DCL: Dynamic Causal Learning for Cross-Modality Cardiac Image Segmentation.

X-ray computed tomography (CT) imaging plays an essential role in disease diagnosis due to its noninvasive, painless mode and superior penetration depth. However, the resolution of the soft tissue ...

Left ventricular (LV) pseudoaneurysm is a rare complication that occurs shortly after myocardial infarction or previous cardiac surgery. We present here a case of an elderly patient with an LV pseudoaneurysm that developed over a long period of time after Dor operation. An 83-year-old man with rapid LV enlargement 18 years after Dor operation presented with dyspnea. We diagnosed his mass as an LV pseudoaneurysm using multiple non-invasive imaging modalities. The suture between the patch and the LV myocardium became fragile and detached over time. However, due to adhesion in the thorax and slow blood flow, the enlargement stopped. Surgery was considered, but due to his age and renal dysfunction, he did not undergo surgery. Conservative treatment with antihypertensive and heart failure therapy was continued. Clinical manifestations of LV pseudoaneurysm vary from asymptomatic to signs of heart failure and even sudden death. Surgery is recommended for LV pseudoaneurysm because of the high risk of rupture, but conservative treatment may be an option that has been developed long after surgery in the era of an increasing number of elderly OMI or post-cardiac surgery patients. Proper diagnosis using multiple imaging modalities may lead to the avoidance of this devastating complication.

Heart disease is the leading cause of death in the modern world. Cardiac image processing is now routinely applied for detecting, classification and diagnosis of heart diseases. One of the most common uses of processing methods that are now widely applied are cardiac segmentation [1] and registration methods [2], that are used in order to extract the detailed anatomy and function of the heart. Automatic segmentation plays a central role when inspecting reconstructed 3D cardiac images from CT or MR scanners. An accurate classification of the different cardiac regions is usually the first step of tasks like: cardiac visualization, coronary artery inspection, measurement of the ejection fraction for the left and right ventricles and wall motion analysis. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time [3]. Cardiac image registration remains a challenge because of the numerous specific problems mainly related to the different motion sources (patient, respiration, heart) and to the specificity of each imaging modality. Up to now, no general method could automatically register any modality with any other modality so far. This paper presents a survey of shape modeling applications to cardiac image analysis from MRI, CT, echocardiography, PET, and SPECT and aims to introduce new methodologies in this field, classify major contributions in image-based cardiac modeling, provide a tutorial to beginners to initiate their own studies, and introduce the major challenges of registration and segmentation and provide practical examples. Image-driven processing methods, such as thresholding, region-based or edge-based techniques, or else pixel classification [4-6], provide a limited framework for further medical image analysis. They can include geometrical information, as well as high level information, in the so-called shape prior based segmentation framework, or through active shape models and active appearance models. At last, atlas guided segmentation also make use of a set of manually segmented images. By using different analysis software of cardiac images, CAD prototypes can be used in clinical routines in order to provide a computer output as a second reader to assist physicians in the detection of abnormalities, quantification of disease progression and differential diagnosis of lesions [7]. Computerized analysis of cardiac images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Several imaging technologies are available for diagnostic cardiac imaging, such as coronary computed tomography angiography (CTA), single photon emission computed tomography (SPECT) positron emission tomography (PET) and magnetic resonance imaging (MRI). Each of these techniques offers unique advantages, but also suffers from specific limitations. Software techniques are being developed to combine cardiac images from different modalities and generate composite multimodality images, allowing better diagnosis than that possible from images analyzed separately. Hybrid scanners (SPECT/CT and PET/CT) have also been proposed for integrated cardiac imaging. Physicians are presented with integrated fused images that contain complementary information from separate scans containing physiological and anatomical information. In this review, we present the latest approaches for integration of cardiac images from multiple modalities.

