HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.

Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study

OBJECTIVES

To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm.

METHODS

We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy.

RESULTS

The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05).

CONCLUSIONS

The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm.

KEY POINTS

• The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets.

Association Between Left Ventricular Global Function Index and Outcomes in Patients With Dilated Cardiomyopathy

Purpose: Left ventricular global function index (LVGFI) assessed using cardiac magnetic resonance (CMR) seems promising in the prediction of clinical outcomes. However, the role of the LVGFI is uncertain in patients with heart failure (HF) with dilated cardiomyopathy (DCM). To describe the association of LVGFI and outcomes in patients with DCM, it was hypothesized that LVGFI is associated with decreased major adverse cardiac events (MACEs) in patients with DCM. Materials and Methods: This prospective cohort study was conducted from January 2015 to April 2020 in consecutive patients with DCM who underwent CMR. The association between outcomes and LVGFI was assessed using a multivariable model adjusted with confounders. LVGFI was the primary exposure variable. The long-term outcome was a composite endpoint, including death or heart transplantation. Results: A total of 334 patients (mean age: 55 years) were included in this study. The average of CMR-LVGFI was 16.53%. Over a median follow-up of 565 days, 43 patients reached the composite endpoint. Kaplan-Meier analysis revealed that patients with LVGFI lower than the cutoff values (15.73%) had a higher estimated cumulative incidence of the endpoint compared to those with LVGFI higher than the cutoff values (P = 0.0021). The hazard of MACEs decreased by 38% for each 1 SD increase in LVGFI (hazard ratio 0.62[95%CI 0.43-0.91]) and after adjustment by 46% (HR 0.54 [95%CI 0.32-0.89]). The association was consistent across subgroup analyses. Conclusion: In this study, an increase in CMR-LVGFI was associated with decreasing the long-term risk of MACEs with DCM after adjustment for traditional confounders.

Reliability of pediatric ventricular function analysis by short-axis "single-cycle-stack-advance" single-shot compressed-sensing cines in minimal breath-hold time

Objectives

Cardiovascular magnetic resonance (CMR) cine imaging by compressed sensing (CS) is promising for patients unable to tolerate long breath-holding. However, the need for a steady-state free-precession (SSFP) preparation cardiac cycle for each slice extends the breath-hold duration (e.g. for 10 slices, 20 cardiac cycles) to an impractical length. We investigated a method reducing breath-hold duration by half and assessed its reliability for biventricular volume analysis in a pediatric population.

Methods

Fifty-five consecutive pediatric patients (median age 12 years, range 7–17) referred for assessment of congenital heart disease or cardiomyopathy were included. Conventional multiple breath-hold SSFP short-axis (SAX) stack cines served as the reference. Real-time CS SSFP cines were applied without the steady-state preparation cycle preceding each SAX cine slice, accepting the limitation of omitting late diastole. The total acquisition time was 1 RR interval/slice. Volumetric analysis was performed for conventional and “single-cycle-stack-advance” (SCSA) cine stacks.

Results

Bland–Altman analyses [bias (limits of agreement)] showed good agreement in left ventricular (LV) end-diastolic volume (EDV) [3.6 mL (− 5.8, 12.9)], LV end-systolic volume (ESV) [1.3 mL (− 6.0, 8.6)], LV ejection fraction (EF) [0.1% (− 4.9, 5.1)], right ventricular (RV) EDV [3.5 mL (− 3.34, 10.0)], RV ESV [− 0.23 mL (− 7.4, 6.9)], and RV EF [1.70%, (− 3.7, 7.1)] with a trend toward underestimating LV and RV EDVs with the SCSA method. Image quality was comparable for both methods (p = 0.37).

Conclusions

LV and RV volumetric parameters agreed well between the SCSA and the conventional sequences. The SCSA method halves the breath-hold duration of the commercially available CS sequence and is a reliable alternative for volumetric analysis in a pediatric population.

Key Points

• Compressed sensing is a promising accelerated cardiovascular magnetic resonance imaging technique.

• We omitted the steady-state preparation cardiac cycle preceding each cine slice in compressed sensing and achieved an acquisition speed of 1 RR interval/slice.

• This modification called “single-cycle-stack-advance” enabled the acquisition of an entire short-axis cine stack in a single short breath hold.

• When tested in a pediatric patient group, the left and right ventricular volumetric parameters agreed well between the “single-cycle-stack-advance” and the conventional sequences.

