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Erschienen in: Journal of Nuclear Cardiology 6/2022

07.12.2021 | Editorial

Deep learning-based attenuation map generation and correction; could it be useful clinically?

verfasst von: Ananya Singh, MSc, Robert J. H. Miller, MD

Erschienen in: Journal of Nuclear Cardiology | Ausgabe 6/2022

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Excerpt

Deep learning (DL) is a branch of machine learning characterized by a multi-layered learning approach. Convolutional neural networks (CNNs) are an example of DL which are commonly applied to imaging data.1,2 DL models are particularly well suited to direct image analysis and image manipulation because each input image pixel can be mapped to one neuron in the first layer of the network. Since neurons are only connected to nearby neurons in the following layer, the structural relationships within the image are preserved through the model layers. …
Literatur
1.
Zurück zum Zitat Krittanawong C, Tunhasiriwet A, Zhang H, Wang Z, Aydar M, Kitai T. Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol 2017;69:2100‐1.CrossRef Krittanawong C, Tunhasiriwet A, Zhang H, Wang Z, Aydar M, Kitai T. Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol 2017;69:2100‐1.CrossRef
2.
Zurück zum Zitat Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol 2019;73:1317‐35.CrossRef Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol 2019;73:1317‐35.CrossRef
3.
Zurück zum Zitat Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Comput Vis Pattern Recog 2015. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Comput Vis Pattern Recog 2015.
4.
Zurück zum Zitat Shi L, Onofrey JA, Liu H, Liu Y-H, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging 2020;47:2383‐95.CrossRef Shi L, Onofrey JA, Liu H, Liu Y-H, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging 2020;47:2383‐95.CrossRef
5.
Zurück zum Zitat Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. Comput Vis Pattern Recog 2017;1125-34. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. Comput Vis Pattern Recog 2017;1125-34.
6.
Zurück zum Zitat Liu H, Wu J, Shi L, Liu Y, Miller EJ, Sinusas AJ et al. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning based attenuation map generation. J Nucl Cardiol 2021 Liu H, Wu J, Shi L, Liu Y, Miller EJ, Sinusas AJ et al. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning based attenuation map generation. J Nucl Cardiol 2021
7.
Zurück zum Zitat Slomka P, Miller RJ, Hu L-H, Germano G, Berman D. Solid-state detector SPECT myocardial perfusion imaging. J Nucl Med 2019;60:1194‐204.CrossRef Slomka P, Miller RJ, Hu L-H, Germano G, Berman D. Solid-state detector SPECT myocardial perfusion imaging. J Nucl Med 2019;60:1194‐204.CrossRef
8.
Zurück zum Zitat Shiri I, AmirMozafari Sabet K, Arabi H, Pourkeshavarz M, Teimourian B, Ay MR et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J Nucl Cardiol 2020 Shiri I, AmirMozafari Sabet K, Arabi H, Pourkeshavarz M, Teimourian B, Ay MR et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J Nucl Cardiol 2020
9.
Zurück zum Zitat Chen X, Zhou B, Shi L, Liu H, Pang Y, Wang R et al. CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network. J Nucl Cardiol 2021 Chen X, Zhou B, Shi L, Liu H, Pang Y, Wang R et al. CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network. J Nucl Cardiol 2021
10.
Zurück zum Zitat Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol 2020;65:055011.CrossRef Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol 2020;65:055011.CrossRef
11.
Zurück zum Zitat Mostafapour S, Gholamiankhah F, Maroofpour S, Momennezhad M, Asadinezhad M, Zakavi SR et al. Deep learning-based attenuation correction in the image domain for myocardial perfusion SPECT imaging. 2021; arXiv:210204915. Mostafapour S, Gholamiankhah F, Maroofpour S, Momennezhad M, Asadinezhad M, Zakavi SR et al. Deep learning-based attenuation correction in the image domain for myocardial perfusion SPECT imaging. 2021; arXiv:​210204915.
12.
