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Background Quantitative cardiovascular magnetic resonance (CMR) T1 and T2 mapping are used to detect diffuse disease such as myocardial fibrosis or edema. However, post gadolinium contrast mapping often lacks visual contrast needed for assessment of focal scar. On the other hand, late gadolinium enhancement (LGE) CMR which nulls the normal myocardium has excellent contrast between focal scar and normal myocardium but has poor ability to detect global disease. The objective of this work is to provide a calculated bright-blood (BB) and dark-blood (DB) LGE based on simultaneous acquisition of T1 and T2 maps, so that both diffuse and focal disease may be assessed within a single multi-parametric acquisition. Methods The prototype saturation recovery-based SASHA T1 mapping may be modified to jointly calculate T1 and T2 maps (known as multi-parametric SASHA) by acquiring additional saturation recovery (SR) images with both SR and T2 preparations. The synthetic BB phase sensitive inversion recovery (PSIR) LGE may be calculated from the post-contrast T1, and the DB PSIR LGE may be calculated from the post-contrast joint T1 and T2 maps. Multi-parametric SASHA maps were acquired free-breathing (45 heartbeats). Protocols were designed to use the same spatial resolution and achieve similar signal-to-noise ratio (SNR) as conventional motion corrected (MOCO) PSIR. The calculated BB and DB LGE were compared with separate free breathing (FB) BB and DB MOCO PSIR acquisitions requiring 16 and 32 heart beats, respectively. One slice with myocardial infarction (MI) was acquired with all protocols within 4 min. Results Multiparametric T1 and T2 maps and calculated BB and DB PSIR LGE images were acquired for patients with subendocardial chronic MI (n = 10), acute MI (n = 3), and myocarditis (n = 1). The contrast-to-noise (CNR) between scar (MI and myocarditis) and remote was 26.6 ± 7.7 and 20.2 ± 7.4 for BB and DB PSIR LGE, and 31.3 ± 10.6 and 21.8 ± 7.6 for calculated BB and DB PSIR LGE, respectively. The CNR between scar and the left ventricualr blood pool was 5.2 ± 6.5 and 29.7 ± 9.4 for conventional BB and DB PSIR LGE, and 6.5 ± 6.0 and 38.6 ± 11.6 for calculated BB and DB PSIR LGE, respectively. Conclusions A single free-breathing acquisition using multi-parametric SASHA provides T1 and T2 maps and calculated BB and DB PSIR LGE images for comprehensive tissue characterization.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post‐MI care. Late gadolinium enhancement‐cardiac magnetic resonance (LGE‐CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE‐CMR is the invasive intravenous introduction of gadolinium‐based contrast agents that present potential high‐risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, a completely non‐invasive methodology is developed to identify the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this approach, the remarkable performance of a multi‐fidelity ML model is demonstrated, which combines rodent‐based in‐silico‐generated training data (low‐fidelity) with very limited patient‐specific human data (high‐fidelity) in predicting LGE ground truth. The results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation‐based data with very small amounts of in vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited. This study presents a non‐invasive machine learning model to identify infarct regions in the left ventricle using cardiac strain data. By combining rodent‐based simulated data with limited human data, the model achieves high accuracy in predicting infarct size and location without the need for gadolinium contrast agents, offering a promising alternative to traditional methods.

Reflecting both the worldwide importance of AF, as well as the worldwide performance of AF ablation, this document is the result of a joint partnership between the HRS, EHRA, ECAS, the Asia Pacific Heart Rhythm Society (APHRS), and the Latin American Society of Cardiac Stimulation and Electrophysiology (Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología [SOLAECE]). The purpose of this 2017 Consensus Statement is to provide a state-of-the-art review of the field of catheter and surgical ablation of AF and to report the findings of a writing group, convened by these five international societies. The writing group is composed of 60 experts representing 11 organizations: HRS, EHRA, ECAS, APHRS, SOLAECE, STS, ACC, American Heart Association (AHA), Canadian Heart Rhythm Society (CHRS), Japanese Heart Rhythm Society (JHRS), and Brazilian Society of Cardiac Arrhythmias (Sociedade Brasileira de Arritmias Cardíacas [SOBRAC]). Rather, the ultimate judgment regarding care of a particular patient must be made by the health care provider and the patient in light of all the circumstances presented by that patient.

