ArticleEJNMMI physics2023
CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning.
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- From Patient Selection to Surveillance: Artificial Intelligence Applications in Radioiodine Therapy - A Systematic Review.Nuclear medicine and molecular imaging · 2026Review
- AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.EJNMMI physics · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- CT-free kidney single-photon emission computed tomography for glomerular filtration rate.Scientific reports · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Article
- Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.EJNMMI physics · 2024Article
- Tc-99m DMSA SPECT for Follow-Up of Non-Operative Treatments in Renal Injuries: A Prospective Single-Center Study.Korean journal of radiology · 2023Article
- Gamma camera-specific reference standards for radioactive iodine uptake measurements.EJNMMI physics · 2023Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeQuantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid.
methodsQuantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification.
resultsThe synthetic μ-map demonstrated a strong correlation (R
conclusionCT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.