373 results found
Wustmann K, Constantine A, Davies JE, et al., 2021, Prognostic implications of pulmonary wave reflection and reservoir pressure in patients with pulmonary hypertension, International Journal of Cardiology: Congenital Heart Disease, Vol: 5, Pages: 1-8, ISSN: 2666-6685
BackgroundRight ventricular (RV) coupling to the pulmonary circulation influences the response of the RV to the increased afterload caused by pulmonary hypertension (PH), which ultimately determines prognosis. A methodology that accounts for pulsatile flow is required when assessing ventriculo-arterial coupling. We applied wave intensity analysis (WIA) methods to assess the compliance of the main pulmonary artery (PA) in patients with or without PH and compared this to PA distensibility, RV function and clinical outcomes.MethodsHigh-fidelity blood pressure and Doppler flow velocity tracings were obtained simultaneously during cardiac catheterisation for suspected PH. RV volumes, main PA distensibility and ventriculo-arterial coupling (Emax/Ea) were analysed using cardiovascular magnetic resonance.ResultsThe study included 17 PH patients and 6 controls. Wave speed, reservoir and excess pressure were higher in PH patients compared to controls (p < 0.01 for all). Waveforms relating to RV ejection, microvascular wave reflection and late systolic proximal deceleration were higher in PH patients compared to controls (p < 0.01 for all) and related to echocardiographic findings, including PA Doppler notching and shortened acceleration time. Wave speed, reservoir pressure and excess pressure were strongly correlated to main PA distensibility, RV function and Emax/Ea. A higher total pressure integral was associated with an increased risk of death (all-cause mortality).ConclusionThe reservoir-excess pressure model, in combination with conventional clinical imaging, provides valuable information on the pathophysiology of PH that standard haemodynamic parameters do not. Future studies should further investigate the prognostic implications of WIA in PH, and its potential role in clinical practice.
Jun C, Zhang H, Mohiaddin R, et al., 2021, Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
Almogheer B, Antonopoulos AS, Azzu A, et al., 2021, Diagnostic and Prognostic Value of Cardiovascular Magnetic Resonance in Neuromuscular Cardiomyopathies, PEDIATRIC CARDIOLOGY, ISSN: 0172-0643
Hatipoglu S, Almogheer B, Mahon C, et al., 2021, Clinical Significance of Partial Anomalous Pulmonary Venous Connections (Isolated and Atrial Septal Defect Associated) Determined by Cardiovascular Magnetic Resonance, CIRCULATION-CARDIOVASCULAR IMAGING, Vol: 14, ISSN: 1941-9651
Bermejo IA, Bautista-Rodriguez C, Fraisse A, et al., 2021, Short-Term sequelae of Multisystem Inflammatory Syndrome in Children Assessed by CMR, JACC-CARDIOVASCULAR IMAGING, Vol: 14, Pages: 1666-1667, ISSN: 1936-878X
Azzu A, Morosin M, Antonopoulos AS, et al., 2021, Cardiac Decompression by Pericardiectomy for Constrictive Pericarditis: Multimodality Imaging to Identify Patients at Risk for Prolonged Inotropic Support., J Cardiovasc Imaging
BACKGROUND: Right ventricular (RV) failure post-pericardiectomy for constrictive pericarditis (CP) has been reported but it remains not well-studied. To investigate imaging parameters that could predict RV function and the outcome of patients post-pericardiectomy. METHODS: We analysed data from a total of 53 CP patients undergoing pericardiectomy. Preoperative, early and at 6 months postoperative echocardiographic (echo) imaging datasets were analysed and correlated with preoperative cardiac magnetic resonance (CMR), cardiac computed tomography scans and histology. The primary endpoint of the study was RV functional status early postoperatively and at 6 months. Secondary endpoint was the need for prolonged inotropic support. RESULTS: A cause of CP was identified in 26 patients (49%). Inotropic support ≥ 48 hours was required in n = 28 (53%) of patients and was correlated with lower preoperative RV areas by echo or RV volumes by CMR (p < 0.05 for all). A pericardial score based on pericardial thickness/calcification and epicardial fat thickness had good diagnostic accuracy to identify patients requiring prolonged use of inotropes (area under the curve, 0.825; 95% confidence interval, 0.674-0.976). Pericardiectomy resulted in RV decompression and impaired RV function early postoperatively (fractional area change: 40.5% ± 8.8% preoperatively vs. 31.4% ± 10.4% early postoperatively vs. 42.5% ± 10.2% at 6 months, p < 0.001). CONCLUSIONS: We show that a smaller RV cavity size and a pericardial scoring system are associated with prolonged inotropic support in CP patients undergoing pericardiectomy. RV systolic impairment post decompression is present in most patients, but it is only transient.
Chen J, Yang G, Khan H, et al., 2021, JAS-GAN: generative adversarial network based joint atrium and scar segmentations on unbalanced atrial targets, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2194
Automated and accurate segmentation of the 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 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.
