Cartesian dictionary-based native T1 and T2 mapping of the myocardium

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.

Retrospective motion correction for cardiac multi-parametric mapping with dictionary matching-based image synthesis and a low-rank constraint

PURPOSE

To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping.

METHODS

The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively.

RESULTS

The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments.

CONCLUSION

The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Free-Breathing Ungated Radial Simultaneous Multi-Slice Cardiac T1 Mapping

BACKGROUND

Modified Look-Locker imaging (MOLLI) T1 mapping sequences are acquired during breath-holding and require ECG gating with consistent R-R intervals, which is problematic for patients with atrial fibrillation (AF). Consequently, there is a need for a free-breathing and ungated framework for cardiac T1 mapping.

PURPOSE

To develop and evaluate a free-breathing ungated radial simultaneous multi-slice (SMS) cardiac T1 mapping (FURST) framework.

STUDY TYPE

Retrospective, nonconsecutive cohort study.

POPULATION

Twenty-four datasets from 17 canine and 7 human subjects (4 males,51 ± 22 $$ 51\pm 22 $$ years; 3 females,56 ± 19 $$ 56\pm 19 $$ years). Canines were from studies involving AF induction and ablation treatment. The human population included separate subjects with suspected microvascular disease, acute coronary syndrome with persistent AF, and transthyretin amyloidosis with persistent AF. The remaining human subjects were healthy volunteers.

FIELD STRENGTH/SEQUENCE

Pre- and post-contrast T1 mapping with the free-breathing and ungated SMS inversion recovery sequence with gradient echo readout and with conventional MOLLI sequences at 1.5 T and 3.0 T.

ASSESSMENT

MOLLI and FURST were acquired in all subjects, and American Heart Association (AHA) segmentation was used for segment-wise analysis. Pre-contrast T1, post-contrast T1, and ECV were analyzed using correlation and Bland-Altman plots in 13 canines and 7 human subjects. T1 difference box plots for repeated acquisitions in four canine subjects were used to assess reproducibility. The PIQUE image quality metric was used to evaluate the perceptual quality of T1 maps.

STATISTICAL TESTS

Paired t-tests were used for all comparisons between FURST and MOLLI, withP < 0.05 $$ P<0.05 $$ indicating statistical significance.

RESULTS

There were no significant differences between FURST and MOLLI pre-contrast T1 reproducibility (25 ± 18 $$ 25\pm 18 $$ and19 ± 16 msec $$ 19\pm 16\ \mathrm{msec} $$ ,P = 0.19 $$ P=0.19 $$ ), FURST and MOLLI ECV (29 % ± 11 % $$ 29\%\pm 11\% $$ and28 % ± 11 % $$ 28\%\pm 11\% $$ ,P = 0.05 $$ P=0.05 $$ ), or FURST and MOLLI PIQUE scores (52 ± 8 $$ 52\pm 8 $$ and53 ± 10 $$ 53\pm 10 $$ ,P = 0.18 $$ P=0.18 $$ ). The ECV mean difference was0.48 $$ 0.48 $$ with95 % CI : 6.0 × 10 - 4 , 0.96 $$ 95\%\mathrm{CI}:\left(6.0\times {10}^{-4},0.96\right) $$ .

CONCLUSIONS

FURST had similar quality pre-contrast T1, post-contrast T1, and ECV maps and similar reproducibility compared to MOLLI.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: 1.

Simultaneous multislice cardiac multimapping based on locally low-rank and sparsity constraints

BACKGROUND

Although quantitative myocardial T1 and T2 mappings are clinically used to evaluate myocardial diseases, their application needs a minimum of six breath-holds to cover three short-axis slices. The purpose of this work is to simultaneously quantify multislice myocardial T1 and T2 across three short-axis slices in one breath-hold by combining simultaneous multislice (SMS) with multimapping.

METHODS

An SMS-Multimapping sequence with multiband radiofrequency (RF) excitations and Cartesian fast low-angle shot readouts was developed for data acquisition. When 3 slices are simultaneously acquired, the acceleration rate is around 12-fold, causing a highly ill-conditioned reconstruction problem. To mitigate image artifacts and noise caused by the ill-conditioning, a reconstruction algorithm based on locally low-rank and sparsity (LLRS) constraints was developed. Validation was performed in phantoms and in vivo imaging, with 20 healthy subjects and 4 patients, regarding regional mean, precision, and scan-rescan reproducibility.

