Free-breathing, non-ECG, continuous myocardial T1 mapping with cardiovascular magnetic resonance multitasking

Free-breathing, fat-corrected T1 mapping of the liver with stack-of-stars MRI, and joint estimation of T1, PDFF, R 2 * , and B 1 +

PURPOSE

Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat andB 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition.

THEORY AND METHODS

Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T.

RESULTS

The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps.

CONCLUSION

The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, andB 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

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.

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.

Deep image prior cine MR fingerprinting with B1 + spin history correction

PURPOSE

To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF).

METHODS

The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements.

RESULTS

DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans.

CONCLUSION

A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.

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.

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.

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.

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.

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.

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.

Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging

PURPOSE

To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes.

METHODS

Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies.

RESULTS

The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required.

CONCLUSION

SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.

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.

MR Multitasking-based multi-dimensional assessment of cardiovascular system (MT-MACS) with extended spatial coverage and water-fat separation

PURPOSE

To extend the MR MultiTasking-based Multidimensional Assessment of Cardiovascular System (MT-MACS) technique with larger spatial coverage and water-fat separation for comprehensive aortocardiac assessment.

METHODS

MT-MACS adopts a low-rank tensor image model for 7D imaging, with three spatial dimensions for volumetric imaging, one cardiac motion dimension for cine imaging, one respiratory motion dimension for free-breathing imaging, one T2-prepared inversion recovery time dimension for multi-contrast assessment, and one T2*-decay time dimension for water-fat separation. Nine healthy subjects were recruited for the 3T study. Overall image quality was scored on bright-blood (BB), dark-blood (DB), and gray-blood (GB) contrasts using a 4-point scale (0-poor to 3-excellent) by two independent readers, and their interreader agreement was evaluated. Myocardial wall thickness and left ventricular ejection fraction (LVEF) were quantified on DB and BB contrasts, respectively. The agreement in these metrics between MT-MACS and conventional breath-held, electrocardiography-triggered 2D sequences were evaluated.

RESULTS

MT-MACS provides both water-only and fat-only images with excellent image quality (average score = 3.725/3.780/3.835/3.890 for BB/DB/GB/fat-only images) and moderate to high interreader agreement (weighted Cohen's kappa value = 0.727/0.668/1.000/1.000 for BB/DB/GB/fat-only images). There were good to excellent agreements in myocardial wall thickness measurements (intraclass correlation coefficients [ICC] = 0.781/0.929/0.680/0.878 for left atria/left ventricle/right atria/right ventricle) and LVEF quantification (ICC = 0.716) between MT-MACS and 2D references. All measurements were within the literature range of healthy subjects.

CONCLUSION

The refined MT-MACS technique provides multi-contrast, phase-resolved, and water-fat imaging of the aortocardiac systems and allows evaluation of anatomy and function. Clinical validation is warranted.

Pilot tone-based prospective correction of respiratory motion for free-breathing myocardial T1 mapping

OBJECTIVE

To provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition.

MATERIALS AND METHODS

During a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior-posterior direction is estimated and two linear models are derived between the PT and heart motion. In the following scan through-plane motion is corrected prospectively with slice tracking based on the PT. In-plane motion is corrected for retrospectively. Our method was evaluated on a motion phantom and 11 healthy subjects.

RESULTS

Non-motion corrected measurements using a moving phantom showed T1 errors of 14 ± 4% (p < 0.05) compared to a reference measurement. The proposed motion correction approach reduced this error to 3 ± 4% (p < 0.05). In vivo the respiratory motion led to an overestimation of T1 values by 26 ± 31% compared to breathhold T1 maps, which was successfully corrected to an average difference of 3 ± 2% (p < 0.05) between our free-breathing approach and breathhold data.

DISCUSSION

Our proposed PT-based motion correction approach allows for T1 mapping during free-breathing with the same accuracy as a corresponding breathhold T1 mapping scan.

