Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT

[1]  Matt A. King,et al.  Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network. , 2022, Medical physics.

[2]  H. Zaidi,et al.  Deep learning–based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[3]  G. Mok,et al.  Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising. , 2021, Quantitative imaging in medicine and surgery.

[4]  Yongyi Yang,et al.  Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging. , 2020, Medical physics.

[5]  M. King,et al.  The clinical utilities of multi-pinhole single photon emission computed tomography. , 2020, Quantitative imaging in medicine and surgery.

[6]  A. Rahmim,et al.  Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[7]  H. Zaidi,et al.  Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks , 2020, Journal of Nuclear Cardiology.

[8]  Ehsan Samei,et al.  Virtual clinical trials in medical imaging: a review , 2020, Journal of medical imaging.

[9]  P Hendrik Pretorius,et al.  Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks , 2020, IEEE Transactions on Medical Imaging.

[10]  Habib Zaidi,et al.  Projection Space Implementation of Deep Learning–Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space , 2020, The Journal of Nuclear Medicine.

[11]  G. Mok,et al.  Interpolated CT for attenuation correction on respiratory gating cardiac SPECT/CT - A simulation study. , 2019, Medical physics.

[12]  Chih-Chieh Liu,et al.  PET Image Denoising Using a Deep Neural Network Through Fine Tuning , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[13]  G. Mok,et al.  Comparison of Different Attenuation Correction Methods for Dual Gating Myocardial Perfusion SPECT/CT , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[14]  A. Einstein High Radiation Doses From SPECT Myocardial Perfusion Imaging in the United States. , 2018, Circulation. Cardiovascular imaging.

[15]  Piotr J Slomka,et al.  Investigation of dose reduction in cardiac perfusion SPECT via optimization and choice of the image reconstruction strategy , 2018, Journal of Nuclear Cardiology.

[16]  M. King,et al.  Evaluation of different respiratory gating schemes for cardiac SPECT , 2018, Journal of Nuclear Cardiology.

[17]  R. Glenn Wells,et al.  Dose reduction is good but it is image quality that matters , 2018, Journal of Nuclear Cardiology.

[18]  Dinggang Shen,et al.  3D conditional generative adversarial networks for high-quality PET image estimation at low dose , 2018, NeuroImage.

[19]  Karen L. Johnson,et al.  Investigation of the physical effects of respiratory motion compensation in a large population of patients undergoing Tc-99m cardiac perfusion SPECT/CT stress imaging , 2017, Journal of Nuclear Cardiology.

[20]  Bang-Hung Yang,et al.  Respiratory average CT for attenuation correction in myocardial perfusion SPECT/CT , 2017, Annals of Nuclear Medicine.

[21]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  N. Better,et al.  D-SPECT: New technology, old tricks , 2016, Journal of Nuclear Cardiology.

[23]  Albert Flotats,et al.  Current worldwide nuclear cardiology practices and radiation exposure: results from the 65 country IAEA Nuclear Cardiology Protocols Cross-Sectional Study (INCAPS) , 2015, European heart journal.

[24]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[25]  Michael Ghaly,et al.  Design of a digital phantom population for myocardial perfusion SPECT imaging research , 2014, Physics in medicine and biology.

[26]  D. Berman,et al.  Myocardial Perfusion Imaging with a Solid-State Camera: Simulation of a Very Low Dose Imaging Protocol , 2013, The Journal of Nuclear Medicine.

[27]  G. Mok,et al.  Infant Cardiac CT Angiography with 64-Slice and 256-Slice CT: Comparison of Radiation Dose and Image Quality Using a Pediatric Phantom , 2012, PloS one.

[28]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[29]  Leonid Tsukerman,et al.  A fast cardiac gamma camera with dynamic SPECT capabilities: design, system validation and future potential , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[30]  Yi-Hwa Liu,et al.  Quantification of nuclear cardiac images: The Yale approach , 2007, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[31]  D. Berman,et al.  Automated quantification of myocardial perfusion SPECT using simplified normal limits , 2004, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[32]  P. J. Ell,et al.  Myocardial perfusion scintigraphy: the evidence , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[33]  B.M.W. Tsui,et al.  A practical projector-backprojector modeling attenuation, detector response, and scatter for accurate scatter compensation in SPECT , 1991, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference.

[34]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.