RESULTS

The phantom imaging shows that SMS-Multimapping with locally low-rank (LLRS) accurately reconstructed multislice T1 and T2 maps despite a six-fold acceleration of scan time. Healthy subject imaging shows that the proposed LLRS algorithm substantially improved image quality relative to split slice-generalized autocalibrating partially parallel acquisition. Compared with modified look-locker inversion recovery (MOLLI), SMS-Multimapping exhibited higher T1 mean (1118 ± 43 ms vs 1190 ± 49 ms, P < 0.01), lower precision (67 ± 17 ms vs 90 ± 17 ms, P < 0.01), and acceptable scan-rescan reproducibility measured by 2 scans 10-min apart (bias = 1.4 ms for MOLLI and 9.0 ms for SMS-Multimapping). Compared with balanced steady-state free precession (bSSFP) T2 mapping, SMS-Multimapping exhibited similar T2 mean (43.5 ± 3.3 ms vs 43.0 ± 3.5 ms, P = 0.64), similar precision (4.9 ± 2.1 ms vs 5.1 ± 1.0 ms, P = 0.93), and acceptable scan-rescan reproducibility (bias = 0.13 ms for bSSFP T2 mapping and 0.55 ms for SMS-Multimapping). In patients, SMS-Multimapping clearly showed the abnormality in a similar fashion as the reference methods despite using only one breath-hold.

CONCLUSION

SMS-Multimapping with the proposed LLRS reconstruction can measure multislice T1 and T2 maps in one breath-hold with good accuracy, reasonable precision, and acceptable reproducibility, achieving a six-fold reduction of scan time and an improvement of patient comfort.

Free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55 T

PURPOSE

To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T.

METHODS

The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence.

RESULTS

The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement.

CONCLUSION

The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.

Evaluation of myocarditis with a free-breathing three-dimensional isotropic whole-heart joint T1 and T2 mapping sequence

BACKGROUND

The diagnosis of myocarditis by cardiovascular magnetic resonance (CMR) requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current two-dimensional (2D) mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing three-dimensional (3D) whole-heart sequence that allows simultaneous T1 and T2 mapping and validated it in patients with suspected myocarditis.

METHODS

Eighteen healthy volunteers and 28 patients with suspected myocarditis underwent conventional 2D T1 and T2 mapping with whole-heart coverage and 3D joint T1/T2 mapping on a 1.5T scanner. Acquisition time, image quality, and diagnostic performance were compared. Qualitative analysis was performed using a 4-point Likert scale. Bland-Altman plots were used to assess the quantitative agreement between 2D and 3D sequences.

RESULTS

The 3D T1/T2 sequence was acquired in 8 min 26 s under free breathing, whereas 2D T1 and T2 sequences were acquired with breath-holds in 11 min 44 s (p = 0.0001). All 2D images were diagnostic. For 3D images, 89% (25/28) of T1 and 96% (27/28) of T2 images were diagnostic with no significant difference in the proportion of diagnostic images for the 3D and 2D T1 (p = 0.2482) and T2 maps (p = 1.0000). Systematic bias in T1 was noted with biases of 102, 115, and 152 ms for basal-apical segments, with a larger bias for higher T1 values. Good agreement between T2 values for 3D and 2D techniques was found (bias of 1.8, 3.9, and 3.6 ms for basal-apical segments). The sensitivity and specificity of the 3D sequence for diagnosing acute myocarditis were 74% (95% confidence interval [CI] 49%-91%) and 83% (36%-100%), respectively, with a c-statistic (95% CI) of 0.85 (0.79-0.91) and no statistically significant difference between the 2D and 3D sequences for the detection of acute myocarditis for T1 (p = 0.2207) or T2 (p = 1.0000).

CONCLUSION

Free-breathing whole-heart 3D joint T1/T2 mapping was comparable to 2D mapping sequences with respect to diagnostic performance, but with the added advantages of free breathing and shorter scan times. Further work is required to address the bias noted at high T1 values, but this did not significantly impact diagnostic accuracy.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

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.

Free-breathing simultaneous native myocardial T1, T2 and T1ρ mapping with Cartesian acquisition and dictionary matching

BACKGROUND

T1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition.

METHODS

The proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases.

