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Mendeley readers
Attention Score in Context
| Title |
NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data
|
|---|---|
| Published in |
Neuroinformatics, January 2023
|
| DOI | 10.1007/s12021-022-09616-0 |
| Pubmed ID | |
| Authors |
Jieqing Jiao, Fiona Heeman, Rachael Dixon, Catriona Wimberley, Isadora Lopes Alves, Juan Domingo Gispert, Adriaan A. Lammertsma, Bart N. M. van Berckel, Casper da Costa-Luis, Pawel Markiewicz, David M. Cash, M Jorge Cardoso, Sebastién Ourselin, Maqsood Yaqub, Frederik Barkhof |
| Abstract |
Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 25% |
| Luxembourg | 2 | 25% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 40% |
| Student > Doctoral Student | 3 | 30% |
| Student > Ph. D. Student | 1 | 10% |
| Professor > Associate Professor | 1 | 10% |
| Unknown | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 30% |
| Physics and Astronomy | 2 | 20% |
| Psychology | 1 | 10% |
| Neuroscience | 1 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 2 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 March 2023.
All research outputs
#5,925,470
of 23,577,761 outputs
Outputs from Neuroinformatics
#113
of 416 outputs
Outputs of similar age
#104,133
of 436,592 outputs
Outputs of similar age from Neuroinformatics
#3
of 12 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 416 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 436,592 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.