Arzanauskaite M, Cookson S, Mohiaddin RH, 2021, Long-standing Cannonball Metastases in Myxoid Chondrosarcoma: Multimodality Appearances of the Radiological Sign., Arch Bronconeumol (Engl Ed)
Almogheer B, Antonopoulos A, Papagkikas P, et al., 2021, The Big Mitral Annulus Calcification (MAC) - Tissue Characterization and Assessment of Haemodynamic Impact Using Cardiac Magnetic Resonance -, CIRCULATION JOURNAL, Vol: 85, Pages: 315-315, ISSN: 1346-9843
Antonopoulos AS, Azzu A, Androulakis E, et al., 2021, Eosinophilic heart disease: diagnostic and prognostic assessment by cardiac magnetic resonance., Eur Heart J Cardiovasc Imaging
AIMS : Eosinophilic heart disease (EHD) is a rare cardiac condition with a wide spectrum of phenotypes. The diagnostic and prognostic value of cardiac magnetic resonance (CMR) in EHD remains unknown. METHODS AND RESULTS : This was a retrospective analysis of 250 patients with eosinophilia referred for a CMR scan (period 2000-2020). CMR data sets and clinical/laboratory data were collected. Patients were followed up for a mean of 24 months (range 1-224) for the composite endpoint of death, acute coronary syndrome, hospitalization for acute heart failure, malignant ventricular arrhythmias, or the need for implantable cardiac defibrillator/pacemaker. The main objectives were to explore the diagnostic value of CMR in EHD; relationships between cardiac function, late gadolinium enhancement (LGE), and EHD phenotypes; and the prognostic value of fibrosis and oedema by CMR. The prevalence of findings compatible with EHD was 39% (patients with cardiac symptoms: 57% vs. screening: 20%, P < 0.001). EHD phenotypes included subendocardial LGE (n = 58), mid-wall/subepicardial LGE (n = 26), pericarditis (n = 5) or dilated cardiomyopathy (n = 8). Myocardial oedema was present in 10% of patients. Intracardiac thrombi (7%) were associated with EHD phenotype (χ2=47.3, P = 1.3×10-8). LGE extent correlated with LVEDVi (rho = 0.268, P = 5.3×10-5) and LVEF (rho=-0.415, P = 8.6×10-11). A CMR scan positive for EHD [hazard ratio (HR) = 5.61, 95% confidence interval (CI): 1.82-17.89, P = 0.0026] or a subendocardial LGE pattern (HR = 5.13, 95% CI: 1.29-20.38, P = 0.020) were independently associated with the composite clinical endpoint. CONCLUSION : The diagnostic yield of CMR screening in patients with persistent eosinophilia, even if asymptomatic, is high. The extent
Mahon C, Mohiaddin RH, 2021, The emerging applications of cardiovascular magnetic resonance imaging in transcatheter aortic valve implantation, CLINICAL RADIOLOGY, Vol: 76, ISSN: 0009-9260
Haldar S, Khan HR, Boyalla V, et al., 2020, Catheter ablation vs. thoracoscopic surgical ablation in long-standing persistent atrial fibrillation: CASA-AF randomized controlled trial., European Heart Journal, Vol: 41, Pages: 4471-4480, ISSN: 0195-668X
AIMS: Long-standing persistent atrial fibrillation (LSPAF) is challenging to treat with suboptimal catheter ablation (CA) outcomes. Thoracoscopic surgical ablation (SA) has shown promising efficacy in atrial fibrillation (AF). This multicentre randomized controlled trial tested whether SA was superior to CA as the first interventional strategy in de novo LSPAF. METHODS AND RESULTS: We randomized 120 LSPAF patients to SA or CA. All patients underwent predetermined lesion sets and implantable loop recorder insertion. Primary outcome was single procedure freedom from AF/atrial tachycardia (AT) ≥30 s without anti-arrhythmic drugs at 12 months. Secondary outcomes included clinical success (≥75% reduction in AF/AT burden); procedure-related serious adverse events; changes in patients' symptoms and quality-of-life scores; and cost-effectiveness. At 12 months, freedom from AF/AT was recorded in 26% (14/54) of patients in SA vs. 28% (17/60) in the CA group [OR 1.128, 95% CI (0.46-2.83), P = 0.83]. Reduction in AF/AT burden ≥75% was recorded in 67% (36/54) vs. 77% (46/60) [OR 1.13, 95% CI (0.67-4.08), P = 0.3] in SA and CA groups, respectively. Procedure-related serious adverse events within 30 days of intervention were reported in 15% (8/55) of patients in SA vs. 10% (6/60) in CA, P = 0.46. One death was reported after SA. Improvements in AF symptoms were greater following CA. Over 12 months, SA was more expensive and provided fewer quality-adjusted life-years (QALYs) compared with CA (0.78 vs. 0.85, P = 0.02). CONCLUSION: Single procedure thoracoscopic SA is not superior to CA in treating LSPAF. Catheter ablation provided greater improvements in symptoms and accrued significantly more QALYs during follow-up than SA. CLINICAL TRIAL REGISTRATION: ISRCTN18250790 and ClinicalTrials.gov: NCT02755688.
