Cardiac Magnetic Resonance Imaging Images Research Articles

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI.

Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. SwingGA pattern achieves the highest sampling efficiency (mSE>0.925) and the minimum azimuthal angle increment (mAD<1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

MSMHSA-DeepLab V3+: An Effective Multi-Scale, Multi-Head Self-Attention Network for Dual-Modality Cardiac Medical Image Segmentation.

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-scale, multi-head self-attention (MSMHSA) mechanism. The incorporation of this mechanism enables us to achieve larger receptive fields, facilitating the accurate segmentation of whole heart structures in both CT and MRI images. Within this network, features extracted from the shallow feature extraction network undergo a MHSA mechanism that closely aligns with human vision, resulting in the extraction of contextual semantic information more comprehensively and accurately. To improve the precision of cardiac substructure segmentation across varying sizes, our proposed method introduces three MHSA networks at distinct scales. This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the accurate segmentation of seven cardiac substructures in both cardiac CT and MRI images. Through comparative experiments with advanced transformer-based models, our study provides compelling evidence that despite the remarkable achievements of transformer-based models, the fusion of CNN models and self-attention remains a simple yet highly effective approach for dual-modality whole heart segmentation.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

Left atrial diastasis strain slope is a marker of hemodynamic recovery in post-ST elevation myocardial infarction: the Laser Atherectomy for STemi, Pci Analysis with Scintigraphy Study (LAST-PASS).

Left atrial (LA) mechanics are strongly linked with left ventricular (LV) filling. The LA diastasis strain slope (LADSS), which spans between the passive and active LA emptying phases, may be a key indicator of the LA-LV interplay during diastole. This study aimed to investigate the LA-LV interdependencies in post-ST elevation myocardial infarction (STEMI), with particular focus on the LADSS. Patients with post-anterior STEMI who received primary percutaneous coronary intervention underwent contrast cardiac magnetic resonance imaging (MRI) during acute (5-9 days post-STEMI) and chronic (at 6 months) phases. The LADSS was categorized into three groups: Groups 1, 2, and 3 representing positive, flat, and negative slopes, respectively. Cross-sectional correlates of LADSS Group 2 or 3 compared to Group 1 were identified, adjusting for demographics, LA indices, and with or without LV indices. The associations of acute phase LADSS with the recovery of LV ejection fraction (LVEF) and scar amount were investigated. Sixty-six acute phase (86.4% male, 63.1 ± 11.8 years) and 59 chronic phase cardiac MRI images were investigated. The distribution across LADSS Groups 1, 2, and 3 in the acute phase was 24.2%, 28.9%, and 47.0%, respectively, whereas in the chronic phase, it was 33.9%, 22.0%, and 44.1%, respectively. LADSS Group 3 demonstrated a higher heart rate than Group 1 in the acute phase (61.9 ± 8.7 vs. 73.5 ± 11.9 bpm, p < 0.01); lower LVEF (48.7 ± 8.6 vs. 41.8 ± 9.9%, p = 0.041) and weaker LA passive strain rate (SR) (-1.1 ± 0.4 vs. -0.7 [-1.2 to -0.6] s-1, p = 0.037) in the chronic phase. Chronic phase Group 3 exhibited weaker LA passive SR [relative risk ratio (RRR) = 8.8, p = 0.012] than Group 1 after adjusting for demographics and LA indices; lower LVEF (RRR = 0.85, p < 0.01), higher heart rate (RRR = 1.1, p = 0.070), and less likelihood of being male (RRR = 0.08, p = 0.058) after full adjustment. Acute phase LADSS Groups 2 and 3 predicted poor recovery of LVEF when adjusted for demographics and LA indices; LADSS Group 2 remained a predictor in the fully adjusted model (β = -5.8, p = 0.013). The LADSS serves both as a marker of current LV hemodynamics and its recovery in post-anterior STEMI. The LADSS is an important index of LA-LV interdependency during diastole. https://clinicaltrials.gov/, identifier NCT03950310.

FDDSeg: Unleashing the power of scribble annotation for cardiac MRI images through feature decomposition distillation.

