejection fraction and volumetry derived from ESPIRiT, and LRT showed no significant systematic difference (P = 0.32).

DATA CONCLUSION

Integration of low-rank reconstruction with ROCK-MUSIC acquisition may be feasible, and semi-automatic hyperparameter tuning could be effective for generating cardiorespiratory resolved images.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

High Spatial-Resolution and Acquisition-Efficiency Cardiac MR T1 Mapping Based on Radial bSSFP and a Low-Rank Tensor Constraint

BACKGROUND

Cardiac T1 mapping is valuable for evaluating myocardial fibrosis, yet its resolution and acquisition efficiency are limited, potentially obscuring visualization of small pathologies.

PURPOSE

To develop a technique for high-resolution cardiac T1 mapping with a less-than-100-millisecond acquisition window based on radial MOdified Look-Locker Inversion recovery (MOLLI) and a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT) constrained reconstruction.

STUDY TYPE

Prospective.

SUBJECTS/PHANTOM

Sixteen healthy subjects (age 25 ± 3 years, 44% females) and 12 patients with suspected cardiomyopathy (age 57 ± 15 years, 42% females), NiCl2-agar phantom.

FIELD STRENGTH/SEQUENCE

3-T, standard MOLLI, radial MOLLI, inversion-recovery spin-echo, late gadolinium enhancement.

ASSESSMENT

SCC-LLRT was compared to a conventional locally low-rank (LLR) method through simulations using Normalized Root-Mean-Square Error (NRMSE) and Structural Similarity Index Measure (SSIM). Radial MOLLI was compared to standard MOLLI across phantom, healthy subjects, and patients. Three independent readers subjectively evaluated the quality of T1 maps using a 5-point scale (5 = best).

STATISTICAL TESTS

Paired t-test, Wilcoxon signed-rank test, intraclass correlation coefficient analysis, linear regression, Bland-Altman analysis. P < 0.05 was considered statistically significant.

RESULTS

In simulations, SCC-LLRT demonstrated a significant improvement in NRMSE and SSIM compared to LLR. In phantom, both radial MOLLI and standard MOLLI provided consistent T1 estimates across different heart rates. In healthy subjects, radial MOLLI exhibited a significantly lower mean T1 (1115 ± 39 msec vs. 1155 ± 36 msec), similar T1 SD (74 ± 14 msec vs. 67 ± 23 msec, P = 0.20), and similar T1 reproducibility (28 ± 18 msec vs. 22 ± 15 msec, P = 0.34) compared to standard MOLLI. In patients, the proposed method significantly improved the sharpness of myocardial boundaries (4.50 ± 0.65 vs. 3.25 ± 0.43), the conspicuity of papillary muscles and fine structures (4.33 ± 0.74 vs. 3.33 ± 0.47), and artifacts (4.75 ± 0.43 vs. 3.83 ± 0.55). The reconstruction time for a single slice was 5.2 hours.

DATA CONCLUSION

The proposed method enables high-resolution cardiac T1 mapping with a short acquisition window and improved image quality.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping ofT 1 , T 2 * , proton density and the field inhomogeneity. However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner. In this work, we propose a faster version of Joint MAPLE which retains the mapping performance of the original version. Coil compression, random slice selection, parameter-specific learning rates and transfer learning are synergistically combined in the proposed framework. It speeds-up the reconstruction time up to 700 times than the original version and processes a whole-brain MEMFA dataset in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE. The mapping performance of the proposed framework is ~2-fold better than the standard and the state-of-the-art evaluated reconstruction techniques on average in terms of the root mean squared error.

Free-breathing three-dimensional simultaneous myocardial T1 and T2 mapping based on multi-parametric SAturation-recovery and Variable-flip-Angle

BACKGROUND

Quantitative myocardial tissue characterization with T1 and T2 parametric mapping can provide an accurate and complete assessment of tissue abnormalities across a broad range of cardiomyopathies. However, current clinical T1 and T2 mapping tools rely predominantly on two-dimensional (2D) breath-hold sequences. Clinical adoption of three-dimensional (3D) techniques is limited by long scan duration. The aim of this study is to develop and validate a time-efficient 3D free-breathing simultaneous T1 and T2 mapping sequence using multi-parametric SAturation-recovery and Variable-flip-Angle (mSAVA).

