Free-breathing whole-heart multi-slice myocardial T1 mapping in 2 minutes

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping

BACKGROUND

The modified Look-Locker inversion recovery (MOLLI) sequence is commonly used for myocardial T1 mapping. However, it acquires images with different inversion times, which causes difficulty in motion correction for respiratory-induced misregistration to a given target image.

HYPOTHESIS

Using a generative adversarial network (GAN) to produce virtual MOLLI images with consistent heart positions can reduce respiratory-induced misregistration of MOLLI datasets.

STUDY TYPE

Retrospective.

POPULATION

1071 MOLLI datasets from 392 human participants.

FIELD STRENGTH/SEQUENCE

Modified Look-Locker inversion recovery sequence at 3 T.

ASSESSMENT

A GAN model with a single inversion time image as input was trained to generate virtual MOLLI target (VMT) images at different inversion times which were subsequently used in an image registration algorithm. Four VMT models were investigated and the best performing model compared with the standard vendor-provided motion correction (MOCO) technique.

STATISTICAL TESTS

The effectiveness of the motion correction technique was assessed using the fitting quality index (FQI), mutual information (MI), and Dice coefficients of motion-corrected images, plus subjective quality evaluation of T1 maps by three independent readers using Likert score. Wilcoxon signed-rank test with Bonferroni correction for multiple comparison. Significance levels were defined as P < 0.01 for highly significant differences and P < 0.05 for significant differences.

RESULTS

The best performing VMT model with iterative registration demonstrated significantly better performance (FQI 0.88 ± 0.03, MI 1.78 ± 0.20, Dice 0.84 ± 0.23, quality score 2.26 ± 0.95) compared to other approaches, including the vendor-provided MOCO method (FQI 0.86 ± 0.04, MI 1.69 ± 0.25, Dice 0.80 ± 0.27, quality score 2.16 ± 1.01).

DATA CONCLUSION

Our GAN model generating VMT images improved motion correction, which may assist reliable T1 mapping in the presence of respiratory motion. Its robust performance, even with considerable respiratory-induced heart displacements, may be beneficial for patients with difficulties in breath-holding.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 1.

STEAM-SASHA: a novel approach for blood- and fat-suppressed native T1 measurement in the right ventricular myocardium

OBJECTIVE

The excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping.

MATERIALS AND METHODS

STEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV).

RESULTS

In phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal.

DISCUSSION

STEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.

DeepFittingNet: A deep neural network-based approach for simplifying cardiac T1 and T2 estimation with improved robustness

PURPOSE

To develop and evaluate a deep neural network (DeepFittingNet) for T1 /T2 estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness.

THEORY AND METHODS

DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and Tx of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T1 mapping sequences, and T2 -prepared balanced SSFP (T2 -prep bSSFP) T2 mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm.

RESULTS

In testing, DeepFittingNet performed T1 /T2 estimation of four sequences with improved robustness in inversion-recovery T1 estimation. The mean bias in phantom T1 and T2 between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T1 /T2 with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T1 /T2 between the two methods.

CONCLUSION

DeepFittingNet trained with simulations of MOLLI, SASHA, and T2 -prep bSSFP performed T1 /T2 estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T1 estimation and had comparable performance in terms of accuracy and precision.

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.

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.

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.

SAturation-recovery and Variable-flip-Angle-based three-dimensional free-breathing cardiovascular magnetic resonance T1 mapping at 3 T

The purpose of the current study was to develop and validate a three-dimensional (3D) free-breathing cardiac T₁ -mapping sequence using SAturation-recovery and Variable-flip-Angle (SAVA). SAVA sequentially acquires multiple electrocardiogram-triggered volumes using a multishot spoiled gradient-echo sequence. The first volume samples the equilibrium signal of the longitudinal magnetization, where a flip angle of 2° is used to reduce the time for the magnetization to return to equilibrium. The succeeding three volumes are saturation prepared with variable delays, and are acquired using a 15° flip angle to maintain the signal-to-noise ratio. A diaphragmatic navigator is used to compensate the respiratory motion. T₁ is calculated using a saturation-recovery model that accounts for the flip angle. We validated SAVA by simulations, phantom, and human subject experiments at 3 T. SAVA was compared with modified Look-Locker inversion recovery (MOLLI) and saturation-recovery single-shot acquisition (SASHA) in vivo. In phantoms, T₁ by SAVA had good agreement with the reference (R²  = 0.99). In vivo 3D T₁ mapping by SAVA could achieve an imaging resolution of 1.25 × 1.25 × 8 mm³ . Both global and septal T₁ values by SAVA (1347 ± 37 and 1332 ± 42 ms) were in between those by SASHA (1612 ± 63 and 1618 ± 51 ms) and MOLLI (1143 ± 59 and 1188 ± 65 ms). According to the standard deviation (SD) and coefficient of variation (CV), T₁ precision measured by SAVA (SD: 99 ± 14 and 60 ± 8 ms; CV: 7.4% ± 0.9% and 4.5% ± 0.6%) was comparable with MOLLI (SD: 99 ± 25 and 46 ± 12 ms; CV: 8.8% ± 2.5% and 3.9% ± 1.1%) and superior to SASHA (SD: 222 ± 89 and 132 ± 33 ms; CV: 13.8% ± 5.5% and 8.1% ± 2.0%). It was concluded that the proposed free-breathing SAVA sequence enables more efficient 3D whole-heart T₁ estimation with good accuracy and precision.

