ArticleFrontiers in medicine2023
Generative adversarial network-based attenuation correction for
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Investigation of Attenuation Correction Methods for Dual-Gated Single Photon Emission Computed Tomography (DG-SPECT).Bioengineering (Basel, Switzerland) · 2025Article
- Development of a population of digital brain phantoms for radionuclide imaging research in Parkinson's disease.EJNMMI physics · 2025Article
- Transfer learning‑based attenuation correction inEJNMMI physics · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.Physics in medicine and biology · 2024Article
- Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.EJNMMI physics · 2024Article
- Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.BJR artificial intelligence · 2024Review
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8 authors.
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Abstract
Background: Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on Methods: Two hundred and sixty patients who underwent Results: The NMSE for Chang's method, DL-AC Conclusion: All DL-based AC methods are superior to Chang-AC. DL-AC
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