Deep Learning-based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

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Abstract

Introduction: To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC) (CTAC). However, CTAC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-attenuation corrected (NC) SPECT, without the need for CT. Methods: SPECT MPI was performed using Tc-99m sestamibi or Tc-99m tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that generates simulated AC SPECT images (DeepAC). The model was trained with short-axis NC and AC images performed in one site (n = 4886) and was tested in patients from two separate external sites (n = 604). We assessed diagnostic accuracy of stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver operating characteristic curve (AUC). We also quantified direct count change between AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in <1 second from NC images, AUC for obstructive CAD was higher for DeepAC TPD (0.79, 95% CI 0.72 – 0.85) compared to NC TPD (0.70, 95% Confidence Intervals (CI) 0.63 – 0.78, p<0.001), and similar to AC TPD (0.81, 95% CI 0.75 – 0.87, P = 0.196). The normalcy rate (defined as stress TPD <3%) in the LLK population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) compared to NC TPD (54.6%, p<0.001 for both).  Positive count change (increase in counts) was significantly higher for AC vs NC (median 9.4, Inter Quartile Range (IQR) 6.0 – 14.2, p<0.001) than for AC vs DeepAC (median 2.4, interquartile range [IQR] 1.3 – 4.2). Conclusion: In an independent external dataset, DeepAC provides improved diagnostic accuracy for obstructive CAD similar to actual AC, as compared to NC images. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions.Copyright © 2022 by the Society of Nuclear Medicine and Molecular Imaging, Inc.