Latest Advances in Image Acceleration: All Dimensions are Fair Game

Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction

OBJECTIVES

To develop a real-time abdominal T₂ mapping method without requiring breath-holding or respiratory-gating.

METHODS

The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T₂ mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T₂ mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set.

RESULTS

The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T₂ mapping. The average NMSE and R² values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T₂ and reference T₂ in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T₂ value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T₂ values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively.

CONCLUSIONS

Free-breathing abdominal T₂ mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T₂ mapping in the abdomen.

KEY POINTS

• MOLED achieves free-breathing abdominal T₂ mapping in about 100 ms, enabling real-time capture of T₂ value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches

Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T₁, T_1ρ, T₂ and T₂* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma

Background and aims

Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.

Methods

The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow F_p, plasma volume fraction v_p, transfer constant K^trans, and extravascular extracellular volume fraction v_e. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.

Results

Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of F_p, v_p, K^trans, and v_e can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.

Conclusion

Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

Free-running 3D whole-heart T1 and T2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction

PURPOSE

To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images, enabling a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3-min scan.

METHODS

Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects.

RESULTS

Good quality 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images were reconstructed for all subjects. Septal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> values using the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>50</mml:mn></mml:mrow> <mml:annotation>$$ 1200\pm 50 $$</mml:annotation></mml:semantics> </mml:math> ms) were higher than those from a 2D MOLLI sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1063</mml:mn> <mml:mo>±</mml:mo> <mml:mn>33</mml:mn></mml:mrow> <mml:annotation>$$ 1063\pm 33 $$</mml:annotation></mml:semantics> </mml:math> ms), which is known to underestimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> , while <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> values from the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 4 $$</mml:annotation></mml:semantics> </mml:math> ms) were in good agreement with those from a 2D GraSE sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 2 $$</mml:annotation></mml:semantics> </mml:math> ms).

CONCLUSION

The proposed technique provides 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images with isotropic spatial resolution in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3.3-min scan.

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.

Free-breathing, non-ECG, simultaneous myocardial T1 , T2 , T2 *, and fat-fraction mapping with motion-resolved cardiovascular MR multitasking

PURPOSE

To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan.

METHODS

The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques.

RESULTS

In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references.

CONCLUSIONS

The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan.

Free-breathing self-gated continuous-IR spiral T1 mapping: Comparison of dual flip-angle and Bloch-Siegert B1-corrected techniques

Purpose

To develop a B1‐corrrected single flip‐angle continuous acquisition strategy with free‐breathing and cardiac self‐gating for spiral T1 mapping, and compare it to a previous dual flip‐angle technique.

Methods

Data were continuously acquired using a spiral‐out trajectory, rotated by the golden angle in time. During the first 2 s, off‐resonance Fermi RF pulses were applied to generate a Bloch‐Siegert shift B1 map, and the subsequent data were acquired with an inversion RF pulse applied every 4 s to create a T1* map. The final T1 map was generated from the B1 and the T1* maps by using a look‐up table that accounted for slice profile effects, yielding more accurate T1 values. T1 values were compared to those from inversion recovery (IR) spin echo (phantom only), MOLLI, SAturation‐recovery single‐SHot Acquisition (SASHA), and previously proposed dual flip‐angle results. This strategy was evaluated in a phantom and 25 human subjects.

Results

The proposed technique showed good agreement with IR spin‐echo results in the phantom experiment. For in‐vivo studies, the proposed technique and the previously proposed dual flip‐angle method were more similar to SASHA results than to MOLLI results.

Conclusions

B1‐corrected single flip‐angle T1 mapping successfully acquired B1 and T1 maps in a free‐breathing, continuous‐IR spiral acquisition, providing a method with improved accuracy to measure T1 using a continuous Look‐Locker acquisition, as compared to the previously proposed dual excitation flip‐angle technique.

