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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 Hier10 archical 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 knowl28 edge 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.

Objectives We 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 ri...

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.

Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility. Accurate left ventricle delineation is a prerequisite for robust and reproducible myocardial velocity estimation. Conventional manual segmentation on this dataset can be time-consuming and subjective, and an effective fully automated delineation method is highly in demand. By leveraging recently proposed deep learning-based semantic segmentation approaches, in this study, we propose a novel fully automated framework incorporating a 3D-UNet backbone architecture with Embedded multichannel Attention mechanism and LSTM based Recurrent neural networks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposed method also utilises the amalgamation of magnitude and phase images as input to realise an information fusion of this multichannel dataset and exploring the correlations of temporal frames via the embedded RNN. By comparing the baseline model of 3D-UNet and ablation studies with and without embedded attentive LSTM modules and various loss functions, we can demonstrate that the proposed model has outperformed the state-of-the-art baseline models with significant improvement.

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., Comment: Accepted by IEEE Journal of Biomedical and Health Informatics

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 seconds 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., 34 pages, 10 figures, 7 tables, accepted by Future Generation Computer Systems journal

Highlights • Propose a fully automatic method for left atrial scar quantification, with promising performance. • Formulate a new framework of scar quantification based on surface projection and graph-cuts framework. • Propose the multi-scale learning CNN, combined with the random shift training strategy, to learn and predict the graph potentials, which significantly improves the performance of the proposed method, and enables the full automation of the framework. • Provide thorough validation and parameter studies for the proposed techniques using fifty-eight clinical images., Graphical abstract The proposed framework for left atrium (LA) scar quantication and analysis. In this framework, we rst use a well-developed multi-atlas whole heart segmentation (MA-WHS) to obtain an initial segmentation of the LA. Then, we project the LA myocardium to generate a surface mesh, where the quantication is performed. At the same time, both the texture and anatomical features of the LA endocardium can be adequately extracted by employing the multi-scale patch (MSP) strategy. Thus, the features of the nodes in the graph are represented by a set of MSPs. Finally, the labeling of scars is achieved by optimizing a cost function based on the graph-cuts framework, whose potentials for edge weights are explicitly learned and predicted by the proposed multi-scale convolutional neural network (MS-CNN)., 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

Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows 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, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel automated framework that improves the standard U-Net based methods on these CMR multi-channel data (magnitude and phase) by cross-channel fusion with attention module and shape information based post-processing to achieve accurate delineation of both epicardium and endocardium 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 enhanced performance compared to standard U-Net based networks trained with single-channel data. Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data., Comment: 7 pages, 3 figures, accepted by SPIE Medical Imaging 2021

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.

Highlights • This work 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. • This work introduces the related information to the challenge, discusses the results from the conventional methods and deep learning-based algorithms, and provides insights to the future research. • The challenge provides a fair and intuitive comparison framework for methods developed and being developed for WHS. • The challenge provides the training datasets with manually delineated ground truths and evaluation for an ongoing development of MM-WHS algorithms., Graphical abstract This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MMWHS) challenge, in conjunction with MICCAI-STACOM 2017. The challenge provides 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 show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. Several of them also reported poor results in the blinded evaluation, probably due to over fitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. 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., 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/).

Assessment of blood flow in the coronary arteries is highly challenging because of their small size, tortuosity, and high mobility. In addition, flow velocities are low (requiring a sequence with high flow sensitivity) and phasic, peaking in diastole for the left anterior descending artery but with approximately equal flow peaks in systole and diastole for the right coronary artery. In the presence of disease, the temporal patterns of flow velocity through the cardiac cycle change, with diastolic flow velocities being reduced before systolic components are compromised. Assessment of total or global coronary flow can be made from measurements in the larger and less mobile coronary sinus, and this has been validated against positon emission tomography (PET). For flow in individual arteries, both breath-hold and navigator gated segmented gradient echo phase velocity mapping approaches have been developed at both 1.5 Tesla (T) and 3 T for the assessment of coronary flow and flow velocity and coronary flow reserve and coronary flow velocity reserve. More recently, the higher efficiency of interleaved spiral phase velocity mapping has allowed acquisitions of coronary blood flow with higher spatial and temporal resolution. Assessment of coronary blood flow and flow reserve has potential for the noninvasive assessment of the physiologic significance of coronary stenosis. In addition, through assessment of changes in cross-sectional area and coronary blood flow in response to isometric hand grip exercise, cardiovascular magnetic resonance provides a noninvasive method for assessing coronary artery endothelial dysfunction, a marker of atherosclerotic disease and an independent predictor of coronary events.

Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it does not always hold that the manually labeled data and the unlabeled data are sitting in the same distribution. In this paper, we alleviate these problems by proposing a discriminative consistent domain generation (DCDG) approach to achieve a semi-supervised learning. The discriminative consistent domain is achieved by a double-sided domain adaptation. The double-sided domain adaptation aims to make a fusion of the feature spaces of labeled data and unlabeled data. In this way, we can fit the differences of various distributions between labeled data and unlabeled data. In order to keep the discriminativeness of generated consistent domain for the task learning, we apply an indirect learning for the double-sided domain adaptation. Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation. We demonstrate the performance of our proposed DCDG on the late gadolinium enhancement cardiac MRI (LGE-CMRI) images acquired from patients with atrial fibrillation in two clinical centers for the segmentation of the left atrium anatomy (LA) and proximal pulmonary veins (PVs). The experiments show that our semi-supervised approach achieves compelling segmentation results, which can prove the robustness of DCDG for the semi-supervised learning using the unlabeled data along with labeled data acquired from a single center or multicenter studies.

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.

Introduction Heart failure develops in 3%–5% of patients with hypertrophic cardiomyopathy (HCM) and is associated with a ten-fold increase in mortality. Myocardial perfusion abnormalities using positron emission tomography (PET) predict patients at increased risk of heart failure and death, however the mechanism of these perfusion defects has not been demonstrated, nor has the added prognostic value of perfusion imaging over late gadolinium imaging been established. Methods We measured simultaneous pressure and flow in the proximal left anterior descending artery in 33 HCM and 20 control patients at rest and during hyperemia, allowing calculation of wave intensity (WIA). Patients underwent quantitative first-pass perfusion cardiovascular magnetic resonance (CMR). We prospectively recruited a further 328 HCM patients for CMR, including adenosine first pass perfusion, LV and LA volumes and late gadolinium enhancement (LGE). We followed patients for development of heart failure, with a composite endpoint of heart failure death, cardiac transplantation and unplanned heart failure hospitalisation. Results Patients with HCM had a lower coronary flow reserve than controls (1.9±0.8 versus 2.7±0.9, p=0.001). Coronary hemodynamics in HCM were characterised by a very large backward compression wave during systole (38%±11% versus 21±6%, p The proportion of waves acting to accelerate coronary flow increased with hyperemia and the magnitude of change was proportional to the myocardial perfusion reserve. (r=0.62, p During a median follow up of 3.2 years, 32 patients met the heart failure endpoint. Baseline left atrial volume indexed to body surface area, LAVi, (HR 1.38 (1.2–1.6), p Discussion Coronary flow in patients with HCM is deranged. Distally, compressive deformation of intra-myocardial blood vessels during systole result in an abnormally large backward compression wave, while proximally, severe LVOT obstruction is associated with an additional deceleration wave. Perfusion abnormalities in HCM are not simply a consequence of supply/demand mismatch or remodelling of the intra-myocardial blood vessels but represent a dynamic interaction with myocardial mechanics which may be amenable to treatment. In our prognostic study, we identified that while replacement fibrosis was a likely mechanism of progression to heart failure, LAVi as a more global measure of ventricular dysfunction was the strongest prognostic marker in HCM. Although perfusion using PET predicted development of heart failure in HCM, perfusion imaging with CMR did not predict outcome. Perfusion defects in HCM are complex and multifactorial, however do not correlate directly with outcome in large, prospective studies and should not be included in risk stratification scores.

Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements (\(p

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model., 8 pages, 4 figures, accepted by MICCAI 2018

We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session. We formulate the joint distribution of images using a multivariate mixture model (MvMM), and employ the maximum likelihood estimator (MLE) for texture classification of the images simultaneously. The MvMM can also embed transformations assigned to the images to correct the misregistration. The iterated conditional mode algorithm is adopted for optimization. This method first extracts the anatomical shape of the LA, and then estimates a prior probability map. It projects the resulting segmentation onto the LA surface, for quantification and analysis of scarring. We applied the proposed method to 36 clinical data sets and obtained promising results (Accuracy: \(0.809\pm .150\), Dice: \(0.556\pm .187\)). We compared the method with the conventional algorithms and showed an evidently and statistically better performance (\(p

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.

