ArticlePhysics in medicine and biology2019
Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 70 citations in OpenAlex.
- Scatter correction for self-collimating SPECT using a 3D U-Net framework.American journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- Optimizing Attenuation Correction inDiagnostics (Basel, Switzerland) · 2025Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.BMC medical imaging · 2024Article
- Activity quantification and dosimetry in radiopharmaceutical therapy with reference toFrontiers in nuclear medicine · 2024Review
- Deep-learning-based methods of attenuation correction for SPECT and PET.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.European journal of nuclear medicine and molecular imaging · 2022Article
- A Path to Qualification of PET/MRI Scanners for Multicenter Brain Imaging Studies: Evaluation of MRI-Based Attenuation Correction Methods Using a Patient Phantom.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.Magnetic resonance in medicine · 2022Article
- A review on AI in PET imaging.Annals of nuclear medicine · 2022Review
- Artificial intelligence with deep learning in nuclear medicine and radiology.EJNMMI physics · 2021Review
- Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2021Article
- Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.PET clinics · 2021Review
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.Journal of magnetic resonance imaging : JMRI · 2021Review
- Quantitative PET in the 2020s: a roadmap.Physics in medicine and biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no direct solution for accurate attenuation and scatter correction, both of which are critical for quantitative PET. To address this problem, we propose joint attenuation and scatter correction (ASC) in image space for non-corrected PET (PET
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.