Cardiac MR: From Theory to Practice

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

Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction

Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction. Four novel DC formulations are developed and evaluated: two gradient decent approaches, a directly solved approach, and a conjugate gradient approach. We applied a U-Net model with and without DC layers to reconstruct T1-weighted images for cardiac MR Multitasking (an advanced multidimensional imaging method), comparing our results to the iteratively reconstructed reference. Experimental results show that the proposed framework significantly improves reconstruction accuracy over the U-Net model without DC, while significantly accelerating the reconstruction over conventional iterative reconstruction.

Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking

PURPOSE

To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping.

METHODS

MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T₁ /T₂ /T_1ρ mapping in the brain was investigated.

RESULTS

For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T₁ /T₂ /T1_ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T₁ /T₂ /T_1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance.

CONCLUSION

Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.

Free-breathing multitasking multi-echo MRI for whole-liver water-specific T1 , proton density fat fraction, and R2∗ quantification

PURPOSE

To develop a 3D multitasking multi-echo (MT-ME) technique for the comprehensive characterization of liver tissues with 5-min free-breathing acquisition; whole-liver coverage; a spatial resolution of 1.5 × 1.5 × 6 mm³ ; and simultaneous quantification of T₁ , water-specific T₁ (T_1w ), proton density fat fraction (PDFF), and R2∗ .

METHODS

Six-echo bipolar spoiled gradient echo readouts following inversion recovery preparation was performed to generate T₁ , water/fat, and R2∗ contrast. MR multitasking was used to reconstruct the MT-ME images with 3 spatial dimensions: 1 T₁ recovery dimension, 1 multi-echo dimension, and 1 respiratory dimension. A basis function-based approach was developed for T_1w quantification, followed by the estimation of R2∗ and T₁ -corrected PDFF. The intrasession repeatability and agreement against references of MT-ME measurements were tested on a phantom and 15 clinically healthy subjects. In addition, 4 patients with confirmed liver diseases were recruited, and the agreement between MT-ME measurements and references was assessed.

RESULTS

MT-ME produced high-quality, coregistered T₁ , T_1w , PDFF, and R2∗ maps with good intrasession repeatability and substantial agreement with references on phantom and human studies. The intra-class coefficients of T₁ , T_1w , PDFF, and R2∗ from the repeat MT-ME measurements on clinically healthy subjects were 0.989, 0.990, 0.999, and 0.988, respectively. The intra-class coefficients of T₁ , PDFF, and R2∗ between the MT-ME and reference measurements were 0.924, 0.987, and 0.975 in healthy subjects and 0.980, 0.999, and 0.998 in patients. The T_1w was independent to PDFF (R = -0.029, P = .904).

CONCLUSION

The proposed MT-ME technique quantifies T₁ , T_1w , PDFF, and R2∗ simultaneously and is clinically promising for the comprehensive characterization of liver tissue properties.

Dual flip-angle IR-FLASH with spin history mapping for B1+ corrected T1 mapping: Application to T1 cardiovascular magnetic resonance multitasking

PURPOSE

To develop a single-scan method for B 1 + -corrected T1 mapping and apply it for free-breathing (FB) cardiac MR multitasking without electrocardiogram (ECG) triggering.

METHODS

One dual flip-angle (2FA) inversion recovery (IR)-FLASH scan provides two observations of T 1 ∗ (apparent T1 ) corresponding to two distinct combinations of the nominal FA α and B 1 + . Spatiotemporally coregistered T1 and B 1 + spin history maps are obtained by fitting the 2FA signal model. T1 estimate accuracy and repeatability for single flip-angle (1FA) and 2FA IR-FLASH sequence MR multitasking were evaluated at 3T. A T1 phantom was first imaged on the scanner table, then on two human subjects' thoraxes in both breath-hold (BH) and FB conditions. IR-turbo spin echo (IR-TSE) static phantom T1 measurements served as reference. In 10 healthy subjects, myocardial T1 was evaluated with ECG-free, FB multitasking sequences alongside ECG-triggered BH MOLLI.