The late gadolinium-enhanced (LGE) MRI technique is a well-validated method for fibrosis detection in the myocardium. With this technique, the altered wash-in and wash-out contrast agent kinetics in fibrotic and healthy myocardium results in scar tissue being seen with high or enhanced signal relative to normal tissue which is ‘nulled’. Recently, great progress on LGE MRI has resulted in improved visualization of fibrosis in the left atrium (LA). This provides valuable information for treatment planning, image-based procedure guidance and clinical management in patients with atrial fibrillation (AF). Nevertheless, precise and objective atrial fibrosis segmentation (AFS) is required for accurate assessment of AF patients using LGE MRI. This is a very challenging task, not only because of the limited quality and resolution of the LGE MRI images acquired in AF but also due to the thinner wall and unpredictable morphology of the LA. Accurate and reliable segmentation of the anatomical structure of the LA myocardium is a prerequisite for accurate AFS. Most current studies rely on manual segmentation of the anatomical structures, which is very labor-intensive and subject to inter- and intra-observer variability. The subsequent AFS is normally based on unsupervised learning methods, e.g., using thresholding, histogram analysis, clustering and graph-cut based approaches, which have variable accuracy. In this study, we present a fully-automated multi-atlas propagation based whole heart segmentation method to derive the anatomical structure of the LA myocardium and pulmonary veins. This is followed by a supervised deep learning method for AFS. Twenty clinical LGE MRI scans from longstanding persistent AF patients were entered into this study retrospectively. We have demonstrated that our fully automatic method can achieve accurate and reliable AFS compared to manual delineated ground truth.

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.

Precise and objective segmentation of atrial scarring (SAS) is a prerequisite for quantitative assessment of atrial fibrillation using non-invasive late gadolinium-enhanced (LGE) MRI. This also requires accurate delineation of the left atrium (LA) and pulmonary veins (PVs) geometry. Most previous studies have relied on manual segmentation of LA wall and PVs, which is a tedious and error-prone procedure with limited reproducibility. There are many attempts on automatic SAS using simple thresholding, histogram analysis, clustering and graph-cut based approaches; however, in general, these methods are considered as unsupervised learning thus subject to limited segmentation accuracy. In this study, we present a fully-automated multi-atlas based whole heart segmentation method to derive the LA and PVs geometry objectively that is followed by a fully automatic deep learning method for SAS. Our deep learning method consists of a feature extraction step via super-pixel over-segmentation and a supervised classification step via stacked sparse auto-encoders. We demonstrate the efficacy of our method on 20 clinical LGE MRI scans acquired from a longstanding persistent atrial fibrillation cohort. Both quantitative and qualitative results show that our fully automatic method obtained accurate segmentation results compared to the manual segmentation based ground truths.

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is a non-invasive technique, which has shown promise in detecting native and post-ablation atrial scarring. To visualize the scarring, a precise segmentation of the left atrium (LA) and pulmonary veins (PVs) anatomy is performed as a first step—usually from an ECG gated CMRI roadmap acquisition—and the enhanced scar regions from the LGE CMRI images are superimposed. The anatomy of the LA and PVs in particular is highly variable and manual segmentation is labor intensive and highly subjective. In this paper, we developed a multi-atlas propagation based whole heart segmentation (WHS) to delineate the LA and PVs from ECG gated CMRI roadmap scans. While this captures the anatomy of the atrium well, the PVs anatomy is less easily visualized. The process is therefore augmented by semi-automated manual strokes for PVs identification in the registered LGE CMRI data. This allows us to extract more accurate anatomy than the fully automated WHS. Both qualitative visualization and quantitative assessment with respect to manual segmented ground truth showed that our method is efficient and effective with an overall mean Dice score of 0.91.

Although the high prevalence of aortic root dilatation in adults with repaired tetralogy of Fallot (rTOF) is well established [(1,2)][1], evidence to guide clinical follow-up and decision making remains sparse. We sought to define the features, determinants, and rate of progression of aortic

Like X-Ray contrast angiography, MR coronary angiograms show the vessel lumens rather than the vessels themselves. Consequently, outward remodeling of the vessel wall, which occurs in subclinical coronary disease before luminal narrowing, cannot be seen. The current gold standard for assessing the coronary vessel wall is intravascular ultrasound, and more recently, optical coherence tomography, both of which are invasive and use ionizing radiation. A noninvasive, low-risk technique for assessing the vessel wall would be beneficial to cardiologists interested in the early detection of preclinical disease and for the safe monitoring of the progression or regression of disease in longitudinal studies. In this review article, the current state of the art in MR coronary vessel wall imaging is discussed, together with validation studies and recent developments. J. Magn. Reson. Imaging 2015;41:1190–1202. © 2014 Wiley Periodicals, Inc.

