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- Title
Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.
- Authors
Liu, Junchi; Yang, Yongyi; Wernick, Miles N.; Pretorius, P. Hendrik; Slomka, Piotr J.; King, Michael A.
- 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.
- Subjects
PERFUSION imaging; IMAGE denoising; PERFUSION; IMAGE analysis; RECEIVER operating characteristic curves; SIGNAL convolution
- Publication
Journal of Nuclear Cardiology, 2022, Vol 29, Issue 5, p2340
- ISSN
1071-3581
- Publication type
Article
- DOI
10.1007/s12350-021-02676-w