Fully-modelled blood-focused variable inversion times for 3D late gadolinium-enhanced imaging.

PURPOSE: Variable heart rate during single-cycle inversion-recovery Late Gadolinium-Enhanced (LGE) scanning degrades image quality, which can be mitigated using Variable Inversion Times (VTIs) in real-time response to R-R interval changes. We investigate in vivo and in simulations an extension of a single-cycle VTI method previously applied in 3D LGE imaging, that now fully models the longitudinal magnetisation (fmVTI). METHODS: The VTI and fmVTI methods were used to perform 3D LGE scans for 28 3D LGE patients, with qualitative image quality scores assigned for left atrial wall clarity and total ghosting. Accompanying simulations of numerical phantom images were assessed in terms of ghosting of normal myocardium, blood, and myocardial scar. RESULTS: The numerical simulations for fmVTI showed a significant decrease in blood ghosting (VTI: 410 ± 710, fmVTI: 68 ± 40, p < 0.0005) and scar ghosting (VTI: 830 ± 1300, fmVTI: 510 ± 730, p < 0.02). Despite this, there was no significant change in qualitative image quality scores, either for left atrial wall clarity (VTI: 2.0 ± 1.0, fmVTI: 1.8 ± 1.0, p > 0.1) or for total ghosting (VTI: 1.9 ± 1.0, fmVTI: 2.0 ± 1.0, p > 0.7). CONCLUSIONS: Simulations indicated reduced ghosting with the fmVTI method, due to reduced Mz variability in the blood signal. However, other sources of phase-encode ghosting and blurring appeared to dominate and obscure this finding in the patient studies available.

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.

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.

Predicting Survival in Repaired Tetralogy of Fallot: A Lesion-Specific and Personalized Approach.

OBJECTIVES: This study sought to identify patients with repaired tetralogy of Fallot (rTOF) at high risk of death and malignant ventricular arrhythmia (VA). BACKGROUND: To date there is no robust risk stratification scheme to predict outcomes in adults with rTOF. METHODS: Consecutive patients were prospectively recruited for late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) to define right and left ventricular (RV, LV) fibrosis in addition to proven risk markers. RESULTS: The primary endpoint was all-cause mortality. Of the 550 patients (median age 32 years, 56% male), 27 died (mean follow-up 6.4 ± 5.8; total 3,512 years). Mortality was independently predicted by RVLGE extent, presence of LVLGE, RV ejection fraction ≤47%, LV ejection fraction ≤55%, B-type natriuretic peptide ≥127 ng/L, peak exercise oxygen uptake (V02) ≤17 mL/kg/min, prior sustained atrial arrhythmia, and age ≥50 years. The weighted scores for each of the preceding independent predictors differentiated a high-risk subgroup of patients with a 4.4%, annual risk of mortality (area under the curve [AUC]: 0.87; P < 0.001). The secondary endpoint (VA), a composite of life-threatening sustained ventricular tachycardia/resuscitated ventricular fibrillation/sudden cardiac death occurred in 29. Weighted scores that included several predictors of mortality and RV outflow tract akinetic length ≥55 mm and RV systolic pressure ≥47 mm Hg identified high-risk patients with a 3.7% annual risk of VA (AUC: 0.79; P < 0.001) RVLGE was heavily weighted in both risk scores caused by its strong relative prognostic value. CONCLUSIONS: We present a score integrating multiple appropriately weighted risk factors to identify the subgroup of patients with rTOF who are at high annual risk of death who may benefit from targeted therapy.

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.

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

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.

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/).

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.

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.

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.

Combined heart-liver transplantation for failing Fontan circulation in a late survivor with single-ventricle physiology.

Management of adults with failing Fontan physiology poses many challenges, especially as transplantation offers the only realistic alternative to palliative care. We present the first combined heart and liver transplant performed in Europe, for a late survivor of single ventricle palliation with the Fontan circulation. In addition to the conventional medical and surgical challenges posed, we highlight the management of the associated multi-organ failure with focus on the liver and novel strategies for assessment and optimization.

Myocardial Architecture, Mechanics, and Fibrosis in Congenital Heart Disease.

Congenital heart disease (CHD) is the most common category of birth defect, affecting 1% of the population and requiring cardiovascular surgery in the first months of life in many patients. Due to advances in congenital cardiovascular surgery and patient management, most children with CHD now survive into adulthood. However, residual and postoperative defects are common resulting in abnormal hemodynamics, which may interact further with scar formation related to surgical procedures. Cardiovascular magnetic resonance (CMR) has become an important diagnostic imaging modality in the long-term management of CHD patients. It is the gold standard technique to assess ventricular volumes and systolic function. Besides this, advanced CMR techniques allow the acquisition of more detailed information about myocardial architecture, ventricular mechanics, and fibrosis. The left ventricle (LV) and right ventricle have unique myocardial architecture that underpins their mechanics; however, this becomes disorganized under conditions of volume and pressure overload. CMR diffusion tensor imaging is able to interrogate non-invasively the principal alignments of microstructures in the left ventricular wall. Myocardial tissue tagging (displacement encoding using stimulated echoes) and feature tracking are CMR techniques that can be used to examine the deformation and strain of the myocardium in CHD, whereas 3D feature tracking can assess the twisting motion of the LV chamber. Late gadolinium enhancement imaging and more recently T1 mapping can help in detecting fibrotic myocardial changes and evolve our understanding of the pathophysiology of CHD patients. This review not only gives an overview about available or emerging CMR techniques for assessing myocardial mechanics and fibrosis but it also describes their clinical value and how they can be used to detect abnormalities in myocardial architecture and mechanics in CHD patients.

