ArticleQuantitative imaging in medicine and surgery2022
Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 53 citations in OpenAlex.
- A projection-domain deep learning approach for respiratory motion correction of myocardial perfusion imaging using a multi-pinhole solid-state SPECT.Medical physics · 2026Article
- 3DAD: Super-Resolution Image Synthesis from Anisotropic CT Images Using a Three-Dimensional Adversarial Diffusion Model.Bioengineering (Basel, Switzerland) · 2026Article
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 2026Article
- SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.EJNMMI physics · 2026Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Shorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views.EJNMMI physics · 2025Article
- Guided synthesis of annotated lung CT images with pathologies using a multi-conditioned denoising diffusion probabilistic model (mDDPM).Physics in medicine and biology · 2025Article
- Article
- An in-silico simulation study to generate computed tomography images from ultrasound data by using deep learning techniques.BJR artificial intelligence · 2025Article
- RISNet: A variable multi-modal image feature fusion adversarial neural network for generating specific dMRI images.PloS one · 2025Article
- Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.EJNMMI physics · 2024Article
- Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data.Annals of nuclear medicine · 2024Article
- TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.Medical image analysis · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- Application of convolutional neural networks in medical images: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Med-cDiff: Conditional Medical Image Generation with Diffusion Models.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Myocardial perfusion (MP) SPECT is a well-established method for diagnosing cardiac disease, yet its radiation risk poses safety concern. This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images. Methods: One hundred male and female patients with different Results: NMSE values of 0.0233±0.004 Conclusions: The Pix2Pix GAN method is effective to reduce the noise level of low dose MP SPECT. Further studies on clinical performance are warranted to demonstrate its full clinical effectiveness.
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