RESULTS

The phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients.

CONCLUSION

The proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.

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.

Clinical evaluation of the Multimapping technique for simultaneous myocardial T1 and T2 mapping

The Multimapping technique was recently proposed for simultaneous myocardial T₁ and T₂ mapping. In this study, we evaluate its correlation with clinical reference mapping techniques in patients with a range of cardiovascular diseases (CVDs) and compare image quality and inter- and intra-observer repeatability. Multimapping consists of an ECG-triggered, 2D single-shot bSSFP readout with inversion recovery and T₂ preparation modules, acquired across 10 cardiac cycles. The sequence was implemented at 1.5T and compared to clinical reference mapping techniques, modified Look-Locker inversion recovery (MOLLI) and T₂ prepared bSSFP with four echo times (T₂bSSFP), and compared in 47 patients with CVD (of which 44 were analyzed). In diseased myocardial segments (defined as the presence of late gadolinium enhancement), there was a high correlation between Multimapping and MOLLI for native myocardium T₁ (r² = 0.73), ECV (r² = 0.91), and blood T₁ (r² = 0.88), and Multimapping and T₂bSSFP for native myocardial T₂ (r² = 0.80). In healthy myocardial segments, a bias for native T₁ (Multimapping = 1,116 ± 21 ms, MOLLI = 1,002 ± 21, P < 0.001), post-contrast T₁ (Multimapping = 479 ± 31 ms, MOLLI = 426 ± 27 ms, 0.001), ECV (Multimapping = 21.5 ± 1.9%, MOLLI = 23.7 ± 2.3%, P = 0.001), and native T₂ (Multimapping = 48.0 ± 3.0 ms, T₂bSSFP = 53.9 ± 3.5 ms, P < 0.001) was observed. The image quality for Multimapping was scored as higher for all mapping techniques (native T₁, post-contrast T₁, ECV, and T₂bSSFP) compared to the clinical reference techniques. The inter- and intra-observer agreements were excellent (intraclass correlation coefficient, ICC > 0.9) for most measurements, except for inter-observer repeatability of Multimapping native T₁ (ICC = 0.87), post-contrast T₁ (ICC = 0.73), and T₂bSSFP native T₂ (ICC = 0.88). Multimapping shows high correlations with clinical reference mapping techniques for T₁, T₂, and ECV in a diverse cohort of patients with different cardiovascular diseases. Multimapping enables simultaneous T₁ and T₂ mapping and can be performed in a short breath-hold, with image quality superior to that of the clinical reference techniques.

Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA

PURPOSE

To develop and validate a new myocardial T₁ mapping sequence (MOSHA) which is based on a combination of the modified Look-Locker inversion recovery (MOLLI) and the saturation recovery single-shot acquisition (SASHA) sequences.

METHODS

Prior studies have shown that myocardial T₁ mapping by SASHA is more accurate but less precise than MOLLI. A new myocardial T₁ mapping technique (MOSHA) based on single-shot acquisitions is developed by combining the MOLLI and SASHA sequences. Phantom and patient studies on 15 patients (9 males, median age 21 years) were performed to validate and compare MOSHA with the MOLLI and SASHA sequences in terms of accuracy and precision.

RESULTS

In the phantom study, MOSHA was as accurate as SASHA (P-value = 0.88) and as precise as MOLLI (P-value = 0.59). Similar trends were observed in the patient study. Compared to SASHA, MOSHA accuracy was comparable for blood pre-contrast (P-value≥0.10) and post-contrast (P-value≥0.70), and for myocardium pre-contrast (P-value = 0.70) and post-contrast (P-value = 0.09). Compared to MOLLI, MOSHA precision was lower for blood pre-contrast (P-value<0.01) and higher for blood post-contrast (P-value≤0.01), and comparable for myocardium pre-contrast (P-value = 0.24) and post-contrast (P-value = 0.07). Synthetic Extracellular volume fraction (ECV) calculated by MOSHA was more precise than those of SASHA and MOLLI (P-value ≤0.01).

CONCLUSION

In phantom studies and patients, MOSHA has comparable accuracy as SASHA and nearly similar precision as MOLLI for T₁ mapping. Precision of MOSHA was better than MOLLI and SASHA in synthetic ECV measurements. Therefore, it may be a superior choice in clinical practice for a precise and accurate calculation of T₁ and ECV.