Effects of renal denervation on vascular remodelling in patients with heart failure and preserved ejection fraction: A randomised control trial.

OBJECTIVE: To assess the effect of renal denervation (RDT) on micro- and macro-vascular function in patients with heart failure with preserved ejection fraction (HFpEF). DESIGN: A prospective, randomised, open-controlled trial with blinded end-point analysis. SETTING: A single-centre London teaching hospital. PARTICIPANTS: Twenty-five patients with HFpEF who were recruited into the RDT-PEF trial. MAIN OUTCOME MEASURES: Macro-vascular: 24-h ambulatory pulse pressure, aorta distensibilty (from cardiac magnetic resonance imaging (CMR), aorta pulse wave velocity (CMR), augmentation index (peripheral tonometry) and renal artery blood flow indices (renal MR). Micro-vascular: endothelial function (peripheral tonometry) and urine microalbuminuria. RESULTS: At baseline, 15 patients were normotensive, 9 were hypertensive and 1 was hypotensive. RDT did not lower any of the blood pressure indices. Though there was evidence of abnormal vascular function at rest, RDT did not affect these at 3 or 12 months follow-up. CONCLUSIONS: RDT did not improve markers of macro- and micro-vascular function.

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.

Mechanisms of Myocardial Ischemia in Hypertrophic Cardiomyopathy: Insights From Wave Intensity Analysis and Magnetic Resonance.

BACKGROUND: Angina is common in hypertrophic cardiomyopathy (HCM) and is associated with abnormal myocardial perfusion. Wave intensity analysis improves the understanding of the mechanics of myocardial ischemia. OBJECTIVES: Wave intensity analysis was used to describe the mechanisms underlying perfusion abnormalities in patients with HCM. METHODS: Simultaneous pressure and flow were measured in the proximal left anterior descending artery in 33 patients with HCM and 20 control patients at rest and during hyperemia, allowing calculation of wave intensity. Patients also underwent quantitative first-pass perfusion cardiac magnetic resonance to measure myocardial perfusion reserve. RESULTS: Patients with HCM had a lower coronary flow reserve than control subjects (1.9 ± 0.8 vs. 2.7 ± 0.9; p = 0.01). Coronary hemodynamics in HCM were characterized by a very large backward compression wave during systole (38 ± 11% vs. 21 ± 6%; p < 0.001) and a proportionately smaller backward expansion wave (27% ± 8% vs. 33 ± 6%; p = 0.006) compared with control subjects. Patients with severe left ventricular outflow tract obstruction had a bisferiens pressure waveform resulting in an additional proximally originating deceleration wave during systole. The proportion of waves acting to accelerate coronary flow increased with hyperemia, and the magnitude of change was proportional to the myocardial perfusion reserve (rho = 0.53; p < 0.01). CONCLUSIONS: Coronary flow in patients with HCM is deranged. Distally, compressive deformation of intramyocardial blood vessels during systole results in an abnormally large backward compression wave, whereas proximally, severe left ventricular outflow tract obstruction is associated with an additional deceleration wave. Perfusion abnormalities in HCM are not simply a consequence of supply/demand mismatch or remodeling of the intramyocardial blood vessels; they represent a dynamic interaction with the mechanics of myocardial ischemia that may be amenable to treatment.

Pulmonary artery diameters, cross sectional areas and area changes measured by cine cardiovascular magnetic resonance in healthy volunteers.

BACKGROUND: We measured by cine cardiovascular magnetic resonance (CMR) main and branch pulmonary artery diameters and cross sectional areas in diastole and systole in order to establish normal ranges and the effects on them of age, gender and body surface area (BSA). Documentation of normal ranges provides a reference for research and clinical investigation in the fields of congenital heart disease, pulmonary hypertension and connective tissue disorders. METHODS: We recruited 120 healthy volunteers: ten males (M) and ten females (F) in each decile between 20 and 79 years, imaging them in a 1.5 Tesla CMR system. Scout acquisitions guided the placement of steady state free precession cine acquisitions transecting the main, right and left pulmonary arteries (MPA, RPA and LPA). Cross sections were rarely quite circular. RESULTS: From all subjects, the means of the greater and lesser orthogonal diastolic diameters in mm were: MPA, 22.9 ± 2.4 (M) and 21.2 ± 2.1 (F), RPA 16.6 ± 2.8 (M) and 14.7 ± 2.2 (F), and LPA 17.3 ± 2.5 (M) and 15.9 ± 2.0 (F), p < 0.0001 between genders in each case. The diastolic diameters increased with BSA and age, and plots are provided for reference. From measurements of minimum diastolic and maximum systolic cross sectional areas, the % systolic distensions were: MPA 42.7 ± 17.2 (M) and 41.8 ± 15.7 (F), RPA 50.6 ± 16.9 (M) and 48.2 ± 14.5 (F), LPA 35.6 ± 10.1 (M) and 35.2 ± 10.3 (F), and there was a decrease in distension with age (p < 0.0001 for the MPA). CONCLUSIONS: Measurements of MPA, RPA and LPA by cine CMR are provided for reference, with documentation of their changes with age and BSA.