Purpose High resolution three-dimensional (3D) late gadolinium enhancement (LGE) imaging is performed with single R-wave gating to minimize lengthy acquisition durations. In patients with atrial fibrillation (AF), heart rate variability results in variable magnetization recovery between sequence repeats, and image quality is often poor. In this study, we implemented and tested a dynamic inversion time (dynamic-TI) scheme designed to reduce sequence sensitivity to heart rate variations. Methods An inversion-prepared 3D segmented gradient echo sequence was modified so that the TI varied automatically from beat-to-beat (dynamic-TI) based on the time since the last sequence repeat. 3D LGE acquisitions were performed in 17 patients prior to radio frequency ablation of persistent AF both with and without dynamic-TI. Qualitative image quality scores, blood signal-to-ghosting ratios (SGRs). and blood–myocardium contrast-to-ghosting ratios (CGRs) were compared. Results Image quality scores were higher with dynamic-TI than without dynamic-TI (2.2 ± 0.9 vs. 1.8 ± 1.1, P = 0.008), as were blood–myocardium CGRs (13.8 ± 7.6 vs. 8.3 ± 6.1, P = 0.003) and blood SGRs (19.6 ± 8.5 vs. 13.1 ± 8.0, P = 0.003). Conclusion The dynamic-TI algorithm improves image quality of 3D LGE imaging in this difficult patient population by reducing the sequence sensitivity to RR interval variations Magn Reson Med 73:646–654, 2015. © 2014 Wiley Periodicals, Inc.

Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform super-resolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information.

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

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

© 1982-2012 IEEE.Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-Assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-The-Art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value.

Cardiac magnetic resonance (CMR) is regarded as the reference examination for cardiac morphology in tetralogy of Fallot (ToF) patients allowing images of high spatial resolution and high contrast. The detailed knowledge of the right ventricular anatomy is critical in ToF management. The segmentation of the right ventricle (RV) in CMR images from ToF patients is a challenging task due to the high shape and image quality variability. In this paper we propose a fully automatic deep learning-based framework to segment the RV from CMR anatomical images of the whole heart. We adopt a 3D multi-scale deep convolutional neural network to identify pixels that belong to the RV. Our robust segmentation framework was tested on 26 ToF patients achieving a Dice similarity coefficient of 0.8281±0.1010 with reference to manual annotations performed by expert cardiologists. The proposed technique is also computationally efficient, which may further facilitate its adoption in the clinical routine.

The aim of this study was to implement a quantitative in vivo cardiac diffusion tensor imaging (DTI) technique that was robust, reproducible, and feasible to perform in patients with cardiovascular disease. A stimulated-echo single-shot echo-planar imaging (EPI) sequence with zonal excitation and parallel imaging was implemented, together with a novel modification of the prospective navigator (NAV) technique combined with a biofeedback mechanism. Ten volunteers were scanned on two different days, each time with both multiple breath-hold (MBH) and NAV multislice protocols. Fractional anisotropy (FA), mean diffusivity (MD), and helix angle (HA) fiber maps were created. Comparison of initial and repeat scans showed good reproducibility for both MBH and NAV techniques for FA (P > 0.22), MD (P > 0.15), and HA (P > 0.28). Comparison of MBH and NAV FA (FAMBHday1 = 0.60 ± 0.04, FANAVday1 = 0.60 ± 0.03, P = 0.57) and MD (MDMBHday1 = 0.8 ± 0.2 × 10(-3) mm(2) /s, MDNAVday1 = 0.9 ± 0.2 × 10(-3) mm(2) /s, P = 0.07) values showed no significant differences, while HA values (HAMBHday1Endo = 22 ± 10°, HAMBHday1Mid-Endo = 20 ± 6°, HAMBHday1Mid-Epi = -1 ± 6°, HAMBHday1Epi = -17 ± 6°, HANAVday1Endo = 7 ± 7°, HANAVday1Mid-Endo = 13 ± 8°, HANAVday1Mid-Epi = -2 ± 7°, HANAVday1Epi = -14 ± 6°) were significantly different. The scan duration was 20% longer with the NAV approach. Currently, the MBH approach is the more robust in normal volunteers. While the NAV technique still requires resolution of some bulk motion sensitivity issues, these preliminary experiments show its potential for in vivo clinical cardiac diffusion tensor imaging and for delivering high-resolution in vivo 3D DTI tractography of the heart.