Cardiac amyloidosis is a form of infiltrative cardiomyopathy typically presenting with progressive heart failure. The clinical presentation and morphological findings often overlap with other cardiovascular diseases, and frequently results in misdiagnosis and consequent under-reporting. Cardiovascular imaging is playing an increasingly important diagnostic and prognostic role in this referral population, and is reducing the reliance on endomyocardial biopsy as a confirmatory testing. Advancements across multiple cardiac imaging modalities, including echocardiography, magnetic resonance imaging, nuclear imaging, and computed tomography, are improving diagnostic accuracy and offering novel approaches to sub-type differentiation and prognostication. This review explores recent advancements in cardiac imaging for the diagnosis, typing, and staging of cardiac amyloidosis, with a focus on new and evolving techniques. Emphasis is also placed on the promise of non-invasive cardiac imaging to provide value across the spectrum of this clinical disease, from early disease identification (prior to the development of increased wall thickness) through to markers of advanced disease associated with early mortality.

The goal of glioma surgery is maximal safe resection in order to provide optimal tumor control and survival benefit to the patient. There are multiple imaging modalities beyond traditional contrast-enhanced magnetic resonance imaging (MRI) that have been incorporated into the preoperative workup of patients presenting with gliomas. The aim of these imaging modalities is to identify cortical and subcortical areas of eloquence, and their relationship to the lesion. In this article, multiple modalities are described with an emphasis on the underlying technology, clinical utilization, advantages, and disadvantages of each. functional MRI and its role in identifying hemispheric dominance and areas of language and motor are discussed. The nuances of magnetoencephalography and transcranial magnetic stimulation in localization of eloquent cortex are examined, as well as the role of diffusion tensor imaging in defining normal white matter tracts in glioma surgery. Lastly, we highlight the role of stimulated Raman spectroscopy in intraoperative histopathological diagnosis of tissue to guide tumor resection. Tumors may shift the normal arrangement of functional anatomy in the brain; thus, utilization of multiple modalities may be helpful in operative planning and patient counseling for successful surgery.

Disease is very old, and nothing about it has changed. It is we who change, and we learn to recognise what was formally imperceptible. J.M. Charcot (1825–1893) Before the development of cardiovascular imaging techniques, clinicians could only imagine how the heart of their patients was contracting. It was not until the introduction of contrast ventriculography and quantitative methods for analysis in the early 1960s that objective data could be substituted for the subjective bedside observations. These developments irrevocably changed cardiology from an art to a science. Computer-aided systems were subsequently developed for more accurate analysis and reproducible measurements. In the past 20 years, advances in digital techniques and the imagination and creativity of many have resulted in an enormous progress in complex cardiac imaging modalities including ultrasound imaging, SPECT, multislice-CT, MR and PET[1]. These advances will undoubtedly accelerate as our reliance on imaging techniques for management of cardiovascular disease will continue to increase[1]. Who could have predicted 30 years ago the impressive evolution of cardiac ultrasound imaging systems? The technique paralleled the developments in microprocessor technology, it did not mimic the existing imaging modalities by providing tomographic images of the heart and it introduced new pathophysiologic and diagnostic concepts. As a consequence, it opened new horizons for clinical research and made unique contributions to our understanding of cardiac disease. With the increasing imaging performance, the number of functions and Doppler assessment of hemodynamics, all of which can be applied in a wide variety of clinical scenarios, it has become the most widely disseminated cardiac imaging technology. Currently, more than one out of every four medical imaging studies is performed worldwide with ultrasound and the proportion is still increasing. With the progress in miniaturisation and microprocessor technology cardiac ultrasound imaging continues to evolve rapidly (Fig. 1). We …

The aim of cardiac imaging is to contribute to the diagnosis of cardiac and vascular diseases by detecting early signs of abnormal activity, and to contribute to therapy follow‐up. Cardiac imaging attempts to detect global or local abnormalities in morphology, function, perfusion, and metabolism of the heart and vessels, including the evaluation of myocardial viability and artery plaques.The main imaging modalities are presented here. X‐ray angiography is useful for assessing heart and vessel anatomy, although it is an invasive modality requiring catheterization. Echocardiography is a widespread, cheap, and non‐invasive modality that can analyze cardiac anatomy, tissue and blood motion, and perfusion. Traditionally, nuclear imaging with thallium or technetium has been used to detect myocardial perfusion defects. Recently, molecular imaging, using positron emission tomography, has been proposed to identify molecular processes like angiogenesis or apoptosis. Cardiac magnetic resonance imaging is attracting increasing interest as it can now produce high‐quality images with good spatial resolution. Apart from evaluating anatomy, it also allows assessment of tissue and blood motion, perfusion, and myocardial viability. Finally, ultrafast computed tomography may also produce high‐resolution images of the heart and great vessels, showing and characterizing their function and assessing plaques in coronary arteries.As will be seen, currently available imaging modalities are complementary, showing different characteristics of cardiovascular structures and helping to achieve an accurate and early diagnosis of cardiovascular diseases.

