Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images

Hage FG, AlJaroudi WA. Review of cardiovascular imaging in the Journal of Nuclear Cardiology in, 2017 Part 2 of 2: Myocardial perfusion imaging. J Nucl Cardiol 2018;25:1390‐9.

Article  PubMed  Google Scholar 

Kuhle WG, Porenta G, Huang SC, Phelps ME, Schelbert HR. Issues in the quantitation of reoriented cardiac PET images. J Nucl Med 1992;33:1235‐42.

CAS  PubMed  Google Scholar 

deKemp RA, Nahmias C. Automated determination of the left ventricular long axis in cardiac positron tomography. Physiol Meas 1996;17:95‐108.

Article  CAS  PubMed  Google Scholar 

Cauvin JC, Boire JY, Maublant JC, Bonny JM, Zanca M, Veyre A. Automatic detection of the left ventricular myocardium long axis and center in thallium-201 single photon emission computed tomography. Eur J Nucl Med 1992;19:1032‐7.

CAS  PubMed  Google Scholar 

van Hastenberg RP, Kemerink GJ, Hasman A. On the generation of short-axis and radial long-axis slices in thallium-201 myocardial perfusion single-photon emission tomography. Eur J Nucl Med 1996;23:924‐31.

Article  PubMed  Google Scholar 

Zhou WH, Garcia EV. Nuclear image-guided approaches for cardiac resynchronization therapy (CRT). Curr Cardiol Rep 2016;18:89.

Article  Google Scholar 

Garcia EV, Van Train K, Maddahi J, Prigent F, Friedman J, Areeda J. Quantification of rotational thallium-201 myocardial tomography. J Nucl Med 1985;26:17‐26.

CAS  PubMed  Google Scholar 

Lancaster JL, Starling MR, Kopp DT, Lasher JC, Blumhardt R. Effect of errors in reangulation on planar and tomographic thallium-201 washout profile curves. J Nucl Med 1986;26:1445‐55.

Google Scholar 

Depuey EG, Garcia EV. Optimal specificity of thallium-201 SPECT through recognition of imaging artifacts. J Nucl Med 1989;30:441‐9.

CAS  PubMed  Google Scholar 

He ZX, Maublant JC, Cauvin JC. Reorientation of the left ventricular long-axis on myocardial transaxial tomograms by a linear fitting method. J Nucl Med Offic Publ Soc Nucl Med 1991;32:1794.

CAS  Google Scholar 

Mullick R, Ezquerra NF. Automatic determination of LV orientation from SPECT data. IEEE Trans Med Imaging 1995;14:88‐99.

Article  CAS  PubMed  Google Scholar 

Germano G. Automatic reorientation of three-dimensional, transaxial myocardial perfusion SPECT. J Nucl Med 1995;36:7.

Google Scholar 

Slomka PJ, Hurwitz GA, Stephenson J, Cradduck T. Automated alignment and sizing of myocardial stress and rest scans to three-dimensional normal templates using an image registration algorithm. J Nucl Med 1995;36:1115‐22.

CAS  PubMed  Google Scholar 

Jin SK, Na Y, Bae KT. Segmentation of ECG-gated multidetector row-CT cardiac images for functional analysis. Paper presented at: Medical Imaging, 2002.

Klein R, Lortie M, Adler A, Beanlands RS, Dekemp R. Fully Automated Software for Polar-Map Registration and Sampling from PET Images. Paper presented at: IEEE Nuclear Science Symposium Conference Record, 2006.

Zhang D, Pretorius PH, Lin K, Miao W, Zhu W. A novel deep-learning–based approach for automatic reorientation of 3D cardiac SPECT images. Eur J Nucl Med Mol Imaging. 2021.

Su YY, Liu Q, Xie WT, Hu PZ. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. Comput Methods Programs Biomed 2022;221:15.

Article  Google Scholar 

Sadak F, Saadat M, Hajiyavand AM. Real-time deep learning-based image recognition for applications in automated positioning and injection of biological cells. Comput Biol Med 2020;125:156.

Article  Google Scholar 

Vigneault DM, Xie W, Ho CY, Bluemke DA, Noble JA. Omega-net: Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. 2017.

Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: A toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010;29:196‐205.

Article  PubMed  Google Scholar 

Shamonin DP, Bron EE, Lelieveldt BPF, Smits M, Klein S, Staring M, et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Front Neuroinform 2014;7:14.

Google Scholar 

Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K. Spatial Transformer Networks. Paper presented at: MIT Press, 2015

Zhao C, Xu Y, He Z, Tang JS, Zhang YJ, Han JG, et al. Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recognit 2021;119:46.

Article  Google Scholar 

Zhao C, Keyak JH, Tang J, Kaneko TS, Zhou W. ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation. Complex Intelligent Systems. 2021.

Wang TH, Lei Y, Tang HP, He Z, Castillo R, Wang C, et al. A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study. J Nucl Cardiol 2020;27:976‐87.

Article  PubMed  Google Scholar 

Wen H, Wei Q, Huang J-L, Tsai S-C, Wang C-Y, Chiang K-F, et al. Analysis on SPECT myocardial perfusion imaging with a tool derived from dynamic programming to deep learning. Optik 2021;240:47.

Article  Google Scholar 

Zhu F, Zhao J, Zhao C, Tang S, Nan J, Li Y, et al. A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images. ArXiv. 2022;abs/2206.03603.

He Z, Fernandes FdA, do Nascimento EA, Garcia EV, Mesquita CT, Zhou W. Incremental value of left ventricular shape parameters measured by gated SPECT MPI in predicting the super-response to CRT. J Nucl Cardiol. 2021.

He Z, Zhang X, Zhao C, Qian Z, Wang Y, Hou X, et al. A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI. 2021.

Zhao C, Vij A, Malhotra S, Tang J, Tang H, Pienta D, et al. Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms. Comput Biol Med 2021;136:58.

Article  Google Scholar 

Tang H, Bober RR, Zhao C, Zhang C, Zhou W. 3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention. J Nucl Cardiol. 2021.

Xu Z, Tang H, Malhotra S, Dong M, Zhao C, Ye Z, et al. Three-dimensional fusion of myocardial perfusion SPECT and invasive coronary angiography guides coronary revascularization. J Nucl Cardiol. 2022.

Zhou W, Hou X, Piccinelli M, Tang X, Tang L, Cao K, et al. 3D fusion of LV venous anatomy on fluoroscopy venograms with epicardial surface on SPECT myocardial perfusion images for guiding CRT LV lead placement. JACC-Cardiovascular Imaging 2014;7:1239‐48.

Article  PubMed  Google Scholar 

留言 (0)

沒有登入
gif