Background— Improvement in outcome of infants born with congenital heart defects has been accompanied by an increasing frequency of late arrhythmias. Ablation is difficult because of multiple tachycardias in the presence of complex anatomy with limited accessibility. We report on remote-controlled ablation using magnetic navigation in conjunction with 3D image integration in patients with previous intra-atrial baffle procedures. Methods and Results— Thirteen patients (8 male; age, 30.5±8 years) with supraventricular tachycardia (SVT) underwent catheter ablation. Group A had a medical history of a Mustard or Senning operation, whereas group B had undergone total cavopulmonary connection. A total of 26 tachycardias were treated in 17 procedures (median cycle length of 280 ms). Group A patients had more inducible SVTs than group B, and all index SVTs were located in the remainder of the morphological right atrium in all but 1 patient. Retrograde access through the aorta was performed and led to successful ablation, using magnetic navigation with a very low total radiation exposure (median of 3.8 minutes in group A versus 5.9 minutes in group B). Only 1 of 13 patients continued to have short-lasting SVTs despite 3 ablation procedures during a median follow-up time of >200 days. Conclusions— Remote-controlled catheter ablation by magnetic navigation in combination with accurate 3D image integration allowed safe and successful elimination of SVTs, using an exclusively retrograde approach, resulting in low radiation exposure for patients after intra-atrial baffle procedures (Mustard, Senning, or total cavopulmonary connection).

This study quantitatively assesses the effectiveness of retrospective beat-to-beat respiratory motion correction (B2B-RMC) at near 100% efficiency using high-resolution coronary artery imaging. Three-dimensional (3D) spiral images were obtained in a coronary respiratory motion phantom with B2B-RMC and navigator gating. In vivo, targeted 3D coronary imaging was performed in 10 healthy subjects using B2B-RMC spiral and navigator gated balanced steady-state free-precession (nav-bSSFP) techniques. Vessel diameter and sharpness in proximal and mid arteries were used as a measure of respiratory motion compensation effectiveness and compared between techniques. Phantom acquisitions with B2B-RMC were sharper than those acquired with navigator gating (B2B-RMC vs. navigator gating: 1.01±0.02 mm(-1) vs. 0.86±0.08 mm(-1), P.05). In vivo B2B-RMC respiratory efficiency was significantly and substantially higher (99.7%±0.5%) than nav-bSSFP (44.0%±8.9%, P.0001). Proximal and mid vessel sharpnesses were similar (B2B-RMC vs. nav-bSSFP, proximal: 1.00±0.14 mm(-1) vs. 1.08±0.11 mm(-1), mid: 1.01±0.11 mm(-1) vs. 1.05±0.12 mm(-1); both P=not significant [ns]). Mid vessel diameters were not significantly different (2.85±0.39 mm vs. 2.80±0.35 mm, P=ns), but proximal B2B-RMC diameters were slightly higher (2.85±0.38 mm vs. 2.70±0.34 mm, P.05), possibly due to contrast differences. The respiratory efficiency of B2B-RMC is less variable and significantly higher than navigator gating. Phantom and in vivo vessel sharpness and diameter values suggest that respiratory motion compensation is equally effective.

A novel technique to guide a subjects' breathing pattern using a respiratory biofeedback (rBF) "game" to improve respiratory efficiency is presented. The continuously adaptive windowing strategy, a fully automatic and highly efficient free-breathing navigator gated technique, is used to acquire the data as it ensures that all potential navigator acceptance windows are possible. This enables the rBF to be fully adaptable to a subject's respiratory pattern. Images of the thoracic aorta acquired using balanced steady-state free precession with continuously adaptive windowing strategy respiratory motion control, with and without rBF, were compared in 10 healthy subjects. Total scan time was reduced by using rBF. The mean scan time was reduced from 7 min 44 s (463 cardiac cycles, ± 127 cc) without rBF to 5 min 43 s (380 cardiac cycles, ± 118 cc) with the use of rBF (P < 0.05). Respiratory efficiency was increased from 45% without rBF to 56% with rBF (P < 0.01). Image quality was the same for both techniques (P = ns). In conclusion, rBF significantly improved respiratory efficiency and reduced acquisition duration without affecting image quality.