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy

OBJECTIVE

To explore the association between three-dimensional (3D) cardiac magnetic resonance (CMR) feature tracking (FT) right ventricular peak global longitudinal strain (RVpGLS) and major adverse cardiovascular events (MACEs) in patients with stage C or D heart failure (HF) with non-ischaemic dilated cardiomyopathy (NIDCM) but without atrial fibrillation (AF).

METHODS

Patients with dilated cardiomyopathy were enrolled in this prospective cohort study. Comprehensive clinical and biochemical analysis and CMR imaging were performed. All patients were followed up for MACEs.

RESULTS

A total of 192 patients (age 53±14 years) were eligible for this study. A combination of cardiovascular death and cardiac transplantation occurred in 18 subjects during the median follow-up of 567 (311, 920) days. Brain natriuretic peptide, creatinine, left ventricular (LV) end-diastolic volume, LV end-systolic volume, right ventricular (RV) end-diastolic volume and RVpGLS from CMR were associated with the outcomes. The multivariate Cox regression model adjusting for traditional risk factors and CMR variables detected a significant association between RVpGLS and MACEs in patients with stage C or D HF with NIDCM without AF. Kaplan-Meier analysis based on RVpGLS cut-off value revealed that patients with RVpGLS <-8.5% showed more favourable clinical outcomes than those with RVpGLS ≥-8.5% (p=0.0037). Subanalysis found that this association remained unchanged.

CONCLUSIONS

RVpGLS-derived from 3D CMR FT is associated with a significant prognostic impact in patients with NIDCM with stage C or D HF and without AF.

Cardiovascular magnetic resonance predictors of heart failure in hypertrophic cardiomyopathy: the role of myocardial replacement fibrosis and the microcirculation

INTRODUCTION

Heart failure (HF) in hypertrophic cardiomyopathy (HCM) is associated with high morbidity and mortality. Predictors of HF, in particular the role of myocardial fibrosis and microvascular ischemia remain unclear. We assessed the predictive value of cardiovascular magnetic resonance (CMR) for development of HF in HCM in an observational cohort study.

METHODS

Serial patients with HCM underwent CMR, including adenosine first-pass perfusion, left atrial (LA) and left ventricular (LV) volumes indexed to body surface area (i) and late gadolinium enhancement (%LGE- as a % of total myocardial mass). We used a composite endpoint of HF death, cardiac transplantation, and progression to NYHA class III/IV.

RESULTS

A total of 543 patients with HCM underwent CMR, of whom 94 met the composite endpoint at baseline. The remaining 449 patients were followed for a median of 5.6 years. Thirty nine patients (8.7%) reached the composite endpoint of HF death (n = 7), cardiac transplantation (n = 2) and progression to NYHA class III/IV (n = 20). The annual incidence of HF was 2.0 per 100 person-years, 95% CI (1.6-2.6). Age, previous non-sustained ventricular tachycardia, LV end-systolic volume indexed to body surface area (LVESVI), LA volume index ; LV ejection fraction, %LGE and presence of mitral regurgitation were significant univariable predictors of HF, with LVESVI (Hazard ratio (HR) 1.44, 95% confidence interval (95% CI) 1.16-1.78, p = 0.001), %LGE per 10% (HR 1.44, 95%CI 1.14-1.82, p = 0.002) age (HR 1.37, 95% CI 1.06-1.77, p = 0.02) and mitral regurgitation (HR 2.6, p = 0.02) remaining independently predictive on multivariable analysis. The presence or extent of inducible perfusion defect assessed using a visual score did not predict outcome (p = 0.16, p = 0.27 respectively).

DISCUSSION

The annual incidence of HF in a contemporary ambulatory HCM population undergoing CMR is low. Myocardial fibrosis and LVESVI are strongly predictive of future HF, however CMR visual assessment of myocardial perfusion was not.

Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping

Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data.

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently ( ∼ 0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

•

Propose a fully automatic method for left atrial scar quantification, with promising performance.

•

Formulate a new framework of scar quantification based on surface projection and graph-cuts framework.

•

Propose the multi-scale learning CNN, combined with the random shift training strategy, to learn and predict the graph potentials, which significantly improves the performance of the proposed method, and enables the full automation of the framework.

•

Provide thorough validation and parameter studies for the proposed techniques using fifty-eight clinical images.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients.

