Free-breathing combined three-dimensional phase sensitive late gadolinium enhancement and T1 mapping for myocardial tissue characterization

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.

Cardiac magnetic resonance imaging in the evaluation and management of mitral valve prolapse - a comprehensive review

Mitral valve prolapse is a common valve disorder that usually has a benign prognosis unless there is significant regurgitation or LV impairment. However, a subset of patients are at an increased risk of ventricular arrhythmias and sudden cardiac death, which has led to the recognition of "arrhythmic mitral valve prolapse" as a clinical entity. Emerging risk factors include mitral annular disjunction and myocardial fibrosis. While echocardiography remains the primary method of evaluation, cardiac magnetic resonance has become crucial in managing this condition. Cine magnetic resonance sequences provide accurate characterization of prolapse and annular disjunction, assessment of ventricular volumes and function, identification of early dysfunction and remodeling, and quantitative assessment of mitral regurgitation when integrated with flow imaging. However, the unique strength of magnetic resonance lies in its ability to identify tissue changes. T1 mapping sequences identify diffuse fibrosis, in turn related to early ventricular dysfunction and remodeling. Late gadolinium enhancement sequences detect replacement fibrosis, an independent risk factor for ventricular arrhythmias and sudden cardiac death. There are consensus documents and reviews on the use of cardiac magnetic resonance specifically in arrhythmic mitral valve prolapse. However, in this article, we propose an algorithm for the broader use of cardiac magnetic resonance in managing this condition in various scenarios. Future advancements may involve implementing techniques for tissue characterization and flow analysis, such as 4D flow imaging, to identify patients with ventricular dysfunction and remodeling, increased arrhythmic risk, and more accurate grading of mitral regurgitation, ultimately benefiting patient selection for surgical therapy.

An Overview of Deep Learning Methods for Left Ventricle Segmentation

Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.

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.

Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques

PURPOSE

We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.

METHODS

Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2.

RESULTS

In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30).

CONCLUSIONS

Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.

Single breath-hold saturation recovery 3D cardiac T1 mapping via compressed SENSE at 3T

Objectives

To propose and validate a novel imaging sequence that uses a single breath-hold whole-heart 3D T1 saturation recovery compressed SENSE rapid acquisition (SACORA) at 3T.

Methods

The proposed sequence combines flexible saturation time sampling, compressed SENSE, and sharing of saturation pulses between two readouts acquired at different RR intervals. The sequence was compared with a 3D saturation recovery single-shot acquisition (SASHA) implementation with phantom and in vivo experiments (pre and post contrast; 7 pigs) and was validated against the reference inversion recovery spin echo (IR-SE) sequence in phantom experiments.

Results

Phantom experiments showed that the T1 maps acquired by 3D SACORA and 3D SASHA agree well with IR-SE. In vivo experiments showed that the pre-contrast and post-contrast T1 maps acquired by 3D SACORA are comparable to the corresponding 3D SASHA maps, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). Mean septal pre-contrast T1 was 1453 ± 44 ms with 3D SACORA and 1460 ± 60 ms with 3D SASHA. Mean septal post-contrast T1 was 824 ± 66 ms and 824 ± 60 ms.

Conclusion

3D SACORA acquires 3D T1 maps in 15 heart beats (heart rate, 60 bpm) at 3T. In addition to its short acquisition time, the sequence achieves good T1 estimation precision and accuracy.

Electronic supplementary material

The online version of this article (10.1007/s10334-020-00848-2) contains supplementary material, which is available to authorized users.

Comparison of free breathing 3D mDIXON with single breath-hold 3D inversion recovery sequences for the assessment of Late Gadolinium Enhancement

PURPOSE

To evaluate the technical and diagnostic performance of three dimensional (3D) mDIXON versus 3D inversion recovery (3D VIAB) and 3D spectral presaturation with inversion recovery (3D SPIR) late gadolinium enhancement (LGE) sequences.