Background Quantification of non-ischemic myocardial scar remains a challenge due to the patchy diffuse nature of fibrosis. Extracellular volume (ECV) to guide late gadolinium enhancement (LGE) analysis may achieve a robust scar assessment. Methods Three cohorts of 80 non-ischemic-training, 20 non-ischemic-validation, and 10 ischemic-validation were prospectively enrolled and underwent 3.0 Tesla cardiac MRI. An ECV cutoff to differentiate LGE scar from non-scar was identified in the training cohort from the receiver-operating characteristic curve analysis, by comparing the ECV value against the visually-determined presence/absence of the LGE scar at the highest signal intensity (SI) area of the mid-left ventricle (LV) LGE. Based on the ECV cutoff, an LGE semi-automatic threshold of n-times of standard-deviation (n-SD) above the remote-myocardium SI was optimized in the individual cases ensuring correspondence between LGE and ECV images. The inter-method agreement of scar amount in comparison with manual (for non-ischemic) or full-width half-maximum (FWHM, for ischemic) was assessed. Intra- and inter-observer reproducibility were investigated in a randomly chosen subset of 40 non-ischemic and 10 ischemic cases. Results The non-ischemic groups were all female with the HIV positive rate of 73.8% (training) and 80% (validation). The ischemic group was all male with reduced LV function. An ECV cutoff of 31.5% achieved optimum performance (sensitivity: 90%, specificity: 86.7% in training; sensitivity: 100%, specificity: 81.8% in validation dataset). The identified n-SD threshold varied widely (range 3 SD–18 SD), and was independent of scar amount (β = −0.01, p  = 0.92). In the non-ischemic cohorts, results suggested that the manual LGE assessment overestimated scar (%) in comparison to ECV-guided analysis [training: 4.5 (3.2–6.4) vs. 0.92 (0.1–2.1); validation: 2.5 (1.2–3.7) vs. 0.2 (0–1.6); P  < 0.01 for both]. Intra- and inter-observer analyses of global scar (%) showed higher reproducibility in ECV-guided than manual analysis with CCC = 0.94 and 0.78 versus CCC = 0.86 and 0.73, respectively ( P  < 0.01 for all). In ischemic validation, the ECV-guided LGE analysis showed a comparable scar amount and reproducibility with the FWHM. Conclusions ECV-guided LGE analysis is a robust scar quantification method for a non-ischemic cohort. Trial registration ClinicalTrials.gov; NCT00000797, retrospectively-registered 2 November 1999; NCT02501811, registered 15 July 2015.

BackgroundAdvances in cancer treatment have revolutionized survival outcomes over the past decades but many anti-cancer treatments have cardiotoxic side effects. Furthermore, many cancer survivors go on to develop cardiovascular disease. Cardiac magnetic resonance imaging (CMR) is a key imaging technique in the assessment and management of cardiovascular disease, but its role in patients with history of cancer has not been fully defined. Female patients are under-represented in cardiovascular clinical trials and may benefit from tailored investigations and treatments. In this study we aimed to evaluate the clinical utility and role of CMR in the management of female patients with history of cancer.MethodsBetween 2013–2023, 126 female patients (mean age 71 +/- 15 years) undergoing cancer treatment or with history of previous cancer treatment were identified. A standard CMR exam included ventricular volumes, function, stress perfusion and late gadolinium enhancement (LGE) imaging was successfully completed in 120/126 of the patients. Details regarding patient’s cancer, treatment and cardiovascular risk factors were obtained from patients’ clinical notes. The indication for scan, CMR findings and impact of CMR results on patients’ clinical outcomes were recorded.ResultsThe indication for cardiac MRI was related to the cancer diagnosis in 64(53%) of the patients; 48% for the assessment of cardiac function in context of cancer treatment and 5% to look for direct cardiac involvement of cancer (e.g. masses). In 47% of the patients, the clinical indication was unrelated to their previous cancer history. The most common types of cancer were found to be breast (46/126, 37%), haematological (25/126, 20%) and lung (16/126, 13%). Whilst the largest proportion of patients (69%), were treated with surgery followed by chemotherapy (65%) and radiotherapy (22%). The most common cardiovascular comorbidities were hypertension (37%), followed by ischemic heart disease (21%), dyslipidaemia (20%), atrial fibrillation (17%) and diabetes (15%). 18% of cancer patients who underwent CMR had left ventricular (LV) systolic dysfunction. Cancer treatment led to immunotherapy related myocarditis in 5/64 (8%) of the patients on active cancer treatment. 4/64 (5%) of the patients were found to have diffuse LGE in keeping with cardiac amyloid (all in the context of myeloma). The number of patients with a new diagnosis of myocardial infarction (subendocardial LGE) in relation to active cancer treatment was only 4/64 (6%). Only 2 patients were diagnosed with intracardiac masses, where histology showed sarcoma in one case and melanoma in the other. 42/64 of the patients on active cancer treatment had completely normal studies which facilitated ongoing cancer treatment. Overall, CMR helped to identify cardiac dysfunction, such as ischemic insult, myocarditis and ventricular systolic impairment as a consequence of cancer treatment in cancer patients. For instance, in 11/22 (50%) patients who had developed LV systolic dysfunction as a direct result of cancer therapy, early identification of systolic impairment alongside aetiology assessment of LV dysfunction by use of cardiac MRI, allowed intensive cardiovascular treatment leading to normalization of LV systolic function subsequently. All patients with cardiac dysfunction post cancer therapy were managed at specialist cardiology clinic and received remodelling agents such as Angiotensin-converting enzyme (ACE) inhibitors and beta blockers in context of LV systolic dysfunction.ConclusionCMR has an important role in aiding clinical decision-making in female patients with cardiac risk factors and a history of cancer and in assessing cardiac toxicity of cancer treatment. It allows early risk stratification and guides preventive and treatment strategies.Abstract 182 Figure 1Examples of late gadolinium enhancement images. A - Mid wall late fibrosis (black arrow) in a patient with breast cancer previously treated with anthracyclines. B - diffuse, circumferential enhancement indicative of AL amyloid in a patient with myeloma. C - inferior myocardial infarction in a patient with previously treated malignancy. D - hypertrophic cardiomyopathy in a patient with breast cancerConflict of Interestnone

