Nipun Manral
YOU?
Author Swipe
View article: AI-quantified epicardial adipose tissue and prediction of future myocardial infarction in patients with cardiometabolic disease: a post-hoc analysis from the SCOT-HEART trial
AI-quantified epicardial adipose tissue and prediction of future myocardial infarction in patients with cardiometabolic disease: a post-hoc analysis from the SCOT-HEART trial Open
View article: AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study Open
View article: Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US population
Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US population Open
View article: Imaging small dynamic lesions using positron emission tomography and computed tomography: an 18F-sodium fluoride valvular phantom study
Imaging small dynamic lesions using positron emission tomography and computed tomography: an 18F-sodium fluoride valvular phantom study Open
Aims 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) detects active microcalcification and predicts adverse outcomes including bioprosthetic valve deterioration. However, measuring small areas of 18F-NaF uptake within movi…
View article: AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study Open
Background Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel a…
View article: Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve–Defined Ischemia and Impaired Myocardial Blood Flow
Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve–Defined Ischemia and Impaired Myocardial Blood Flow Open
Background: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratificat…
View article: Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry
Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry Open
View article: Deep learning-based plaque quantification from coronary computed tomography angiography: external validation and comparison with intravascular ultrasound
Deep learning-based plaque quantification from coronary computed tomography angiography: external validation and comparison with intravascular ultrasound Open
Background Atherosclerotic plaque quantification from coronary computed tomography angiography (CTA) enables accurate assessment of coronary artery disease burden, progression, and prognosis. However, quantitative plaque analysis is time-c…
View article: Deep Learning From Coronary Computed Tomography Angiography for Atherosclerotic Plaque and Stenosis Quantification and Cardiac Risk Prediction
Deep Learning From Coronary Computed Tomography Angiography for Atherosclerotic Plaque and Stenosis Quantification and Cardiac Risk Prediction Open