In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging

Relationship between cardiac mechanical properties and cardiac magnetic resonance imaging at rest in childhood acute lymphoblastic leukemia survivors

The characterization of cardiac mechanical properties may contribute to better understanding of doxorubicin-induced cardiotoxicity. Our study aims to investigate the relationship between cardiac mechanical properties, T1 and T2 relaxation times and partition coefficient. Fifty childhood acute lymphoblastic leukemia survivors underwent a cardiac magnetic resonance (CMR) at rest on a 3T MRI system and included a standard ECG-gated 3(3)3(3)5 MOLLI sequence for T1 mapping and an ECG-gated T2-prepared TrueFISP sequence for T2 mapping. Partition coefficient, ejection fraction, end-diastolic volume (EDV) and end-systolic volume (ESV) were calculated. CircAdapt model was used to study cardiac mechanical performance (left ventricle stiffness (LVS), contractility (LVC) and pressure (Pmin and Pmax), cardiac work efficiency (CWE) and ventricular arterial coupling). In the whole cohort, our results showed that LVC (R2 = 69.2%, r = 0.83), Pmin (R2 = 62.9%, r = 0.79) and Pmax can be predicted by significant CMR parameters, while T1 (R2 = 23.2%, r = 0.48) and partition coefficient (R2 = 13.8%, r = 0.37) can be predicted by significant cardiac mechanical properties. In SR group LVS (R2 = 94.8%, r = 0.97), LVC (R2 = 93.7%, r = 0.96) and Pmin (R2 = 90.6%, r = 0.95) can be predicted by significant cardiac mechanical properties, while in HR + DEX group CWE (R2 = 49.8%, r = 0.70) can be predicted by significant cardiac mechanical properties. Partition coefficient (R2 = 72.6%, r = 0.85) can be predicted by significant CMR parameters in SR group. Early characterization of cardiac mechanical properties from CMR parameters has the potential to early detect doxorubicin-induced cardiotoxicity.

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping

Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot

BACKGROUND

Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows.

METHODS

Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores.

RESULTS

The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas.

CONCLUSIONS

Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.

MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging

Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of -9 ± 16 ml, -1 ± 10 ml, -2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.

Ultrafast Computation of Left Ventricular Ejection Fraction by Using Temporal Intensity Variation in Cine Cardiac Magnetic Resonance

Cardiac magnetic resonance enables comprehensive cardiac evaluation; however, intense time and labor requirements for data acquisition and processing have discouraged many clinicians from using it. We have developed an alternative image-processing algorithm that requires minimal user interaction: an ultrafast algorithm that computes left ventricular ejection fraction (LVEF) by using temporal intensity variation in cine balanced steady-state free precession (bSSFP) short-axis images, with or without contrast medium. We evaluated the algorithm's performance against an expert observer's analysis for segmenting the LV cavity in 65 study participants (LVEF range, 12%-70%). In 12 instances, contrast medium was administered before cine imaging. Bland-Altman analysis revealed quantitative effects of LV basal, midcavity, and apical morphologic variation on the algorithm's accuracy. Total computation time for the LV stack was <2.5 seconds. The algorithm accurately delineated endocardial boundaries in 1,132 of 1,216 slices (93%). When contours in the extreme basal and apical slices were not adequate, they were replaced with manually drawn contours. The Bland-Altman mean differences were <1.2 mL (0.8%) for end-diastolic volume, <5 mL (6%) for end-systolic volume, and <3% for LVEF. Standard deviation of the difference was ≤4.1% of LV volume for all sections except the midcavity in end-systole (8.3% of end-systolic volume). We conclude that temporal intensity variation-based ultrafast LVEF computation is clinically accurate across a range of LV shapes and wall motions and is suitable for postcontrast cine SSFP imaging. Our algorithm enables real-time processing of cine bSSFP images on a commercial scanner console within 3 seconds in an unobtrusive automated process.

A kinematic model-based analysis framework for 3D Cine-DENSE-validation with an axially compressed gel phantom and application in sheep before and after antero-apical myocardial infarction

PURPOSE

Myocardial strain is increasingly used to assess left ventricular (LV) function. Incorporation of LV deformation into finite element (FE) modeling environment with subsequent strain calculation will allow analysis to reach its full potential. We describe a new kinematic model-based analysis framework (KMAF) to calculate strain from 3D cine-DENSE (displacement encoding with stimulated echoes) MRI.