Cardiovascular diseases can be diagnosed with computer assistance when using the magnetic resonance imaging (MRI) image that is produced by the MRI sensor. Deep learning-based scribbling MRI image segmentation has demonstrated impressive results recently. However, the majority of current approaches possess an excessive number of model parameters and do not completely utilize scribbling annotations. To develop a feature decomposition distillation deep learning method, named FDDSeg, for scribble-supervised cardiac MRI image segmentation. Public ACDC and MSCMR cardiac MRI datasets were used to evaluate the segmentation performance of FDDSeg. FDDSeg adopts a scribble annotation reuse policy to help provide accurate boundaries, and the intermediate features are split class region and class-free region by using the pseudo labels to further improve feature learning. Effective distillation knowledge is then captured by feature decomposition. FDDSeg was compared with 7 state-of-the-art methods, MAAG, ShapePU, CycleMix, Dual-Branch, ZscribbleSeg, Perturbation Dual-Branch as well as ScribbleVC on both ACDC and MSCMR datasets. FDDSeg is shown to perform the best in DSC(89.05% and 88.75%), JC(80.30% and 79.78%) as well as HD95(5.76% and 4.44%) metrics with only 2.01M of parameters. FDDSeg methods can segment cardiac MRI images more precise with only scribble annotations at lower computation cost, which may help increase the efficiency of quantitative analysis of cardiac. Code and models are available at: https://github.com/labiip/FDDSeg.

Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Left atrium size and function is an important but less studied prognostic marker for many cardiovascular diseases [1]. Cardiac Magnetic Resonance Imaging (MRI) can capture atrial structure with a high temporal and spatial resolution. However, diagnoses and accurate measurement of volumes from MRI often rely on time-consuming manual processing, or needs correction of software segmentation error by clinicians. Robust, automated methods are highly desirable to assist morphological analysis of dynamic changes in the heart. Ultimately this facilitates the wider application of cardiac MRI in early identification and prevention of potential cardiovascular disease. Purpose To introduce a semi-supervised teacher-student deep neural networks (requiring less labelled data) for accurate segmentation of left atrium from cardiac MRI images, and compare their performance with conventional supervised learning. Methods The basic structure for a mean teacher deep neural network is shown in Fig.1. Network training starts in a supervised manner. At each MRI batch input, both the student and the teacher model evaluate the images with applied noise. The student network is updated by gradient descent and segmentation loss is obtained by comparing its prediction with the label. The exponential moving average (EMA) of the student weight is then migrated to update the teacher network. Consistency loss between the student’s and teacher’s prediction (filtered by uncertainty map in Uncertainty-Aware Mean Teacher, UA-MT [2]) is obtained to jointly update the student. Unlabeled images continually improve the network without using supervised loss. We compared three semi-supervised teacher-student networks UA-MT, Hierarchical Consistency Regularized Mean Teacher (HCR-MT) and Semi-supervised Contrastive Consistency model (SCC), with the supervised network TransUNnet [3], using benchmark cardiac MRI dataset from MICCAI 2018 Atrial Segmentation Challenge [4]. Out of 20 MRI scans (1760 images), 16 scans were used for training, and 4 scans for testing. For semi-supervised learning, 75% of the training set was used as labelled data and 25% was assumed to be unlabeled. In the supervised learning, all labels were enabled. Results Example segmentation results from three semi-supervised models are shown in Fig 2. For comparison, the fully supervised TransUNnet produced an accuracy of 76%. Benefiting from effective teacher-student learning, the semi-supervised models achieved a superior accuracy of 89%, while also requiring less labelled data. Conclusion The proposed teacher-student deep neural network facilitates effective semi-supervised learning, and outperforms supervised TransUNet in segmenting the left atrium in cardiac MRI. Our semi-supervised approach uses less training data, reducing tedious and time-consuming manual labelling tasks, ultimately unlocking the ability to effectively utilise the full power of deep learning in medical applications.

The association with organ procurement techniques and early cardiac transplant outcomes using cardiac MRI.

