ArticleIEEE transactions on computational imaging2019
PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior.
Article in IEEE transactions on computational imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Elevated locus coeruleus metabolism provides resilience against cognitive decline in preclinical Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer's disease: Challenges and opportunities.Neuroscience and biobehavioral reviews · 2023Review
- Effect of Denoising and DeblurringMetabolites · 2022Article
- Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.PET clinics · 2021Review
- Longitudinal predictive modeling of tau progression along the structural connectome.NeuroImage · 2021Article
- CT-less Direct Correction of Attenuation and Scatter in the Image Space Using Deep Learning for Whole-Body FDG PET: Potential Benefits and Pitfalls.Radiology. Artificial intelligence · 2021Article
- Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2021Article
- PET image super-resolution using generative adversarial networks.Neural networks : the official journal of the International Neural Network Society · 2020Article
- Super-Resolution PET Imaging Using Convolutional Neural Networks.IEEE transactions on computational imaging · 2020Article
- PET image denoising using unsupervised deep learning.European journal of nuclear medicine and molecular imaging · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework relies on image-domain post-processing of already-reconstructed PET images by means of spatially-variant deconvolution stabilized by an MR-based joint entropy penalty function. The method is validated through simulation studies based on the BrainWeb digital phantom, experimental studies based on the Hoffman phantom, and clinical neuroimaging studies pertaining to aging and Alzheimer's disease. The developed technique was compared with direct deconvolution and deconvolution stabilized by a quadratic difference penalty, a total variation penalty, and a Bowsher penalty. The BrainWeb simulation study showed improved image quality and quantitative accuracy measured by contrast-to-noise ratio, structural similarity index, root-mean-square error, and peak signal-to-noise ratio generated by this technique. The Hoffman phantom study indicated noticeable improvement in the structural similarity index (relative to the MR image) and gray-to-white contrast-to-noise ratio. Finally, clinical amyloid and tau imaging studies for Alzheimer's disease showed lowering of the coefficient of variation in several key brain regions associated with two target pathologies.
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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.