METHODS

Cine-DENSE allows measurement of 3D myocardial displacement with high spatial accuracy. The KMAF framework uses cine cardiovascular magnetic resonance (CMR) to facilitate cine-DENSE segmentation, interpolates cine-DENSE displacement, and kinematically deforms an FE model to calculate strain. This framework was validated in an axially compressed gel phantom and applied in 10 healthy sheep and 5 sheep after myocardial infarction (MI).

RESULTS

Excellent Bland-Altman agreement of peak circumferential (E_cc ) and longitudinal (E_ll ) strain (mean difference = 0.021 ± 0.04 and -0.006 ± 0.03, respectively), was found between KMAF estimates and idealized FE simulation. E_rr had a mean difference of -0.014 but larger variation (±0.12). Cine-DENSE estimated end-systolic (ES) E_cc , E_ll and E_rr exhibited significant spatial variation for healthy sheep. Displacement magnitude was reduced on average by 27%, 42%, and 56% after MI in the remote, adjacent and MI regions, respectively.

CONCLUSIONS

The KMAF framework allows accurate calculation of 3D LV E_cc and E_ll from cine-DENSE.

Effect of Low-Sodium versus Conventional Sodium Dialysate on Left Ventricular Mass in Home and Self-Care Satellite Facility Hemodialysis Patients: A Randomized Clinical Trial

BACKGROUND

Fluid overload in patients undergoing hemodialysis contributes to cardiovascular morbidity and mortality. There is a global trend to lower dialysate sodium with the goal of reducing fluid overload.

METHODS

To investigate whether lower dialysate sodium during hemodialysis reduces left ventricular mass, we conducted a randomized trial in which patients received either low-sodium dialysate (135 mM) or conventional dialysate (140 mM) for 12 months. We included participants who were aged >18 years old, had a predialysis serum sodium ≥135 mM, and were receiving hemodialysis at home or a self-care satellite facility. Exclusion criteria included hemodialysis frequency >3.5 times per week and use of sodium profiling or hemodiafiltration. The main outcome was left ventricular mass index by cardiac magnetic resonance imaging.

RESULTS

The 99 participants had a median age of 51 years old; 67 were men, 31 had diabetes mellitus, and 59 had left ventricular hypertrophy. Over 12 months of follow-up, relative to control, a dialysate sodium concentration of 135 mmol/L did not change the left ventricular mass index, despite significant reductions at 6 and 12 months in interdialytic weight gain, in extracellular fluid volume, and in plasma B-type natriuretic peptide concentration (ratio of intervention to control). The intervention increased intradialytic hypotension (odds ratio [OR], 7.5; 95% confidence interval [95% CI], 1.1 to 49.8 at 6 months and OR, 3.6; 95% CI, 0.5 to 28.8 at 12 months). Five participants in the intervention arm could not complete the trial because of hypotension. We found no effect on health-related quality of life measures, perceived thirst or xerostomia, or dietary sodium intake.

CONCLUSIONS

Dialysate sodium of 135 mmol/L did not reduce left ventricular mass relative to control, despite improving fluid status.

CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER

The Australian New Zealand Clinical Trials Registry, ACTRN12611000975998.

Efficient estimation of load-free left ventricular geometry and passive myocardial properties using principal component analysis

Models of cardiac mechanics require a well-defined reference geometry from which deformations and hence myocardial strain and stress can be calculated. In the in vivo beating heart, the load-free (LF) geometry generally cannot be measured directly, since, in many cases, there is no stage at which the lumen pressures and contractile state are all zero. Therefore, there is a need for an efficient method to estimate the LF geometry, which is essential for an accurate mechanical simulation of left ventricular (LV) mechanics, and for estimations of passive and contractile constitutive parameters of the heart muscle. In this paper, we present a novel method for estimating both the LF geometry and the passive stiffness of the myocardium. A linear combination of principal components from a population of diastolic displacements is used to construct the LF geometry. For each estimate of the LF geometry and tissue stiffness, LV inflation is simulated, and the model predictions are compared with surface data at multiple stages during passive diastolic filling. The feasibility of this method was demonstrated using synthetically deformation data that were generated using LV models derived from clinical magnetic resonance image data, and the identifiability of the LF geometry and passive stiffness parameters were analysed using Hessian metrics. Applications of this method to clinical data would improve the accuracy of constitutive parameter estimation and allow a better simulation of LV wall strains and stresses.

Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank

PURPOSE

To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI-tagged images.

MATERIALS AND METHODS

In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis.

RESULTS

Within the test set, myocardial end-systolic circumferential Green strain errors were -0.001 ± 0.025, -0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6-8 minutes per slice for the manual analysis.

CONCLUSION

The fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack.Published under a CC BY 4.0 license. Supplemental material is available for this article.

A distance map regularized CNN for cardiac cine MR image segmentation

PURPOSE

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images.

METHODS

We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel-wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per-pixel segmentation when a new patient cine MR image is provided as an input.

RESULTS

We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84 ± 0.03 and 0.91 ± 0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56 ± 0.28 to 0.80 ± 0.14.

CONCLUSIONS

We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state-of-the-art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non-invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Left Ventricular Diastolic Myocardial Stiffness and End-Diastolic Myofibre Stress in Human Heart Failure Using Personalised Biomechanical Analysis

Understanding the aetiology of heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction requires knowledge of biomechanical factors such as diastolic myocardial stiffness and stress. Cine CMR images and intra-ventricular pressure recordings were acquired in 8 HFrEF, 11 HFpEF and 5 control subjects. Diastolic myocardial stiffness was estimated using biomechanical models and found to be greater in HFrEF (6.4 ± 1.2 kPa) than HFpEF (2.7 ± 0.6 kPa, p < 0.05) and also greater than control (1.2 ± 0.4 kPa, p < 0.005). End-diastolic mid-ventricular myofibre stress derived from the personalised biomechanics model was higher in HFrEF (2.9 ± 0.3 kPa) than control (0.9 ± 0.3 kPa, p < 0.01). Chamber stiffness, measured from the slope of the diastolic pressure-volume relationship, is determined by the intrinsic tissue properties as well as the size and shape of the heart, and was unable to distinguish between any of the three groups (p > 0.05). Personalised biomechanical analysis may provide more specific information about myocardial mechanical behaviour than global chamber indices, which are confounded by variations in ventricular geometry.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression

BACKGROUND

Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment.

PURPOSE

To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans.

STUDY TYPE

Cross-sectional survey; diagnostic accuracy.

SUBJECTS

In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database.

FIELD STRENGTH/SEQUENCE

1.5T, steady-state free precession.

ASSESSMENT

Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume.

STATISTICAL TESTS

Paired t-tests compared to previous work.

RESULTS

Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase.

DATA CONCLUSION

A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.

Comparison of effects of losartan and metoprolol on left ventricular and aortic function at rest and during exercise in chronic aortic regurgitation

Aortic regurgitation (AR) increases the hemodynamic load on both the left ventricle (LV) and the aorta. Vasodilators and beta-blockers both reduce systemic blood pressure, but their relative effects on the LV and aortic function and aortic regurgitant fraction in chronic AR are uncertain. We aimed to compare short-term effects of losartan and metoprolol on LV and aortic function in asymptomatic patients with chronic moderate to severe AR, both at rest and during exercise, using cardiac magnetic resonance (CMR) imaging. 17 chronic AR patients were randomized to 4-6 weeks losartan followed by metoprolol, or vice versa, in a cross-over design. Aortic regurgitant fraction, aortic distensibility, pulse wave velocity and LV function were assessed at rest and after moderate exercise stress (29 ± 7 W, heart rate increase 25 ± 6 bpm) using CMR. Chronic AR patients on metoprolol had a significantly lower mean heart rate, cardiac power index and rate-pressure product, than on losartan (all p < 0.01). However, aortic regurgitant fraction was greater on metoprolol compared to losartan (by 7 ± 11%, p = 0.02). Metoprolol was also associated with a greater reduction in aortic distensibility during exercise than losartan (- 2.4 ± 1.5 × 10^-3 vs - 1.7 ± 2.1 × 10^-3 mmHg^-1 respectively, p = 0.04). End-diastolic volume index was higher on metoprolol than losartan at exercise (difference 6.6 ± 7.8 ml/m², p < 0.01), as was end-systolic volume index (difference 4.0 ± 5.2 ml/m², p < 0.01). Losartan and metoprolol have significantly different short-term effects on aortic regurgitation and LV and aortic function in chronic AR. Further research is required to determine the long-term clinical significance of these changes.

An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

Cardiac magnetic resonance perfusion examinations enable noninvasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine, or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information, or cross correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum-of-squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of nonrigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project ( www.cardiacatlas.org).