RESULTS

For phantom-on-table T1 estimates, 2FA agreed better with IR-TSE (intraclass correlation coefficient [ICC] = 0.996, mean error ± SD = -1.6% ± 1.9%) than did 1FA (ICC = 0.922; mean error ± SD = -4.3% ± 12%). For phantom-on-thorax, 2FA was more repeatable and robust to respiration than 1FA (coefficient of variation [CoV] = 1.2% 2FA, = 11.3% 1FA). In vivo, in intrasession T1 repeatability, 2FA (septal CoV = 2.4%, six-segment CoV = 4.4%) outperformed 1FA (septal CoV = 3.1%, six-segment CoV = 5.5%). In six-segment T1 homogeneity, 2FA (CoV = 7.9%) also outperformed 1FA (CoV = 11.1%).

CONCLUSION

The 2FA IR-FLASH improves T1 estimate accuracy and repeatability over 1FA IR-FLASH, and enables single-scan B 1 + -corrected T1 mapping without BHs or ECG when used with MR multitasking.

Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T₁ . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.

Free-breathing 3D cardiac T1 mapping with transmit B1 correction at 3T

PURPOSE

To develop a cardiac T₁ mapping method for free-breathing 3D T₁ mapping of the whole heart at 3 T with transmit B₁ ( B 1 + ) correction.

METHODS

A free-breathing, electrocardiogram-gated inversion-recovery sequence with spoiled gradient-echo readout was developed and optimized for cardiac T₁ mapping at 3 T. High-frame-rate dynamic images were reconstructed from sparse (k,t)-space data acquired along a stack-of-stars trajectory using a subspace-based method for accelerated imaging. Joint T₁ and flip-angle estimation was performed in T₁ mapping to improve its robustness to B 1 + inhomogeneity. Subject-specific timing of data acquisition was used in the estimation to account for natural heart-rate variations during the imaging experiment.

RESULTS

Simulations showed that accuracy and precision of T₁ mapping can be improved with joint T₁ and flip-angle estimation and optimized electrocardiogram-gated spoiled gradient echo-based inversion-recovery acquisition scheme. The phantom study showed good agreement between the T₁ maps from the proposed method and the reference method. Three-dimensional cardiac T₁ maps (40 slices) were obtained at a 1.9-mm in-plane and 4.5-mm through-plane spatial resolution from healthy subjects (n = 6) with an average imaging time of 14.2 ± 1.6 minutes (heartbeat rate: 64.2 ± 7.1 bpm), showing myocardial T₁ values comparable to those obtained from modified Look-Locker inversion recovery. The proposed method generated B 1 + maps with spatially smooth variation showing 21%-32% and 11%-15% variations across the septal-lateral and inferior-anterior regions of the myocardium in the left ventricle.

CONCLUSION

The proposed method allows free-breathing 3D T₁ mapping of the whole heart with transmit B₁ correction in a practical imaging time.

Intradiscal quantitative chemical exchange saturation transfer MRI signal correlates with discogenic pain in human patients

Low back pain (LBP) is often a result of a degenerative process in the intervertebral disc. The precise origin of discogenic pain is diagnosed by the invasive procedure of provocative discography (PD). Previously, we developed quantitative chemical exchange saturation transfer (qCEST) magnetic resonance imaging (MRI) to detect pH as a biomarker for discogenic pain. Based on these findings we initiated a clinical study with the goal to evaluate the correlation between qCEST values and PD results in LBP patients. Twenty five volunteers with chronic low back pain were subjected to T2-weighted (T2w) and qCEST MRI scans followed by PD. A total of 72 discs were analyzed. The average qCEST signal value of painful discs was significantly higher than non-painful discs (p = 0.012). The ratio between qCEST and normalized T2w was found to be significantly higher in painful discs compared to non-painful discs (p = 0.0022). A receiver operating characteristics (ROC) analysis indicated that qCEST/T2w ratio could be used to differentiate between painful and non-painful discs with 78% sensitivity and 81% specificity. The results of the study suggest that qCEST could be used for the diagnosis of discogenic pain, in conjunction with the commonly used T2w scan.

Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of > 98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.

Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping

PURPOSE

This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping.

METHODS

An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver).

RESULTS

ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers.

CONCLUSION

This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

Dual-excitation flip-angle simultaneous cine and T1 mapping using spiral acquisition with respiratory and cardiac self-gating

PURPOSE

To develop a free-breathing cardiac self-gated technique that provides cine images and B1+ slice profile-corrected T₁ maps from a single acquisition.

METHODS

Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T₁ maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects.

RESULTS

The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T₁ values were similar to those of the inversion-recovery spin-echo technique in phantom, and had a higher global T₁ value than that of MOLLI in human subjects.

CONCLUSIONS

Cine and T₁ mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T₁ maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T₁ while accounting for B1+ and slice profile effect on the apparent T1∗ .

Electrocardiogram-less, free-breathing myocardial extracellular volume fraction mapping in small animals at high heart rates using motion-resolved cardiovascular magnetic reesonance multitasking: a feasibility study in a heart failure with preserved ejection fraction rat model

BACKGROUND

Extracellular volume fraction (ECV) quantification with cardiovascular magnetic resonance (CMR) T1 mapping is a powerful tool for the characterization of focal or diffuse myocardial fibrosis. However, it is technically challenging to acquire high-quality T1 and ECV maps in small animals for preclinical research because of high heart rates and high respiration rates. In this work, we developed an electrocardiogram (ECG)-less, free-breathing ECV mapping method using motion-resolved CMR Multitasking on a 9.4 T small animal CMR system. The feasibility of characterizing diffuse myocardial fibrosis was tested in a rat heart failure model with preserved ejection fraction (HFpEF).

METHODS

High-salt fed rats diagnosed with HFpEF (n = 9) and control rats (n = 9) were imaged with the proposed ECV Multitasking technique. A 25-min exam, including two 4-min T1 Multitasking scans before and after gadolinium injection, were performed on each rat. It allows a cardiac temporal resolution of 20 ms for a heart rate of ~ 300 bpm. Myocardial ECV was calculated from the hematocrit (HCT) and fitted T1 values of the myocardium and the blood pool. Masson's trichrome stain was used to measure the extent of fibrosis. Welch's t-test was performed between control and HFpEF groups.

RESULTS

ECV was significantly higher in the HFpEF group (22.4% ± 2.5% vs. 18.0% ± 2.1%, P = 0.0010). A moderate correlation between the ECV and the extent of fibrosis was found (R = 0.59, P = 0.0098).

CONCLUSIONS

Motion-resolved ECV Multitasking CMR can quantify ECV in the rat myocardium at high heart rates without ECG triggering or respiratory gating. Elevated ECV found in the HFpEF group is consistent with previous human studies and well correlated with histological data. This technique has the potential to be a viable imaging tool for myocardial tissue characterization in small animal models.

Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer

PURPOSE

To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T₁ mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm³ , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg).

METHODS

Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T₁ recovery dimension for dynamic T₁ quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T₁ maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups.

RESULTS

The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively).

CONCLUSION

The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.

Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis

PURPOSE

To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min.

METHODS

MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined.

RESULTS

Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone.

CONCLUSION

MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.

Prospective correction of patient-specific respiratory motion in myocardial T1 and T2 mapping

PURPOSE

Respiratory motion in cardiovascular MRI presents a challenging problem with many potential solutions. Current approaches require breath-holds, apply retrospective image registration, or significantly increase scan time by respiratory gating. Myocardial T₁ and T₂ mapping techniques are particularly sensitive to motion as they require multiple source images to be accurately aligned prior to the estimation of tissue relaxation. We propose a patient-specific prospective motion correction (PROCO) strategy that corrects respiratory motion on the fly with the goal of reducing the spatial variation of myocardial parametric mapping techniques.