Zurück zum Zitat Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M Jr, Gerlach J, 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 2013;54:221‐8.CrossRef Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M Jr, Gerlach J, 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 2013;54:221‐8.CrossRef
13.
Zurück zum Zitat Huang J-Y, Huang C-K, Yen R-F, Wu H-Y, Tu Y-K, Cheng M-F, et al. Diagnostic performance of attenuation-corrected myocardial perfusion imaging for coronary artery disease: A systematic review and meta-analysis. J Nucl Med 2016;57:1893‐8.CrossRef Huang J-Y, Huang C-K, Yen R-F, Wu H-Y, Tu Y-K, Cheng M-F, et al. Diagnostic performance of attenuation-corrected myocardial perfusion imaging for coronary artery disease: A systematic review and meta-analysis. J Nucl Med 2016;57:1893‐8.CrossRef
14.
Zurück zum Zitat Otaki Y, Singh A, Miller RJH, Kavanagh P, Sharir T, Fish M et al. Clinical deployment of explainable deep learning to improve myocardial perfusion imaging. JACC Cardiovasc Imaging 2021 Otaki Y, Singh A, Miller RJH, Kavanagh P, Sharir T, Fish M et al. Clinical deployment of explainable deep learning to improve myocardial perfusion imaging. JACC Cardiovasc Imaging 2021
15.
Zurück zum Zitat Hacker M, Becker C. The incremental value of coronary artery calcium scores to myocardial single photon emission computer tomography in risk assessment. J Nucl Cardiol 2011;18:700-11; quiz 12-6. Hacker M, Becker C. The incremental value of coronary artery calcium scores to myocardial single photon emission computer tomography in risk assessment. J Nucl Cardiol 2011;18:700-11; quiz 12-6.
16.
Zurück zum Zitat Mouden M, Ottervanger JP, Timmer JR, Reiffers S, Oostdijk AH, Knollema S, et al. The influence of coronary calcium score on the interpretation of myocardial perfusion imaging. J Nucl Cardiol 2014;21:368‐74.CrossRef Mouden M, Ottervanger JP, Timmer JR, Reiffers S, Oostdijk AH, Knollema S, et al. The influence of coronary calcium score on the interpretation of myocardial perfusion imaging. J Nucl Cardiol 2014;21:368‐74.CrossRef
17.
Zurück zum Zitat Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, et al. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: A prospective study. Cardiovasc Res 2019;116:2216‐25.CrossRef Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, et al. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: A prospective study. Cardiovasc Res 2019;116:2216‐25.CrossRef
18.
Zurück zum Zitat Trpkov C, Savtchenko A, Liang Z, Feng P, Southern DA, Wilton SB et al. Visually estimated coronary artery calcium score improves SPECT-MPI risk stratification. IJC Heart Vasc 2021;35:100827. Trpkov C, Savtchenko A, Liang Z, Feng P, Southern DA, Wilton SB et al. Visually estimated coronary artery calcium score improves SPECT-MPI risk stratification. IJC Heart Vasc 2021;35:100827.
19.
Zurück zum Zitat Sharma V, Mughal L, Dimitropoulos G, Sheikh A, Griffin M, Moss A et al. The additive prognostic value of coronary calcium score (CCS) to single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI)-real world data from a single center. J Nucl Cardiol 2019 Sharma V, Mughal L, Dimitropoulos G, Sheikh A, Griffin M, Moss A et al. The additive prognostic value of coronary calcium score (CCS) to single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI)-real world data from a single center. J Nucl Cardiol 2019
20.
Zurück zum Zitat Thompson RC, McGhie AI, Moser KW, O’Keefe JH Jr, Stevens TL, House J, et al. Clinical utility of coronary calcium scoring after nonischemic myocardial perfusion imaging. J Nucl Cardiol 2005;12:392‐400.CrossRef Thompson RC, McGhie AI, Moser KW, O’Keefe JH Jr, Stevens TL, House J, et al. Clinical utility of coronary calcium scoring after nonischemic myocardial perfusion imaging. J Nucl Cardiol 2005;12:392‐400.CrossRef
Metadaten
Titel
Deep learning-based attenuation map generation and correction; could it be useful clinically?
verfasst von
Ananya Singh, MSc
Robert J. H. Miller, MD
Publikationsdatum
07.12.2021
Verlag
Springer International Publishing
Erschienen in
Journal of Nuclear Cardiology / Ausgabe 6/2022
Print ISSN: 1071-3581
Elektronische ISSN: 1532-6551
DOI
https://doi.org/10.1007/s12350-021-02875-5

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