METHODS

A total of 78 patients (50 males and 28 females, age 49 ± 18 years) with 1.5 T CMR examination including three different 3D LGE sequences (3D mDIXON, 3D VIAB, and 3D SPIR) were evaluated for technical and diagnostic performance by two readers. Qualitative scores and quantitative signal and contrast-to-noise ratios were compared among sequences. Qualitative comparisons were made using Friedman and Wilcoxon signed rank tests. Quantitative comparisons were made using one way ANOVA. Reader agreements were tested using Cohen's Kappa. Any p-value <0.05 was significant.

RESULTS

19 out of 78 patients (24 %) were excluded due to poor (grade 4) image quality and 29 patients were excluded due to absence of LGE. For the remaining 30 patients, free breathing 3D mDIXON showed higher confidence in diagnosis of subepicardial LGE (p-value < 0.05). 3D mDIXON outperformed 3D SPIR in both visualization of LGE (p = 0.02) and quality of fat suppression (p = 0.001). Nevertheless, 3D mDIXON showed lower image quality compared to the other two sequences.

CONCLUSION

Free breathing 3D mDIXON is a diagnostic problem-solving tool, especially when making a diagnosis of subepicardial enhancement and/or fat suppression is needed, owing to its high spatial resolution and robust fat suppression. Choice of 3D LGE sequence should be based on patient's breath-hold ability, diagnostic needs, and institutional availability considering the strengths and limitations of each sequence.

Free-breathing simultaneous myocardial T1 and T2 mapping with whole left ventricle coverage

PURPOSE

To develop a free-breathing sequence, that is, Multislice Joint T₁ -T₂ , for simultaneous measurement of myocardial T₁ and T₂ for multiple slices to achieve whole left-ventricular coverage.

METHODS

Multislice Joint T₁ -T₂ adopts slice-interleaved acquisition to collect 10 single-shot electrocardiogram-triggered images for each slice prepared by saturation and T₂ preparation to simultaneously estimate myocardial T₁ and T₂ and achieve whole left-ventricular coverage. Prospective slice-tracking using a respiratory navigator and retrospective image registration are used to reduce through-plane and in-plane motion, respectively. Multislice Joint T₁ -T₂ was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation-recovery single-shot acquisition and T₂ -prepared balanced Steady-State Free Precession (T₂ -prep SSFP) sequences.

RESULTS

Phantom T₁ and T₂ from Multislice Joint T₁ -T₂ had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T₁ -T₂ yielded T₁ and T₂ maps of nine left-ventricular slices in 1.4 minutes. The mean left-ventricular T₁ difference between saturation-recovery single-shot acquisition and Multislice Joint T₁ -T₂ across healthy subjects and patients was 191 ms (1564 ± 60 ms versus 1373 ± 50 ms; P < .05) and 111 ms (1535 ± 49 ms vs 1423 ± 49 ms; P < .05), respectively. The mean difference in left-ventricular T₂ between T₂ -prep SSFP and Multislice Joint T₁ -T₂ across healthy subjects and patients was -6.3 ms (42.4 ± 1.4 ms vs 48.7 ± 2.5; P < .05) and -5.7 ms (41.6 ± 2.5 ms vs 47.3 ± 2.7; P < .05), respectively.

CONCLUSION

Multislice Joint T₁ -T₂ enables quantification of whole left-ventricular T₁ and T₂ during free breathing within a clinically feasible scan time of less than 2 minutes.

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

PURPOSE

To develop and validate a saturation-delay-inversion recovery preparation, slice tracking and multi-slice based sequence for measuring whole-heart native T₁ .

METHOD

The proposed free-breathing sequence performs T₁ mapping of multiple left-ventricular slices by slice-interleaved acquisition to collect 10 electrocardiogram-triggered single-shot slice-selective images for each slice. A saturation-delay-inversion recovery pulse is used for T₁ preparation. Prospective slice tracking by the diaphragm navigator and retrospective registration are used to reduce through-plane and in-plane motion, respectively. The proposed sequence was validated in both phantom and human subjects (12 healthy subjects and 15 patients who were referred for a clinical cardiac MR exam) and compared with saturation recovery single-shot acquisition (SASHA) and modified Look-Locker inversion recovery (MOLLI).

