ArticlePhysics in medicine and biology2021
Deconvolution-based partial volume correction of PET images with parallel level set regularization.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- 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
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- On the effect of reference and sample volume on magnetic particle imaging quantification.Journal of magnetism and magnetic materials · 2025Article
- On the partial volume effect in magnetic particle imaging.Physics in medicine and biology · 2025Article
- Partial volume correction for Lu-177-PSMA SPECT.EJNMMI physics · 2024Article
- Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning.Brain informatics · 2023Article
- Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to NasopharyngealCancers · 2023Article
- PET evaluation of cholinergic system differences in progressive supranuclear palsy and age-matched controls using [Nuclear medicine and biologyArticle
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Authors and funding
7 authors.
Funding
Abstract
The partial volume effect (PVE), caused by the limited spatial resolution of positron emission tomography (PET), degrades images both qualitatively and quantitatively. Anatomical information provided by magnetic resonance (MR) images has the potential to play an important role in partial volume correction (PVC) methods. Post-reconstruction MR-guided PVC methods typically use segmented MR tissue maps, and further, assume that PET activity distribution is uniform in each region, imposing considerable constraints through anatomical guidance. In this work, we present a post-reconstruction PVC method based on deconvolution with parallel level set (PLS) regularization. We frame the problem as an iterative deconvolution task with PLS regularization that incorporates anatomical information without requiring MR segmentation or assuming uniformity of PET distributions within regions. An efficient algorithm for non-smooth optimization of the objective function (invoking split Bregman framework) is developed so that the proposed method can be feasibly applied to 3D images and produces sharper images compared to PLS method with smooth optimization. The proposed method was evaluated together with several other PVC methods using both realistic simulation experiments based on the BrainWeb phantom as well as
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