METHODS

mSAVA acquires four volumes for simultaneous whole-heart T1 and T2 mapping. We validated mSAVA using simulations, phantoms, and in-vivo experiments at 3T in 11 healthy subjects and 11 patients with diverse cardiomyopathies. T1 and T2 values by mSAVA were compared with modified Look-Locker inversion recovery (MOLLI) and gradient and spin echo (GraSE), respectively. The clinical performance of mSAVA was evaluated against late gadolinium enhancement (LGE) imaging in patients.

RESULTS

Phantom T1 and T2 by mSAVA showed a strong correlation to reference sequences (R2 = 0.98 and 0.99). In-vivo imaging with an imaging resolution of 1.5 × 1.5 × 8 mm3 could be achieved. Myocardial T1 and T2 of healthy subjects by mSAVA were 1310 ± 46 and 44.6 ± 2.0 ms, respectively, with T1 standard deviation higher than MOLLI (105 ± 12 vs 60 ± 16 ms) and T2 standard deviation lower than GraSE (4.5 ± 0.8 vs 5.5 ± 1.0 ms). mSAVA T1 and T2 maps presented consistent findings in patients undergoing LGE. Myocardial T1 and T2 of all patients by mSAVA were 1421 ± 79 and 47.2 ± 3.3 ms, respectively.

CONCLUSION

mSAVA is a fast 3D technique promising for clinical whole-heart T1 and T2 mapping.

Free-breathing MRI techniques for fat and R2* quantification in the liver

OBJECTIVE

To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities.

MATERIALS AND METHODS

This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification.

RESULTS

Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity.

DISCUSSION

As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.

Structural Defect-Enabled Magnetic Neutrality Nanoprobes for Ultra-High-Field Magnetic Resonance Imaging of Isolated Tumor Cells in Vivo

The identification of metastasis "seeds," isolated tumor cells (ITCs), is of paramount importance for the prognosis and tailored treatment of metastatic diseases. The conventional approach to clinical ITCs diagnosis through invasive biopsies is encumbered by the inherent risks of overdiagnosis and overtreatment. This underscores the pressing need for noninvasive ITCs detection methods that provide histopathological-level insights. Recent advancements in ultra-high-field (UHF) magnetic resonance imaging (MRI) have ignited hope for the revelation of minute lesions, including the elusive ITCs. Nevertheless, currently available MRI contrast agents are susceptible to magnetization-induced strong T2-decaying effects under UHF conditions, which compromises T1 MRI capability and further impedes the precise imaging of small lesions. Herein, this study reports a structural defect-enabled magnetic neutrality nanoprobe (MNN) distinguished by its paramagnetic properties featuring an exceptionally low magnetic susceptibility through atomic modulation, rendering it almost nonmagnetic. This unique characteristic effectively mitigates T2-decaying effect while concurrently enhancing UHF T1 contrast. Under 9 T MRI, the MNN demonstrates an unprecedentedly low r2/r1 value (≈1.06), enabling noninvasive visualization of ITCs with an exceptional detection threshold of ≈0.16 mm. These high-performance MNNs unveil the domain of hitherto undetectable minute lesions, representing a significant advancement in UHF-MRI for diagnostic purposes and fostering comprehensive metastasis research.

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.

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

OBJECTIVE

To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction.

METHODS

We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of "contrast-weighted" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion.

RESULTS

We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases.

CONCLUSION

The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction.

SIGNIFICANCE

Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.

Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS

PURPOSE

To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling.

THEORY AND METHODS

A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold.

RESULTS

Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time.

CONCLUSION

The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias

BACKGROUND

Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. Clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias.

PURPOSE

To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time-resolved cine images.

STUDY TYPE

Prospective.

SUBJECTS

Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm.

FIELD STRENGTH/SEQUENCE

Free-running balanced steady state free precession (bSSFP) with sorted golden-step, reference real-time sequence.

ASSESSMENT

Each subject underwent both the sorted golden-step bSSFP and the reference Cartesian real-time imaging. Golden-step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k-means clustering. Image quality (4-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, and ventricular function were assessed.

STATISTICAL TESTS

Paired t-tests, Friedman test, regression analysis, Fleiss' Kappa, Bland-Altman analysis. Significance level P < 0.05.

