Cine Cardiac Magnetic Resonance Research Articles

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.

The effect of Trimetazidine on the left ventricular global longitudinal strain assessed by cardiac magnetic resonance in stable angina patients with previous myocardial infarction and left ventricular

Abstract Introduction Trimetazidine (TMZ) is an anti-ischemic metabolic agent that is indicated in adults as add-on therapy for the symptomatic treatment of patients with stable angina pectoris (SA). Global left ventricular (LV) systolic function is an important predictor of outcomes in patients after myocardial infarction (MI). Cardiac magnetic resonance (CMR) imaging has now evolved into a major tool for the evaluation of patients with LV dysfunction, providing precise measurements of ejection fraction (EF) and viability assessment but also now allowing assessment of global longitudinal strain (GLS) from standard cine CMR images. We hypothesized that the use of TMZ 80 mg as an anti-ischemic agent in patients with SA, history of MI, reduced EF and in the presence of viable myocardium may produce an early modification of GLS. Methods TRIM-AZ - was a non-interventional, prospective Azerbaijan study to analyze the capacity of short-term therapy with TMZ 80 mg on LV GLS evaluated by CMR in patients with previous MI. Patients with a confirmed diagnosis of heart failure (HF) with impaired LV systolic function and SA in the presence of viable myocardium were included. The treatment of all patients was optimized according to the latest ESC guidelines before entry in the study. Patients were randomized into two groups, one received TMZ 80 mg on top of guidelines directed medical therapies, and the other was the control group. A CMR as well as a transthoracic echography were performed for all patients at the inclusion visit and 8 weeks later. Results 52 patients were included in the study, 30 patients in the TMZ group and 22 in the control group,. At baseline, the mean age was 62±7 years , 63% were smokers, 60% hypertensive, 60% diabetics, 48% had a family history of MI, 12% had typical symptoms of SA while 88% had atypical form (e.g. like shortness of breath). All patients received baseline therapy with ARNI/ACE-i, SGLT2 inhibitors, beta-blockers, MRA, ASA, and statin. 8 weeks after the initiation of TMZ, there were no significant changes in LVEF assessed both by Echo and CMR (29,8%±3,2% vs 30,1%±3,5% by echo in TMZ group; 31,3%±2,7% vs 31,6%±2,7% by echo in the control group; 25,9%±3,1% vs 26,9±4,1% by CMR in TMZ group; 26,9%±2,1% vs 30,1%±2,3% by CMR in the control group), however GLS was significantly improved in TMZ group (-9,9%±1,1% to -12,7%±1,2%, p=0,00), but not in control group (-9,3%±1,3% vs -10,3%±1,4% p=0,3). No adverse event related to TMZ use was reported during the study. Conclusion These findings suggest that the use of trimetazidine in patients with stable angina , previous MI and heart failure in the presence of viable myocardium could improve the left ventricular global longitudinal strain assessed by CMR.

Prognostic value of enhanced cine cardiac MRI-based radiomics in dilated cardiomyopathy

BackgroundEarly precise identification of high-risk dilated cardiomyopathy (DCM) phenotype is essential for clinical decision-making and patient surveillance. The aim of the study was to assess the prognostic value of enhanced cine cardiac magnetic resonance (CMR)-based radiomics in DCM. MethodsWe prospectively enrolled 401 (training set: 281; test set: 120) DCM patients. Radiomic features were extracted from enhanced cine images of entire left ventricular wall and selected by the least absolute shrinkage and selection operator. Different predictive models were built using logistic regression classifier to predict all-cause mortality and heart transplantation. Model performances were compared with the area under the receiver operating characteristic curves (AUCs). Kaplan-Meier curves, log-rank test, and Cox regression were used for survival analysis. ResultsEndpoint events occurred in 65 patients over a median follow-up period of 25.4 months. 13 radiomic features were finally selected. The Rad_Combined model integrating clinical characteristics, CMR parameters and radiomics features achieved the best performance with an AUC of 0.836 and 0.835 in the training and test sets, respectively. High-risk groups with endpoint events defined by the Rad_Combined model had significantly shorter survival time than low-risk group in both the training [Hazard Ratio (HR) = 7.74, P < 0.001] and test sets (HR = 4.84, P < 0.001). ConclusionThe Rad_Combined model might serve as an effective tool to help risk stratification and clinical decision-making for patients with DCM. Trial registrationChinese Clinical Trial Registry, ChiCTR1800017058 by the ethics committee of West China hospital,Sichuan University.

