Liu, JunchiYang, YongyiWernick, Miles N.Pretorius, P. HendrikSlomka, Piotr J.King, Michael A2022-08-232022-08-232021-07-192021-09-03<p>Liu J, Yang Y, Wernick MN, Pretorius PH, Slomka PJ, King MA. Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising. J Nucl Cardiol. 2021 Jul 19. doi: 10.1007/s12350-021-02676-w. Epub ahead of print. PMID: 34282538. <a href="https://doi.org/10.1007/s12350-021-02676-w">Link to article on publisher's site</a></p>1071-3581 (Linking)10.1007/s12350-021-02676-w34282538https://hdl.handle.net/20.500.14038/48544BACKGROUND: 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.en-USSPECT-MPIdeep learningnoise-to-noise trainingpost-reconstruction filteringArtificial Intelligence and RoboticsDiagnosisInvestigative TechniquesRadiologyJournal Articlehttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1660&amp;context=radiology_pubs&amp;unstamped=1https://escholarship.umassmed.edu/radiology_pubs/64324651279radiology_pubs/643