METHODS

A rapid, patient-specific training scan was performed to characterize respiration-induced motion of the heart relative to a diaphragmatic navigator, and a parametric mapping pulse sequence utilized the resulting motion model to prospectively update the scan plane in real-time. Midventricular short-axis T₁ and T₂ maps were acquired under breath-hold or free-breathing conditions with and without PROCO in 7 healthy volunteers and 3 patients. T₁ and T₂ were measured in 6 segments and compared to reference standard breath-hold measurements using Bland-Altman analysis.

RESULTS

PROCO significantly reduced the spatial variation of parametric maps acquired during free-breathing, producing limits of agreement of -47.16 to 30.98 ms (T₁ ) and -1.35 to 4.02 ms (T₂ ), compared to -67.77 to 74.34 ms (T₁ ) and -2.21 to 5.62 ms (T₂ ) for free-breathing acquisition without PROCO.

CONCLUSION

Patient-specific respiratory PROCO method significantly reduced the spatial variation of myocardial T₁ and T₂ mapping, while allowing for 100% efficient free-breathing acquisitions.

Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T1 and T2 quantification

This study introduces a technique called cine magnetic resonance fingerprinting (cine-MRF) for simultaneous T₁ , T₂ and ejection fraction (EF) quantification. Data acquired with a free-running MRF sequence are retrospectively sorted into different cardiac phases using an external electrocardiogram (ECG) signal. A low-rank reconstruction with a finite difference sparsity constraint along the cardiac motion dimension yields images resolved by cardiac phase. To improve SNR and precision in the parameter maps, these images are nonrigidly registered to the same phase and matched to a dictionary to generate T₁ and T₂ maps. Cine images for computing left ventricular volumes and EF are also derived from the same data. Cine-MRF was tested in simulations using a numerical relaxation phantom. Phantom and in vivo scans of 19 subjects were performed at 3 T during a 10.9 seconds breath-hold with an in-plane resolution of 1.6 x 1.6 mm² and 24 cardiac phases. Left ventricular EF values obtained with cine-MRF agreed with the conventional cine images (mean bias -1.0%). Average myocardial T₁ times in diastole/systole were 1398/1391 ms with cine-MRF, 1394/1378 ms with ECG-triggered cardiac MRF (cMRF) and 1234/1212 ms with MOLLI; and T₂ values were 30.7/30.3 ms with cine-MRF, 32.6/32.9 ms with ECG-triggered cMRF and 37.6/41.0 ms with T₂ -prepared FLASH. Cine-MRF and ECG-triggered cMRF relaxation times were in good agreement. Cine-MRF T₁ values were significantly longer than MOLLI, and cine-MRF T₂ values were significantly shorter than T₂ -prepared FLASH. In summary, cine-MRF can potentially streamline cardiac MRI exams by combining left ventricle functional assessment and T₁ -T₂ mapping into one time-efficient acquisition.

Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding

PURPOSE

To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging.

METHODS

The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B₀ -update algorithm and a shot-to-shot B₀ variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new "temporal-variant" of CAIPI encoding is proposed to further improve performance.

RESULTS

The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B₀ -informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T 2 ∗ and tissue phase maps at 1 mm isotropic resolution were acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding.

CONCLUSION

The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.

Magnetic resonance multitasking for multidimensional assessment of cardiovascular system: Development and feasibility study on the thoracic aorta

PURPOSE

To develop an MR multitasking-based multidimensional assessment of cardiovascular system (MT-MACS) with electrocardiography-free and navigator-free data acquisition for a comprehensive evaluation of thoracic aortic diseases.

METHODS

The MT-MACS technique adopts a low-rank tensor image model with a cardiac time dimension for phase-resolved cine imaging and a T₂ -prepared inversion-recovery dimension for multicontrast assessment. Twelve healthy subjects and 2 patients with thoracic aortic diseases were recruited for the study at 3 T, and both qualitative (image quality score) and quantitative (contrast-to-noise ratio between lumen and wall, lumen and wall area, and aortic strain index) analyses were performed in all healthy subjects. The overall image quality was scored based on a 4-point scale: 3, excellent; 2, good; 1, fair; and 0, poor. Statistical analysis was used to test the measurement agreement between MT-MACS and its corresponding 2D references.