Dark papillary muscles sign is a new prognostic indicator in patients with dilated cardiomyopathy: A multi-center study

ObjectivesThe prognostic value of left ventricular (LV) papillary muscle anomalies in dilated cardiomyopathy (DCM) patients is unclear. The objective of this study was to evaluate the prognostic significance of LV papillary muscle anomalies in DCM patients using cardiac magnetic resonance (CMR). Methods369 DCM patients who underwent CMR at two Chinese medical facilities from January 2019 to June 2023 were retrospectively and consecutively included in total. The various features of the LV papillary muscles were taken into consideration: thickness, attachment, supernumerary papillary muscles, angles, and signal intensity. The end-systolic signal hypointensity of both papillary muscles in early post-contrast cine CMR images was identified as Dark-Paps. Major adverse cardiac events (MACEs) were assessed, and all patients were followed up. Results119 patients (32.2 %) had Dark-Paps and 141 patients (38.2 %) experienced MACE during a median follow-up of 22 months. According to Kaplan-Meier curve analysis, patients who had Dark-Paps had a lower survival rate free from MACE (log-rank, p < 0.001). Dark-Paps maintained an independent predictor of MACE in a multivariate model that included left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) extent (HR: 3.49; p < 0.001). Furthermore, adding Dark-Paps to the multivariate model greatly enhanced the prognostic role of endpoint events (C-statistic improvement: 0.652–0.777, Delong test: p < 0.001). ConclusionDark-Paps is a potent independent indicator of major adverse cardiac events in dilated cardiomyopathy patients. In addition, Dark-Paps can provide additional prognostic value over the multivariable baseline clinical model.

Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on contrast-free CMR are desirable to overcome the limitations associated with contrast enhancement. We introduce a novel deep generative learning method, termed cine-generated enhancement (CGE), which transforms standard contrast-free cine CMR into LGE-equivalent images for MI assessment. CGE features with multislice spatiotemporal feature extractor, enhancement contrast modulation, and sophisticated loss function. Data from 430 patients with acute MI from 3 centers were collected. After image quality control, 1525 pairs (289 patients) of center I were used for training, and 293 slices (52 patients) of the same center were reserved for internal testing. The 40 patients (401 slices) of the other 2 centers were used for external testing. The CGE robustness was further tested in 20 normal subjects in a public cine CMR data set. CGE images were compared with LGE for image quality assessment and MI quantification regarding scar size and transmurality. The CGE method produced images of superior quality to LGE in both internal and external data sets. There was a significant (P<0.001) correlation between CGE and LGE measurements of scar size (Pearson correlation, 0.79/0.80; intraclass correlation coefficient, 0.79/0.77) and transmurality (Pearson correlation, 0.76/0.64; intraclass correlation coefficient, 0.76/0.63) in internal/external data set. Considering all data sets, CGE demonstrated high sensitivity (91.27%) and specificity (95.83%) in detecting scars. Realistic enhancement images were obtained for the normal subjects in the public data set without false positive subjects. CGE achieved superior image quality to LGE and accurate scar delineation in patients with acute MI of both internal and external data sets. CGE can significantly simplify the CMR examination, reducing scan times and risks associated with gadolinium-based contrasts, which are crucial for acute patients.

Computational modeling of left ventricular flow using PC-CMR-derived four-dimensional wall motion.

