Abstract
Background
We previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dose clinical acquisitions.
Methods
To quantify perfusion-defect detection accuracy, we conducted a receiver-operating characteristic (ROC) analysis on reconstructed images with and without processing by the DL network using a set of clinical SPECT-MPI data from 190 subjects. For perfusion-defect detection hybrid studies were used as ground truth, which were created from clinically normal studies with simulated realistic lesions inserted. We considered ordered-subset expectation-maximization (OSEM) reconstruction with corrections for attenuation, resolution, and scatter and with 3D Gaussian post-filtering. Total perfusion deficit (TPD) scores, computed by Quantitative Perfusion SPECT (QPS) software, were used to evaluate the reconstructed images.
Results
Compared to reconstruction with optimal Gaussian post-filtering (sigma = 1.2 voxels), further DL denoising increased the area under the ROC curve (AUC) from 0.80 to 0.88 (P-value < 10−4). For reconstruction with less Gaussian post-filtering (sigma = 0.8 voxels), thus better spatial resolution, DL denoising increased the AUC value from 0.78 to 0.86 (P-value < 10−4) and achieved better spatial resolution in reconstruction.
Conclusions
DL denoising can effectively improve the detection of abnormal defects in standard-dose SPECT-MPI images over conventional reconstruction.
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Abbreviations
- SPECT:
-
Single-photon emission computed tomography
- MPI:
-
Myocardial perfusion imaging
- LV:
-
Left ventricular
- OSEM:
-
Ordered-subset expectation-maximization
- AC:
-
Attenuation correction
- SC:
-
Scatter correction
- RC:
-
Resolution correction
- AUC:
-
Area under the ROC curve
- TPD:
-
Total perfusion deficit
- DL:
-
Deep learning
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Acknowledgments
This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number R01 HL154687.
Disclosure
The University of Massachusetts had a research agreement with Philips Healthcare at the time some of this work was performed. The authors declare that they have no conflict of interest.
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Liu, J., Yang, Y., Wernick, M.N. et al. Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising. J. Nucl. Cardiol. 29, 2340–2349 (2022). https://doi.org/10.1007/s12350-021-02676-w
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DOI: https://doi.org/10.1007/s12350-021-02676-w