Publications
431 results found
Xing X, Del Ser J, Wu Y, et al., 2023, HDL: hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis, IEEE Journal of Biomedical and Health Informatics, Vol: 27, Pages: 5134-5142, ISSN: 2168-2194
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure indigital healthcare. A core part of digital healthcare twins is model based data synthesis, which permits the generation of realisticmedical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing themin reality. Unfortunately, algorithms for cardiac data synthesis havebeen so far scarcely studied in the literature. An important imagingmodality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides aquantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complexacquisition produce make it more urgent to produce syntheticdigital twins of this imaging modality. In this study, we proposea hybrid deep learning (HDL) network, especially for synthetic 3DirMVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporallydown-sampled magnitude CINE images (six times), our proposedalgorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventriclesegmentation (DICE=0.92). These performance scores indicate thatour proposed HDL algorithm can be implemented in real-worlddigital twins for myocardial velocity mapping data simulation. Tothe best of our knowledge, this work is the first one in the literatureinvestigating digital twins of the 3Dir MVM CMR, which has showngreat potential for improving the efficiency of clinical studies viasynthesised cardiac data.
Zhou Z, Gao Y, Zhang W, et al., 2023, Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study, EUROPEAN RADIOLOGY, Vol: 33, Pages: 678-689, ISSN: 0938-7994
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- Citations: 2
Ferreira PF, Banerjee A, Scott AD, et al., 2022, Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold, JOURNAL OF MAGNETIC RESONANCE IMAGING, Vol: 56, Pages: 1691-1704, ISSN: 1053-1807
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- Citations: 4
Hatipoglu S, Gatehouse P, Krupickova S, et al., 2022, Reliability of pediatric ventricular function analysis by short-axis "single-cycle-stack-advance" single-shot compressed-sensing cines in minimal breath-hold time, EUROPEAN RADIOLOGY, Vol: 32, Pages: 2581-2593, ISSN: 0938-7994
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- Citations: 3
Xing X, Wu Y, Firmin D, et al., 2022, Synthetic velocity mapping cardiac MRI coupled with automated left ventricle segmentation, Publisher: ArXiv
Temporal patterns of cardiac motion provide important information for cardiacdisease diagnosis. This pattern could be obtained by three-directional CINEmulti-slice left ventricular myocardial velocity mapping (3Dir MVM), which is acardiac MR technique providing magnitude and phase information of themyocardial motion simultaneously. However, long acquisition time limits theusage of this technique by causing breathing artifacts, while shortening thetime causes low temporal resolution and may provide an inaccurate assessment ofcardiac motion. In this study, we proposed a frame synthesis algorithm toincrease the temporal resolution of 3Dir MVM data. Our algorithm is featured by1) three attention-based encoders which accept magnitude images, phase images,and myocardium segmentation masks respectively as inputs; 2) three decodersthat output the interpolated frames and corresponding myocardium segmentationresults; and 3) loss functions highlighting myocardium pixels. Our algorithmcan not only increase the temporal resolution 3Dir MVMs, but can also generatesthe myocardium segmentation results at the same time.
Chen Y, Schönlieb C-B, Liò P, et al., 2022, AI-based reconstruction for fast MRI – a systematic review and meta-analysis, Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), Vol: 110, Pages: 224-245, ISSN: 0018-9219
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep neural networks and CS algorithms are being integrated to redefine the state of the art of fast MRI. The past several years have witnessed substantial growth in the complexity, diversity, and performance of deep learning-based CS techniques that are dedicated to fastMRI. In this meta-analysis, we systematically review the deep learning-based CS techniques for fast MRI, describe key model designs, highlight breakthroughs, and discuss promising directions. We have also introduced a comprehensive analysis framework and a classification system to assess the pivotal role of deep learning in CS-based accelerationfor MRI.
Jun C, Zhang H, Mohiaddin R, et al., 2022, Adaptive hierarchical dual consistency for semi-supervised left atrium segmentation on cross-domain data, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 420-433, ISSN: 0278-0062
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 mainlyconsists 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 fromdifferent centres and a 3D CT dataset. Compared to otherstate-of-the-art methods, our proposed AHDC achievedhigher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.