Cardiac Diffusion: Technique and Practical Applications

The 3D microarchitecture of the cardiac muscle underlies the mechanical and electrical properties of the heart. Cardiomyocytes are arranged helically through the depth of the wall, and their shortening leads to macroscopic torsion, twist, and shortening during cardiac contraction. Furthermore, cardiomyocytes are organized in sheetlets separated by shear layers, which reorientate, slip, and shear during macroscopic left ventricle (LV) wall thickening. Cardiac diffusion provides a means for noninvasive interrogation of the 3D microarchitecture of the myocardium. The fundamental principle of MR diffusion is that an MRI signal is attenuated by the self-diffusion of water in the presence of large diffusion-encoding gradients. Since water molecules are constrained by the boundaries in biological tissue (cell membranes, collagen layers, etc.), depicting their diffusion behavior elucidates the shape of the myocardial microarchitecture they are embedded in. Cardiac diffusion therefore provides a noninvasive means to understand not only the dynamic changes in cardiac microstructure of healthy myocardium during cardiac contraction but also the pathophysiological changes in the presence of disease. This unique and innovative technology offers tremendous potential to enable improved clinical diagnosis through novel microstructural and functional assessment. in vivo cardiac diffusion methods are immediately translatable to patients, opening new avenues for diagnostic investigation and treatment evaluation in a range of clinically important cardiac pathologies. This review article describes the 3D microstructure of the LV, explains in vivo and ex vivo cardiac MR diffusion acquisition and postprocessing techniques, as well as clinical applications to date. Level of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:348-368.

Microvascular Dysfunction in Dilated Cardiomyopathy: A Quantitative Stress Perfusion Cardiovascular Magnetic Resonance Study

OBJECTIVES

This study sought to quantify myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) in dilated cardiomyopathy (DCM) and examine the relationship between myocardial perfusion and adverse left ventricular (LV) remodeling.

BACKGROUND

Although regarded as a nonischemic condition, DCM has been associated with microvascular dysfunction, which is postulated to play a role in its pathogenesis. However, the relationship of the resulting perfusion abnormalities to myocardial fibrosis and the degree of LV remodeling is unclear.

METHODS

A total of 65 patients and 35 healthy control subjects underwent adenosine (140 μg/kg/min) stress perfusion cardiovascular magnetic resonance with late gadolinium enhancement imaging. Stress and rest MBF and MPR were derived using a modified Fermi-constrained deconvolution algorithm.

RESULTS

Patients had significantly higher global rest MBF compared with control subjects (1.73 ± 0.42 ml/g/min vs. 1.14 ± 0.42 ml/g/min; p < 0.001). In contrast, global stress MBF was significantly lower versus control subjects (3.07 ± 1.02 ml/g/min vs. 3.53 ± 0.79 ml/g/min; p = 0.02), resulting in impaired MPR in the DCM group (1.83 ± 0.58 vs. 3.50 ± 1.45; p < 0.001). Global stress MBF (2.70 ± 0.89 ml/g/min vs. 3.44 ± 1.03 ml/g/min; p = 0.017) and global MPR (1.67 ± 0.61 vs. 1.99 ± 0.50; p = 0.047) were significantly reduced in patients with DCM with LV ejection fraction ≤35% compared with those with LV ejection fraction >35%. Segments with fibrosis had lower rest MBF (mean difference: -0.12 ml/g/min; 95% confidence interval: -0.23 to -0.01 ml/g/min; p = 0.035) and lower stress MBF (mean difference: -0.15 ml/g/min; 95% confidence interval: -0.28 to -0.03 ml/g/min; p = 0.013).

CONCLUSIONS

Patients with DCM exhibit microvascular dysfunction, the severity of which is associated with the degree of LV impairment. However, rest MBF is elevated rather than reduced in DCM. If microvascular dysfunction contributes to the pathogenesis of DCM, then the underlying mechanism is more likely to involve stress-induced repetitive stunning rather than chronic myocardial hypoperfusion.

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm² ± 2.8 vs 5.5 cm² ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI

PURPOSE

Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF.

METHODS

Our fully automatic pipeline uniquely combines: (a) a multiatlas-based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long-standing AF.

RESULTS

Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91.

CONCLUSION

Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.

Identification of myocardial diffuse fibrosis by 11 heartbeat MOLLI T 1 mapping: averaging to improve precision and correlation with collagen volume fraction

OBJECTIVES

Our objectives involved identifying whether repeated averaging in basal and mid left ventricular myocardial levels improves precision and correlation with collagen volume fraction for 11 heartbeat MOLLI T 1 mapping versus assessment at a single ventricular level.