RESULTS

The MT-MACS images reconstructed from acquisitions as short as 6 minutes demonstrated good or excellent image quality for bright-blood (2.58 ± 0.46), dark-blood (2.58 ± 0.50), and gray-blood (2.17 ± 0.53) contrast weightings, respectively. The contrast-to-noise ratios for the three weightings were 49.2 ± 12.8, 20.0 ± 5.8 and 2.8 ± 1.8, respectively. There were good agreements in the lumen and wall area (intraclass correlation coefficient = 0.993, P < .001 for lumen; intraclass correlation coefficient = 0.969, P < .001 for wall area) and strain (intraclass correlation coefficient = 0.947, P < .001) between MT-MACS and conventional 2D sequences.

CONCLUSION

The MT-MACS technique provides high-quality, multidimensional images for a comprehensive assessment of the thoracic aorta. Technical feasibility was demonstrated in healthy subjects and patients with thoracic aortic diseases. Further clinical validation is warranted.

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.

Free-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction

Mapping the longitudinal relaxation time constant (T1) of the myocardium using Magnetic Resonance Imaging (MRI) is an emerging technique for quantitative assessment of the morphology and viability of the myocardium. However, three-dimensional (3D) T1 mapping of the heart is challenging due to the high dimensionality of the signal and the presence of cardiac and respiratory motions. We propose a subspace-based method for free-breathing 3D T1 mapping of the heart without respiratory gating. The image function is represented as a high-order partially separable (PS) function to explore the inherent spatiotemporal correlations of the underlying signal. A special data acquisition scheme enabled by the high-order PS model is used for sparse sampling of the (k,t)-space, where complementary sparse datasets are acquired, each covering only a small portion of the (k,t)-space to characterize a single subspace (spatial or temporal). High-resolution dynamic MR images are reconstructed from the highly undersampled (k,t)-space using low-rank tensor and sparsity constraints. We demonstrate the feasibility of our proposed method using in vivo data obtained from healthy subjects on a 3T MR scanner. The proposed method can enable new clinical applications of T1 mapping in cardiac MR.

Three-dimensional simultaneous brain T1 , T2 , and ADC mapping with MR Multitasking

PURPOSE

To develop a simultaneous T₁ , T₂ , and ADC mapping method that provides co-registered, distortion-free images and enables multiparametric quantification of 3D brain coverage in a clinically feasible scan time with the MR Multitasking framework.

METHODS

The T₁ /T₂ /diffusion weighting was generated by a series of T₂ preparations and diffusion preparations. The underlying multidimensional image containing 3 spatial dimensions, 1 T₁ weighting dimension, 1 T₂ -preparation duration dimension, 1 b-value dimension, and 1 diffusion direction dimension was modeled as a 5-way low-rank tensor. A separate real-time low-rank model incorporating time-resolved phase correction was also used to compensate for both inter-shot and intra-shot phase inconsistency induced by physiological motion. The proposed method was validated on both phantom and 16 healthy subjects. The quantification of T₁ /T₂ /ADC was evaluated for each case. Three post-surgery brain tumor patients were scanned for demonstration of clinical feasibility.

RESULTS

Multitasking T₁ /T₂ /ADC maps were perfectly co-registered and free from image distortion. Phantom studies showed substantial quantitative agreement ( R 2 = 0.999 ) with reference protocols for T₁ /T₂ /ADC. In vivo studies showed nonsignificant T₁ (P = .248), T₂ (P = .97), ADC (P = .328) differences among the frontal, parietal, and occipital regions. Although Multitasking showed significant differences of T₁ (P = .03), T₂ (P < .001), and ADC (P = .001) biases against the references, the mean bias estimates were small (ΔT₁ % < 5%, ΔT₂ % < 7%, ΔADC% < 5%), with all intraclass correlation coefficients greater than 0.82 indicating "excellent" agreement. Patient studies showed that Multitasking T₁ /T₂ /ADC maps were consistent with the clinical qualitative images.