In today’s digital trend, deep learning technology has become an indispensable innovation power in the field of graphic design. In order to better integrate traditional visual elements from graphic design into graphic design, this paper proposes a graphic design layout generation model based on the LayoutGAN model. Taking Chinese traditional paper-cutting art as an example, we construct a dataset of visual elements and use Gaussian filtering, the LGE model, and the OTSU algorithm to preprocess the images of visual elements in paper-cutting art. The LayoutGAN model utilizes the layout element wireframe rendering module to enhance the extraction of visual elements of traditional art and a 2-stage training strategy is employed for model optimization training. To demonstrate the feasibility of applying the model in the reconstruction of visual elements of graphic design art, it was tested experimentally. After the LGE model was used for the image enhancement of the visual elements of paper-cutting art, the image enhancement effect was improved by 30.68%, and the segmentation accuracy and the mIoU of the traditional art visual elements were 98.24% and 97.58%, respectively. The PSNR value of the graphic design generated using the LayoutGAN model is 21.27, and the subjective evaluation scores are all above 3. The reconstruction of traditional visual elements in graphic design can be achieved using deep learning models, and the original information is preserved to the maximum extent.

Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classification of prior information U-Net (ICPIU-Net) to efficiently segment the left ventricle (LV) myocardium, myocardial infarction (MI), and microvascular-obstructed (MVO) tissues from late gadolinium enhancement magnetic resonance (LGE-MR) images. Our approach was developed using two subnets cascaded to first segment the LV cavity and myocardium. Then, we used inclusion and classification constraint networks to improve the resulting segmentation of the diseased regions within the pre-segmented LV myocardium. This network incorporates the inclusion and classification information of the LGE-MRI to maintain topological constraints of pathological areas. In the testing stage, the outputs of each segmentation network obtained with specific estimated parameters from training were fused using the majority voting technique for the final label prediction of each voxel in the LGE-MR image. The proposed method was validated by comparing its results to manual drawings by experts from 50 LGE-MR images. Importantly, compared to various deep learning-based methods participating in the EMIDEC challenge, the results of our approach have a more significant agreement with manual contouring in segmenting myocardial diseases.

In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for the quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).