Improving accuracy of myocardial T1 estimation in MyoMapNet

PURPOSE

To improve the accuracy and robustness of T₁ estimation by MyoMapNet, a deep learning-based approach using 4 inversion-recovery T₁ -weighted images for cardiac T₁ mapping.

METHODS

MyoMapNet is a fully connected neural network for T₁ estimation of an accelerated cardiac T₁ mapping sequence, which collects 4 T₁ -weighted images by a single Look-Locker inversion-recovery experiment (LL4). MyoMapNet was originally trained using in vivo data from the modified Look-Locker inversion recovery sequence, which resulted in significant bias and sensitivity to various confounders. This study sought to train MyoMapNet using signals generated from numerical simulations and phantom MR data under multiple simulated confounders. The trained model was then evaluated by phantom data scanned using new phantom vials that differed from those used for training. The performance of the new model was compared with modified Look-Locker inversion recovery sequence and saturation-recovery single-shot acquisition for measuring native and postcontrast T₁ in 25 subjects.

RESULTS

In the phantom study, T₁ values measured by LL4 with MyoMapNet were highly correlated with reference values from the spin-echo sequence. Furthermore, the estimated T₁ had excellent robustness to changes in flip angle and off-resonance. Native and postcontrast myocardium T₁ at 3 Tesla measured by saturation-recovery single-shot acquisition, modified Look-Locker inversion recovery sequence, and MyoMapNet were 1483 ± 46.6 ms and 791 ± 45.8 ms, 1169 ± 49.0 ms and 612 ± 36.0 ms, and 1443 ± 57.5 ms and 700 ± 57.5 ms, respectively. The corresponding extracellular volumes were 22.90% ± 3.20%, 28.88% ± 3.48%, and 30.65% ± 3.60%, respectively.

CONCLUSION

Training MyoMapNet with numerical simulations and phantom data will improve the estimation of myocardial T₁ values and increase its robustness to confounders while also reducing the overall T₁ mapping estimation time to only 4 heartbeats.

Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach

PURPOSE

To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4).

METHOD

We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI.

RESULTS

MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively.

CONCLUSION

A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

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.

Right Ventricular Function and T1-Mapping in Boys With Duchenne Muscular Dystrophy

BACKGROUND

Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in-depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied.

PURPOSE

To evaluate several analysis methods and identify the most reliable one to measure RV pre- and postcontrast T1 (RV-T1) and to characterize myocardial remodeling in the RV of boys with DMD.

STUDY TYPE

Prospective.

POPULATION

Boys with DMD (N = 27) and age-/sex-matched healthy controls (N = 17) from two sites.

FIELD STRENGTH/SEQUENCE

3.0 T using balanced steady state free precession, motion-corrected phase sensitive inversion recovery and modified Look-Locker inversion recovery sequences.

ASSESSMENT

Biventricular mass (Mi), end-diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre- and postcontrast myocardial T1 maps. The RV-T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC).

STATISTICAL TESTS

The Wilcoxon rank sum test was used to compare RV-T1 differences between DMD boys with negative LGE(-) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV-T1 with functional measures was performed. A P-value <0.05 was considered statistically significant.

RESULTS

A 1-pixel thick RV circumferential ROI proved most reliable (ICC > 0.91) for assessing RV-T1. Precontrast RV-T1 was significantly higher in boys with DMD compared to controls. Both LGE(-) and LGE(+) boys had significantly elevated precontrast RV-T1 compared to controls (1543 [1489-1597] msec and 1550 [1402-1699] msec vs. 1436 [1399-1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%, P = 0.31) and significantly reduced RVMi (29.8 [9.7] g vs. 48.0 [15.7] g), RVEDVi (69.8 [29.7] mL/m² vs. 89.1 [21.9] mL/m² ), and TAE (22.0 [3.2] cm vs. 26.0 [4.7] cm). Significant correlations were found between precontrast RV-T1 and RVEF (β = -0.48%/msec) and between LV-T1 and LVEF (β = -0.51%/msec).

DATA CONCLUSION

Precontrast RV-T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.