4D modelling for rapid assessment of biventricular function in congenital heart disease

Although more patients with congenital heart disease (CHD) are now living longer due to better surgical interventions, they require regular imaging to monitor cardiac performance. There is a need for robust clinical tools which can accurately assess cardiac function of both the left and right ventricles in these patients. We have developed methods to rapidly quantify 4D (3D + time) biventricular function from standard cardiac MRI examinations. A finite element model was interactively customized to patient images using guide-point modelling. Computational efficiency and ability to model large deformations was improved by predicting cardiac motion for the left ventricle and epicardium with a polar model. In addition, large deformations through the cycle were more accurately modeled using a Cartesian deformation penalty term. The model was fitted to user-defined guide points and image feature tracking displacements throughout the cardiac cycle. We tested the methods in 60 cases comprising a variety of congenital heart diseases and showed good correlation with the gold standard manual analysis, with acceptable inter-observer error. The algorithm was considerably faster than standard analysis and shows promise as a clinical tool for patients with CHD.

Automated Segmentation of Light-Sheet Fluorescent Imaging to Characterize Experimental Doxorubicin-Induced Cardiac Injury and Repair

This study sought to develop an automated segmentation approach based on histogram analysis of raw axial images acquired by light-sheet fluorescent imaging (LSFI) to establish rapid reconstruction of the 3-D zebrafish cardiac architecture in response to doxorubicin-induced injury and repair. Input images underwent a 4-step automated image segmentation process consisting of stationary noise removal, histogram equalization, adaptive thresholding, and image fusion followed by 3-D reconstruction. We applied this method to 3-month old zebrafish injected intraperitoneally with doxorubicin followed by LSFI at 3, 30, and 60 days post-injection. We observed an initial decrease in myocardial and endocardial cavity volumes at day 3, followed by ventricular remodeling at day 30, and recovery at day 60 (P < 0.05, n = 7-19). Doxorubicin-injected fish developed ventricular diastolic dysfunction and worsening global cardiac function evidenced by elevated E/A ratios and myocardial performance indexes quantified by pulsed-wave Doppler ultrasound at day 30, followed by normalization at day 60 (P < 0.05, n = 9-20). Treatment with the γ-secretase inhibitor, DAPT, to inhibit cleavage and release of Notch Intracellular Domain (NICD) blocked cardiac architectural regeneration and restoration of ventricular function at day 60 (P < 0.05, n = 6-14). Our approach provides a high-throughput model with translational implications for drug discovery and genetic modifiers of chemotherapy-induced cardiomyopathy.

Left ventricular function and regional strain with subtly-tagged steady-state free precession feature tracking

PURPOSE

To provide regional strain and ventricular volume from a single acquisition, using subtly tagged steady-state free precession (SubTag SSFP) feature tracking.

MATERIALS AND METHODS

The effects on regional strain of tag strength in gradient recalled echo (GRE) tagging, flip angle in untagged balanced SSFP, and both in SubTag SSFP were examined in the mid left ventricle of 15 healthy volunteers at 3T. Optimal parameters were determined from varying both tag strength and SSFP flip angle using full tag saturation GRE as the reference standard. SubTag SSFP was acquired in 15 additional healthy volunteers for whole-heart volume and strain assessment using the optimized parameters. Values measured by two image analysts were compared to clinical reference standards from untagged SSFP (volumes) and GRE tagging (strains).

RESULTS

Regional strain accuracy was maintained with decreasing total tagging flip angle (β); less than 3% differences for β ≥ 26°. For untagged SSFP flip angle (α), whole-wall strain differences became statistically significant when α < 40°. A SubTag SSFP acquisition with α = 40° and β = 46° showed the best combination of tagging strength, blood-myocardial contrast, and tag persistence at end-systole for regional strain estimation. SubTag SSFP also showed excellent agreement with untagged SSFP for volumetrics (percent difference: end-diastolic volume = 0.6%, end-systolic volume = 0.4%, stroke volume = 1.2%, ejection fraction = 0.6%, mass = 1.1%).

CONCLUSION

Feature tracking for regional myocardial strain assessment is dependent on image features, mainly the tag strength, persistence, and image contrast. SubTag SSFP balances these criteria to provide accurate regional strain and volumetric assessment in a single acquisition.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2018;47:787-797.

