Title

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising

UMMS Affiliation

Department of Radiology

Publication Date

2021-07-19

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Diagnosis | Investigative Techniques | Radiology

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.

Keywords

SPECT-MPI, deep learning, noise-to-noise training, post-reconstruction filtering

DOI of Published Version

10.1007/s12350-021-02676-w

Source

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/Book/Conference Title

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

Related Resources

Link to Article in PubMed

PubMed ID

34282538

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