| Title |
NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis
|
|---|---|
| Published in |
Neuroinformatics, December 2017
|
| DOI | 10.1007/s12021-017-9352-y |
| Pubmed ID | |
| Authors |
Pawel J. Markiewicz, Matthias J. Ehrhardt, Kjell Erlandsson, Philip J. Noonan, Anna Barnes, Jonathan M. Schott, David Atkinson, Simon R. Arridge, Brian F. Hutton, Sebastien Ourselin |
| 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Finland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 22% |
| Researcher | 13 | 18% |
| Student > Master | 8 | 11% |
| Student > Doctoral Student | 4 | 5% |
| Student > Bachelor | 4 | 5% |
| Other | 12 | 16% |
| Unknown | 16 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 16% |
| Computer Science | 10 | 14% |
| Medicine and Dentistry | 9 | 12% |
| Physics and Astronomy | 7 | 10% |
| Neuroscience | 7 | 10% |
| Other | 7 | 10% |
| Unknown | 21 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 July 2021.
All research outputs
#6,110,089
of 23,016,919 outputs
Outputs from Neuroinformatics
#118
of 406 outputs
Outputs of similar age
#122,400
of 441,819 outputs
Outputs of similar age from Neuroinformatics
#4
of 9 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 406 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 70% of its peers.
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We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.