Image-Based Investigation of Human in Vivo Myofibre Strain

Cardiac myofibre deformation is an important determinant of the mechanical function of the heart. Quantification of myofibre strain relies on 3D measurements of ventricular wall motion interpreted with respect to the tissue microstructure. In this study, we estimated in vivo myofibre strain using 3D structural and functional atlases of the human heart. A finite element modelling framework was developed to incorporate myofibre orientations of the left ventricle (LV) extracted from 7 explanted normal human hearts imaged ex vivo with diffusion tensor magnetic resonance imaging (DTMRI) and kinematic measurements from 7 normal volunteers imaged in vivo with tagged MRI. Myofibre strain was extracted from the DTMRI and 3D strain from the tagged MRI. We investigated: i) the spatio-temporal variation of myofibre strain throughout the cardiac cycle; ii) the sensitivity of myofibre strain estimates to the variation in myofibre angle between individuals; and iii) the sensitivity of myofibre strain estimates to variations in wall motion between individuals. Our analysis results indicate that end systolic (ES) myofibre strain is approximately homogeneous throughout the entire LV, irrespective of the inter-individual variation in myofibre orientation. Additionally, inter-subject variability in myofibre orientations has greater effect on the variabilities in myofibre strain estimates than the ventricular wall motions. This study provided the first quantitative evidence of homogeneity of ES myofibre strain using minimally-invasive medical images of the human heart and demonstrated that image-based modelling framework can provide detailed insight to the mechanical behaviour of the myofibres, which may be used as a biomarker for cardiac diseases that affect cardiac mechanics.

Estimation of myocardial strain from non-rigid registration and highly accelerated cine CMR

Sparsely sampled cardiac cine accelerated acquisitions show promise for faster evaluation of left-ventricular function. Myocardial strain estimation using image feature tracking methods is also becoming widespread. However, it is not known whether highly accelerated acquisitions also provide reliable feature tracking strain estimates. Twenty patients and twenty healthy volunteers were imaged with conventional 14-beat/slice cine acquisition (STD), 4× accelerated 4-beat/slice acquisition with iterative reconstruction (R4), and a 9.2× accelerated 2-beat/slice real-time acquisition with sparse sampling and iterative reconstruction (R9.2). Radial and circumferential strains were calculated using non-rigid registration in the mid-ventricle short-axis slice and inter-observer errors were evaluated. Consistency was assessed using intra-class correlation coefficients (ICC) and bias with Bland-Altman analysis. Peak circumferential strain magnitude was highly consistent between STD and R4 and R9.2 (ICC = 0.876 and 0.884, respectively). Average bias was -1.7 ± 2.0 %, p < 0.001, for R4 and -2.7 ± 1.9 %, p < 0.001 for R9.2. Peak radial strain was also highly consistent (ICC = 0.829 and 0.785, respectively), with average bias -11.2 ± 18.4 %, p < 0.001, for R4 and -15.0 ± 21.2 %, p < 0.001 for R9.2. STD circumferential strain could be predicted by linear regression from R9.2 with an R² of 0.82 and a root mean squared error of 1.8 %. Similarly, radial strain could be predicted with an R² of 0.67 and a root mean squared error of 21.3 %. Inter-observer errors were not significantly different between methods, except for peak circumferential strain R9.2 (1.1 ± 1.9 %) versus STD (0.3 ± 1.0 %), p = 0.011. Although small systematic differences were observed in strain, these were highly consistent with standard acquisitions, suggesting that accelerated myocardial strain is feasible and reliable in patients who require short acquisition durations.

Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences

Anatomical cine cardiovascular magnetic resonance (CMR) imaging is widely used to assess the systolic cardiac function because of its high soft tissue contrast. Assessment of diastolic LV function has not regularly been performed due the complex and time consuming procedures. This study presents a semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images. The proposed method is based on three main steps: (1) non-rigid registration, which yields a sequence of endocardial boundary points over the cardiac cycle based on a user-provided contour on the first frame; (2) LV volume and filling rate computations over the cardiac cycle; and (3) automated detection of the peak values of early (E) and late ventricular (A) filling waves. In 47 patients cine CMR imaging and Doppler-echocardiographic imaging were performed. CMR measurements of peak values of the E and A waves as well as the deceleration time were compared with the corresponding values obtained in Doppler-Echocardiography. For the E/A ratio the proposed algorithm for CMR yielded a Cohen's kappa measure of 0.70 and a Gwet's AC1 coefficient of 0.70.

