An assessment of attenuation correction of SPECT MPI images generated by deep learning model
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https://doi.org/10.54939/1859-1043.j.mst.CSCE5.2021.93-101Keywords:
Single photon emission computed tomography (SPECT); Attenuation Correction (AC); Non-Attenuation Correction (NC); Deep Learning (DL); Computer-aided diagnosis (CAD); Myocardial Perfusion Imaging (MPI).Abstract
This article evaluates the effectiveness of using a deep learning network model to generate reliable attenuation corrected the single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). The authors collected myocardial perfusion imaging data of 88 patients from a SPECT/CT machine, with an average age of 62.47 years. Then, two datasets are created from the original data: set A includes the deep learning-based attenuation corrected images (Generated Attenuation Correction - GenAC), and the non-attenuation corrected images; set B contains only non-attenuation corrected images. These datasets were diagnosed by two doctors (in which, one has 7 years of experience and the other has 10 years of experience in reading SPECT MPI). The doctors diagnose based on the image data without knowing which dataset it belongs to. The sensitivity, specificity, diagnostic accuracy, and lesion rate were evaluated between the two data sets. Results: The average specificity, sensitivity, and accuracy of the set with the deep learning-based attenuation corrected images were 0.87, 0.86, 0.86, while the results with the non-attenuation corrected images are 0.69, 0.83, and 0.78.
References
[1]. Goff D. C., Jr., et al, "2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology", American Heart Association Task Force on Practice Guidelines, Circulation, 129, 25 Suppl 2, Jun. 2014, 49-73, doi: 10.1161/01.cir.0000437741.48606.98.
[2]. Einstein AJ, "Effects of radiation exposure from cardiac imaging: how good are the data?", J Am Coll Cardiol 59, 2012, pp. 553–565, doi: 10.1016/j.jacc.2011.08.079
[3]. Peter L. Tilkemeier, MD, "Standardized reporting of radionuclide myocardial perfusion and function", Journal of Nuclear Cardiology (2009), doi: https://doi.org/10.1007/s12350-009-9095-8.
[4]. Bateman TM, Dilsizian V, Beanlands RS, DePuey EG, Heller GV, Wolinsky DA, "American society of nuclear cardiology position statement", Journal of Nuclear Cardiology, 2016, doi:10.1007/s12350-016-0626-9.
[5]. Technavio (2017), "Global SPECT Market 2017-2021", https://www.technavio.com/report/globalmedicalimagingglobalspectmarket2017-2021
[6]. Jha AK, Zhu Y, Clarkson E, Kupinski MA, Frey EC (2018), "Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution", arXiv preprint. arXiv:180701767.
[7]. Hesse B, et al, "EANM/ESC procedural guidelines for myocardial perfusion imaging in nuclear cardiology", European journal of nuclear medicine and molecular imaging, 32(7), 2005, pp. 855-897, doi: 10.1007/s00259-005-1779-y.
[8]. Holly T. A., et al, "Single photon-emission computed tomography", J Nucl Cardiol, 17(5), 2010, pp. 941-973, doi: 10.1007/s12350-010-9246-y.
[9]. Chien-Shun Lo, Chuin-Mu Wang, "Support vector machine for breast MR image classification", Computers & Mathematics with Applications, Volume 64, Issue 5, 2012, pp. 1153-1162, doi: 10.1016/j.camwa.2012.03.033.
[10]. Z. Camlica, H. R. Tizhoosh and F. Khalvati, "Medical Image Classification via SVM Using LBP Features from Saliency-Based Folded Data," 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, 2015, pp. 128-132, doi: 10.1109/ICMLA.2015.131.
[11]. Jiang, Yun & Li, Zhanhuai & Zhang, Longbo & Sun, Peng, “An Improved SVM Classifier for Medical Image Classification”, 2007, pp. 764-773, doi: 10.1007/978-3-540-73451-2_80.
[12]. Hongkai Wang, Zongwei Zhou, "Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images", EJNMMI Research, 2017, doi: 10.1186/s13550-017-0260-9.
[13]. Ryo Nakazato, Balaji K. Tamarappoo, Xingping Kang, Arik Wolak, Faith Kite1, Sean W. Hayes, Louise E.J. Thomson, John D. Friedman, Daniel S. Berman and Piotr J. Slomka, "Quantitative Upright–Supine High-Speed SPECT Myocardial Perfusion Imaging for Detection of Coronary Artery Disease: Correlation with Invasive Coronary Angiography", Journal of Nuclear Medicine, vol. 51(11), 2010, pp. 1724-1731, doi: 10.2967/jnumed.110.078782.
[14]. Yann LeCun, Yoshua Bengio & Geoffrey Hinton, "Deep learning", Nature, vol. 521(7553) 2015, pp. 436-444, doi: 10.1038/nature14539.
[15]. Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; et al, "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs", JAMA, vol. 316(22), Dec. 2016, pp. 2402-2410, doi: 10.1001/jama.2016.17216.
[16]. Betancur, Commandeur, Motlagh, "Deep Learning for Prediction of Obstructive Disease from Fast Myocardial Perfusion SPECT", JACC: Cardiovascular Imaging, vol. 11(11), 2018, pp. 1654-1663, doi: 10.1016/j.jcmg.2018.01.020.
[17]. Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, ICLR (2015).
[18]. Nguyễn Thành Trung, Nguyễn Chí Thành, Đặng Hoàng Minh, Nguyễn Thái Hà, Nguyễn Đức Thuận, “3D Convolutional Auto-Encoder for Attenuation Correction of Cardiac SPECT Images”, Proceedings of the 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE2020), 2021,
[19]. Nguyễn Thành Trung, Nguyễn Chí Thành, Đặng Hoàng Minh, Nguyễn Thái Hà, Nguyễn Đức Thuận (2020). “3D Unet Generative Adversarial Network for Attenuation Correction of Spect Images”. Proceedings of the 2020 4th International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom2020), Vietnam, Aug 2020, doi: 10.1109/SigTelCom49868.2020.9199018