MATERIALS AND METHODS

For assessment of T 1 mapping precision, a cohort of 15 healthy volunteers underwent two CMR scans on separate days using an 11 heartbeat MOLLI with a 5(3)3 beat scheme to measure native T 1 and a 4(1)3(1)2 beat post-contrast scheme to measure post-contrast T 1, allowing calculation of partition coefficient and ECV. To assess correlation of T 1 mapping with collagen volume fraction, a separate cohort of ten aortic stenosis patients scheduled to undergo surgery underwent one CMR scan with this 11 heartbeat MOLLI scheme, followed by intraoperative tru-cut myocardial biopsy. Six models of myocardial diffuse fibrosis assessment were established with incremental inclusion of imaging by averaging of the basal and mid-myocardial left ventricular levels, and each model was assessed for precision and correlation with collagen volume fraction.

RESULTS

A model using 11 heart beat MOLLI imaging of two basal and two mid ventricular level averaged T 1 maps provided improved precision (Intraclass correlation 0.93 vs 0.84) and correlation with histology (R 2 = 0.83 vs 0.36) for diffuse fibrosis compared to a single mid-ventricular level alone. ECV was more precise and correlated better than native T 1 mapping.

CONCLUSION

T 1 mapping sequences with repeated averaging could be considered for applications of 11 heartbeat MOLLI, especially when small changes in native T 1/ECV might affect clinical management.

Feasibility of cardiovascular magnetic resonance derived coronary wave intensity analysis

BACKGROUND

Wave intensity analysis (WIA) of the coronary arteries allows description of the predominant mechanisms influencing coronary flow over the cardiac cycle. The data are traditionally derived from pressure and velocity changes measured invasively in the coronary artery. Cardiovascular magnetic resonance (CMR) allows measurement of coronary velocities using phase velocity mapping and derivation of central aortic pressure from aortic distension. We assessed the feasibility of WIA of the coronary arteries using CMR and compared this to invasive data.

METHODS

CMR scans were undertaken in a serial cohort of patients who had undergone invasive WIA. Velocity maps were acquired in the proximal left anterior descending and proximal right coronary artery using a retrospectively-gated breath-hold spiral phase velocity mapping sequence with high temporal resolution (19 ms). A breath-hold segmented gradient echo sequence was used to acquire through-plane cross sectional area changes in the proximal ascending aorta which were used as a surrogate of an aortic pressure waveform after calibration with brachial blood pressure measured with a sphygmomanometer. CMR-derived aortic pressures and CMR-measured velocities were used to derive wave intensity. The CMR-derived wave intensities were compared to invasive data in 12 coronary arteries (8 left, 4 right). Waves were presented as absolute values and as a % of total wave intensity. Intra-study reproducibility of invasive and non-invasive WIA was assessed using Bland-Altman analysis and the intraclass correlation coefficient (ICC).

RESULTS

The combination of the CMR-derived pressure and velocity data produced the expected pattern of forward and backward compression and expansion waves. The intra-study reproducibility of the CMR derived wave intensities as a % of the total wave intensity (mean ± standard deviation of differences) was 0.0 ± 6.8%, ICC = 0.91. Intra-study reproducibility for the corresponding invasive data was 0.0 ± 4.4%, ICC = 0.96. The invasive and CMR studies showed reasonable correlation (r = 0.73) with a mean difference of 0.0 ± 11.5%.

CONCLUSION

This proof of concept study demonstrated that CMR may be used to perform coronary WIA non-invasively with reasonable reproducibility compared to invasive WIA. The technique potentially allows WIA to be performed in a wider range of patients and pathologies than those who can be studied invasively.

Zebrafish Model for Functional Screening of Flow-Responsive Genes

Supplemental Digital Content is available in the text.

Objective—

Atherosclerosis is initiated at branches and bends of arteries exposed to disturbed blood flow that generates low shear stress. This mechanical environment promotes lesions by inducing endothelial cell (EC) apoptosis and dysfunction via mechanisms that are incompletely understood. Although transcriptome-based studies have identified multiple shear-responsive genes, most of them have an unknown function. To address this, we investigated whether zebrafish embryos can be used for functional screening of mechanosensitive genes that regulate EC apoptosis in mammalian arteries.

Approach and Results—

First, we demonstrated that flow regulates EC apoptosis in developing zebrafish vasculature. Specifically, suppression of blood flow in zebrafish embryos (by targeting cardiac troponin) enhanced that rate of EC apoptosis (≈10%) compared with controls exposed to flow (≈1%). A panel of candidate regulators of apoptosis were identified by transcriptome profiling of ECs from high and low shear stress regions of the porcine aorta. Genes that displayed the greatest differential expression and possessed 1 to 2 zebrafish orthologues were screened for the regulation of apoptosis in zebrafish vasculature exposed to flow or no-flow conditions using a knockdown approach. A phenotypic change was observed in 4 genes; p53-related protein (PERP) and programmed cell death 2–like protein functioned as positive regulators of apoptosis, whereas angiopoietin-like 4 and cadherin 13 were negative regulators. The regulation of perp, cdh13, angptl4, and pdcd2l by shear stress and the effects of perp and cdh13 on EC apoptosis were confirmed by studies of cultured EC exposed to flow.