RESULTS

The DRACO method had the highest percent of images with scores ≥3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k-means and real-time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k-means and between DRACO vs. real-time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real-time.

CONCLUSION

DRACO with time-resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

Diffusion Tensor Phenomapping of the Healthy and Pressure-Overloaded Human Heart

Current techniques to image the microstructure of the heart with diffusion tensor MRI (DTI) are highly under-resolved. We present a technique to improve the spatial resolution of cardiac DTI by almost 10-fold and leverage this to measure local gradients in cardiomyocyte alignment or helix angle (HA). We further introduce a phenomapping approach based on voxel-wise hierarchical clustering of these gradients to identify distinct microstructural microenvironments in the heart. Initial development was performed in healthy volunteers (n=8). Thereader, subjects with severe but well-compensated aortic stenosis (AS, n=10) were compared to age-matched controls (CTL, n=10). Radial HA gradient was significantly reduced in AS (8.0±0.8°/mm vs. 10.2±1.8°/mm, p=0.001) but the other HA gradients did not change significantly. Four distinct microstructural clusters could be idenJfied in both the CTL and AS subjects and did not differ significantly in their properties or distribution. Despite marked hypertrophy, our data suggest that the myocardium in well-compensated AS can maintain its microstructural coherence. The described phenomapping approach can be used to characterize microstructural plasticity and perturbation in any organ system and disease.

Flow independent black blood imaging with a large FOV from the neck to the aortic arch: A feasibility study at 3 tesla

PURPOSE

To investigate the feasibility of obtaining black-blood imaging with a large FOV from the neck to the aortic arch at 3 T using a newly modified Relaxation-Enhanced Angiography without Contrast and Triggering for Black-Blood Imaging (REACT-BB).

MATERIALS AND METHODS

REACT-BB provides black-blood images by adjusting the inversion time (TI) in REACT to the null point of blood. The optimal TI for REACT-BB was investigated in 10 healthy volunteers with TI varied from 200 ms to 1400 ms. Contrast ratios were calculated between muscle and three branch arteries of the aortic arch. Additionally, a comparison between REACT-BB and MPRAGE involved evaluating the depiction of high-intensity plaques in 222 patients with stroke or transient ischemic attack. Measurements included plaque-to-muscle signal intensity ratios (PMR), plaque volumes, and carotid artery stenosis rates in 60 patients with high-intensity plaques in carotid arteries.

RESULTS

REACT-BB with TI = 850 ms produced the black-blood image with the best contrast between blood and background tissues. REACT-BB outperformed MPRAGE in depicting high-intensity plaques in the aortic arch (55.4% vs 45.5%) and exhibited superior overall image quality in visual assessment (3.31 ± 0.70 vs 2.89 ± 0.73; p < 0.05). Although the PMR of REACT-BB was significantly lower than MPRAGE (2.227 ± 0.601 vs 2.285 ± 0.662; P < 0.05), a strong positive correlation existed between REACT-BB and MPRAGE (ρ = 0.935; P < 0.05), and all high-intensity plaques that MPRAGE detected were clearly detected by REACT-BB.

CONCLUSION

REACT-BB provides black-blood images with uniformly suppressed fat and blood signals over a large FOV from the neck to the aortic arch with comparable or better high-signal plaque depiction than MPRAGE.

Non-electrocardiogram-gated, free-breathing, off-resonance reduced, high-resolution, whole-heart myocardial T2 * mapping at 3 T within 5 min

PURPOSE

Widely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated.

METHODS

A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol.

RESULTS

Spatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias.

CONCLUSION

The proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.

Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain

PURPOSE

Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system.

METHODS

Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.

RESULTS

The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol.

CONCLUSION

Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.