RESULTS

Phantom T₁ measured by the proposed sequence had excellent agreement (R²  = 0.99) with the ground-truth T₁ and was insensitive to heart rate. In both healthy subjects and patients, the proposed sequence yielded nine left-ventricular T₁ maps per volume in less than 2 minutes (healthy volunteers: 1.8 ± 0.4 minutes; patients: 1.9 ± 0.2 minutes). The average T₁ of whole left ventricle for all healthy subjects and patients were 1560 ± 61 and 1535 ± 49 ms by SASHA, 1208 ± 42 and 1233 ± 56 ms by MOLLI5(3)3, and 1397 ± 34 and 1433 ± 56 ms by the proposed sequence, respectively. The corresponding coefficient of variation of T₁ were 6.2 ± 1.4% and 5.8 ± 1.6%, 5.3 ± 1.1% and 5.1 ± 0.8%, and 4.9 ± 0.8% and 4.5 ± 0.8%, respectively.

CONCLUSION

The proposed sequence enables quantification of whole heart T₁ with good accuracy and precision in less than 2 minutes during free breathing.

T1 Mapping Tissue Heterogeneity Provides Improved Risk Stratification for ICDs Without Needing Gadolinium in Patients With Dilated Cardiomyopathy

OBJECTIVES

This study sought to determine whether myocardial tissue heterogeneity scanned by native T₁ mapping could improve risk stratification in patients with nonischemic dilated cardiomyopathy (NICM) evaluated for primary prevention by ICD.

BACKGROUND

The benefit of insertable cardiac-defibrillator (ICD) as primary prevention ICD in patients with NICM remains to be fully clarified.

METHODS

A total of 115 NICM candidates for primary prevention and 55 healthy controls with similar distributions of age and sex were prospectively enrolled. Imaging was performed at 1.5-T using a protocol that included cine magnetic resonance for left ventricular function, late gadolinium enhancement (LGE) for focal scarring, and 5-slice native T₁ mapping for diffuse fibrosis and heterogeneity. The last method was assessed by mean absolute deviation of the segmental pixel-SD from the average pixel-SD (Mad-SD). The primary endpoint was a composite of appropriate ICD therapy and sudden cardiac death.

RESULTS

During a median follow-up of 24 months, 13 patients (11%) experienced the primary endpoint. Dichotomized Mad-SD >0.24 provided a comparable outcome to the presence of LGE for the primary endpoint (annual event rate: 9.8% vs. 10.9%). The integration of Mad-SD to global native T₁ showed excellent arrhythmic event-free survival (annual event rate: 0%), and high sensitivity of 85% (95% confidence interval [CI]: 55% to 98%) and moderate specificity of 72% (95% CI: 62% to 80%), with a C-statistic of 0.76 (95% CI: 0.64 to 0.87), which was comparable to the presence, location, or extent of LGE in its ability to predict arrhythmic events.

CONCLUSIONS

Combined myocardium tissue heterogeneity and interstitial fibrosis assessment by native T₁ mapping is an important predictor of ventricular tachycardia and ventricular fibrillation and provides additive risk stratification for primary prevention ICD in NICM patients without the need for gadolinium contrast.

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.

Whole-heart T1 mapping using a 2D fat image navigator for respiratory motion compensation

Purpose

To combine a 3D saturation‐recovery‐based myocardial T₁ mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat‐iNAV) to allow 3D T₁ maps with 100% respiratory scan efficiency and predictable scan time.

Methods

Data from T₁ phantom and 10 subjects were acquired at 1.5T. For respiratory motion compensation, a 2D fat‐iNAV was acquired before each 3D SASHA k‐space segment to correct for 2D translational motion in a beat‐to‐beat fashion. The effect of the fat‐iNAV on the 3D SASHA T1 estimation was evaluated on the T₁ phantom. For 3 representative subjects, the proposed free‐breathing 3D SASHA with fat‐iNAV was compared to the original implementation with the diaphragmatic navigator. The 3D SASHA with fat‐iNAV was compared to the breath‐hold 2D SASHA sequence in terms of accuracy and precision.

