Abstract
Background
The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts.
Methods
By moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 (\(4\times 19\)) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software.
Results
Multi-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies.
Conclusion
Increasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.
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Abbreviations
- CVD:
-
Cardiovascular disease
- SPECT:
-
Single photon emission computed tomography
- DNM:
-
GE discovery NM
- CZT:
-
Cadmium zinc telluride
- AC:
-
Attenuation correction
- FOV:
-
Field of view
- MLEM:
-
Maximum likelihood expectation maximization
- DS:
-
Defect size
- Tc-99m:
-
Technetium-99m
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Acknowledgements
Authors would like to thank all the members in the Yale Translational Research Imaging Center and the Yale Clinical Nuclear Cardiology Laboratory.
Funding
This work is supported by the NIH Grants R01HL154345 and S10RR025555.
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Huidong Xie, Stephanie Thorn, Xiongchao Chen, Bo Zhou, Hui Liu, Zhao Liu, Supum Lee, Ge Wang, Yi-Hwa Liu, Albert J. Sinusas, and Chi Liu declare that they do not have relevant conflicts of interest to disclose. All authors read and approved the final manuscript.
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The use of animal and anonymized human data in this study was approved by the Institutional Animal Care & Use Committee (IACUC) and the Institutional Review Board (IRB) of Yale University, respectively.
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Xie, H., Thorn, S., Chen, X. et al. Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction. J. Nucl. Cardiol. 30, 86–100 (2023). https://doi.org/10.1007/s12350-022-02972-z
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DOI: https://doi.org/10.1007/s12350-022-02972-z