Heart transplantation (HT) has historically been limited by organ availability. Use of donation after circulatory death (DCD) donors addresses this limitation by utilizing previously unused hearts through use of the Organ Care System (OCS). This study aimed to determine the impact of procurement and transportation method on allograft structure and function using early post-transplant cardiac magnetic resonance imaging (MRI). Patients who underwent HT at our institution from February 1, 2020, through April 30, 2021 who underwent cardiac MRI imaging<60 days from transplant were included. Recipient and donor characteristics, clinical outcomes, and MRI findings were compared between those who underwent DCD transplantation using the OCS device (DCD-OCS), brain dead donation (DBD) using the OCS device (DBD-OCS), and DBD transported via cold storage (DBD-cold storage) using one-way analysis of variance. A total of 85 patients underwent HT with a cardiac MRI during the study period. Thirty-one (36%) patients received a DCD organ, 16 (19%) received a DBD-OCS organ and 38 (45%) received a DBD-cold storage organ. Rates of primary graft dysfunction (PGD) were significantly higher in DCD transplants (19.5% DCD vs. .0% DBD-OCS and 5.3% DBD-cold storage; p<.050 across three groups), but with no differences in mortality or rejection. There were no differences in cardiac MRI findings between the three transplant types, including presence of gadolinium hyperenhancement after transplant (all p>.050). We observed no differences in early cardiac MRI findings between patients that received DCD and DBD-OCS heart transplants compared with those receiving DBD-cold storage transplants.

Precise Identification and Quantitative Analysis of Right Ventricle Using Hierarchical Intensity Clustering with Markov Random Field for Cardiac Mr Images

Precise localization and quantitative analysis of the right ventricle from cardiac magnetic resonance imaging (CMRI) images is imperative for assessing cardiopulmonary and cardiovascular mal-functionalities. Due to its poorly defined borders, precise contouring of the RV in CMRI images continues to be difficult. An approach for contouring the right ventricle based on a hierarchical intensity-based clustering method followed by Markov random field is proposed in this paper to overcome this difficulty.Because our method offers localization for each time step, it enables comprehensive right ventricle analysis during the whole cardiac-cycle. It also allows automatic prediction systems of volumetric parameters such as end systole and end diastolestages.48 human participants' cardiac MRI scans were used to validate the method. The presented scheme yielded significantly less variance than (approximately one half) compared to the reference standard of manually determined RV contours by clinical experts. This approach obtained mean Dice-Coefficient and Haussdorff-Distance of 0.92 and 5.25 mm 0.94 and 5.68 mm on the validation and tests. Further the results are evaluated using diagnosis metrics such as end diastole volume, end systole volume, and ejection fraction. Our model heading towards accurate RV localization at endocardium borders in cardiac MRI.

Adenosine triphosphate (ATP): a safe and effective vasodilator for stress perfusion cardiac magnetic resonance imaging

To evaluate the efficiency and safety of adenosine triphosphate (ATP) as a stress agent in a cohort of patients undergoing stress perfusion cardiac magnetic resonance imaging (CMRI). This retrospective study was conducted between December 2019 and October 2021. The study recruited patients who underwent stress perfusion CMRI using ATP as a vasodilator. Adverse events, such as chest pain, flushing, dyspnoea, headache, and splenic switch-off (SSO) phenomenon, were evaluated in the patients who underwent stress perfusion CMRI. The study included 107 patients (age range: 53±11 years; male:female, 62%:38%). The haemodynamic response (heart rate increased by≥10 beats/min) was quick and observed within 2 minutes of ATP infusion. Scanning was stopped in three patients because of atrioventricular block. CMRI images of seven out of 104 patients were excluded from the final analysis because of inferior quality. During ATP infusion, 37/107 patients (35%) experienced mild adverse events, such as chest pain, flushing, dyspnoea, headache, and atrioventricular block. Myocardial infarction and bronchospasms were not observed during ATP infusion. SSO, a marker of adequate stress, was observed in 91% (94/103) of the patients who underwent stress perfusion CMRI. As a coronary vasodilator, ATP was safe for stress perfusion CMRI. In addition, the adverse events during ATP infusion were mild, which were relieved within 2 minutes of ATP injection cessation. SSO could serve as an indicator of stress success in ATP stress perfusion CMRI.