Results

In the phantom study, the Bland‐Altman plot shows that the 2D fat‐iNAVs does not affect the T₁ quantification of the 3D SASHA acquisition (0 ± 12.5 ms). For the in vivo study, the 2D fat‐iNAV permits to estimate the respiratory motion of the heart, while allowing for 100% scan efficiency, improving the precision of the T₁ measurement compared to non‐motion‐corrected 3D SASHA. However, the image quality achieved with the proposed 3D SASHA with fat‐iNAV is lower compared to the original implementation, with reduced delineation of the myocardial borders and papillary muscles.

Conclusions

We demonstrate the feasibility to combine the 3D SASHA T₁ mapping imaging sequence with a 2D fat‐iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time.

Cardiac magnetic resonance T1 mapping. Part 1: Aspects of acquisition and evaluation

While an enormous number of studies have documented pathological alterations of the myocardial native longitudinal relaxation time (T1) and the fraction of the extracellular myocardial volume (ECV), it has also become clear that continuously evolving T1 mapping sequence, acquisition and evaluation techniques have a substantial impact on quantitative results, making the translation of reported findings into routine clinical use particularly challenging. To provide a basis for the discussion of pathological myocardial T1 and ECV alterations, the present review aims to summarize the methodological aspects of myocardial T1 mapping along with technical and physiological factors influencing results and normal ranges of myocardial native T1 and ECV reported across studies.

Three-dimensional free breathing whole heart cardiovascular magnetic resonance T1 mapping at 3 T

BACKGROUND

This study demonstrates a three-dimensional (3D) free-breathing native myocardial T1 mapping sequence at 3 T.

METHODS

The proposed sequence acquires three differently T1-weighted volumes. The first two volumes receive a saturation pre-pulse with different recovery time. The third volume is acquired without magnetization preparation and after a significant recovery time. Respiratory navigator gating and volume-interleaved acquisition are adopted to mitigate misregistration. The proposed sequence was validated through simulation, phantom experiments and in vivo experiments in 12 healthy adult subjects.

RESULTS

In phantoms, good agreement on T1 measurement was achieved between the proposed sequence and the reference inversion recovery spin echo sequence (R2 = 0.99). Homogeneous 3D T1 maps were obtained from healthy adult subjects, with a T1 value of 1476 ± 53 ms and a coefficient of variation (CV) of 6.1 ± 1.4% over the whole left-ventricular myocardium. The averaged septal T1 was 1512 ± 60 ms with a CV of 2.1 ± 0.5%.

CONCLUSION

Free-breathing 3D native T1 mapping at 3 T is feasible and may be applicable in myocardial assessment. The proposed 3D T1 mapping sequence is suitable for applications in which larger coverage is desired beyond that available with single-shot parametric mapping, or breath-holding is unfeasible.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

Temporally resolved parametric assessment of Z-magnetization recovery (TOPAZ): Dynamic myocardial T1 mapping using a cine steady-state look-locker approach

PURPOSE

To develop and evaluate a cardiac phase-resolved myocardial T₁ mapping sequence.

METHODS

The proposed method for temporally resolved parametric assessment of Z-magnetization recovery (TOPAZ) is based on contiguous fast low-angle shot imaging readout after magnetization inversion from the pulsed steady state. Thereby, segmented k-space data are acquired over multiple heartbeats, before reaching steady state. This results in sampling of the inversion-recovery curve for each heart phase at multiple points separated by an R-R interval. Joint T₁ and B1+ estimation is performed for reconstruction of cardiac phase-resolved T₁ and B1+ maps. Sequence parameters are optimized using numerical simulations. Phantom and in vivo imaging are performed to compare the proposed sequence to a spin-echo reference and saturation pulse prepared heart rate-independent inversion-recovery (SAPPHIRE) T₁ mapping sequence in terms of accuracy and precision.