CONCLUSION

The Multitasking approach simultaneously quantifies T₁ /T₂ /ADC with substantial agreement with the references and is promising for clinical applications.

Myocardial T1 and ECV Measurement: Underlying Concepts and Technical Considerations

Myocardial native T1 and extracellular volume fraction (ECV) mapping have emerged as cardiac magnetic resonance biomarkers providing unique insight into cardiac pathophysiology. Single breath-hold acquisition techniques, available on clinical scanners across multiple vendor platforms, have made clinical T1 and ECV mapping a reality. Although the relationship between changes in native T1 and alterations in cardiac microstructure is complex, an understanding of how edema, blood volume, myocyte and interstitial expansion, lipids, and paramagnetic substances affect T1 and ECV can provide insight into how and why these parameters change in various cardiac pathologies. The goals of this state-of-the-art review will be to review factors influencing native T1 and ECV, to describe how native T1 and ECV are measured, to discuss potential challenges and pitfalls in clinical practice, and to describe new T1 mapping techniques on the horizon.

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.

Free-running 3D whole heart myocardial T1 mapping with isotropic spatial resolution

PURPOSE

To develop a free-running (free-breathing, retrospective cardiac gating) 3D myocardial T1 mapping with isotropic spatial resolution.

METHODS

The free-running sequence is inversion recovery (IR)-prepared followed by continuous 3D golden angle radial data acquisition. 1D respiratory motion signal is extracted from the k-space center of all spokes and used to bin the k-space data into different respiratory states, enabling estimation and correction of 3D translational respiratory motion, whereas cardiac motion is recorded using electrocardiography and synchronized with data acquisition. 3D translational respiratory motion compensated T1 maps at diastole and systole were generated with 1.5 mm isotropic spatial resolution with low-rank inversion and high-dimensionality patch-based undersampled reconstruction. The technique was validated against conventional methods in phantom and 9 healthy subjects.

RESULTS

Phantom results demonstrated good agreement (R2 = 0.99) of T1 estimation with reference method. Homogeneous systolic and diastolic 3D T1 maps were reconstructed from the proposed technique. Diastolic septal T1 estimated with the proposed method (1140 ± 36 ms) was comparable to the saturation recovery single-shot acquisition (SASHA) sequence (1153 ± 49 ms), but was higher than the modified Look-Locker inversion recovery (MOLLI) sequence (1037 ± 33 ms). Precision of the proposed method (42 ± 8 ms) was comparable to MOLLI (41 ± 7 ms) and improved with respect to SASHA (87 ± 19 ms).

CONCLUSIONS

The proposed free-running whole heart T1 mapping method allows for reconstruction of isotropic resolution 3D T1 maps at different cardiac phases, serving as a promising tool for whole heart myocardial tissue characterization.

Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac T 1 Mapping

Multi-dimensional, multi-contrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T 1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe inter-dimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T 1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T 1 maps acquired in six healthy volunteers. All methods provided comparable T 1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T 1 mapping at high spatio-temporal resolutions.

Cardiac T1 mapping: Techniques and applications

A key advantage of cardiac magnetic resonance (CMR) imaging over other cardiac imaging modalities is the ability to perform detailed tissue characterization. CMR techniques continue to evolve, with advanced imaging sequences being developed to provide a reproducible, quantitative method of tissue interrogation. The T₁ mapping technique, a pixel-by-pixel method of quantifying T₁ relaxation time of soft tissues, has been shown to be promising for characterization of diseased myocardium in a wide variety of cardiomyopathies. In this review, we describe the basic principles and common techniques for T₁ mapping and its use for native T₁ , postcontrast T₁ , and extracellular volume mapping. We will review a wide range of clinical applications of the technique that can be used for identification and quantification of myocardial edema, fibrosis, and infiltrative diseases with illustrative clinical examples. In addition, we will explore the current limitations of the technique and describe some areas of ongoing development. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1336-1356.

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.