CONCLUSION

Semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images provides mitral inflow measurements comparable to Doppler-Echocardiography.

Creating shape templates for patient specific biventricular modeling in congenital heart disease

Survival rates for infants with congenital heart disease (CHD) are improving, resulting in a growing population of adults with CHD. However, the analysis of left and right ventricular function is very time-consuming owing to the variety of congenital morphologies. Efficient customization of patient geometry and function depends on high quality shape templates specifically designed for the application. In this paper, we combine a method for creating finite element shape templates with an interactive template customization to patient MRI examinations. This enables different templates to be chosen depending on patient morphology. To demonstrate this pipeline, a new biventricular template with 162 elements was created and tested in place of an existing 82-element template. The method was able to provide fast interactive biventricular analysis with 0.31 sec per edit response time. The new template was customized to 13 CHD patients with similar biventricular topology, showing improved performance over the previous template and good agreement with clinical indices.

An interactive tool for rapid biventricular analysis of congenital heart disease

Cardiac malformations are the most common birth defect. Better interventions in early life have improved mortality for children with congenital heart disease, but heart failure is a significant problem in adulthood. These patients require regular imaging and analysis of biventricular (left and right ventricular) function. In this study, we describe a rapid method to analyse left and right ventricular shape and function from cardiac MRI examinations. A 4D (3D+time) finite element model template is interactively customized to the anatomy and motion of the biventricular unit. The method was validated in 17 patients and 10 ex-vivo hearts. Interactive model updates were achieved through preconditioned conjugate gradient optimization on a multithread system, and by precomputing points predicted from a coarse mesh optimization.

Information maximizing component analysis of left ventricular remodeling due to myocardial infarction

BACKGROUND

Although adverse left ventricular shape changes (remodeling) after myocardial infarction (MI) are predictive of morbidity and mortality, current clinical assessment is limited to simple mass and volume measures, or dimension ratios such as length to width ratio. We hypothesized that information maximizing component analysis (IMCA), a supervised feature extraction method, can provide more efficient and sensitive indices of overall remodeling.

METHODS

IMCA was compared to linear discriminant analysis (LDA), both supervised methods, to extract the most discriminatory global shape changes associated with remodeling after MI. Finite element shape models from 300 patients with myocardial infarction from the DETERMINE study (age 31-86, mean age 63, 20 % women) were compared with 1991 asymptomatic cases from the MESA study (age 44-84, mean age 62, 52 % women) available from the Cardiac Atlas Project. IMCA and LDA were each used to identify a single mode of global remodeling best discriminating the two groups. Logistic regression was employed to determine the association between the remodeling index and MI. Goodness-of-fit results were compared against a baseline logistic model comprising standard clinical indices.

RESULTS

A single IMCA mode simultaneously describing end-diastolic and end-systolic shapes achieved best results (lowest Deviance, Akaike information criterion and Bayesian information criterion, and the largest area under the receiver-operating-characteristic curve). This mode provided a continuous scale where remodeling can be quantified and visualized, showing that MI patients tend to present larger size and more spherical shape, more bulging of the apex, and thinner wall thickness.

CONCLUSIONS

IMCA enables better characterization of global remodeling than LDA, and can be used to quantify progression of disease and the effect of treatment. These data and results are available from the Cardiac Atlas Project ( http://www.cardiacatlas.org ).

Right ventricular segmentation in cardiac MRI with moving mesh correspondences

This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84±0.11 for the epicardium and 0.79±0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82±0.14, which demonstrates the robustness of the algorithm.

Rapid D-Affine biventricular cardiac function with polar prediction

Although many solutions have been proposed for left ven-tricular functional analysis of the heart, right and left (bi-) ventricular function has been problematic due to the complex geometry and large motions. Biventricular function is particularly important in congenital heart disease, the most common type of birth defects. We describe a rapid interactive analysis tool for biventricular function which incorporates 1) a 3D+ time finite element model of biventricular geometry, 2) a fast prediction step which estimates an initial geometry in a polar coordinate system, and 3) a Cartesian update which penalizes deviations from affine transformations (D-Affine) from a prior. Solution times were very rapid, enabling interaction in real time using guide point modeling. The method was applied to 13 patients with congenital heart disease and compared with the clinical gold standard of manual tracing. Results between the methods showed good correlation (R2 > 0.9) and good precision (volume < 17 ml; mass < 11g) for both chambers.