Background Hypertrophic cardiomyopathy (HCM) is characterized by increased left ventricular wall thickness, cardiomyocyte hypertrophy, and fibrosis. Adverse cardiac risk characterization has been performed using late gadolinium enhancement (LGE), native T1, and extracellular volume (ECV). Relaxation time constants are affected by background field inhomogeneity. T1ρ utilizes a spin-lock pulse to decrease the effect of unwanted relaxation. The objective of this study was to study T1ρ as compared to T1, ECV, and LGE in HCM patients. Methods HCM patients were recruited as part of the Novel Markers of Prognosis in Hypertrophic Cardiomyopathy study, and healthy controls were matched for comparison. In addition to cardiac functional imaging, subjects underwent T1 and T1ρ cardiovascular magnetic resonance imaging at short-axis positions at 1.5T. Subjects received gadolinium and underwent LGE imaging 15–20 min after injection covering the entire heart. Corresponding basal and mid short axis LGE slices were selected for comparison with T1 and T1ρ. Full-width half-maximum thresholding was used to determine the percent enhancement area in each LGE-positive slice by LGE, T1, and T1ρ. Two clinicians independently reviewed LGE images for presence or absence of enhancement. If in agreement, the image was labeled positive (LGE + +) or negative (LGE −−); otherwise, the image was labeled equivocal (LGE + −). Results In 40 HCM patients and 10 controls, T1 percent enhancement area (Spearman’s rho = 0.61, p < 1e-5) and T1ρ percent enhancement area (Spearman’s rho = 0.48, p < 0.001e-3) correlated with LGE percent enhancement area. T1 and T1ρ percent enhancement areas were also correlated (Spearman’s rho = 0.28, p = 0.047). For both T1 and T1ρ, HCM patients demonstrated significantly longer relaxation times compared to controls in each LGE category (p < 0.001 for all). HCM patients also showed significantly higher ECV compared to controls in each LGE category (p < 0.01 for all), and LGE −− slices had lower ECV than LGE + + (p = 0.01). Conclusions Hyperenhancement areas as measured by T1ρ and LGE are moderately correlated. T1, T1ρ, and ECV were elevated in HCM patients compared to controls, irrespective of the presence of LGE. These findings warrant additional studies to investigate the prognostic utility of T1ρ imaging in the evaluation of HCM patients.

Background We examined the dynamic response of the myocardium to infarction in a longitudinal porcine study using relaxometry, functional as well as diffusion cardiovascular magnetic resonance (CMR). We sought to compare non contrast CMR methods like relaxometry and in-vivo diffusion to contrast enhanced imaging and investigate the link of microstructural and functional changes in the acute and chronically infarcted heart. Methods CMR was performed on five myocardial infarction pigs and four healthy controls. In the infarction group, measurements were obtained 2 weeks before 90 min occlusion of the left circumflex artery, 6 days after ischemia and at 5 as well as 9 weeks as chronic follow-up. The timing of measurements was replicated in the control cohort. Imaging consisted of functional cine imaging, 3D tagging, T2 mapping, native as well as gadolinium enhanced T1 mapping, cardiac diffusion tensor imaging, and late gadolinium enhancement imaging. Results Native T1, extracellular volume (ECV) and mean diffusivity (MD) were significantly elevated in the infarcted region while fractional anisotropy (FA) was significantly reduced. During the transition from acute to chronic stages, native T1 presented minor changes (< 3%). ECV as well as MD increased from acute to the chronic stages compared to baseline: ECV: 125 ± 24% (day 6) 157 ± 24% (week 5) 146 ± 60% (week 9), MD: 17 ± 7% (day 6) 33 ± 14% (week 5) 29 ± 15% (week 9) and FA was further reduced: − 31 ± 10% (day 6) − 38 ± 8% (week 5) − 36 ± 14% (week 9). T2 as marker for myocardial edema was significantly increased in the ischemic area only during the acute stage (83 ± 3 ms infarction vs. 58 ± 2 ms control p < 0.001 and 61 ± 2 ms in the remote area p < 0.001). The analysis of functional imaging revealed reduced left ventricular ejection fraction, global longitudinal strain and torsion in the infarct group. At the same time the transmural helix angle (HA) gradient was steeper in the chronic follow-up and a correlation between longitudinal strain and transmural HA gradient was detected (r = 0.59 with p < 0.05). Comparing non-gadolinium enhanced data T2 mapping showed the largest relative change between infarct and remote during the acute stage (+ 33 ± 4% day 6, with p = 0.013 T2 vs. MD, p = 0.009 T2 vs. FA and p = 0.01 T2 vs. T1) while FA exhibited the largest relative change between infarct and remote during the chronic follow-up (+ 31 ± 2% week 5, with p = N.S. FA vs. MD, p = 0.03 FA vs. T2 and p = 0.003 FA vs. T1). Overall, diffusion parameters provided a higher contrast (> 23% for MD and > 27% for FA) during follow-up compared to relaxometry (T1 17–18%/T2 10–20%). Conclusion During chronic follow-up after myocardial infarction, cardiac diffusion tensor imaging provides a higher sensitivity for mapping microstructural alterations when compared to non-contrast enhanced relaxometry with the added benefit of providing directional tensor information to assess remodelling of myocyte aggregate orientations, which cannot be otherwise assessed.