Scott A, Jackson T, Khalique Z, et al., 2022, Development of a CMR compatible large animal isolated heart model for direct comparison of beating and arrested hearts, NMR in Biomedicine, Vol: 35, ISSN: 0952-3480
BackgroundCardiac motion results in image artefacts and quantification errors in many cardiovascular magnetic resonance (CMR) techniques, including microstructural assessment using diffusion tensor cardiovascular magnetic resonance (DT-CMR). Here we develop a CMR compatible isolated perfused porcine heart model that allows comparison of data obtained in beating and arrested states.Methods10 porcine hearts (8/10 for protocol optimisation) were harvested using a donor heart retrieval protocol and transported to the remote CMR facility. Langendorff perfusion in a 3D printed chamber and perfusion circuit re-established contraction. Hearts were imaged using cine, parametric mapping and STEAM DT-CMR at cardiac phases with the minimum and maximum wall thickness. High potassium and lithium perfusates were then used to arrest the heart in a slack and contracted state respectively. Imaging was repeated in both arrested states. After imaging, tissue was removed for subsequent histology in a location matched to the DT-CMR data using fiducial markers.ResultsRegular sustained contraction was successfully established in 6/10 hearts, including the final 5 hearts. Imaging was performed in 4 hearts and one underwent the full protocol including co-localised histology. Image quality was good and there was good agreement between DT-CMR data in equivalent beating and arrested states. Despite the use of autologous blood and dextran within the perfusate, T2, DT-CMR measures and an increase in mass was consistent with development of myocardial edema resulting in failure to achieve a true diastolic-like state. A contiguous stack of 313 5μm histological sections at and a 100μm thick section showing cell morphology on 3D fluorescent confocal microscopy co-localised to DT-CMR data were obtained.ConclusionsA CMR compatible isolated perfused beating heart setup for large animal hearts allows direct comparisons of beating and arrested heart data with subsequent co-localised histology without
Chen J, Yang G, Khan H, et al., 2022, JAS-GAN: generative adversarial network based joint atrium and scar segmentations on unbalanced atrial targets, IEEE Journal of Biomedical and Health Informatics, Vol: 26, Pages: 103-114, 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.
Yang G, Liu T, Zhou Z, et al., 2021, Association between left ventricular global function index and outcomes in patients with dilated cardiomyopathy, Frontiers in Cardiovascular Medicine, Vol: -, ISSN: 2297-055X
Purpose: Left ventricular global function index (LVGFI) assessed using cardiac magnetic resonance (CMR) seems promising in the prediction of clinical outcomes. However, the role of the LVGFI is uncertain in patients with heart failure (HF) with dilated cardiomyopathy (DCM). To describe the association of LVGFI and outcomes in patients with DCM, it was hypothesized that LVGFI is associated with decreased major adverse cardiac events (MACEs) in patients with DCM. Materials and Methods: This prospective cohort study was conducted from January 2015 to April 2020 in consecutive patients with DCM who underwent CMR. The association between outcomes and LVGFI was assessed using a multivariable model adjusted with confounders. LVGFI was the primary exposure variable. The long-term outcome was a composite endpoint, including death or heart transplantation. Results: A total of 334 patients (mean age: 55 years) were included in this study. The average of CMR-LVGFI was 16.53%. Over a median follow-up of 565 days, 43 patients reached the composite endpoint. Kaplan–Meier analysis revealed that patients with LVGFI lower than the cutoff values (15.73%) had a higher estimated cumulative incidence of the endpoint compared to those with LVGFI higher than the cutoff values (P=0.0021). The hazard of MACEs decreased by 38% for each 1 SD increase in LVGFI (hazard ratio 0.62[95%CI 0.43-0.91]) and after adjustment by 46% (HR 0.54 [95%CI 0.32-0.89]). The association was consistent across subgroup analyses.Conclusion: In this study, an increase in CMR-LVGFI was associated with decreasing the long-term risk of MACEs with DCM after adjustment for traditional confounders.