Atlas-based quantification of cardiac remodeling due to myocardial infarction

Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and end-systolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects.

Multi-atlas segmentation with augmented features for cardiac MR images

Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupé et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.

Atlas-based Anatomical Modeling and Analysis of Heart Disease

Heart shape and function are major determinants of disease severity and predictors of future morbidity and mortality. Many studies now rely on non-invasive cardiac imaging techniques to quantify structural and functional changes. Statistical anatomical modeling of heart shape and motion provides a new tool for the quantification and evaluation of heart disease. This review surveys recent progress in the evaluation of statistical shape measures across populations and sub-cohorts, and highlights collaborative efforts to facilitate data sharing and atlas-based shape analysis.

Left ventricular shape variation in asymptomatic populations: the Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Although left ventricular cardiac geometric indices such as size and sphericity characterize adverse remodeling and have prognostic value in symptomatic patients, little is known of shape distributions in subclinical populations. We sought to quantify shape variation across a large number of asymptomatic volunteers, and examine differences among sub-cohorts.

METHODS

An atlas was constructed comprising 1,991 cardiovascular magnetic resonance (CMR) cases contributed from the Multi-Ethnic Study of Atherosclerosis baseline examination. A mathematical model describing regional wall motion and shape was used to establish a coordinate map registered to the cardiac anatomy. The model was automatically customized to left ventricular contours and anatomical landmarks, corrected for breath-hold mis-registration between image slices. Mathematical techniques were used to characterize global shape distributions, after removal of translations, rotations, and scale due to height. Differences were quantified among ethnicity, sex, smoking, hypertension and diabetes sub-cohorts.

RESULTS

The atlas construction process yielded accurate representations of global shape (errors between manual and automatic surface points in 244 validation cases were less than the image pixel size). After correction for height, the dominant shape component was associated with heart size, explaining 32% of the total shape variance at end-diastole and 29% at end-systole. After size, the second dominant shape component was sphericity at end-diastole (13%), and concentricity at end-systole (10%). The resulting shape components distinguished differences due to ethnicity and risk factors with greater statistical power than traditional mass and volume indices.

CONCLUSIONS

We have quantified the dominant components of global shape variation in the adult asymptomatic population. The data and results are available at cardiacatlas.org. Shape distributions were principally explained by size, sphericity and concentricity, which are known correlates of adverse outcomes. Atlas-based global shape analysis provides a powerful method for quantifying left ventricular shape differences in asymptomatic populations.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00005487.

Comparison of magnetic resonance feature tracking for systolic and diastolic strain and strain rate calculation with spatial modulation of magnetization imaging analysis

PURPOSE

To compare cardiovascular magnetic resonance-feature tracking (CMR-FT) with spatial modulation of magnetization (SPAMM) tagged imaging for the calculation of short and long axis Lagrangian strain measures in systole and diastole.

MATERIALS AND METHODS

Healthy controls (n = 35) and patients with dilated cardiomyopathy (n = 10) were identified prospectively and underwent steady-state free precession (SSFP) cine imaging and SPAMM imaging using a gradient-echo sequence. A timed offline analysis of images acquired at identical horizontal long and short axis slice positions was performed using CMR-FT and dynamic tissue-tagging (CIMTag2D). Agreement between strain and strain rate (SR) values calculated using these two different methods was assessed using the Bland-Altman technique.

RESULTS

Across all participants, there was good agreement between CMR-FT and CIMTag for calculation of peak systolic global circumferential strain (-22.7 ± 6.2% vs. -22.5 ± 6.9%, bias 0.2 ± 4.0%) and SR (-1.35 ± 0.42 1/s vs. -1.22 ± 0.42 1/s, bias 0.13 ± 0.33 1/s) and early diastolic global circumferential SR (1.21 ± 0.44 1/s vs. 1.07 ± 0.30 1/s, bias -0.14 ± 0.34 1/s) at the subendocardium. There was satisfactory agreement for derivation of peak systolic global longitudinal strain (-18.1 ± 5.0% vs. -16.7 ± 4.8%, bias 1.3 ± 3.8%) and SR (-1.04 ± 0.29 1/s vs. -0.95 ± 0.32 1/s, bias 0.09 ± 0.26 1/s). The weakest agreement was for early diastolic global longitudinal SR (1.10 ± 0.40 1/s vs. 0.67 ± 0.32 1/s, bias -0.42 ± 0.40 1/s), although the correlation remained significant (r = 0.42, P < 0.01). CMR-FT generated these data over four times quicker than CIMTag.

