ArticleEJNMMI physics2022
DeepPVC: prediction of a partial volume-corrected map for brain positron emission tomography studies via a deep convolutional neural network.
Article in EJNMMI physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.EJNMMI research · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook.European journal of nuclear medicine and molecular imaging · 2026Review
- Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models.Life (Basel, Switzerland) · 2024Article
- Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.Quantitative imaging in medicine and surgery · 2024Article
- Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines.Physics in medicine and biology · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundPartial volume correction with anatomical magnetic resonance (MR) images (MR-PVC) is useful for accurately quantifying tracer uptake on brain positron emission tomography (PET) images. However, MR segmentation processes for MR-PVC are time-consuming and prevent the widespread clinical use of MR-PVC. Here, we aimed to develop a deep learning model to directly predict PV-corrected maps from PET and MR images, ultimately improving the MR-PVC throughput.
methodsWe used MR T1-weighted and [
resultsA high SSIM (0.884 ± 0.021) and ICC (0.921 ± 0.042) were observed in the validation and test data (SSIM, 0.876 ± 0.028; ICC, 0.894 ± 0.051). The computation time required to predict a PV-corrected map for a participant (48 s without a graphics processing unit) was much shorter than that for the RBV PVC and MR segmentation processes.
conclusionThese results suggest that the deepPVC model directly predicts PV-corrected maps from MR and PET images and improves the throughput of MR-PVC by skipping the MR segmentation processes.
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