Cardiac MRI Left Atrial Strain Associated With New-Onset Atrial Fibrillation in Patients With ST-Segment Elevation Myocardial Infarction.

Left atrial (LA) strain is associated with structural remodeling of the LA. Whether there is an association between LA strain obtained by cardiac magnetic resonance imaging (MRI) and new-onset atrial fibrillation (AF) after ST-segment elevation myocardial infarction (STEMI) is unclear. To investigate the relationship between LA strain and new-onset AF after STEMI. Retrospective. Three hundred and seventy-nine STEMI patients were enrolled, of which 26 had new-onset AF. 3.0 T, balanced turbo field echo sequence. Patients were divided into w/o AF group and new-onset AF group. Cardiac MRI images were analyzed using cardiovascular imaging software CVI 42 (Circle Cardiovascular Imaging, Canada). An automatic tracing algorithm was applied to obtain strain values. The reservoir strain, conduit strain, and booster strain were included in model 1, model 2, and model 3, respectively. Student's t-test, Mann-Whiney U test, and chi-square test were performed. Variables with a P ≤ 0.05 were incorporated into the logistic regression analysis. Area under curve of receiver operating characteristic was used to assess the ability of LA strain to identify new-onset AF. Bayesian information criterion, Akaike information criterion, and C-index were used to make comparisons between three models. P < 0.05 was considered statistically significant. Three models were used to assess LA strain identification ability for new-onset AF. After including multiple factors, right coronary artery (RCA), LVEF, and reservoir strain were still risk factors for new-onset AF in model 1. In model 2, age, RCA, LVEF, and conduit strain were still risk factors for new-onset AF. In model 3, RCA, LVEF, LVEDVi, and booster strain were still risk factors for new-onset AF. Model 2 has a stronger identification ability than others. LA strain associated with new-onset AF after STEMI. The model including conduit strain was the best-fit one. 4 TECHNICAL EFFICACY: Stage 3.

Deep Learning Approach for Automatic Segmentation and Functional Assessment of LV in Cardiac MRI

The early diagnosis of cardiovascular diseases (CVDs) can effectively prevent them from worsening. The source of the disease can be effectively detected through analysis with cardiac magnetic resonance imaging (CMRI). The segmentation of the left ventricle (LV) in CMRI images plays an indispensable role in the diagnosis of CVDs. However, the automated segmentation of LV is a challenging task, as it is confused with neighboring regions in the cardiac MRI. Deep learning models are effective in performing such complex segmentation because of the high performing convolutional neural networks (CNN). However, since segmentation using CNN involves the pixel-level classification of the image, it lacks the contextual information that is highly desirable in analyzing medical images. In this research, we propose a modified U-Net model to accurately segment the LV using context-enabled segmentation. The proposed model achieves the automatic segmentation and quantitative assessment of LV. The proposed model achieves the state-of-the-art accuracy by effectively utilizing various hyperparameters, such as batch size, batch normalization, activation function, loss function and dropout. Our method demonstrated a statistical significance in the endo- and epicardial walls with a dice score of 0.96 and 0.93, respectively, an average perpendicular distance of 1.73 and percentage of good contours of 96.22 were achieved. Furthermore, a high positive correlation of 0.98 between the clinical parameters, such as ejection fraction, end diastolic volume (EDV), end systolic volume (ESV) and gold standard was obtained.