Purpose To quantitatively assess the performance and reproducibility of 3D spiral coronary artery wall imaging with beat-to-beat respiratory-motion-correction (B2B-RMC) compared to navigator gated 2D spiral and turbo-spin-echo (TSE) acquisitions. Materials and Methods High-resolution (0.7 × 0.7 mm) cross-sectional right coronary wall acquisitions were performed in 10 subjects using four techniques (B2B-RMC 3D spiral with alternate (2RR) and single (1RR) R-wave gating, navigator-gated 2D spiral (2RR) and navigator-gated 2D TSE (2RR)) on two occasions. Wall thickness measurements were compared with repeated measures analysis of variance (ANOVA). Reproducibility was assessed with the intraclass correlation coefficient (ICC). Results In all, 91% (73/80) of acquisitions were successful (failures: four TSE, two 3D spiral (1RR) and one 3D spiral (2RR)). Respiratory efficiency of the B2B-RMC was less variable and substantially higher than for navigator gating (99.6 ± 1.2% vs. 39.0 ± 7.5%, P < 0.0001). Coronary wall thicknesses (± standard deviation [SD]) were not significantly different: 1.10 ± 0.14 mm (3D spiral (2RR)), 1.20 ± 0.16 mm (3D spiral (1RR)), 1.14 ± 0.15 mm (2D spiral), and 1.21 ± 0.17 mm (TSE). Wall thickness reproducibility ranged from good (ICC = 0.65, 3D spiral (1RR)) to excellent (ICC = 0.87, 3D spiral (2RR)). Conclusion High-resolution 3D spiral imaging with B2B-RMC permits coronary vessel wall assessment over multiple thin contiguous slices in a clinically feasible duration. Excellent reproducibility of the technique potentially enables studies of disease progression/regression. J. Magn. Reson. Imaging 2011;33:77–86. © 2010 Wiley-Liss, Inc.

A fully automatic and highly efficient free-breathing navigator gated technique, continuously adaptive windowing strategy (CLAWS), is presented. Using a novel and dynamic acquisition strategy that ensures all potential navigator acceptance windows are possible, CLAWS acquires an image with the highest possible efficiency regardless of variations in the respiratory pattern. Unnecessary prolongation of scan durations due to respiratory drift or navigator acceptance window adjustments are avoided. As CLAWS requires no setting of the acceptance window, nor monitoring of the navigator traces during the scan, operator dependence is minimized and ease of use improved. CLAWS was compared against a standard accept/reject algorithm (ARA) and an end-expiratory following ARA (EE-ARA) in 20 healthy subjects and 10 patients (ARA only). The respiratory efficiency was compared against the retrospectively determined best possible respiratory efficiency for each acquisition. On average, the difference between CLAWS scan times and best possible scan times was 0.6% (± 1.3%). For the ARA and EE-ARA techniques, mean differences were 14.4% (± 20.9%) and 32.6 ± 10.9%, respectively. Had the CLAWS algorithm been used with the ARA and EE-ARA traces, mean differences would have been 0.2% (± 1.1%) and 0.5% (± 1.7%), respectively. Image quality was the same for all techniques: respiratory gating, motion artifacts, navigator, and coronary artery imaging.

Purpose To compare the effectiveness and reproducibility of a new black-blood sequence vs. a conventional bright-blood gradient-echo T2* sequence for myocardial iron overload measurement in thalassemia. Materials and Methods Twenty thalassemia patients were studied. Black-blood sequence images were acquired in diastole after a double inversion recovery (DIR) preparation pulse. Bright-blood sequence images were acquired in both early systole and late diastole. The data were randomized and the T2* analysis was performed blindly by two independent observers. Results The T2* values from the black-blood sequence were comparable to those of the conventional bright-blood sequence (25.7 ± 12.9 msec vs. 26.4 ± 14.2 msec in early systole, P = 0.44; and 25.2 ± 13.1 msec in late diastole, P = 0.41). The coefficient of variation (CV) for black-blood image T2* analysis was 4.1% compared with 8.9% (early systole P = 0.03) and 7.8% (late diastole P = 0.05) for bright-blood image analysis. Conclusion The black-blood T2* technique yields high-contrast myocardial images, provides clearly depicted myocardial borders, and avoids blood signal contamination of the myocardium while yielding improvements in interobserver variability. J. Magn. Reson. Imaging 2007;25:1205–1209. © 2007 Wiley-Liss, Inc.

Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Global normalized intensity threshold levels T PRE = 1 1/4 and T POST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies.

Background Ventricular fibrosis in adult congenital heart disease (ACHD) has been shown to relate to adverse clinical markers.[1,2] Novel free-breathing, motion-corrected late gadolinium enhancement cardiovascular magnetic resonance (LGE CMR) has been shown to be advantageous in quantifying LV LGE, but has not been evaluated for RV LGE. [3] It may therefore also be beneficial in ACHD patients, particularly with RV LGE patterns. We sought to investigate its clinical utility in ACHD patients undergoing LGE CMR.