Intracardiac hemodynamics plays a crucial role in the onset and development of cardiac and valvular diseases. Simulations of blood flow in the left ventricle (LV) have provided valuable insight into assessing LV hemodynamics. While fully coupled fluid-solid modelings of the LV remain challenging due to the complex passive-active behavior of the LV wall myocardium, the integration of imaging-driven quantification of structural motion with computational fluid dynamics (CFD) modeling in the LV holds the promise of feasible and clinically translatable characterization of patient-specific LV hemodynamics. In this study, we propose to integrate two magnetic resonance imaging (MRI) modalities with the moving-boundary CFD method to characterize intracardiac LV hemodynamics. Our method uses the standard cine cardiac magnetic resonance (CMR) images to estimate four-dimensional myocardial motion, eliminating the need for involved myocardial material modeling to capture LV wall behavior. In conjunction with CMR, phase contrast-MRI (PC-MRI) was used to measure temporal blood inflow rates at the mitral orifice, serving as an additional boundary condition. Flow patterns, including velocity streamlines, vortex rings, and kinetic energy, were characterized and compared to the available data. Moreover, relationships between LV wall kinematic markers and flow characteristics were determined without myocardial material modeling and using a non-rigid image registration (NRIR) method. The fidelity of the simulation was quantitatively evaluated by validating the flow rate at the aortic outflow tract against respective PC-MRI measures. The proposed methodology offers a novel and feasible toolset that works with standard PC-CMR protocols to improve the clinical assessment of LV characteristics in prognostic studies and surgical planning.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respectto expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65, and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification. Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGE-CMR prescription, particularly in cases where indication is uncertain.

Differentiating Left Ventricular Remodeling in Aortic Stenosis From Systemic Hypertension.

Left ventricular (LV) hypertrophy occurs in both aortic stenosis (AS) and systemic hypertension (HTN) in response to wall stress. However, differentiation of hypertrophy due to these 2 etiologies is lacking. The aim was to study the 3-dimensional geometric remodeling pattern in severe AS pre- and postsurgical aortic valve replacement and to compare with HTN and healthy controls. Ninety-one subjects (36 severe AS, 19 HTN, and 36 healthy controls) underwent cine cardiac magnetic resonance. Cardiac magnetic resonance was repeated 8 months post-aortic valve replacement (n=18). Principal component analysis was performed on the 3-dimensional meshes reconstructed from 109 cardiac magnetic resonance scans of 91 subjects at end-diastole. Principal component analysis modes were compared across experimental groups together with conventional metrics of shape, strain, and scar. A unique AS signature was identified by wall thickness linked to a LV left-right axis shift and a decrease in short-axis eccentricity. HTN was uniquely linked to increased septal thickness. Combining these 3 features had good discriminative ability between AS and HTN (area under the curve, 0.792). The LV left-right axis shift was not reversible post-aortic valve replacement, did not associate with strain, age, or sex, and was predictive of postoperative LV mass regression (R2=0.339, P=0.014). Unique remodeling signatures might differentiate the etiology of LV hypertrophy. Preliminary findings suggest that LV axis shift is characteristic in AS, is not reversible post-aortic valve replacement, predicts mass regression, and may be interpreted to be an adaptive mechanism.

Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance

BackgroundCardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine.MethodsA fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects.ResultsThe mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91−0.98 and 0.89−0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99−1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001).ConclusionFully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment.Trials registrationClinicalTrials.gov: NCT05114785.Relevance statementIntegrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes.Key points• Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination.• This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution.• LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation.• Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.Graphical

MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains; and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {CHA}_2\ ext {DS}_2\ ext {-VASc}$$\\end{document} score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

Focal ischemic myocardial fibrosis assessed by late gadolinium enhancement cardiovascular magnetic resonance in patients with hypertrophic cardiomyopathy