An image understanding system is discussed that will analyze cardiac dysfunction found by combining information from multiple, noninvasive imaging modality studies. The system is a model for intelligent multimodality image understanding in general, but is applied to a very specific problem: detection and rating of left ventricular (LV) aneurysms. Key features of the system are: 1) its ability to handle and quantify uncertain or partial image-derived information in a concise way using probabilistic evidential reasoning, 2) its ability to fuse pertient relative information from independent diagnostic images of the same patient to achieve an algorithm-assembled, consensus, quantitative opinion of cardiac shape and motion, and finally 3) to arrive at a decision level set of numbers that quantify and localize left ventricular aneurysm formation for each patient. The availability of data such as described in 3) will enable more precise prognostic or diagnostic risk classification for patients, making therapy alternatives more rational. Because of the subjective probabilistic reasoning strategy (based on the principle of maximum entropy) the final quantitative results will carry not only the system’s assessment of a particular patient’s heart, but also the degree of confidence that the automated analysis system has in the result it presents. This confidence is increased when similar LV motion and shape is perceived by the multiple imaging modalities.

Objective: The objective of this narrative review was to summarize and critically appraise current clinical applications of artificial intelligence (AI) in cardiology, with particular emphasis on arrhythmia detection and cardiovascular imaging modalities. Methods: A selective narrative review of peer-reviewed literature published between 2015 and 2025 was performed using PubMed, Scopus, and Web of Science. Studies were selected based on clinical relevance, validation methodology, and reported diagnostic or prognostic performance in human populations. Results: AI-based algorithms demonstrate diagnostic performance comparable to expert interpretation in atrial fibrillation (AF) detection as well as across echocardiography, coronary computed tomography angiography (CCTA), cardiac magnetic resonance imaging (CMR), and intravascular optical coherence tomography (OCT). Beyond diagnostic accuracy, AI has been shown to reduce analysis time and interobserver variability in controlled and retrospective study settings. However, the majority of available studies are retrospective, rely on curated datasets, and lack prospective validation demonstrating a direct impact on clinical outcomes. Conclusions: AI constitutes a valuable decision-support tool in contemporary cardiology, enhancing diagnostic efficiency and risk stratification across multiple imaging and electrophysiological modalities. Nevertheless, broader clinical adoption will require rigorous external validation, improved model interpretability, and prospective outcome-driven studies. This review uniquely integrates evidence across electrophysiology and multiple imaging modalities to identify shared translational challenges and near-term clinical opportunities for AI in cardiology.

Objectives: Cardiac Image Segmentation field have a lot of difficulties when we take the big changes in sequences of images that are of different types. In image sequences, Segmentation of objects which are not fixed is more challenging. To handle such situations, use of Improved Genetic Algorithm for Image Segmentation of Cardiac images is presented.Methods: We propose an algorithm based on Improved Genetic Algorithm, for segmentation of medical image sequences, which uses K-mean clustering. For clustering in the feature space, we used feature vector of two- dimension. Findings: In our paper, for Cardiac Image Segmentation, we are presenting a state of art review of various methods and techniques. Various sequences of Cardiac image have been Registered, and then for segmentation process, single image is used. Novelty/Improvement: Satisfactory results have been given by the experiments done on Cardiac images.

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility. The codes are available at https://github.com/shuowang26/SRHeart.

