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Mendeley readers
Attention Score in Context
| Title |
True 4D Image Denoising on the GPU
|
|---|---|
| Published in |
International Journal of Biomedical Imaging, October 2011
|
| DOI | 10.1155/2011/952819 |
| Pubmed ID | |
| Authors |
Anders Eklund, Mats Andersson, Hans Knutsson |
| Abstract |
The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly. |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Poland | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 29 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 31% |
| Student > Ph. D. Student | 8 | 25% |
| Other | 3 | 9% |
| Student > Master | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 22% |
| Engineering | 7 | 22% |
| Medicine and Dentistry | 3 | 9% |
| Agricultural and Biological Sciences | 2 | 6% |
| Neuroscience | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 9 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 22 June 2011.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from International Journal of Biomedical Imaging
#154
of 168 outputs
Outputs of similar age
#132,769
of 144,007 outputs
Outputs of similar age from International Journal of Biomedical Imaging
#5
of 5 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 168 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 144,007 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.