Epicardial adipose tissue volume and density is associated with cardiac dysfunction in asymptomatic people with type 2 diabetes

Abstract Background Type 2 diabetes (T2D) is associated with several perturbations of cardiac structure and function, which are precursors to the development of heart failure. Excess accumulation of epicardial adipose tissue (EAT) may contribute to cardiac dysfunction in individuals with T2D. Lipid-laden adipocytes have a lower computed tomography (CT) attenuation and can be readily identified using cardiac CT. Using a multimodality cardiac imaging approach, we aimed to assess the association of total and low attenuation EAT volume with early markers of cardiac dysfunction in people with T2D. Methods Prospective case-control study, in which participants with and without T2D and no known cardiovascular disease, underwent comprehensive cardiovascular phenotyping including multiparametric cardiac magnetic resonance imaging (MRI), echocardiography and non-contrast cardiac CT. EAT volume was measured from CT scans using a deep learning method and volumes indexed to body surface area. Total EAT was defined according to CT adipose tissue attenuation range of −30 to −190 Hounsfield Units (HU) and low attenuation EAT as −90 to −190 HU. Left ventricular (LV) volumes, function and strain measurements were derived from cardiac MRI images and diastolic function also assessed using echocardiography. Markers of early cardiac dysfunction in those with T2D were assessed for associations with EAT in T2D participants using multivariable linear regression analyses. Results Two hundred and fifty-four participants were included: demographic, anthropometric and imaging variables are displayed in Table 1. Subjects with T2D had increased LV concentric remodelling (higher LV mass/volume ratio), diastolic dysfunction (lower circumferential peak early diastolic strain rate (PEDSR) and average E/e') and reduced systolic function (global longitudinal strain, GLS) compared with controls. Total and low attenuation indexed EAT volumes were 1.6-fold and 2-fold higher, respectively, in participants with T2D compared to controls (Figure 1). After adjustment for age, gender, ethnicity, insulin resistance, systolic blood pressure and waist/hip ratio, total and low attenuation indexed EAT volume were independently associated with LV mass/volume ratio (total indexed EAT volume: β=0.002, p=0.02, low attenuation indexed EAT volume: β=0.004, p=0.01) and LV GLS (total indexed EAT volume: β=−0.02, p&amp;lt;0.01, low attenuation indexed EAT volume: β=−0.04, p=0.02) in subjects with T2D, but not indices of diastolic dysfunction. Conclusion Total and low attenuation EAT volumes are higher in individuals with T2D, and excess EAT accumulation is independently associated with early markers of cardiac dysfunction. Further studies into the underlying mechanisms of this interaction may facilitate the development of interventions targeted at EAT, which could mitigate against the development of heart failure in people with T2D. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): United Kingdom National Institute for Health Research

Making Semi-Automatic Segmentation Method to be Automatic Using Deep Learning for Biventricular Segmentation

Ventricular Segmentation or Delineation of Cardiac Magnetic Resonance Imaging (CMRI) is significant in obtaining the cardiac contractile function, which in turn is taken as input for diagnosing Cardio Vascular Diseases (CVD). Many automatic and semi-automatic methods were evolved to meet the constraints of diagnosing CVDs. Among these, semi-automatic methods require user intervention for delineation of ventricles, which consumes time and leads to intra and inter-observability, as with manual delineation. Thus, the automatic method is suggested by most of the researchers to address the above-stated problem. We proposed Saliency-based Active contour U-Net (SACU-Net) for automatic bi-ventricular segmentation which is found to surpass the existing highest developed methods regarding closeness to the gold standard. Three schemes are used by our proposed algorithm, namely 1. Saliency Detection Scheme for Region of Interest (ROI) Localization to concentrate only on Object of Interest, 2. Drop-out embedded U-net for Initial Contour evolution that performs initial segmentation and 3. Local-Global-based Regional active Contour (LGRAC) to fine-tune and avoid leaking, merging of ventricles during Delineation. We used three datasets namely Automatic Cardiac Diagnosing Challenge (ACDC) of MICCAI 2017, Right Ventricular Segmentation Challenge (RVSC) of MICCAI 2012, and Sunny Brook (SB) of MICCAI 2009 dataset to test the adaptability nature of our algorithm over different scanner resolutions and protocols. 100 and 50 CMRI Images of ACDC were used for training and testing respectively which obtained average Dice Coefficient (DC) metric of 0.963, 0.934, and 0.948 for Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and Right Ventricular Cavity (RVC) respectively. 32 and 16 CMRI Images of RVSC are used for preparing and experimenting respectively, which obtained an average DC metric of 0.95 for RVC.30 and 15 CMRI Images of SB are used for preparing and experimenting respectively, which obtained average DC metric of 0.96 and 0.97 for LVC and LVM, respectively. Hausdorff Distance (HD) Metrics are also calculated to learn the distance of proposed delineated ventricles to reach the gold standard. The above resultant metrics show the robustness of our proposed SACU-Net in the segmentation of ventricles of CMRI than previous methods.

Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.

This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients with cardiac valve replacement was analyzed in this study. In this study, the filling completion operation, cross entropy loss function, and guidance unit were introduced and optimized based on the U-Net network. The heart MRI image segmentation model ML-Net was established. We compared the Dice, Hausdorff distance (HD), and percentage of area difference (PAD) values between ML-Net and other algorithms. The MRI image features of 82 patients with valvular heart disease who underwent cardiac valve replacement were analyzed. According to different nursing methods, they were randomly divided into the control group (routine nursing) and the intervention group (continuous nursing), with 41 cases in each group. The Glasgow Outcome Scale (GOS) score and the Self-rating Anxiety Scale (SAS) were compared between the two groups to assess the degree of anxiety of patients and the survival status at 6 months, 1 year, 2 years, and 3 years after discharge. The results showed that the Dice coefficient, HD, and PAD of the ML-Net algorithm were (0.896 ± 0.071), (5.66 ± 0.45) mm, and (15.34 ± 1.22) %, respectively. The Dice, HD, and PAD values of the ML-Net algorithm were all statistically different from those of the convolutional neural networks (CNN), fully convolutional networks (FCN), SegNet, and U-Net algorithms (P < 0.05). Atrial, ventricular, and aortic abnormalities can be seen in MRI images of patients with valvular heart disease. The cardiac blood flow signal will also be abnormal. The GOS score of the intervention group was significantly higher than that of the control group (P < 0.01). The SAS score was lower than that of the control group (P < 0.05). The survival rates of patients with valvular heart disease at 6 months, 1 year, 2 years, and 3 years after discharge were significantly higher than those in the control group (P < 0.05). The abovementioned results showed that an effective segmentation model for cardiac MRI images was established in this study. Continuous nursing played an important role in the postoperative recovery of discharged patients after cardiac valve replacement. This study provided a reference value for the diagnosis and prognosis of valvular heart disease.

Assessment of superior cavo-pulmonary anastomoses (Glenn shunt) by cardiac magnetic resonance imaging in comparison with multi-slice computed tomography

BackgroundMulti-slice computed tomography (MSCT) angiography is the gold standard imaging modality to evaluate the patency of Glenn shunt and the presence of veno–veno collaterals. The goal of this study is to evaluate the ability of two cardiac magnetic resonance imaging (MRI) techniques to assess the patency of Glenn shunt and the presence of veno–veno collaterals compared to MSCT angiography.ResultsPatients with Glenn shunt had MSCT angiography and cardiac MRI using two techniques: TWIST (Time-resolved angiography With Stochastic Trajectories) and the three-dimensional (3D) post-contrast whole heart techniques. MSCT angiography and cardiac MRI images were post-processed for quantitative and qualitative assessment of Glenn shunt and veno–veno collaterals. Our study included 29 patients (17 male, 59%) with Glenn shunt, the median age was 22 years (range 3–36 years). 3D post-contrast whole heart images give similar results compared to MSCT angiography results in the evaluation of Glenn shunt and veno–veno collaterals, 100% agreement in Glenn shunt visualization and agreement was 86.2% in the detection of veno–veno collaterals with a perfect agreement (kappa = 1) as regards their proximal connection to superior vena cava (SVC). While TWIST showed lower agreement compared to MSCT angiography results, 87.5% agreement in Glenn shunt visualization and agreement was 68.9% in the detection of veno–veno collaterals with poor agreement (kappa = 0.266) as regards their proximal connection to SVC.Conclusions3D post-contrast whole heart MRI images have similar results as MSCT angiography in the evaluation of superior cavo-pulmonary anastomosis and can be a good and safer alternative to MSCT angiography.

