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Background <p>Improving the image quality of cardiac gating myocardial perfusion single-photon emission computed tomography (CG MP-SPECT) is crucial for accurate diagnosis. Diffusion model (DM) has recently shown promise in MP-SPECT image denoising, but traditional DM typically require extensive computational resources and prolonged processing times. The study aims to develop and evaluate a lightweight generalized DM for efficient denoising in CG MP-SPECT images.</p> Methods <p>We propose a novel step mapping generalized diffusion model (SMGDiff) that incorporates cardiac-gated MP-SPECT images as diffusion endpoints instead of traditional Gaussian noise, alongside a novel mean-preserving degradation operator to significantly reduce sampling steps and inference time. Additionally, a stepwise mapping and error optimization module (SMEO) was designed to precisely calibrate stepwise features using contextual information, thereby minimizing cumulative errors during reconstruction. A retrospective dataset of 50 MP-SPECT scans from 36 patients was used, each gated into 8 (CG-8) or 16 (CG-16) cardiac phases, generating 400/800 image pairs for CG-8/CG-16, respectively. The dataset was divided into training (35 scans), validation (5 scans), and testing (10 scans). Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized mean square error (NMSE), joint histogram, linear regression analysis and a paired two-tail t-test were employed for quantitative evaluation. Two board-certified nuclear medicine physicians performed a blinded and randomized reader study on resulted images. Images were rated on 5-point Likert scales for image quality and diagnostic confidence, with significance evaluated by Wilcoxon signed-rank tests.</p> Results <p>The SMGDiff model with 5 diffusion steps (SMGDiff-5) achieved the best overall performance across all evaluation metrics for both gating configurations. SMGDiff-5 also demonstrated superior computational efficiency, requiring only 0.024 s per slice compared to 4.982 s for the 1000-step diffusion model. Furthermore, SMGDiff-5 significantly outperformed established deep learning methods including CNN, U-Net, GAN, and the denoising diffusion probabilistic model, as evidenced by higher PSNR and SSIM and lower NMSE (p &lt; 0.05). Joint histogram and linear regression analyses confirmed these quantitative findings. Reader study results aligned with the quantitative trends which SMGDiff-5 received the highest or near-reference ratings for image quality (CG-8 4.725; CG-16 4.550) and diagnostic confidence (CG-8 4.600; CG-16 4.525), clearly above original gated images and comparable to static MP-SPECT.</p> Conclusions <p>The proposed SMGDiff-5 model provides robust and efficient denoising of CG MP-SPECT images, offering superior performance compared to traditional deep learning methods with significantly reduced computational demand.</p>

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SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月17日

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  • Chunhao Li,
  • Jiangshan Huang,
  • Jiahui Dong,
  • Chunlei Liu,
  • Yankun Shi,
  • Yu Du,
  • Zhonglin Lu,
  • Chunjie Cao,
  • Greta S. P. Mok,
  • Hui Wang,
  • Jingzhang Sun

摘要

Background

Improving the image quality of cardiac gating myocardial perfusion single-photon emission computed tomography (CG MP-SPECT) is crucial for accurate diagnosis. Diffusion model (DM) has recently shown promise in MP-SPECT image denoising, but traditional DM typically require extensive computational resources and prolonged processing times. The study aims to develop and evaluate a lightweight generalized DM for efficient denoising in CG MP-SPECT images.

Methods

We propose a novel step mapping generalized diffusion model (SMGDiff) that incorporates cardiac-gated MP-SPECT images as diffusion endpoints instead of traditional Gaussian noise, alongside a novel mean-preserving degradation operator to significantly reduce sampling steps and inference time. Additionally, a stepwise mapping and error optimization module (SMEO) was designed to precisely calibrate stepwise features using contextual information, thereby minimizing cumulative errors during reconstruction. A retrospective dataset of 50 MP-SPECT scans from 36 patients was used, each gated into 8 (CG-8) or 16 (CG-16) cardiac phases, generating 400/800 image pairs for CG-8/CG-16, respectively. The dataset was divided into training (35 scans), validation (5 scans), and testing (10 scans). Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized mean square error (NMSE), joint histogram, linear regression analysis and a paired two-tail t-test were employed for quantitative evaluation. Two board-certified nuclear medicine physicians performed a blinded and randomized reader study on resulted images. Images were rated on 5-point Likert scales for image quality and diagnostic confidence, with significance evaluated by Wilcoxon signed-rank tests.

Results

The SMGDiff model with 5 diffusion steps (SMGDiff-5) achieved the best overall performance across all evaluation metrics for both gating configurations. SMGDiff-5 also demonstrated superior computational efficiency, requiring only 0.024 s per slice compared to 4.982 s for the 1000-step diffusion model. Furthermore, SMGDiff-5 significantly outperformed established deep learning methods including CNN, U-Net, GAN, and the denoising diffusion probabilistic model, as evidenced by higher PSNR and SSIM and lower NMSE (p < 0.05). Joint histogram and linear regression analyses confirmed these quantitative findings. Reader study results aligned with the quantitative trends which SMGDiff-5 received the highest or near-reference ratings for image quality (CG-8 4.725; CG-16 4.550) and diagnostic confidence (CG-8 4.600; CG-16 4.525), clearly above original gated images and comparable to static MP-SPECT.

Conclusions

The proposed SMGDiff-5 model provides robust and efficient denoising of CG MP-SPECT images, offering superior performance compared to traditional deep learning methods with significantly reduced computational demand.