CONCLUSION

There is sufficient agreement between systolic and diastolic strain measures calculated using CMR-FT and myocardial tagging for CMR-FT to be considered as a potentially feasible and rapid alternative.

Quantification of circumferential, longitudinal, and radial global fractional shortening using steady-state free precession cines: a comparison with tissue-tracking strain and application in Fabry disease

PURPOSE

Conventional calculation of myocardial strain requires tissue-tracking. A surrogate for strain called global fractional shortening (GFS) is proposed based on changes in dimensions of endocardial and epicardial surfaces without tissue-tracking.

METHODS

Three-dimensional endocardial and epicardial left ventricular surfaces traced at end-diastole and end-systole using conventional steady-state free precession cine images were used to calculate GFScc (circumferential), GFSll (longitudinal), and GFSrr (radial) using fractional length changes in each direction over the heart surface. GFS values were validated using finite element models (FEM) and in vivo using tagging-derived strains (εcc ,εll ,εrr ) in patients with a wide range of ejection fraction (EF) and diagnosis (n=32). GFS was also measured in 31 patients with Fabry disease and matched healthy controls.

RESULTS

GFS values were within 3% of average FEM-derived Lagrangian strains and had good agreement in vivo (GFScc  =-14 ± 4%, εcc  =-14 ± 4%, R(2) =0.85; GFSll  =-12 ± 4%, εll  =-12 ± 4%, R(2) =0.72; GFSrr =46 ± 21%). εrr could not be measured reliably from tagging. Compared with healthy controls with matched EF, patients with Fabry disease had significantly increased GFScc (Endo) (-28 ± 3% versus -25 ± 2%), decreased GFScc(Epi) (-10 ± 2% versus -11 ± 2%) and decreased GFSll for all components.

CONCLUSION

GFS yields similar values to conventionally measured strains without requiring tissue-tracking. Compared with controls, patients with Fabry disease have significant differences in several GFS components.

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

Atlas-based analysis of cardiac shape and function: correction of regional shape bias due to imaging protocol for population studies

BACKGROUND

Cardiovascular imaging studies generate a wealth of data which is typically used only for individual study endpoints. By pooling data from multiple sources, quantitative comparisons can be made of regional wall motion abnormalities between different cohorts, enabling reuse of valuable data. Atlas-based analysis provides precise quantification of shape and motion differences between disease groups and normal subjects. However, subtle shape differences may arise due to differences in imaging protocol between studies.

METHODS

A mathematical model describing regional wall motion and shape was used to establish a coordinate system registered to the cardiac anatomy. The atlas was applied to data contributed to the Cardiac Atlas Project from two independent studies which used different imaging protocols: steady state free precession (SSFP) and gradient recalled echo (GRE) cardiovascular magnetic resonance (CMR). Shape bias due to imaging protocol was corrected using an atlas-based transformation which was generated from a set of 46 volunteers who were imaged with both protocols.

RESULTS

Shape bias between GRE and SSFP was regionally variable, and was effectively removed using the atlas-based transformation. Global mass and volume bias was also corrected by this method. Regional shape differences between cohorts were more statistically significant after removing regional artifacts due to imaging protocol bias.

CONCLUSIONS

Bias arising from imaging protocol can be both global and regional in nature, and is effectively corrected using an atlas-based transformation, enabling direct comparison of regional wall motion abnormalities between cohorts acquired in separate studies.

Cardiovascular magnetic resonance: deeper insights through bioengineering

Heart disease is the main cause of morbidity and mortality worldwide, with coronary artery disease, diabetes, and obesity being major contributing factors. Cardiovascular magnetic resonance (CMR) can provide a wealth of quantitative information on the performance of the heart, without risk to the patient. Quantitative analyses of these data can substantially augment the diagnostic quality of CMR examinations and can lead to more effective characterization of disease and quantification of treatment benefit. This review provides an overview of the current state of the art in CMR with particular regard to the quantification of motion, both microscopic and macroscopic, and the application of bioengineering analysis for the evaluation of cardiac mechanics. We discuss the current clinical practice and the likely advances in the next 5-10 years, as well as the ways in which clinical examinations can be augmented by bioengineering analysis of strain, compliance, and stress.