BackgroundIn patients with hypertrophic cardiomyopathy (HCM), ischemic myocardial fibrosis assessed by late gadolinium enhancement (I-LGE) using cardiovascular magnetic resonance (CMR) have been reported. However, the clinical significance of I-LGE has not been completely understood. We aim to evaluate the I-LGE differ phenotypically from HCM without LGE or nonischemic myocardial fibrosis assessed by late gadolinium enhancement (NI-LGE) in the left ventricle (LV).MethodsThe patients with HCM whom was underwent CMR were enrolled, using cine cardiac magnetic resonance to evaluate LV function and LGE to detect the myocardial fibrosis. Three groups were assorted: 1) HCM without LGE; 2) HCM with LGE involved the subendocardial layer was defined as I-LGE; 3) HCM with LGE not involved the subendocardial layer was defined as NI-LGE.ResultsWe enrolled 122 patients with HCM in the present study. LGE was detected in 58 of 122 (48%) patients with HCM, and 22 (18%) of patients reported I-LGE. HCM with I-LGE had increased higher left ventricular mass index (LVMI) (P < 0.0001) than HCM with NI-LGE or without LGE. In addition, HCM with I-LGE had a larger LV end- systolic volume (P = 0.045), lower LV ejection fraction (LVEF) (P = 0.026), higher LV myocardial mass (P < 0.001) and thicker LV wall (P < 0.001) more than HCM without LGE alone. The I-LGE were significantly associated with LVEF (OR: 0.961; P = 0.016), LV mass (OR: 1.028; P < 0.001), and maximal end-diastolic LVWT (OR: 1.567; P < 0.001). On multivariate analysis, LVEF (OR: 0.948; P = 0.013) and maximal end-diastolic LVWT (OR: 1.548; P = 0.001) were associated with higher risk for I-LGE compared to HCM without LGE. Noticeably, the maximal end-diastolic LVWT (OR: 1.316; P = 0.011) was the only associated with NI-LGE compared to HCM without LGE.ConclusionsI-LGE is not uncommon in patients with HCM. HCM with I-LGE was associated with significant LV hypertrophy, extensive LGE and poor LV ejection fraction. We should consider focal ischemic myocardial fibrosis when applying LGE to risk stratification for HCM.

DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach

ObjectiveThis work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).MethodsThis retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.ResultsA total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47–1.00]) and lowest Brier score (0.19, 95% CI [0.13–0.27]), showing competitive diagnostic accuracy and calibration. At the Youden’s index, sensitivity was 0.72 (95% CI [0.68–0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.ConclusionThe current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.Clinical relevance statementA machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.Key Points• The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols.• Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies.• Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.Graphical

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis.

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.

Comparing Strain Assessment in Compressed Sensing and Conventional Cine MRI.

The aim of this study is to assess the feasibility of compressed sensing (CS) acceleration methods compared to conventional segmented cine (Seg) cardiac magnetic resonance (CMR) for evaluating left ventricular (LV) function and strain by feature tracking (FT). In this prospective study, 45 healthy volunteers underwent CMR imaging used Seg, threefold (CS3), fourfold (CS4), and eightfold (CS8) CS acceleration. Cine images were scored for quality (1-5 scale). LV volumetric and functional parameters and global longitudinal (GLS), circumferential (GCS), and radial strains (GRS) were quantified. LV volumetric and functional parameters exhibited no differences between Seg and all CS cines (all P > 0.05). The strains were similar for Seg, CS3, and CS4 (all P > 0.05). Similarly, no significant differences were observed in GRS and GCS between Seg and CS8 (all P > 0.05), but the global longitudinal strain was significantly lower for CS8 versus Seg (P < 0.001). Image quality declined with CS acceleration, especially in long-axis views with CS8. CS cine MRI at acceleration factor 4 maintained good image quality and accurate measurements of LV function and strain, although there was a slight reduction in the quality of long-axis images and GLS with CS8. CS acceleration up to a factor of 4 enabled fast CMR evaluations, making it suitable for clinical use.

The effects of flip angle and gadolinium contrast agent on single breath-hold compressed sensing cardiac magnetic resonance cine for biventricular global strain assessment.