Azzu A, Antonopoulos AS, Almogheer B, et al., 2020, A case report of a primary cardiac lymphoma causing superior vena cava obstruction: the value of multimodality imaging in the clinical workup, EUROPEAN HEART JOURNAL-CASE REPORTS, Vol: 4
Altamar IB, Whittaker E, Herberg J, et al., 2020, Short-term Sequalae of Children With Paediatric Inflammatory Multisystem Syndrome Temporarily Associated With Sars-cov-2 Infection (pims-ts) Assessed by Cardiovascular Magnetic Resonance, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
Leiner T, Bogaert J, Friedrich MG, et al., 2020, SCMR Position Paper (2020) on clinical indications for cardiovascular magnetic resonance, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 22, ISSN: 1097-6647
Krupickova S, Voges I, Mohiaddin R, 2020, Role of cardiovascular magnetic resonance in an adolescent with a giant intrapericardial mass, CARDIOLOGY IN THE YOUNG, Vol: 30, Pages: 1524-1526, ISSN: 1047-9511
Yang G, Chen J, Gao Z, et al., 2020, Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention, Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, Vol: 107, Pages: 215-228, ISSN: 0167-739X
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.
Li L, Wu F, Yang G, et al., 2020, Atrial scar quantification via multi-scale CNN in the graph-cuts framework, Medical Image Analysis, Vol: 60, ISSN: 1361-8415
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scarassessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can bechallenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cutsframework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scaleconvolutional 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 shownto evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could befurther improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposedmethod 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 methodis fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promisingand can be potentially useful in diagnosis and prognosis of AF.
Zhuang X, Li L, Payer C, et al., 2019, Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge, Medical Image Analysis, Vol: 58, ISSN: 1361-8415
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 functionsof 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 neededfor 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 methodologiesand 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-dimensionalcardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environmentswith manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelvegroups, have been evaluated. The results showed that the performance of CT WHS was generally better than thatof MRI WHS. The segmentation of the substructures for different categories of patients could present different levelsof challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methodsdemonstrated 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 computationalefficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, conti
Chen J, Zhang H, Zhang Y, et al., 2019, Discriminative consistent domain generation for semi-supervised learning, International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 595-604, ISSN: 0302-9743
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.
Arzanauskaite M, Vassiliou VS, Robertus JL, et al., 2019, Primary tumors of the aorta and pulmonary arteries: insights from cardiovascular magnetic resonance, JACC: Cardiovascular Imaging, Vol: 12, Pages: 2065-2070, ISSN: 1936-878X
Khan H, Haldar S, Boyalla V, et al., 2019, Left atrial reverse remodelling is not associated with improved success in treatment of long standing persistent atrial fibrillation, Publisher: OXFORD UNIV PRESS, Pages: 250-250, ISSN: 2047-2404
Khalique Z, Hatipoglu S, Rosendahl U, et al., 2019, Unusual Complicated Fungal Endocarditis in a Patient With Vascular Ehlers-Danlos Syndrome, ANNALS OF THORACIC SURGERY, Vol: 107, Pages: E269-E271, ISSN: 0003-4975
Li L, Yang G, Wu F, et al., 2019, Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework, Pages: 152-160, ISSN: 0302-9743
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerges as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging. In this study, we proposed a fully automated method based on the graph-cut framework, where the potential of the graph is learned on a surface mesh of the left atrium (LA), using an equidistant projection and a deep neural network (DNN). For validation, we employed 100 datasets with manual delineation. The results showed that the performance of the proposed method was improved and converged with respect to the increased size of training patches, which provide important features of the structural and texture information learned by the DNN. The segmentation could be further improved when the contribution from the t-link and n-link is balanced, thanks to the inter-relationship learned by the DNN for the graph-cut algorithm. Compared with the existing methods which mostly acquired an initialization from manual delineation of the LA or LA wall, our method is fully automated and has demonstrated great potentials in tackling this task. The accuracy of quantifying the LA scars using the proposed method was 0.822, and the Dice score was 0.566. The results are promising and the method can be useful in diagnosis and prognosis of AF.
Yang G, Chen J, Gao Z, et al., 2018, Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 1123-1127, ISSN: 1557-170X
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.
Chen J, Yang G, Gao Z, et al., 2018, Multiview two-task recursive attention model for left atrium and atrial scars segmentation, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Publisher: Springer, Pages: 455-463, ISSN: 0302-9743
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.
Seitzer M, Yang G, Schlemper J, et al., 2018, Adversarial and perceptual refinement for compressed sensing MRI reconstruction, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages: 232-240, ISSN: 0302-9743
© Springer Nature Switzerland AG 2018. 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<0.01) over the state-of-the-art in both a human observer study and the semantic interpretability score.
Wu F, Li L, Yang G, et al., 2018, Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages: 604-612, ISSN: 0302-9743
© 2018, Springer Nature Switzerland AG. 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±150, Dice: 0.556±187). We compared the method with the conventional algorithms and showed an evidently and statistically better performance (p < 0.03).
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.