RESULTS

In phantom, TOPAZ T₁ values with integrated B1+ correction are in good agreement with spin-echo T₁ values (normalized root mean square error = 4.2%) and consistent across the cardiac cycle (coefficient of variation = 1.4 ± 0.78%) and different heart rates (coefficient of variation = 1.2 ± 1.9%). In vivo imaging shows no significant difference in TOPAZ T₁ times between the cardiac phases (analysis of variance: P = 0.14, coefficient of variation = 3.2 ± 0.8%), but underestimation compared with SAPPHIRE (T₁ time ± precision: 1431 ± 56 ms versus 1569 ± 65 ms). In vivo precision is comparable to SAPPHIRE T₁ mapping until middiastole (P > 0.07), but deteriorates in the later phases.

CONCLUSIONS

The proposed sequence allows cardiac phase-resolved T₁ mapping with integrated B1+ assessment at a temporal resolution of 40 ms. Magn Reson Med 79:2087-2100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Motion-robust cardiac B1+ mapping at 3T using interleaved bloch-siegert shifts

PURPOSE

To develop and evaluate a robust motion-insensitive Bloch-Siegert shift based B1+ mapping method in the heart.

METHODS

Cardiac Bloch-Siegert B1+ mapping was performed with interleaved positive and negative off-resonance shifts and diastolic spoiled gradient echo imaging in 12 heartbeats. Numerical simulations were performed to study the impact of respiratory motion. The method was compared with three-dimensional (3D) actual flip angle imaging (AFI) and two-dimensional (2D) saturated double angle method (SDAM) in phantom scans. Cardiac B1+ maps of three different views were acquired in six healthy volunteers using Bloch-Siegert and SDAM during breath-hold and free breathing. In vivo maps were evaluated for inter-view consistency using the correlation coefficients of the B1+ profiles along the lines of intersection between the views.

RESULTS

For the Bloch-Siegert sequence, numerical simulations indicated high similarity between breath-hold and free breathing scans, and phantom results indicated low deviation from the 3D AFI reference (normalized root mean square error [NRMSE] = 2.0%). Increased deviation was observed with 2D SDAM (NRMSE = 5.0%) due to underestimation caused by imperfect excitation slice profiles. Breath-hold and free breathing Bloch-Siegert in vivo B1+ maps were visually comparable with no significant difference in the inter-view consistency (P > 0.36). SDAM showed strongly impaired B1+ map quality during free breathing. Inter-view consistency was significantly lower than with the Bloch-Siegert method (breath-hold: P = 0.014, free breathing: P < 0.0001).

CONCLUSION

The proposed interleaved Bloch-Siegert sequence enables cardiac B1+ mapping with improved inter-view consistency and high resilience to respiratory motion. Magn Reson Med 78:670-677, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Simultaneous multislice imaging for native myocardial T1 mapping: Improved spatial coverage in a single breath-hold

PURPOSE

To develop a saturation recovery myocardial T1 mapping method for the simultaneous multislice acquisition of three slices.

METHODS

Saturation pulse-prepared heart rate independent inversion recovery (SAPPHIRE) T1 mapping was implemented with simultaneous multislice imaging using FLASH readouts for faster coverage of the myocardium. Controlled aliasing in parallel imaging (CAIPI) was used to achieve minimal noise amplification in three slices. Multiband reconstruction was performed using three linear reconstruction methods: Slice- and in-plane GRAPPA, CG-SENSE, and Tikhonov-regularized CG-SENSE. Accuracy, spatial variability, and interslice leakage were compared with single-band T1 mapping in a phantom and in six healthy subjects.

RESULTS

Multiband phantom T1 times showed good agreement with single-band T1 mapping for all three reconstruction methods (normalized root mean square error <1.0%). The increase in spatial variability compared with single-band imaging was lowest for GRAPPA (1.29-fold), with higher penalties for Tikhonov-regularized CG-SENSE (1.47-fold) and CG-SENSE (1.52-fold). In vivo multiband T1 times showed no significant difference compared with single-band (T1 time ± intersegmental variability: single-band, 1580 ± 119 ms; GRAPPA, 1572 ± 145 ms; CG-SENSE, 1579 ± 159 ms; Tikhonov, 1586 ± 150 ms [analysis of variance; P = 0.86]). Interslice leakage was smallest for GRAPPA (5.4%) and higher for CG-SENSE (6.2%) and Tikhonov-regularized CG-SENSE (7.9%).