Cardiac Atlases are promising tools for the interpretation of functional and anatomical structures of the heart. Myocardial viability is reflected by both global and regional contractile abnormalities. Atlases incorporating contractility information of a population can assist the diagnosis of myocardial disease and myocardial infarction. For the analysis of myocardial contractility phase-contrast MRI (PC-MRI) is emerging as a valuable clinical tool. The myocardial velocity distribution depicted by PC-MRI provides important insights into the intrinsic mechanics of the heart. As with many imaging techniques, there is an inherent trade-off between imaging resolution and noise. The main purpose of this study is to reduce the noise exhibited in phase-contrast MRI by applying a total variation restoration algorithm. The restoration algorithm has been evaluated on a spiral phase-contrast MRI sequence from a group of normal subjects. The results have shown that the proposed method is able to restore the myocardial velocity distribution whilst preserving the fidelity of the underlying contractile behavior.

Background Temporal patterns of coronary blood flow velocity can provide important information on disease state and are currently assessed invasively using a Doppler guidewire. A non-invasive alternative would be beneficial as it would allow study of a wider patient population and serial scanning. Methods A retrospectively-gated breath-hold spiral phase velocity mapping sequence (TR 19 ms) was developed at 3 Tesla. Velocity maps were acquired in 8 proximal right and 15 proximal left coronary arteries of 18 subjects who had previously had a Doppler guidewire study at the time of coronary angiography. Cardiovascular magnetic resonance (CMR) velocity-time curves were processed semi-automatically and compared with corresponding invasive Doppler data. Results When corrected for differences in heart rate between the two studies, CMR mean velocity through the cardiac cycle, peak systolic velocity (PSV) and peak diastolic velocity (PDV) were approximately 40 % of the peak Doppler values with a moderate - good linear relationship between the two techniques (R2: 0.57, 0.64 and 0.79 respectively). CMR values of PDV/PSV showed a strong linear relationship with Doppler values with a slope close to unity (0.89 and 0.90 for right and left arteries respectively). In individual vessels, plots of CMR velocities at all cardiac phases against corresponding Doppler velocities showed a consistent linear relationship between the two with high R2 values (mean +/−SD: 0.79 +/−.13). Conclusions High temporal resolution breath-hold spiral phase velocity mapping underestimates absolute values of coronary flow velocity but allows accurate assessment of the temporal patterns of blood flow.

Purpose To develop a reproducible fast spin-echo (FSE) technique for accurate myocardial T2 measurement with application to iron overload assessment in thalassemia. Materials and Methods An FSE sequence was developed to permit acquisition of multiple TE images in one breathhold (BH-FSE). A dynamic black-blood scheme was introduced to better cancel blood signal. A nonselective refocusing train was also adopted to suppress stimulated echoes. The optimized technique was tested on phantoms and then applied to 10 normal volunteers and 10 thalassemia patients. Interstudy reproducibility was measured on all the 20 subjects. Results The mean difference in T2 values was 1.7% from phantom experiments between BH-FSE and the conventional spin-echo (SE) technique. High contrast BH-FSE images were acquired from human subjects, with minimal stimulated echoes and effective blood suppression (P = 0.0005). The coefficient of variation for interstudy reproducibility was 4.3%. T2 values from thalassemia patients were substantially lower than those from the normal subjects (45.2 ± 26.1 msec vs. 56.9 ± 8.4ms, P = 0.02). Conclusion The dynamic black-blood T2 sequence is a fast reproducible acquisition that compares favorably with conventional techniques, is robust to motion artifacts, and yields high blood-myocardium contrast. This technique may provide a useful tool in thalassemia and other scenarios requiring myocardial T2 quantification. J. Magn. Reson. Imaging 2006. © 2006 Wiley-Liss, Inc.

Background Qualitative and quantitative assessment of renal blood flow is valuable in the evaluation of patients with renal and renovascular diseases as well as in patients with heart failure. The temporal pattern of renal flow velocity through the cardiac cycle provides important information about renal haemodynamics. High temporal resolution interleaved spiral phase velocity mapping could potentially be used to study temporal patterns of flow and measure resistive and pulsatility indices which are measures of downstream resistance. Methods A retrospectively gated breath-hold spiral phase velocity mapping sequence (TR 19 ms) was developed at 3 Tesla. Phase velocity maps were acquired in the proximal right and left arteries of 10 healthy subjects in each of two separate scanning sessions. Each acquisition was analysed by two independent observers who calculated the resistive index (RI), the pulsatility index (PI), the mean flow velocity and the renal artery blood flow (RABF). Inter-study and inter-observer reproducibility of each variable was determined as the mean +/− standard deviation of the differences between paired values. The effect of background phase errors on each parameter was investigated. Results RI, PI, mean velocity and RABF per kidney were 0.71+/− 0.06, 1.47 +/− 0.29, 253.5 +/− 65.2 mm/s and 413 +/− 122 ml/min respectively. The inter-study reproducibilities were: RI −0.00 +/− 0.04 , PI −0.03 +/− 0.17, mean velocity −6.7 +/− 31.1 mm/s and RABF per kidney 17.9 +/− 44.8 ml/min. The effect of background phase errors was negligible (

