Artificial Intelligence in Nuclear Cardiology

Dey D. Slomka P.J. Leeson P. et al.

Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review.

J Am Coll Cardiol. 73: 1317-1335Obermeyer Z. Emanuel E.J.

Predicting the future - big data, machine learning, and clinical medicine.

N Engl J Med. 375: 1216-1219Segar M.W. Patel K.V. Ayers C. et al.

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

Eur J Heart Fail. 22: 148-158Krittanawong C. Tunhasiriwet A. Zhang H. et al.

Deep learning with unsupervised feature in echocardiographic imaging.

J Am Coll Cardiol. 69: 2100-2101Betancur J. Commandeur F. Motlagh M. et al.

Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study.

JACC Cardiovascular imaging. 11: 1654-1663Betancur J. Hu L.H. Commandeur F. et al.

Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study.

J Nucl Med. 60: 664-670Lundervold A.S. Lundervold A.

An overview of deep learning in medical imaging focusing on MRI.

Z Med Phys. 29: 102-127Ramon A.J. Yang Y. Pretorius P.H. et al.

Improving diagnostic accuracy in low-dose SPECT myocardial perfusion imaging with convolutional denoising networks.

IEEE Trans Med imaging. 39: 2893-2903Ronneberger O. Fischer P. Brox T.

U-net: convolutional networks for biomedical image segmentation.

MICCAI. : 234-241

A K-fold averaging cross-validation procedure.

J Nonparametr Stat. 27: 167-179Cawley G.C. Talbot N.L.C.

On over-fitting in model selection and subsequent selection bias in performance evaluation.

J Machine Learn Res. 11: 2079-2107Riley R.D. Ensor J. Snell K.I. et al.

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.

BMJ. 353: i3140Miller R.J.H. Sabovčik F. Cauwenberghs N. et al.

Temporal shift and predictive performance of machine learning for heart transplant outcomes.

J Heart Lung Transplant. 41: 928-936

Miller R.J.H., Rozanski A., Slomka P.J., et al., Development and validation of ischemia risk scores, J Nucl Cardiol, 2022. doi: 10.1007/s12350-022-02976-9. Online ahead of print.

Van Calster B. Vickers A.J.

Calibration of risk prediction models: impact on decision-analytic performance.

Med Decis Making. 35: 162-169Ramon A.J. Yang Y. Pretorius P.H. et al.

Initial investigation of low-dose SPECT-MPI via deep learning.

IEEE Nucl Sci Symp. : 1-3Song C. Yang Y. Wernick M.N. et al.

Low-dose cardiac-gated spect studies using a residual convolutional neural network.

IEEE Int Symp Biomed Imaging. 1: 653-656

Wang B. and Liu H., FBP-Net for direct reconstruction of dynamic PET images, Phys Med Biol, 65 (23), 2020, 1-16.

Shiri I. AmirMozafari Sabet K. Arabi H. et al.

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.

J Nucl Cardiol. 28: 2761-2779Dorbala S. Di Carli M.F. Delbeke D. et al.

SNMMI/ASNC/SCCT guideline for cardiac SPECT/CT and PET/CT 1.0.

J Nucl Med. 54: 1485-1507Arsanjani R. Xu Y. Hayes S.W. et al.

Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population.

J Nucl Med. 54: 221-228

Prevalence of misregistration between SPECT and CT for attenuation-corrected myocardial perfusion SPECT.

J Nucl Cardiol. 14: 200-206Ko C.-L. Cheng M.-F. Yen R.-F. et al.

Automatic alignment of CZT myocardial perfusion SPECT and external non-contrast CT by deep-learning model and dynamic data generation.

J Nucl Med. 60: 570Shi L. Onofrey J.A. Liu H. et al.

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

Eur J Nucl Med Mol Imaging. 47: 2383-2395Li T. Zhang M. Qi W. et al.

Motion correction of respiratory-gated PET images using deep learning based image registration framework.

Phys Med Biol. 65: 155003Guo X. Zhou B. Pigg D. et al.

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network.

Med Image Anal. 80: 102524Shi L. Lu Y. Dvornek N. et al.

Automatic inter-frame patient motion correction for dynamic cardiac PET using deep learning.

IEEE Trans Med Imaging. 40: 3293-3304Hagio T. Poitrasson-Rivière A. Moody J.B. et al.

Virtual” attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.

