Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.

Ventricular tachycardia (VT) is a severe arrhythmia commonly treated with implantable cardioverter defibrillators, antiarrhythmic drugs and catheter ablation (CA). Although CA is effective in reducing recurrent VT, its impact on survival remains uncertain, especially in patients with extensive scarring. Stereotactic arrhythmia radioablation (STAR) has emerged as a novel treatment for VT in patients unresponsive to CA, leveraging techniques from stereotactic body radiation therapy used in cancer treatments.

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance.

The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.

The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses.

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Background: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality.

Purpose: To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA.

Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study.

Purpose: Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis.

F-sodium fluoride positron emission tomography following coronary computed tomography angiography in predicting long-term coronary events: a 5-year follow-up study.

Purpose: The predictive value of F-sodium fluoride (F-NaF) positron emission tomography (PET) in combination with coronary computed tomography (CT) angiography (CCTA) for future coronary events has attracted interest. We evaluated the potential of F-NaF PET/CT following CCTA to predict major coronary events (MACE) during a 5-year follow-up period.

Methods: Forty patients with coronary atherosclerotic lesions detected on CCTA underwent F-NaF PET/CT examination.

The reliability and utility of on-site CT-derived fractional flow reserve (FFR) based on fluid structure interactions: comparison with FFR based on computational fluid dynamics, invasive FFR, and resting full-cycle ratio.

Fractional flow reserve (FFR) derived off-site by coronary computed tomography angiography (CCTA) (FFR) is obtained by applying the principles of computational fluid dynamics. This study aimed to validate the overall reliability of on-site CCTA-derived FFR based on fluid structure interactions (CT-FFR) and assess its clinical utility compared with FFR, invasive FFR, and resting full-cycle ratio (RFR). We calculated the CT-FFR for 924 coronary vessels in 308 patients who underwent CCTA for clinically suspected coronary artery disease.

Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT.

The main purpose of pre-transcatheter aortic valve implantation (TAVI) cardiac computed tomography (CT) for patients with severe aortic stenosis is aortic annulus measurements. However, motion artifacts present a technical challenge because they can reduce the measurement accuracy of the aortic annulus. Therefore, we applied the recently developed second-generation whole-heart motion correction algorithm (SnapShot Freeze 2.

Native Myocardial T1 Value in Predicting 1-Year Outcomes in Patients with Nonischemic Dilated Cardiomyopathy Experiencing Recent Heart Failure.

The evidence for the clinical implications, especially the short-term utility, of native myocardial T1 value (T1) on cardiac magnetic resonance (CMR) in nonischemic dilated cardiomyopathy (NIDCM) is scant. We investigated the potential of T1 to assess left ventricular (LV) myocardial characteristics and predict 1-year outcomes in patient with NIDCM experiencing recent heart failure (HF).Forty-five patients with NIDCM and HF symptoms within 3 months underwent CMR with cine, non-contrast T1 mapping, and late gadolinium enhancement (LGE).

A longitudinal pilot study to assess temporal changes in coronary arterial F-sodium fluoride uptake.

Purpose: How coronary arterial F-sodium fluoride (F-NaF) uptake on positron emission tomography changes over the long term and what clinical factors impact the changes remain unclear. We sought to investigate the topics in this study.

Methods: We retrospectively studied 15 patients with ≥1 coronary atherosclerotic lesion/s detected on cardiac computed tomography who underwent baseline and follow-up (interval of >3 years) F-NaF positron emission tomography/computed tomography scans.