T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction

The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction. Tensor low-rank methods preserve the inherent high-dimensional structure of data, allowing for improved extraction and utilization of intrinsic low-rank characteristics. However, most current methods are still confined to utilizing low-rank structures either in the image domain or predefined transformed domains. Designing an optimal transformation adaptable to dynamic MRI reconstruction through manual efforts is inherently challenging. In this paper, we propose a deep unrolling network that utilizes the convolutional neural network (CNN) to adaptively learn the transformed domain for leveraging tensor low-rank priors. Under the supervised mechanism, the learning of the tensor low-rank domain is directly guided by the reconstruction accuracy. Specifically, we generalize the traditional t-SVD to a transformed version based on arbitrary high-dimensional unitary transformations and introduce a novel unitary transformed tensor nuclear norm (UTNN). Subsequently, we present a dynamic MRI reconstruction model based on UTNN and devise an efficient iterative optimization algorithm using ADMM, which is finally unfolded into the proposed T2LR-Net. Experiments on two dynamic cardiac MRI datasets demonstrate that T2LR-Net outperforms the state-of-the-art optimization-based and unrolling network-based methods.

Cardiac MRI at Low Field Strengths

Cardiac MR imaging is well established for assessment of cardiovascular structure and function, myocardial scar, quantitative flow, parametric mapping, and myocardial perfusion. Despite the clear evidence supporting the use of cardiac MRI for a wide range of indications, it is underutilized clinically. Recent developments in low-field MRI technology, including modern data acquisition and image reconstruction methods, are enabling high-quality low-field imaging that may improve the cost-benefit ratio for cardiac MRI. Studies to-date confirm that low-field MRI offers high measurement concordance and consistent interpretation with clinical imaging for several routine sequences. Moreover, low-field MRI may enable specific new clinical opportunities for cardiac imaging such as imaging near metal implants, MRI-guided interventions, combined cardiopulmonary assessment, and imaging of patients with severe obesity. In this review, we discuss the recent progress in low-field cardiac MRI with a focus on technical developments and early clinical validation studies. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1)

Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.

Data-driven system matrix manipulation enabling fast functional imaging and intra-image nonrigid motion correction in tomography

Tomographic imaging modalities are described by large system matrices. Sparse sampling and tissue motion degrade system matrix and image quality. Various existing techniques improve the image quality without correcting the system matrices. Here, we compress the system matrices to improve computational efficiency (e.g., 42 times) using singular value decomposition and fast Fourier transform. Enabled by the efficiency, we propose (1) fast sparsely sampling functional imaging by incorporating a densely sampled prior image into the system matrix, which maintains the critical linearity while mitigating artifacts and (2) intra-image nonrigid motion correction by incorporating the motion as subdomain translations into the system matrix and reconstructing the translations together with the image iteratively. We demonstrate the methods in 3D photoacoustic computed tomography with significantly improved image qualities and clarify their applicability to X-ray CT and MRI or other types of imperfections due to the similarities in system matrices.

mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping

PURPOSE

To develop a method for rapid sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO).

METHODS

A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method withR = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard.

RESULTS

The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal.

CONCLUSION

mcLARO enabled fast sub-millimeter T1 , T2 ,T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.

Feasibility of Free-Breathing, Non-ECG-Gated, Black-Blood Cine Magnetic Resonance Images With Multitasking in Measuring Left Ventricular Function Indices

OBJECTIVE

To clinically validate the feasibility and accuracy of cine images acquired through the multitasking method, with no electrocardiogram gating and free-breathing, in measuring left ventricular (LV) function indices by comparing them with those acquired through the balanced steady-state free precession (bSSFP) method, with multiple breath-holds and electrocardiogram gating.

MATERIALS AND METHODS

Forty-three healthy volunteers (female:male, 30:13; mean age, 23.1 ± 2.3 years) and 36 patients requiring an assessment of LV function for various clinical indications (female:male, 22:14; 57.8 ± 11.3 years) were enrolled in this prospective study. Each participant underwent cardiac magnetic resonance imaging (MRI) using the multiple breath-hold bSSFP method and free-breathing multitasking method. LV function parameters were measured for both MRI methods. Image quality was assessed through subjective image quality scores (1 to 5) and calculation of the contrast-to-noise ratio (CNR) between the myocardium and blood pool. Differences between the two MRI methods were analyzed using the Bland-Altman plot, paired t-test, or Wilcoxon signed-rank test, as appropriate.

