Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising
Department of Radiology
Artificial Intelligence and Robotics | Diagnosis | Investigative Techniques | Radiology
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.
SPECT-MPI, deep learning, noise-to-noise training, post-reconstruction filtering
DOI of Published Version
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. Link to article on publisher's site
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Liu J, Yang Y, Wernick MN, Pretorius PH, Slomka PJ, King MA. (2021). Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising. Radiology Publications. https://doi.org/10.1007/s12350-021-02676-w. Retrieved from https://escholarship.umassmed.edu/radiology_pubs/643