Conclusions—

We conclude that a zebrafish model of flow manipulation coupled to gene knockdown can be used for functional screening of mechanosensitive genes in vascular ECs, thus providing potential therapeutic targets to prevent or treat endothelial injury at atheroprone sites.

Validation of T2* in-line analysis for tissue iron quantification at 1.5 T

BACKGROUND

There is a need for improved worldwide access to tissue iron quantification using T2* cardiovascular magnetic resonance (CMR). One route to facilitate this would be simple in-line T2* analysis widely available on MR scanners. We therefore compared our clinically validated and established T2* method at Royal Brompton Hospital (RBH T2*) against a novel work-in-progress (WIP) sequence with in-line T2* measurement from Siemens (WIP T2*).

METHODS

Healthy volunteers (n = 22) and patients with iron overload (n = 78) were recruited (53 males, median age 34 years). A 1.5 T study (Magnetom Avanto, Siemens) was performed on all subjects. The same mid-ventricular short axis cardiac slice and transaxial slice through the liver were used to acquire both RBH T2* images and WIP T2* maps for each participant. Cardiac white blood (WB) and black blood (BB) sequences were acquired. Intraobserver, interobserver and interstudy reproducibility were measured on the same data from a subset of 20 participants.

RESULTS

Liver T2* values ranged from 0.8 to 35.7 ms (median 5.1 ms) and cardiac T2* values from 6.0 to 52.3 ms (median 31 ms). The coefficient of variance (CoV) values for direct comparison of T2* values by RBH and WIP were 6.1-7.8 % across techniques. Accurate delineation of the septum was difficult on some WIP T2* maps due to artefacts. The inability to manually correct for noise by truncation of erroneous later echo times led to some overestimation of T2* using WIP T2* compared with the RBH T2*. Reproducibility CoV results for RBH T2* ranged from 1.5 to 5.7 % which were better than the reproducibility of WIP T2* values of 4.1-16.6 %.

CONCLUSIONS

Iron estimation using the T2* CMR sequence in combination with Siemens' in-line data processing is generally satisfactory and may help facilitate global access to tissue iron assessment. The current automated T2* map technique is less good for tissue iron assessment with noisy data at low T2* values.

Radiation in medicine: Origins, risks and aspirations

The use of radiation in medicine is now pervasive and routine. From their crude beginnings 100 years ago, diagnostic radiology, nuclear medicine and radiation therapy have all evolved into advanced techniques, and are regarded as essential tools across all branches and specialties of medicine. The inherent properties of ionizing radiation provide many benefits, but can also cause potential harm. Its use within medical practice thus involves an informed judgment regarding the risk/benefit ratio. This judgment requires not only medical knowledge, but also an understanding of radiation itself. This work provides a global perspective on radiation risks, exposure and mitigation strategies.

Spiral tissue phase velocity mapping in a breath-hold with non-cartesian SENSE

PURPOSE

Tissue phase velocity mapping (TPVM) is capable of reproducibly measuring regional myocardial velocities. However acquisition durations of navigator gated techniques are long and unpredictable while current breath-hold techniques have low temporal resolution. This study presents a spiral TPVM technique which acquires high resolution data within a clinically acceptable breath-hold duration.

METHODS

Ten healthy volunteers are scanned using a spiral sequence with temporal resolution of 24 ms and spatial resolution of 1.7 × 1.7 mm. Retrospective cardiac gating is used to acquire data over the entire cardiac cycle. The acquisition is accelerated by factors of 2 and 3 by use of non-Cartesian SENSE implemented on the Gadgetron GPU system resulting in breath-holds of 17 and 13 heartbeats, respectively. Systolic, early diastolic, and atrial systolic global and regional longitudinal, circumferential, and radial velocities are determined.

RESULTS

Global and regional velocities agree well with those previously reported. The two acceleration factors show no significant differences for any quantitative parameter and the results also closely match previously acquired higher spatial resolution navigator-gated data in the same subjects.

CONCLUSION

By using spiral trajectories and non-Cartesian SENSE high resolution, TPVM data can be acquired within a clinically acceptable breath-hold.