RESULTS

LV ejection fraction (LVEF) was not significantly different between the two MRI methods (P = 0.222 in healthy volunteers and P = 0.343 in patients). LV end-diastolic mass was slightly overestimated with multitasking in both healthy volunteers (multitasking vs. bSSFP, 60.5 ± 10.7 g vs. 58.0 ± 10.4 g, respectively; P < 0.001) and patients (69.4 ± 18.1 g vs. 66.8 ± 18.0 g, respectively; P = 0.003). Acceptable and comparable image quality was achieved for both MRI methods (multitasking vs. bSSFP, 4.5 ± 0.7 vs. 4.6 ± 0.6, respectively; P = 0.203). The CNR between the myocardium and blood pool showed no significant differences between the two MRI methods (18.89 ± 6.65 vs. 18.19 ± 5.83, respectively; P = 0.480).

CONCLUSION

Multitasking-derived cine images obtained without electrocardiogram gating and breath-holding achieved similar image quality and accurate quantification of LVEF in healthy volunteers and patients.

Dynamic cardiac MRI with high spatiotemporal resolution using accelerated spiral-out and spiral-in/out bSSFP pulse sequences at 1.5 T

OBJECTIVE

To develop two spiral-based bSSFP pulse sequences combined with L + S reconstruction for accelerated ungated, free-breathing dynamic cardiac imaging at 1.5 T.

MATERIALS AND METHODS

Tiny golden angle rotated spiral-out and spiral-in/out bSSFP sequences combined with view-sharing (VS), compressed sensing (CS), and low-rank plus sparse (L + S) reconstruction were evaluated and compared via simulation and in vivo dynamic cardiac imaging studies. The proposed methods were then validated against the standard cine, in terms of quantitative image assessment and qualitative quality rating.

RESULTS

The L + S method yielded the least residual artifacts and the best image sharpness among the three methods. Both spiral cine techniques showed clinically diagnostic images (score > 3). Compared to standard cine, there were significant differences in global image quality and edge sharpness for spiral cine techniques, while there was significant difference in image contrast for the spiral-out cine but no significant difference for the spiral-in/out cine. There was good agreement in left ventricular ejection fraction for both the spiral-out cine (- 1.6 [Formula: see text] 3.1%) and spiral-in/out cine (- 1.5 [Formula: see text] 2.8%) against standard cine.

DISCUSSION

Compared to the time-consuming standard cine (~ 5 min) which requires ECG-gating and breath-holds, the proposed spiral bSSFP sequences achieved ungated, free-breathing cardiac movies at a similar spatial (1.5 × 1.5 × 8 mm3) and temporal resolution (36 ms) per slice for whole heart coverage (10-15 slices) within 45 s, suggesting the clinical potential for improved patient comfort or for imaging patients with arrhythmias or who cannot hold their breath.

Learned Tensor Low-CP-Rank and Bloch Response Manifold Priors for Non-Cartesian MRF Reconstruction

Magnetic resonance fingerprinting (MRF) can rapidly perform simultaneous imaging of multiple tissue parameters. However, the rapid acquisition schemes used in MRF inevitably introduce aliasing artifacts in the recovered tissue fingerprints, reducing the accuracy of the predicted parameter maps. Current regularized reconstruction methods are based on iterative procedures which are usually time-consuming. In addition, most of the current deep learning-based methods for MRF often lack interpretability owing to the black-box nature, and most deep learning-based methods are not applicable for non-Cartesian scenarios, which limits the practical applications. In this paper, we propose a joint reconstruction model incorporating MRF-physics prior and the data correlation constraint for non-Cartesian MRF reconstruction. To avoid time-consuming iterative procedures, we unroll the reconstruction model into a deep neural network. Specifically, we propose a learned CANDECOMP/PARAFAC (CP) decomposition module to exploit the tensor low-rank priors of high-dimensional MRF data, which avoids computationally burdensome singular value decomposition. Inspired by the MRF-physics, we also propose a Bloch response manifold module to learn the mapping between reconstructed MRF data and the multiple parameter maps. Numerical experiments show that the proposed network can reconstruct high-quality MRF data and multiple parameter maps within significantly reduced computational time.

High-fidelity direct contrast synthesis from magnetic resonance fingerprinting

PURPOSE

This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations.

METHODS

To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others.

RESULTS

In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images.

CONCLUSION

We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).

SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS

PURPOSE

To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary.

METHODS

An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models.

RESULTS

Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min.

CONCLUSION

The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning

PURPOSE

To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors.