Fair MJ, Gatehouse PD, Firmin DN, 2021, Minimisation of slab-selective radiofrequency excitation pulse durations constrained by an acceptable aliasing coefficient, MAGNETIC RESONANCE IMAGING, Vol: 81, Pages: 94-100, ISSN: 0730-725X
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Wu Y, Tang Z, Li B, et al., 2021, Recent advances in fibrosis and scar segmentation from cardiac MRI: A state-of-the-art review and future perspectives, Frontiers in Physiology, Vol: 12, Pages: 1-23, ISSN: 1664-042X
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
Liu T, Gao Y, Wang H, et al., 2021, Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy, Heart, Vol: 107, Pages: 1233-1239, ISSN: 1355-6037
Objective To explore the association between three-dimensional (3D) cardiac magnetic resonance (CMR) feature tracking (FT) right ventricular peak global longitudinal strain (RVpGLS) and major adverse cardiovascular events (MACEs) in patients with stage C or D heart failure (HF) with non-ischaemic dilated cardiomyopathy (NIDCM) but without atrial fibrillation (AF).Methods Patients with dilated cardiomyopathy were enrolled in this prospective cohort study. Comprehensive clinical and biochemical analysis and CMR imaging were performed. All patients were followed up for MACEs.Results A total of 192 patients (age 53±14 years) were eligible for this study. A combination of cardiovascular death and cardiac transplantation occurred in 18 subjects during the median follow-up of 567 (311, 920) days. Brain natriuretic peptide, creatinine, left ventricular (LV) end-diastolic volume, LV end-systolic volume, right ventricular (RV) end-diastolic volume and RVpGLS from CMR were associated with the outcomes. The multivariate Cox regression model adjusting for traditional risk factors and CMR variables detected a significant association between RVpGLS and MACEs in patients with stage C or D HF with NIDCM without AF. Kaplan-Meier analysis based on RVpGLS cut-off value revealed that patients with RVpGLS <−8.5% showed more favourable clinical outcomes than those with RVpGLS ≥−8.5% (p=0.0037). Subanalysis found that this association remained unchanged.Conclusions RVpGLS-derived from 3D CMR FT is associated with a significant prognostic impact in patients with NIDCM with stage C or D HF and without AF.
Yang G, Zhang H, Firmin D, et al., 2021, Recent advances in artificial intelligence for cardiac imaging, COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, Vol: 90, ISSN: 0895-6111
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- Citations: 4
Kuang M, Wu Y, Alonso-Álvarez D, et al., 2021, Three-dimensional embedded attentive RNN (3D-EAR) segmentor for leftventricle delineation from myocardial velocity mapping, Publisher: arXiv
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measureglobal and regional myocardial velocities with proved reproducibility. Accurateleft ventricle delineation is a prerequisite for robust and reproduciblemyocardial velocity estimation. Conventional manual segmentation on thisdataset can be time-consuming and subjective, and an effective fully automateddelineation method is highly in demand. By leveraging recently proposed deeplearning-based semantic segmentation approaches, in this study, we propose anovel fully automated framework incorporating a 3D-UNet backbone architecturewith Embedded multichannel Attention mechanism and LSTM based Recurrent neuralnetworks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposedmethod also utilises the amalgamation of magnitude and phase images as input torealise an information fusion of this multichannel dataset and exploring thecorrelations of temporal frames via the embedded RNN. By comparing the baselinemodel of 3D-UNet and ablation studies with and without embedded attentive LSTMmodules and various loss functions, we can demonstrate that the proposed modelhas outperformed the state-of-the-art baseline models with significantimprovement.
Raphael C, Mitchell F, Kanaganayagam G, et al., 2021, Cardiovascular magnetic resonance predictors of heart failure in hypertrophic cardiomyopathy: the role of myocardial replacement fibrosis and the microcirculation, Journal of Cardiovascular Magnetic Resonance, Vol: 26, ISSN: 1097-6647
IntroductionHeart failure (HF) in hypertrophic cardiomyopathy (HCM) is associated with high morbidity and mortality. Predictors of HF, in particular the role of myocardial fibrosis and microvascular ischemia remain unclear. We assessed the predictive value of cardiovascular magnetic resonance (CMR) for development of HF in HCM in an observational cohort study.MethodsSerial patients with HCM underwent CMR, including adenosine first-pass perfusion, left atrial (LA) and left ventricular (LV) volumes indexed to body surface area (i) and late gadolinium enhancement (%LGE- as a % of total myocardial mass). We used a composite endpoint of HF death, cardiac transplantation, and progression to NYHA class III/IV.ResultsA total of 543 patients with HCM underwent CMR, of whom 94 met the composite endpoint at baseline. The remaining 449 patients were followed for a median of 5.6 years. Thirty nine patients (8.7%) reached the composite endpoint of HF death (n = 7), cardiac transplantation (n = 2) and progression to NYHA class III/IV (n = 20). The annual incidence of HF was 2.0 per 100 person-years, 95% CI (1.6–2.6). Age, previous non-sustained ventricular tachycardia, LV end-systolic volume indexed to body surface area (LVESVI), LA volume index ; LV ejection fraction, %LGE and presence of mitral regurgitation were significant univariable predictors of HF, with LVESVI (Hazard ratio (HR) 1.44, 95% confidence interval (95% CI) 1.16–1.78, p = 0.001), %LGE per 10% (HR 1.44, 95%CI 1.14–1.82, p = 0.002) age (HR 1.37, 95% CI 1.06–1.77, p = 0.02) and mitral regurgitation (HR 2.6, p = 0.02) remaining independently predictive on multivariable analysis. The presence or extent of inducible perfusion defect assessed using a visual score did not predict outcome (p = 0.16, p = 0.27 respectively).DiscussionThe annual incidence of HF in a contemporary ambulatory HCM population undergoing CMR
Wu Y, Hatipoglu S, Alonso-Álvarez D, et al., 2021, Fast and automated segmentation for the three-directional multi-slice cine myocardial velocity mapping, Diagnostics, Vol: 11, ISSN: 2075-4418
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.