In the field of cardiac MR image segmentation, active contour models, or snakes have been extensively used, owing to their promising results and to the numerous extensions proposed to improve their performance. This paper explores a methodology for evaluating cardiac MR image segmentation algorithms, which assesses the distance between computer-generated and the observer's hand-outlined boundaries. This metric was applied to various external force extensions of the traditional snake, since no systematic comparison has been performed. Cardiac MRI from six patients were analyzed. Imaging was performed on a 1.5 T MR scanner with ECG-gated balanced steady-state free precession (b-SSFP) sequences. Segmentation performances were established for traditional snake, gradient vector flow snake, standard- and guided- pressure force-based snake. The use of a pre-treatment with non-linear anisotropic filtering was also compared to non-filtered images. Agreement between manual and segmentation algorithms was satisfactory for ejection fraction for every segmentation scheme. However end-systolic and end-diastolic volumes were systematically underestimated. The developed regional error metric provided a more rigorous evaluation of the segmentation schemes in comparison to the classical derived parameters based on left ventricle volume estimation, usually used in functional cardiac MR studies. These derived parameters can furthermore mask local segmentation errors.

Combat-related thoracic trauma (CRTT) is a significant contributor to morbidity and mortality of the casualties from Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF). Penetrating, blunt, and blast injuries are the most common mechanisms of trauma to the chest. Imaging plays a key role in the battlefield management of CRTT casualties. This work discusses the imaging manifestations of thoracic injuries from blunt trauma and blast injury, emphasizing epidemiology and diagnostic clues seen during OEF and OIF. The assessment of radiologic findings in patients who suffer from combat-related blunt thoracic trauma and blast injury is the basis of this work. The imaging modalities for this work include multi-detector computed tomography (MDCT) and chest radiography. Multiple imaging modalities are available to imagers on or near the battlefront, including radiography, fluoroscopy, and MDCT. MDCT with multi-planar reconstructions is the most sensitive imaging modality available in combat hospitals for the evaluation of CRTT. In modern combat, blunt and blast injuries account for a significant portion of CRTT. Individual body armor converts penetrating trauma to blunt trauma, leading to pulmonary contusion that accounted for 50.2% of thoracic injuries during OIF and OEF. Flail chest, a subset of blunt chest injury, is caused by significant blunt force to the chest and occurs four times as frequently in combat casualties when compared with the civilian population. Imaging features of CRTT have significant diagnostic and prognostic value. Pulmonary contusions on chest radiography appear as patchy consolidations in the acute setting with ill-defined and non-segmental borders. MDCT of the chest is a superior imaging modality in diagnosing and evaluating pulmonary contusion. Contusions on MDCT appear as crescentic ground-glass opacities (opacities through which lung interstitium and vasculature are still visible) and areas of consolidation that often do not respect the anatomic boundaries of the affected lobes. Additionally, small pulmonary contusions may exhibit sub-pleural sparing and may distinguish contusion from pneumonia or other lung pathology. Although pulmonary laceration is typically the result of penetrating trauma, laceration may also be caused by displaced rib fractures or significant shearing forces on the lung without penetrating injury. Because of elastic recoil of the normal pulmonary parenchyma surrounding the injury, pulmonary lacerations may present as late as 48-72 h after injury. Pulmonary lacerations may appear similar to pulmonary contusions on chest radiography initially and will require MDCT for definitive diagnosis. Blast injury is a defining injury of modern combat. Blast lung injury is initially diagnosed with chest radiography, where the pattern of lung opacities has previously been described by clinicians as "batwing" or "butterfly" because of its central appearance in the lung. "Peribronchovascular" may be a more accurate description of primary blast lung based on its appearance on MDCT. This pattern may differentiate primary blast lung injury from other causes of thoracic trauma. CRTT continues to be a significant contributor to the morbidity and mortality of those injured during OEF and OIF. The distinct injury patterns and atypical imaging manifestations of blunt trauma and blast lung injury are important to recognize early because of the acuity of this patient population and the influence of accurate diagnosis on clinical management.

Conventional biomedical imaging modalities in wide clinical use, such as ultrasound imaging, X-ray computed tomography, magnetic resonance imaging, and positron emission tomography, can provide morphological, anatomical, and functional information about biological tissues. However, single mode imaging in conventional medicine provides only limited information for definitive diagnoses. Thus, combinational diagnosis using multiple imaging modalities has become increasingly important. Recently, photoacoustic imaging (PAI) has gained significant attention, and several PAI prototypes have been used in clinical trials. At the same time, PAI has been tested in combination with conventional imaging modalities. For all these imaging modalities, various contrast-enhancing agents have been developed for various purposes. In this review article, we will focus on recent progress in developing dual mode contrast agents for PAI in combination with other conventional imaging modalities.