METHODS

Eighteen subjects were imaged using a whole-brain quantitative T1 -T2 -T1ρ MR multitasking sequence. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 gradient echo, and T2 fluid-attenuated inversion recovery were acquired as target images. A 2D U-Net-based neural network was trained to synthesize conventional weighted images from MR multitasking spatial factors. Quantitative assessment and image quality rating by two radiologists were performed to evaluate the quality of deep-learning-based synthesis, in comparison with Bloch-equation-based synthesis from MR multitasking quantitative maps.

RESULTS

The deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis.

CONCLUSION

A deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.

Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.

Accelerated dynamic MR imaging with joint balanced low-rank tensor and sparsity constraints

BACKGROUND

Dynamic magnetic resonance imaging (DMRI) is an essential medical imaging technique, but the slow data acquisition process limits its further development.

PURPOSE

By exploiting the inherent spatio-temporal correlation of MR images, low-rank tensor based methods have been developed to accelerate imaging. However, the tensor rank used by these methods is defined by an unbalanced matricization scheme, which cannot capture the global correlation of DMR data efficiently during the reconstruction process.

METHODS

In this paper, an effective reconstruction model is proposed to achieve accurate reconstruction by using the tensor train (TT) rank defined by a well-balanced matricization scheme to exploit the hidden correlation of DMR data and combining sparsity. Meanwhile, the ket augmentation (KA) technology is introduced to preprocess the DMR data into a higher-order tensor through block structure addressing, which further improves the ability of TT rank to explore the local information of the image. In order to solve the proposed model, the alternating direction method of multipliers (ADMM) is used to decompose the optimization problem into several unconstrained subproblems.

RESULTS

The performance of the proposed method was validated on the 3D DMR image dataset by using different sampling trajectories and sampling rates. Extensive numerical experiments demonstrate that the reconstruction quality of the proposed method is superior to several state-of-the-art reconstruction methods.

CONCLUSIONS

The proposed method successfully utilizes the TT rank to explore the global correlation of DMR images, enabling more detailed information of the image to be captured. Besides, with the sparse priors, the proposed method can further improve the overall reconstruction quality for highly undersampled MR images.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

ABSTRACT

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI

PURPOSE

To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model.

METHODS

Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints.

RESULTS

An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments.

CONCLUSION

Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.

Magnetic resonance myocardial T1ρ mapping : Technical overview, challenges, emerging developments, and clinical applications

The potential of cardiac magnetic resonance to improve cardiovascular care and patient management is considerable. Myocardial T1-rho (T1ρ) mapping, in particular, has emerged as a promising biomarker for quantifying myocardial injuries without exogenous contrast agents. Its potential as a contrast-agent-free ("needle-free") and cost-effective diagnostic marker promises high impact both in terms of clinical outcomes and patient comfort. However, myocardial T1ρ mapping is still at a nascent stage of development and the evidence supporting its diagnostic performance and clinical effectiveness is scant, though likely to change with technological improvements. The present review aims at providing a primer on the essentials of myocardial T1ρ mapping, and to describe the current range of clinical applications of the technique to detect and quantify myocardial injuries. We also delineate the important limitations and challenges for clinical deployment, including the urgent need for standardization, the evaluation of bias, and the critical importance of clinical testing. We conclude by outlining technical developments to be expected in the future. If needle-free myocardial T1ρ mapping is shown to improve patient diagnosis and prognosis, and can be effectively integrated in cardiovascular practice, it will fulfill its potential as an essential component of a cardiac magnetic resonance examination.

Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI

PURPOSE

To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions.

THEORY AND METHODS

A self-supervised eXtra dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid motion fields were incorporated into this architecture by estimating motion fields from a lower resolution motion resolved (XD-GRASP) reconstruction. Motion compensated XD-MBDL was evaluated on lung UTE datasets with and without contrast and compared to constrained reconstructions and variants of self-supervised MBDL that do not account for dynamic respiratory states or leverage motion correction.

RESULTS

Images reconstructed using XD-MBDL demonstrate improved image quality as measured by apparent SNR (aSNR), contrast to noise ratio (CNR), and visual assessment relative to self-supervised MBDL approaches that do not account for dynamic respiratory states, XD-GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction time relative to both XD-GRASP and iMoCo.

CONCLUSION

A method was developed to allow self-supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with graphics processing unit (GPU)-based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user-selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.