A. Shanbhag

发表

Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans

D. Dey, D. Berman, P. Slomka, 2022, European Journal of Nuclear Medicine and Molecular Imaging.

Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method

D. Dey, S. Achenbach, M. Dweck, 2022, Medical Imaging.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

D. Dey, H. Gransar, P. Slomka, 2022, JACC. Cardiovascular imaging.

Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging

P. Slomka, A. Sinusas, Yi-Hwa Liu, 2022, Journal of Nuclear Cardiology.

Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans

D. Dey, D. Berman, P. Slomka, 2022, European Journal of Nuclear Medicine and Molecular Imaging.

Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks

D. Dey, T. Bateman, E. Miller, 2022, Medical Imaging.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

D. Dey, S. Achenbach, H. Gransar, 2022, The Lancet. Digital health.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

D. Dey, P. Kaufmann, P. Slomka, 2023, European Journal of Nuclear Medicine and Molecular Imaging.

Transferring deep convolutional network representations from SPECT to improve PET cardiac outcome prediction

D. Dey, H. Gransar, P. Slomka, 2023, Medical Imaging.

Pseudo-contrast cardiac CT angiography derived from non-contrast CT using conditional generative adversarial networks

D. Dey, P. Slomka, C. Kȩpka, 2023, Medical Imaging.

Deep Learning–Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT

D. Dey, P. Kaufmann, P. Slomka, 2022, The Journal of Nuclear Medicine.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

M. Motwani, D. Dey, P. Kaufmann, 2023, NPJ digital medicine.

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events

D. Dey, D. Berman, P. Slomka, 2022, The Journal of Nuclear Medicine.

Deep Learning From Coronary Computed Tomography Angiography for Atherosclerotic Plaque and Stenosis Quantification and Cardiac Risk Prediction

D. Dey, D. Berman, S. Achenbach, 2021, SSRN Electronic Journal.

Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography

D. Dey, P. Slomka, M. Dweck, 2023, Medical Imaging.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging.

P. Slomka, A. Sinusas, A. Feher, 2023, The international journal of cardiovascular imaging.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

P. Slomka, A. Sinusas, A. Feher, 2023, European journal of nuclear medicine and molecular imaging.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging

D. Dey, T. Ruddy, P. Kavanagh, 2024, npj Digit. Medicine.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

D. Dey, P. Slomka, A. Sinusas, 2024, EBioMedicine.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging

A. Shanbhag, Leonidas Miras, B. Bednarski, 2024, The Journal of Nuclear Medicine.

Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI

D. Dey, A. Shanbhag, Robert J. H. Miller, 2024, medRxiv.

Deep Learning–Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis

D. Dey, P. Kavanagh, Robert J. H. Miller, 2024, The Journal of Nuclear Medicine.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

D. Dey, T. Ruddy, P. Kavanagh, 2024, Nature communications.