Abstract
PurposeThis work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.MethodsClinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (− 3 to + 3) grading scheme.ResultsThe highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland–Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively.ConclusionThe noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.
Highlights
Single-photon emission computed tomography (SPECT) is a widely used molecular imaging modality in various clinical domains, including the assessment of cardiovascular diseases [1]
Qualitative assessment images, whereas notable signal loss and/or noise-induced pseudo-signals were observed in the low-dose images
The reconstructed non-gated images for patients diagnosed with normal perfusion, low-risk, and intermediate-risk are presented in Supplemental Figures 2-4
Summary
Single-photon emission computed tomography (SPECT) is a widely used molecular imaging modality in various clinical domains, including the assessment of cardiovascular diseases [1]. To achieve high-quality images in nuclear medicine, a sufficient dose of radiopharmaceuticals should be injected. Reducing the injected dose beyond the prescribed limit would lead to poor signal-to-noise ratio (SNR) and low-quality images, hampering diagnostic performance [4, 5]. Since SPECT is considered the second leading contributor to radiation dose among medical imaging modalities (with approximately 90% stress imaging studies performed annually in the USA), concerns about the radiation risks of this imaging modality have increased [6,7,8]. Multiple studies have been conducted to cope with the challenge of reducing the injected activity of radiopharmaceuticals in nuclear medicine imaging without sacrificing the diagnostic/clinical value. The proposed strategies fall into four categories: statistical iterative image reconstruction, post-reconstruction filtering or post-processing, recent advances in hardware, and machine learning techniques [8, 9]
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