ArticleNeuroinformatics2018
NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.
Article in Neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- PET Image Reconstruction Using Deep Diffusion Image Prior.IEEE transactions on medical imaging · 2026Article
- Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.EJNMMI research · 2026Article
- The control costs of human brain dynamics.Network neuroscience (Cambridge, Mass.) · 2025Article
- Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner.PloS one · 2024Article
- An energy costly architecture of neuromodulators for human brain evolution and cognition.Science advances · 2023Article
- Guided block matching and 4-D transform domain filter projection denoising method for dynamic PET image reconstruction.EJNMMI physics · 2023Article
- Software compatibility analysis for quantitative measures of [EJNMMI research · 2023Article
- PARALLELPROJ-an open-source framework for fast calculation of projections in tomography.Frontiers in nuclear medicine · 2023Article
- Quantification of amyloid PET for future clinical use: a state-of-the-art review.European journal of nuclear medicine and molecular imaging · 2022Review
- Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software.Medical physics · 2022Article
- The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact.Frontiers in neurology · 2022Review
- Imitation learning for improved 3D PET/MR attenuation correction.Medical image analysis · 2021Article
- Article
- Micro-Networks for Robust MR-Guided Low Count PET Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Modified Look-Locker Inverse-Recovery (MOLLI) Sequence of Quantitative Imaging in Dirty Magnetic Resonance Longitudinal Relaxation Time Diagnostic Value of GE Combined with Longitudinal Relaxation Time Quantitative Imaging for Myocardial Amyloidosis.Journal of healthcare engineering · 2021Article
- Kinfitr - an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability.EJNMMI research · 2020Article
- Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL-MRI: proof of concept.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2019Article
- APPIAN: Automated Pipeline for PET Image Analysis.Frontiers in neuroinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.
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.