Purpose To improve 3D volume-selective turbo spin echo (TSE) carotid artery wall imaging by incorporating navigators to reduce artifacts caused by swallowing. Materials and Methods Images were acquired on a Siemens Magnetom Sonata 1.5T scanner. 3D volume-selective TSE scans of the carotid arteries were acquired in six healthy volunteers. A cross-pair navigator placed on the back of the tongue was used to detect swallowing and movement. Two swallowing patterns were tested: 1) a single swallow approximately halfway through the scan time, at the center of kz, and 2) repeated swallowing as often as possible throughout the scan period. Images were acquired with and without navigators for comparison. Signal intensity in the lumen was quantified for the quality of blood suppression, and the clarity of the vessel wall in the common carotid was ranked by four independent blinded observers. Results In general, lower signal intensity was recorded in the lumen, and decreased blurring and ghosting were observed on scans with navigator control. This reduction in lumen signal intensity signifies an improvement in the black-blood imaging technique. The differences likely reflect the improved double inversion/blood suppression efficiency due to cycles being rejected when the heart rate changed at the point of swallowing, or decreased motional blurring/ghosting of tissue when the navigator is used, or a combination of these two effects. A statistical analysis of image quality showed a significant difference between navigated and non-navigated scans as scored by four independent, blinded observers. For both swallowing patterns, the mean score for the navigator images was on average 0.6 greater than that of non-navigator images (on a scoring scale of 0–5, where 0 = no vessel visible, and 5 = good delineation and blood suppression) and P-values for all observers were less than 0.01. Overall, the central swallow scans were scored higher than the repeated swallow scans. One reason for this may be the fact that the heart rate increased on swallowing, and this often lasted for one or two cardiac cycles after the navigator returned to the normal acceptance position. The effect of the increased heart rate after swallowing is likely to have an effect on double inversion blood suppression efficiency. Therefore, the increased amount of heart rate changes with repeated swallowing may have a greater adverse effect, even if the navigator rejects data views during the swallowing motion. Conclusion The clarity of vessel wall delineation and the apparent efficiency of blood suppression are reduced by swallowing during acquisition. Both motion blurring and quality of blood suppression are factors that can be improved with the use of a navigator accept/reject method. J. Magn. Reson. Imaging 2005. © 2005 Wiley-Liss, Inc.

A review of cardiovascular clinical and research applications of MRI phase-contrast velocity imaging, also known as velocity mapping or flow imaging. Phase-contrast basic principles, advantages, limitations, common pitfalls and artefacts are described. It can measure many different aspects of the complicated blood flow in the heart and vessels: volume flow (cardiac output, shunt, valve regurgitation), peak blood velocity (for stenosis), patterns and timings of velocity waveforms and flow distributions within heart chambers (abnormal ventricular function) and vessels (pulse-wave velocity, vessel wall disease). The review includes phase-contrast applications in cardiac function, heart valves, congenital heart diseases, major blood vessels, coronary arteries and myocardial wall velocity.

The purpose of this study was to evaluate the feasibility and accuracy of combined coronary and perfusion cardiovascular magnetic resonance (CMR) in the assessment of coronary artery stenosis. Thirty-five consecutive patients (27 men, eight women, age range 34-81 years), undergoing cardiac catheterization, were assessed with 3D coronary CMR and rest-stress perfusion CMR. Significant coronary stenosis was determined by vessel narrowing or signal loss with coronary CMR, and by abnormal contrast enhancement with perfusion CMR. Coronary artery diameter stenosis greater than 50% was considered significant with conventional cardiac catheterization. Seventeen patients had significant coronary artery disease, and in these there were 35 significant stenoses on cardiac catheterization. All left main stem arteries were normal on both cardiac catheterization and coronary CMR. For the diagnosis of coronary artery stenosis, coronary CMR had a sensitivity of 92% for the left anterior descending artery (LAD), 79% for the right coronary artery (RCA), but only 13% for the circumflex coronary artery (LCX). Perfusion CMR had corresponding sensitivities of 69%, 86%, and 63%, respectively. For all arteries the accuracies for coronary and perfusion CMR were 67% and 72%, respectively. Combining coronary and perfusion CMR improved the accuracy to 77%. These data demonstrate that in patients with suspected coronary artery disease, combined coronary and perfusion CMR is feasible, increases the accuracy of detection of significant coronary stenosis, and offers the possibility of combined anatomical and hemodynamic assessment of coronary artery stenosis.