ArticleIEEE transactions on radiation and plasma medical sciences2018
MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging.
Article in IEEE transactions on radiation and plasma medical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2026Article
- MR-guided reconstruction of PET data in spinal cord PET/MRI.Frontiers in nuclear medicine · 2025Article
- Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.Nuklearmedizin. Nuclear medicine · 2023Review
- AI for PET image reconstruction.The British journal of radiology · 2023Review
- Generative Adversarial Network-Enhanced Ultra-Low-Dose [AJNR. American journal of neuroradiology · 2023Article
- Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images.Journal of digital imaging · 2023Article
- Pre-training via Transfer Learning and Pretext Learning a Convolutional Neural Network for Automated Assessments of Clinical PET Image Quality.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- HYPR4D kernel method on TOF PET data with validations including image-derived input function.EJNMMI physics · 2022Article
- Deep Kernel Representation for Image Reconstruction in PET.IEEE transactions on medical imaging · 2022Article
- Hybrid kernelised expectation maximisation for Bremsstrahlung SPECT reconstruction in SIRT withEJNMMI physics · 2022Article
- True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.European journal of nuclear medicine and molecular imaging · 2021Article
- Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Micro-Networks for Robust MR-Guided Low Count PET Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Theranostic SPECT reconstruction for improved resolution: application to radionuclide therapy dosimetry.EJNMMI physics · 2021Article
- Article
- Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.NeuroImage · 2021Article
- PET-enabled dual-energy CT: image reconstruction and a proof-of-concept computer simulation study.Physics in medicine and biology · 2020Article
- Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.European journal of nuclear medicine and molecular imaging · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
PET image reconstruction is highly susceptible to the impact of Poisson noise, and if shorter acquisition times or reduced injected doses are used, the noisy PET data become even more limiting. The recent development of kernel expectation maximisation (KEM) is a simple way to reduce noise in PET images, and we show in this work that impressive dose reduction can be achieved when the kernel method is used with MR-derived kernels. The kernel method is shown to surpass maximum likelihood expectation maximisation (MLEM) for the reconstruction of low-count datasets (corresponding to those obtained at reduced injected doses) producing visibly clearer reconstructions for unsmoothed and smoothed images, at all count levels. The kernel EM reconstruction of 10% of the data had comparable whole brain voxel-level error measures to the MLEM reconstruction of 100% of the data (for simulated data, at 100 iterations). For regional metrics, the kernel method at reduced dose levels attained a reduced coefficient of variation and more accurate mean values compared to MLEM. However, the advances provided by the kernel method are at the expense of possible over-smoothing of features unique to the PET data. Further assessment on clinical data is required to determine the level of dose reduction that can be routinely achieved using the kernel method, whilst maintaining the diagnostic utility of the scan.
Indexed as
Identifiers
What OpenQuestion holds
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.