Due to its potential to significantly reduce scanning time while delivering accurate results for cardiac volume function, compressed sensing (CS) has gained traction in cardiovascular magnetic resonance (CMR) cine. However, further investigation is necessary to explore its feasibility and impact on myocardial strain results. A total of 102 participants [75 men, 46.5 ± 17.1 (SD) years] were included in this study. Each patient underwent four consecutive cine sequences with the same slice localization, including the reference multi-breath-hold balanced steady-state free precession (bSSFPref) cine, the CS cine with the same flip angle as bSSFPref before (CS45) and after (eCS45) contrast enhancement, and the CS cine (eCS70) with a 70-degree flip angle after contrast enhancement. Biventricular strain parameters were derived from cine images. Two-tailed paired t-tests were used for data analysis. Global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS) were observed to be significantly lower in comparison to those obtained from bSSFPref sequences for both the right and left ventricles (all p < 0.001). No significant difference was observed on biventricular GRS-LAX (long-axis) and GLS values derived from enhanced and unenhanced CS cine sequences with the same flip angle, but remarkable reductions were noted in GRS-SAX (short-axis) and GCS values (p < 0.001). After contrast injection, a larger flip angle caused a significant elevation in left ventricular strain results (p < 0.001) but did not affect the right ventricle. The increase in flip angle appeared to compensate for contrast agent affection on left ventricular GRS-SAX, GCS values, and right ventricular GRS-LAX, GLS values. Despite incorporating gadolinium contrast agents and applying larger flip angles, single breath-hold CS cine sequences consistently yielded diminished strain values for both ventricles when compared with conventional cine sequences. Prior to employing this single breath-hold CS cine sequence to refine the clinical CMR examination procedure, it is crucial to consider its impact on myocardial strain results.

Cardiovascular magnetic resonance imaging-derived intraventricular pressure gradients in ST-segment elevation myocardial infarction: a long-term follow-up study.

Recently, novel post-processing tools have become available that measure intraventricular pressure gradients (IVPGs) on routinely obtained long-axis cine cardiac magnetic resonance (CMR) images. IVPGs provide a comprehensive overview of both systolic and diastolic left ventricular (LV) functions. Whether IVPGs are associated with clinical outcome after ST-elevation myocardial infarction (STEMI) is currently unknown. Here, we investigated the association between CMR-derived LV-IVPGs and major adverse cardiovascular events (MACE) in a large reperfused STEMI cohort with long-term outcome. In this prospectively enrolled multi-centre cohort study, 307 patients underwent CMR within 14 days after the first STEMI. LV-IVPGs (from apex-to-base) were estimated on the long-axis cine images. During a median follow-up of 9.7 (5.9-12.5) years, MACE (i.e. composite of cardiovascular death and de novo heart failure hospitalisation) occurred in 49 patients (16.0%). These patients had larger infarcts, more often microvascular injury, and impaired LV-IVPGs. In univariable Cox regression, overall LV-IVPG was significantly associated with MACE and remained significantly associated after adjustment for common clinical risk factors (hazard ratio (HR) 0.873, 95% confidence interval (CI) 0.794-0.961, P = 0.005) and myocardial injury parameters (HR 0.906, 95% CI 0.825-0.995, P = 0.038). However, adjusted for LV ejection fraction and LV global longitudinal strain (GLS), overall LV-IVPG does not provide additional prognostic information (HR 0.959, 95% CI 0.866-1.063, P = 0.426). Early after STEMI, CMR-derived LV-IVPGs are univariably associated with MACE and this association remains significant after adjustment for common clinical risk factors and measures of infarct severity. However, LV-IVPGs do not add prognostic value to LV ejection fraction and LV GLS.

Kiosk 6R-FA-05 - Cardiac Magnetic Resonance Cine Images Derived-radiomics for the Prediction of Event FBee Survival in Patients with St-segment Elevation Myocardial Infarction

Kiosk 6R-FA-05 - Cardiac Magnetic Resonance Cine Images Derived-radiomics for the Prediction of Event FBee Survival in Patients with St-segment Elevation Myocardial Infarction