CONCLUSION

Multiband accelerated myocardial T1 mapping demonstrated the potential for single-breath-hold T1 quantification in 16 American Heart Association segments over three slices. A 1.2- to 1.4-fold higher in vivo spatial variability was observed, where GRAPPA-based reconstruction showed the highest homogeneity and the least interslice leakage. Magn Reson Med 78:462-471, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Comparison of spoiled gradient echo and steady-state free-precession imaging for native myocardial T1 mapping using the slice-interleaved T1 mapping (STONE) sequence

Cardiac T1 mapping allows non-invasive imaging of interstitial diffuse fibrosis. Myocardial T1 is commonly calculated by voxel-wise fitting of the images acquired using balanced steady-state free precession (SSFP) after an inversion pulse. However, SSFP imaging is sensitive to B1 and B0 imperfection, which may result in additional artifacts. A gradient echo (GRE) imaging sequence has been used for myocardial T1 mapping; however, its use has been limited to higher magnetic field to compensate for the lower signal-to-noise ratio (SNR) of GRE versus SSFP imaging. A slice-interleaved T1 mapping (STONE) sequence with SSFP readout (STONE-SSFP) has been recently proposed for native myocardial T1 mapping, which allows longer recovery of magnetization (>8 R-R) after each inversion pulse. In this study, we hypothesize that a longer recovery allows higher SNR and enables native myocardial T1 mapping using STONE with GRE imaging readout (STONE-GRE) at 1.5T. Numerical simulations and phantom and in vivo imaging were performed to compare the performance of STONE-GRE and STONE-SSFP for native myocardial T1 mapping at 1.5T. In numerical simulations, STONE-SSFP shows sensitivity to both T2 and off resonance. Despite the insensitivity of GRE imaging to T2 , STONE-GRE remains sensitive to T2 due to the dependence of the inversion pulse performance on T2 . In the phantom study, STONE-GRE had inferior accuracy and precision and similar repeatability as compared with STONE-SSFP. In in vivo studies, STONE-GRE and STONE-SSFP had similar myocardial native T1 times, precisions, repeatabilities and subjective T1 map qualities. Despite the lower SNR of the GRE imaging readout compared with SSFP, STONE-GRE provides similar native myocardial T1 measurements, precision, repeatability, and subjective image quality when compared with STONE-SSFP at 1.5T.

Black-blood native T1 mapping: Blood signal suppression for reduced partial voluming in the myocardium

PURPOSE

To study the feasibility of black-blood contrast in native T₁ mapping for reduction of partial voluming at the blood-myocardium interface.

METHODS

A saturation pulse prepared heart-rate-independent inversion recovery (SAPPHIRE) T₁ mapping sequence was combined with motion-sensitized driven-equilibrium (MSDE) blood suppression for black-blood T₁ mapping at 3 Tesla. Phantom scans were performed to assess the T₁ time accuracy. In vivo black-blood and conventional SAPPHIRE T₁ mapping was performed in eight healthy subjects and analyzed for T₁ times, precision, and inter- and intraobserver variability. Furthermore, manually drawn regions of interest (ROIs) in all T₁ maps were dilated and eroded to analyze the dependence of septal T₁ times on the ROI thickness.

RESULTS

Phantom results and in vivo myocardial T₁ times show comparable accuracy with black-blood compared to conventional SAPPHIRE (in vivo: black-blood: 1562 ± 56 ms vs. conventional: 1583 ± 58 ms, P = 0.20); Using black-blood SAPPHIRE precision was significantly lower (standard deviation: 133.9 ± 24.6 ms vs. 63.1 ± 6.4 ms, P < .0001), and blood T₁ time measurement was not possible. Significantly increased interobserver interclass correlation coefficient (ICC) (0.996 vs. 0.967, P = 0.011) and similar intraobserver ICC (0.979 vs. 0.939, P = 0.11) was obtained with the black-blood sequence. Conventional SAPPHIRE showed strong dependence on the ROI thickness (R² = 0.99). No such trend was observed using the black-blood approach (R² = 0.29).

