Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.

Background: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients.

On-Site Computed Tomography-Derived Fractional Flow Reserve to Guide Management of Patients With Stable Coronary Artery Disease: The TARGET Randomized Trial.

Background: Computed tomography-derived fractional flow reserve (CT-FFR) using on-site machine learning enables identification of both the presence of coronary artery disease and vessel-specific ischemia. However, it is unclear whether on-site CT-FFR improves clinical or economic outcomes when compared with the standard of care in patients with stable coronary artery disease.

Methods: In total, 1216 patients with stable coronary artery disease and an intermediate stenosis of 30% to 90% on coronary computed tomographic angiography were randomized to an on-site CT-FFR care pathway using machine learning or to standard care in 6 Chinese medical centers.

One-stop patient-specific myocardial blood flow quantification technique based on allometric scaling law.

Establishing a patient-specific and non-invasive technique to derive blood flow as well as coronary structural information from one single cardiac CT imaging modality. 336 patients with chest pain or ST segment depression on electrocardiogram were retrospectively enrolled. All patients underwent adenosine-stressed dynamic CT myocardial perfusion imaging (CT-MPI) and coronary computed tomography angiography (CCTA) in sequence.

A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation.

Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the diagnostic performance, a deep learning-based, fully automatic, and clinical-ready framework was developed.

A prospective randomized trial comparing microwave and radiofrequency ablation for the treatment of liver metastases using a dual ablation system ─ The Mira study.

Purpose: The aim of this study was to prospectively compare the therapy response and safety of microwave (MWA) and radiofrequency ablation (RFA) for the treatment of liver metastases using a dual ablation system.

Methods: Fifty patients with liver metastases (23 men, mean age: 62.8 ± 11.

Prevalence and Associated Risk Factors of Pulmonary Embolism in Children and Young Adults With Nephrotic Syndrome: A Chinese Large Cohort Study.

Purpose: Nephrotic syndrome (NS) is highly associated with an increased risk of pulmonary embolism (PE) in children and young adults. However, few studies have specified the risk factors of PE in children and young adults with NS. We sought to determine the prevalence and associated factors of PE confirmed with computed tomography pulmonary angiography in Chinese children and young adults with NS.

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography.

Background: Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone.

Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease.

Objective: To investigate the utility of coronary CT angiography-derived fractional flow reserve (FFR) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE).

Methods: This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death.

Different posterior hippocampus and default mode network modulation in young APOE ε4 carriers: a functional connectome-informed phenotype longitudinal study.

To determine the functional connectome change pattern based on subregions of the hippocampus in young APOEε4 carriers during a 3-year follow-up. All the participants (n = 213) were tested for resting-state functional MRI, neuropsychological scales, and APOE genotype. The age- and sex-matched APOE ε4/ε3 (23.

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases.

Current Status of Etiology, Epidemiology, Clinical Manifestations and Imagings for COVID-19.

Coronavirus disease 2019 (COVID-19) is a transmissible respiratory disease that was initially reported in Wuhan, China in December 2019. With the alarming levels of COVID-19 spread worldwide, the World Health Organization characterized COVID-19 as a pandemic. Over the past several months, chest CT has played a vital role in early identification, disease severity assessment, and dynamic disease course monitoring of COVID-19.

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement.

Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.

Purpose: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA).

Materials And Methods: In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.

Uric Acid Has Different Effects on Spontaneous Brain Activities of Males and Females: A Cross-Sectional Resting-State Functional MR Imaging Study.

Objective: To explore the relationship among serum uric acid (SUA) levels in different states of disease, human cognition, and spontaneous brain activities by resting-state functional MRI (rs-fMRI).

Methods: We prospectively recruited 100 subjects (age 58 ± 11 years, 55 females) who underwent fasting blood sampling, cognitive tests and rs-fMRI scans. The subjects were divided into two groups by sex and each sex group was further stratified into three subgroups according to SUA level in different states of disease.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

Objectives: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.

Methods: Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR.

Feasibility of extracellular volume quantification using dual-energy CT.

ObjectiveTo assess the feasibility of dual energy CT (DECT) to derive myocardial extracellular volume (ECV) and detect myocardial ECV differences without a non-contrast acquisition, compared to single energy CT (SECT). MethodsSubjects (n = 35) with focal fibrosis (n = 17), diffuse fibrosis (n = 10), and controls (n = 9) underwent non-contrast and delayed acquisitions to calculate SECT-ECV. DECT-ECV was calculated using the delayed acquisition and the derived virtual non-contrast images.

Dual-energy CT-based iodine quantification to differentiate abdominal malignant lymphoma from lymph node metastasis.

Purpose: To investigate the value of dual-energy computed tomography (DECT)-derived iodine and fat quantification in differentiating malignant abdominal lymphoma from lymph node metastasis.

Materials And Methods: In this retrospective study, 59 patients (39 men; mean age, 62.7 years) with histopathologically-confirmed diagnosis of either malignant lymphoma or lymph node metastasis were included.