Eur J Nucl Med Mol Imaging. 49: 3140-3149Chen X.C. Zhou B. Shi L.Y. et al.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

J Nucl Cardiol. 29: 2235-2250Shanbhag A.D. Miller R.J.H. Pieszko K. et al.

Deep learning-based attenuation correction improves diagnostic accuracy of cardiac SPECT.

J Nucl Med. jnumed: 264429Trpkov C. Savtchenko A. Liang Z. et al.

Visually estimated coronary artery calcium score improves SPECT-MPI risk stratification.

Int J Cardiol Heart Vasc. 35: 100827Wolterink J.M. Leiner T. de Vos B.D. et al.

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

Med Image Anal. 34: 123-136Takx R.A. de Jong P.A. Leiner T. et al.

Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability.

PLoS One. 9: e91239Zeleznik R. Foldyna B. Eslami P. et al.

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nat Commun. 12: 715Isgum I. de Vos B.D. Wolterink J.M. et al.

Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT.

J Nucl Cardiol. 25: 2133-2142Pieszko K. Shanbhag A.D. Lemley M. et al.

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

Eur J Nucl Med Mol Imaging. 49: 4122-4132Pieszko K. Shanbhag A. Killekar A. et al.

Deep learning of coronary calcium scores from PET/CT attenuation maps accurately predicts adverse cardiovascular events.

JACC Cardiovasc Imaging. ()Miller R.J.H. Pieszko K. Shanbhag A. et al.

Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events.

J Nucl Med. jnumed: 264423Eisenberg E. McElhinney P.A. Commandeur F. et al.

Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects.

Circ Cardiovasc Imaging. 13: e009829Arsanjani R. Xu Y. Dey D. et al.

Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.

J Nucl Med. 54: 549-555Eisenberg E. Miller R.J.H. Hu L.H. et al.

Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT.

J Nucl Cardiol. 29: 2295-2307Miller R.J.H. Hauser M.T. Sharir T. et al.

Machine learning to predict abnormal myocardial perfusion from pre-test features.

J Nucl Cardiol. 29: 2393-2403Spier N. Nekolla S. Rupprecht C. et al.

Classification of polar maps from cardiac perfusion imaging with graph-convolutional neural networks.

Sci Rep. 9: 7569Otaki Y. Singh A. Kavanagh P. et al.

Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease.

JACC Cardiovasc Imaging. 15: 1091-1102Miller R.J.H. Singh A. Otaki Y. et al.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

Eur J Nucl Med Mol Imaging. ()Togo R. Hirata K. Manabe O. et al.

Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps.

Comput Biol Med. 104: 81-86Halme H.L. Ihalainen T. Suomalainen O. et al.

Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.

EJNMMI Res. 12: 27Betancur J. Otaki Y. Motwani M. et al.

Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning.

JACC Cardiovasc Imaging. 11: 1000-1009Hu L.H. Betancur J. Sharir T. et al.

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

Eur Heart J Cardiovasc Imaging. 21: 549-559Hu L.H. Miller R.J.H. Sharir T. et al.

Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT.

Eur Heart J Cardiovasc Imaging. 22: 705-714Juarez-Orozco L.E. Martinez-Manzanera O. van der Zant F.M. et al.

Deep learning in quantitative PET myocardial perfusion imaging: a study on cardiovascular event prediction.

JACC Cardiovasc Imaging. 13: 180-182Singh A. Kwiecinski J. Miller R.J.H. et al.

Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images.

Circ Cardiovasc Imaging. 15: e014526Singh A. Miller R.J.H. Otaki Y. et al.

Direct risk assessment from myocardial perfusion imaging using explainable deep learning.

JACC Cardiovasc Imaging. S1936-878X: 00484-00493Haro Alonso D. Wernick M.N. Yang Y. et al.

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.

J Nucl Cardiol. 26: 1746-1754Rios R. Miller R.J.H. Hu L.H. et al.

Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.

Cardiovasc Res. 118: 2152-2164Rios R. Miller R.J.H. Manral N. et al.

Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: insights from REFINE SPECT registry.

Comput Biol Med. 145: 105449Selvaraju R.R. Cogswell M. Das A. et al.

Grad-cam: visual explanations from deep networks via gradient-based localization.

IEEE Int Conf Comput Vis. : 618-626Miller R.J.H. Kuronuma K. Singh A. et al.

Explainable deep learning improves physician interpretation of myocardial perfusion imaging.

J Nucl Med. 63: 1768-1774

留言 (0)

沒有登入
gif