Wu Y, Hatipoglu S, Alonso-Álvarez D, et al., 2021, Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR, Medical Imaging 2021: Computer-Aided Diagnosis, Publisher: SPIE, Pages: 1-7
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 UNet 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.
Chen Y, Firmin D, Yang G, 2021, Wavelet Improved GAN for MRI reconstruction, Medical Imaging Conference - Physics of Medical Imaging, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
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Fair MJ, Gatehouse PD, Reyes E, et al., 2020, Initial investigation of free-breathing 3D whole-heart stress myocardial perfusion MRI., Glob Cardiol Sci Pract, Vol: 2020, ISSN: 2305-7823
Objective: Myocardial first-pass perfusion imaging with MRI is well-established clinically. However, it is potentially weakened by limited myocardial coverage compared to nuclear medicine. Clinical evaluations of whole-heart MRI perfusion by 3D methods, while promising, have to date had the limit of breathhold requirements at stress. This work aims to develop a new free-breathing 3D myocardial perfusion method, and to test its performance in a small patient population. Methods: This work required tolerance to respiratory motion for stress investigations, and therefore employed a "stack-of-stars" hybrid Cartesian-radial MRI acquisition method. The MRI sequence was highly optimised for rapid acquisition and combined with a compressed sensing reconstruction. Stress and rest datasets were acquired in four healthy volunteers, and in six patients with coronary artery disease (CAD), which were compared against clinical reference information. Results: This free-breathing method produced datasets that appeared consistent with clinical reference data in detecting moderate-to-strong induced perfusion abnormalities. However, the majority of the mild defects identified clinically were not detected by the method, potentially due to the presence of transient myocardial artefacts present in the images. Discussion: The feasibility of detecting CAD using this 3D first-pass perfusion sequence during free-breathing is demonstrated. Good agreement on typical moderate-to-strong CAD cases is promising, however, questions still remain on the sensitivity of the technique to milder cases.
Ferreira PF, Martin RR, Scott AD, et al., 2020, Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation, Magnetic Resonance in Medicine, Vol: 84, Pages: 2801-2814, ISSN: 0740-3194
PurposeIn this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT‐CMR) data powered by deep learning.MethodsA U‐Net based convolutional neural network was developed and trained to segment the heart in short‐axis DT‐CMR images. This was used as the basis to automate and enhance several stages of the DT‐CMR tensor calculation workflow, including image registration and removal of data corrupted with artifacts, and to segment the left ventricle. Previously collected and analyzed scans (348 healthy scans and 144 cardiomyopathy patient scans) were used to train and validate the U‐Net. All data were acquired at 3 T with a STEAM‐EPI sequence. The DT‐CMR postprocessing and U‐Net training/testing were performed with MATLAB and Python TensorFlow, respectively.ResultsThe U‐Net achieved a median Dice coefficient of 0.93 [0.92, 0.94] for the segmentation of the left‐ventricular myocardial region. The image registration of diffusion images improved with the U‐Net segmentation (P < .0001), and the identification of corrupted images achieved an F1 score of 0.70 when compared with an experienced user. Finally, the resulting tensor measures showed good agreement between an experienced user and the fully automated method.ConclusionThe trained U‐Net successfully automated the DT‐CMR postprocessing, supporting real‐time results and reducing human workload. The automatic segmentation of the heart improved image registration, resulting in improvements of the calculated DT parameters.