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning.

This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertension (PH). The heart MRI images were detected based on the You Only Look Once (YOLO) algorithm, and then the MRI image right ventricle segmentation algorithm was established based on the convolutional neural network (CNN) algorithm. The segmentation effect of the right ventricle in cardiac MRI images was evaluated regarding intersection-over-union (IOU), Dice coefficient, accuracy, and Jaccard coefficient. 30 cases of PH patients were taken as the research object. According to different treatments, they were rolled into control group (conventional treatment) and Cur group (conventional treatment + Cur), with 15 cases in each group. Changes in the scores of the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) of the two groups of patients before and after treatment were analyzed. It was found that the average IOU of the heart target detection frame of the MRI image and the true bounding box before correction was 0.7023, and the IOU after correction was 0.9016. The Loss of the MRI image processed by the CNN algorithm was 0.05, which was greatly smaller than those processed by other algorithms. The Dice coefficient, Jaccard coefficient, and accuracy of the MRI image processed by CNN were 0.89, 0.881, and 0.994, respectively. The MRI images of PH patients showed that the anterior wall of the right ventricle was notably thickened, and the main pulmonary artery was greatly widened. After treatment, the SAR and SDS scores of the two groups were lower than those before treatment (P < 0.05), and the SAR and SDS scores of the curcumin group were lower than those of the control group (P < 0.05). To sum up, the right ventricular segmentation ability of MRI images based on deep learning was improved, and Cur can remarkably alleviate the psychological state of PH patients, which provided a reference for the diagnosis and treatment for PH patients.

Active Contour driven by geometric mean and standard deviation-based energy fitting model for the left ventricle segmentation from cardiac magnetic resonance images

This paper proposes a geometric mean and standard deviation-based energy fitting model to improve the accuracy of segmentation of the left ventricle from cardiac Magnetic Resonance Imaging (MRI). Energy-fitting-based active contour models emerged so far suffer either from intensity inhomogeneity or gives wrong segmentation result due to an inappropriate initial contour. Thus, accurate and robust segmentation of the left ventricle from cardiac MRI still a challenging problem. Therefore, to tackle both the problems, a geometric mean-based energy-fitting model is proposed. Unlike the recent energy-fitting-based models which use the arithmetic mean to calculate the local energy, the proposed method uses geometric mean and scaled standard deviation to compute the energy functional which drives the active contour to the region of interest. In addition to that completely removes the initial contour problem by automating it according to the input. The initial contour in the proposed model is a circle its radius and the center are calculated from the input sample itself. This initial contour is an appropriate and automated one that helps to reduce the computation time for segmentation. Experiments are conducted on cardiac MRI images the result obtained is compared with ground truth and evaluated by Average perpendicular distance (APD) and DICE similarity coefficient. Further the visual, as well as evaluated parameters, evidences that the proposed model performs better than the existing methods.

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.

Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping.

The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten-fold cross-validation of over 2900 cardiac MRI images. Then, the fine-tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.

Residual Learning: A New Paradigm to Improve Deep Learning-Based Segmentation of the Left Ventricle in Magnetic Resonance Imaging Cardiac Images.

Background:Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images.Methods:In this article, a new concept called residual learning is utilized to improve the performance of deep learning schemes against gradient vanishing problems. For this purpose, the Residual Network of Residual Network (i.e., Residual of Residual) substructure is utilized inside the main deep learning architecture (e.g., Unet), which provides more significant detection indexes.Results and Conclusion:The proposed method's performances and its alternatives were evaluated on Sunnybrook Cardiac Data as a reliable dataset in the left ventricle segmentation. The results show that the detection parameters are improved at least by 5%, 3.5%, 8.1%, and 11.4% compared to its deep alternatives in terms of Jaccard, Dice, precision, and false-positive rate indexes, respectively. These improvements were made when the recall parameter was reduced to a negligible value (i.e., approximately 1%). Overall, the proposed method can be used as a suitable tool for more accurate detection of the left ventricle in MRI images.