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The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database. [ABSTRACT FROM PUBLISHER]

Background: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference. Methods: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1−4), slice thicknesses (0.4−8 mm), and virtual monoenergetic imaging levels (VMI, 40−90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland–Altman plots, and Lin's concordance correlation coefficient (CCC) were used. Results: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1–1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV). Conclusions: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV. Relevance statement: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. Key points: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance (CMR) has emerged as a useful tool in the diagnostic work-up of patients with clinically suspected acute myocarditis (AM), yet the diagnosis remains challenging. The purpose of this proof-of-concept study was to evaluate if data-driven texture analysis has the feasibility to automatically distinguish between patients with and without CMR-verified AM using T2-weighted, late gadolinium enhancement, and CINE imaging. In particular, the present study investigated if functional CINE imaging could be used as a novel tissue characterization technique. Twenty patients with clinically suspected AM, separated into CMR-verified (n = 10) and non CMR-verified (n = 10) AM according to the Lake Louise criteria, were retrospectively included. Texture features were extracted from the images, compared on a group level, and correlated to the diagnostic outcome (CMR-verified versus non CMR-verified AM). Several features showed good to excellent reproducibility with very large differences between the groups, and moderate to strong correlation with the diagnostic outcome, suggesting that CMR texture analysis is a promising diagnostic tool for patients with clinically suspected AM. Furthermore, findings indicate that CINE imaging, which is currently used for the evaluation of cardiac function, might be a useful non-contrast-based technique for tissue characterization in patients with clinically suspected AM. [ABSTRACT FROM AUTHOR]

Preeclampsia is a maternal hypertensive disease, complicating 2–8% of all pregnancies. It has been linked to a 2–7-fold increased risk for the development of cardiovascular disease, including heart failure, later in life. A total of 40% of formerly preeclamptic women develop preclinical heart failure, which may further deteriorate into clinical heart failure. Noninvasive cardiac imaging could assist in the early detection of myocardial abnormalities, especially in the preclinical stage, when these changes are likely to be reversible. Moreover, imaging studies can improve our insights into the relationship between preeclampsia and heart failure and can be used for monitoring. Cardiac ultrasound is used to assess quantitative changes, including the left ventricular cavity volume and wall thickness, myocardial mass, systolic and diastolic function, and strain. Cardiac magnetic resonance imaging may be of additional diagnostic value to assess diffuse and focal fibrosis and perfusion. After preeclampsia, sustained elevated myocardial mass along with reduced myocardial circumferential and longitudinal strain and decreased diastolic function is reported. These findings are consistent with the early phases of heart failure, referred to as preclinical (asymptomatic) or B-stage heart failure. In this review, we will provide an up-to-date overview of the potential of cardiac magnetic resonance imaging and echocardiography in identifying formerly preeclamptic women who are at high risk for developing heart failure. The potential contribution to early cardiac screening of women with a history of preeclampsia and the pros and cons of these imaging modalities are outlined. Finally, recommendations for future research are presented. [ABSTRACT FROM AUTHOR]

Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is valuable for diagnosis and assessment of the severity of various myocardial diseases owing to its potential to visualize myocardial scars. T1 mapping is complementary to LGE because it can quantify the degree of myocardial fibrosis or edema. As such, T1-weighted imaging techniques, including LGE using an inversion recovery sequence, contribute to cardiac MRI. T2-weighted imaging is widely used to characterize the tissue of many organs. T2-weighted imaging is used in cardiac MRI to identify myocardial edema related to chest pain, acute myocardial diseases, or severe myocardial injuries. However, it is difficult to determine the presence and extent of myocardial edema because of the low contrast between normal and diseased myocardium and image artifacts of T2-weighted images and the lack of an established method to quantify the images. T2 mapping quantifies myocardial T2 values and help identify myocardial edema. The T2 values are significantly related to the clinical symptoms or severity of nonischemic cardiomyopathy. Texture analysis is a postprocessing method to quantify tissue alterations that are reflected in the T2-weighted images. Texture analysis provides a variety of parameters, such as skewness, entropy, and grey-scale non-uniformity, without the need for additional sequences. The abnormal signal intensity on T2-weighted images or T2 values may correspond to not only myocardial edema but also other tissue alterations. In this review, the techniques of cardiac T2 mapping and texture analysis and their clinical relevance are described. [ABSTRACT FROM AUTHOR]

• For Gabor, gradient, HOG and wavelet features the number of intensity levels minimally affects the classification. • Normalization method and number of intensity levels influence the classification for GLCM and GRLM features. • For MRI and CT images, the lowest classification error is usually obtained with a large number of intensity levels. • For MRI and CT images, the recommended normalization method is 1%–99%. • For images with additive noise and GLCM or GRLM features, ±3σ normalization and low number of grey levels is recommended. Image texture is a very important component in many types of images, including medical images. Medical images are often corrupted by noise and affected by artifacts. Some of the texture-based features that should describe the structure of the tissue under examination may also reflect, for example, the uneven sensitivity of the scanner within the tissue region. This in turn may lead to an inappropriate description of the tissue or incorrect classification. To limit these phenomena, the analyzed regions of interest are normalized. In texture analysis methods, image intensity normalization is usually followed by a reduction in the number of levels coding the intensity. The aim of this work was to analyze the impact of different image normalization methods and the number of intensity levels on texture classification, taking into account noise and artifacts related to uneven background brightness distribution. Analyses were performed on four sets of images: modified Brodatz textures, kidney images obtained by means of dynamic contrast-enhanced magnetic resonance imaging, shoulder images acquired as T2-weighted magnetic resonance images and CT heart and thorax images. The results will be of use for choosing a particular method of image normalization, based on the types of noise and distortion present in the images. [ABSTRACT FROM AUTHOR]

Object recognition and localization is still a very challenging problem, despite recent advances in deep learning (DL) approaches, especially for objects with varying shapes and appearances. Statistical models, such as an Active Shape Model (ASM), rely on a parametric model of the object, allowing an easy incorporation of prior knowledge about shape and appearance in a principled way. To take advantage of these benefits, this paper proposes a new ASM framework that addresses two tasks: $(i)$ comparing the performance of several image features used to extract observations from an input image; and $(ii)$ improving the performance of the model fitting by relying on a probabilistic framework that allows the use of multiple observations and is robust to the presence of outliers. The goal in $(i)$ is to maximize the quality of the observations by exploring a wide set of handcrafted features (HOG, SIFT, and texture templates) and more recent DL-based features. Regarding $(ii)$ , we use the Generalized Expectation-Maximization algorithm to deal with outliers and to extend the fitting process to multiple observations. The proposed framework is evaluated in the context of facial landmark fitting and the segmentation of the endocardium of the left ventricle in cardiac magnetic resonance volumes. We experimentally observe that the proposed approach is robust not only to outliers, but also to adverse initialization conditions and to large search regions (from where the observations are extracted from the image). Furthermore, the results of the proposed combination of the ASM with DL-based features are competitive with more recent DL approaches (e.g. FCN , U-Net and CNN Cascade), showing that it is possible to combine the benefits of statistical models and DL into a new deep ASM probabilistic framework. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients. [ABSTRACT FROM AUTHOR]