CONCLUSION

Black-blood SAPPHIRE successfully eliminates partial voluming at the blood pool in native myocardial T₁ mapping while providing accurate T₁ times, albeit at a reduced precision. Magn Reson Med 78:484-493, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption

The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.

Reproducibility of myocardial T1 and T2 relaxation time measurement using slice-interleaved T1 and T2 mapping sequences

PURPOSE

To assess measurement reproducibility and image quality of myocardial T₁ and T₂ maps using free-breathing slice-interleaved T₁ and T₂ mapping sequences at 1.5 Tesla (T).

MATERIALS AND METHODS

Eleven healthy subjects (33 ± 16 years; 6 males) underwent a slice-interleaved T₁ and T₂ mapping test/retest cardiac MR study at 1.5T on 2 days. For each day, subjects were imaged in two sessions with removal out of the magnet and repositioning before the subsequent session. We studied measurement reproducibility as well as the required sample size for sufficient statistical power to detect a predefined change in T₁ and T₂ . In a separate prospective study, we assessed T₁ and T₂ map image quality in 241 patients (54 ± 15 years; 73 women) with known/suspected cardiovascular disease referred for clinical cardiac MR. A subjective quality score was used to assess a segment-based image quality.

RESULTS

In the healthy cohort, the slice-interleaved T₁ measurements were highly reproducible, with global coefficients of variation (CVs) of 2.4% between subjects, 2.1% between days, and 1.7% between sessions. Slice-interleaved T₂ mapping sequences provided similar reproducibility with global CVs of 7.2% between subjects, 6.3% between days, and 5.0 between sessions. A lower variability resulted in a reduction of the required number of subjects to achieve a certain statistical power when compared with other T₁ mapping sequences. In the subjective image quality assessment, >80% of myocardial segments had interpretable data.

CONCLUSION

Slice-interleaved T₁ and T₂ mapping sequences yield highly reproducible T₁ and T₂ measurements with >80% of interpretable myocardial segments. J. Magn. Reson. Imaging 2016;44:1159-1167.

A swine model of infarct-related reentrant ventricular tachycardia: Electroanatomic, magnetic resonance, and histopathological characterization

BACKGROUND

Human ventricular tachycardia (VT) after myocardial infarction usually occurs because of subendocardial reentrant circuits originating in scar tissue that borders surviving myocardial bundles. Several preclinical large animal models have been used to further study postinfarct reentrant VT, but with varied experimental methodologies and limited evaluation of the underlying substrate or induced arrhythmia mechanism.

OBJECTIVE

We aimed to develop and characterize a swine model of scar-related reentrant VT.

METHODS

Thirty-five Yorkshire swine underwent 180-minute occlusion of the left anterior descending coronary artery. Thirty-one animals (89%) survived the 6-8-week survival period. These animals underwent cardiac magnetic resonance imaging followed by electrophysiology study, detailed electroanatomic mapping, and histopathological analysis.

RESULTS

Left ventricular (LV) ejection fraction measured using CMR imaging was 36% ± 6.6% with anteroseptal wall motion abnormality and late gadolinium enhancement across 12.5% ± 4.1% of the LV surface area. Low voltage measured using endocardial electroanatomic mapping encompassed 11.1% ± 3.5% of the LV surface area (bipolar voltage ≤1.5 mV) with anterior, anteroseptal, and anterolateral involvement. Reentrant circuits mapped were largely determined by functional rather than fix anatomical barriers, consistent with "pseudo-block" due to anisotropic conduction. Sustained monomorphic VT was induced in 28 of 31 swine (90%) (67 VTs; 2.4 ± 1.1; range 1-4) and characterized as reentry. VT circuits were subendocardial, with an arrhythmogenic substrate characterized by transmural anterior scar with varying degrees of fibrosis and myocardial fiber disarray on the septal and lateral borders.

CONCLUSION

This is a well-characterized swine model of scar-related subendocardial reentrant VT. This model can serve as the basis for further investigation in the physiology and therapeutics of humanlike postinfarction reentrant VT.