Rajakulasingam R, Nielles-Vallespin S, Ferreira PF, et al., 2020, Diffusion tensor cardiovascular magnetic resonance detects altered myocardial microstructure in patients with acute st-elevation myocardial infarction, European-Society-of-Cardiology (ESC) Congress, Publisher: OXFORD UNIV PRESS, Pages: 208-208, ISSN: 0195-668X
Nielles-Vallespin S, Ferreira PF, Scott A, et al., 2020, Diffusion tensor cardiovascular magnetic resonance predicts adverse remodelling after myocardial infarction, European-Society-of-Cardiology (ESC) Congress, Publisher: OXFORD UNIV PRESS, Pages: 216-216, ISSN: 0195-668X
Nielles-Vallespin S, Scott A, Ferreira P, et al., 2020, Cardiac Diffusion: Technique and Practical Applications, JOURNAL OF MAGNETIC RESONANCE IMAGING, Vol: 52, Pages: 348-368, ISSN: 1053-1807
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- Citations: 22
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.
Khalique Z, Ferreira PF, Scott AD, et al., 2020, Diffusion tensor cardiovascular magnetic resonance in cardiac amyloidosis, Circulation: Cardiovascular Imaging, Vol: 13, ISSN: 1941-9651
Background Cardiac amyloidosis (CA) is a disease of interstitial myocardial infiltration, usually by light chains or transthyretin. We used diffusion tensor cardiovascular magnetic resonance (DT-CMR) to noninvasively assess the effects of amyloid infiltration on the cardiac microstructure. Methods DT-CMR was performed at diastole and systole in 20 CA, 11 hypertrophic cardiomyopathy, and 10 control subjects with calculation of mean diffusivity, fractional anisotropy, and sheetlet orientation (secondary eigenvector angle). Results Mean diffusivity was elevated and fractional anisotropy reduced in CA compared with both controls and hypertrophic cardiomyopathy (P<0.001). In CA, mean diffusivity was correlated with extracellular volume (r=0.68, P=0.004), and fractional anisotropy was inversely correlated with circumferential strain (r=-0.65, P=0.02). In CA, diastolic secondary eigenvector angle was elevated, and secondary eigenvector angle mobility was reduced compared with controls (both P<0.001). Diastolic secondary eigenvector angle was correlated with amyloid burden measured by extracellular volume in transthyretin, but not light chain amyloidosis. Conclusions DT-CMR can characterize the microstructural effects of amyloid infiltration and is a contrast-free method to identify the location and extent of the expanded disorganized myocardium. The diffusion biomarkers mean diffusivity and fractional anisotropy effectively discriminate CA from hypertrophic cardiomyopathy. DT-CMR demonstrated that failure of sheetlet relaxation in diastole correlated with extracellular volume in transthyretin, but not light chain amyloidosis. This indicates that different mechanisms may be responsible for impaired contractility in CA, with an amyloid burden effect in transthyretin, but an idiosyncratic effect in light chain amyloidosis. Consequently, DT-CMR offers a contrast-free tool to identify novel pathophysiology, improve diagnostics, and monitor disease through noninvasive micr
Khalique Z, Ferreira P, Scott A, et al., 2020, Diffusion tensor cardiovascular magnetic resonance: a clinical perspective, JACC: Cardiovascular Imaging, Vol: 13, Pages: 1235-1255, ISSN: 1936-878X
Imaging the heart is central to cardiac phenotyping but in clinical practice this has been restricted to macroscopic interrogation. Diffusion tensor cardiovascular magnetic resonance (DT-CMR) is a novel, non-invasive technique which is beginning to unlock details of this microstructure in humans in-vivo. DT-CMR demonstrates the helical cardiomyocyte arrangement that drives rotation and torsion. Sheetlets (functional units of cardiomyocytes, separated by shear layers) have been shown to reorientate between diastole and systole, revealing how microstructural function facilitates cardiac thickening. Measures of tissue diffusion can also be made; fractional anisotropy (a measure of myocyte organisation) and mean diffusivity (a measure of myocyte packing). Abnormal myocyte orientation and sheetlet function has been demonstrated in congenital heart disease, cardiomyopathy and after myocardial infarction. It is too early to predict the clinical importance of DT-CMR, but such unique in-vivo information will likely prove valuable in early diagnosis and risk prediction of cardiac dysfunction and arrhythmias.
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.
Stoeck CT, Scott AD, Ferreira PF, et al., 2020, Motion-induced signal loss in In vivo cardiac diffusion-weighted imaging, Journal of Magnetic Resonance Imaging, Vol: 51, Pages: 319-320, ISSN: 1053-1807
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
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