| Title |
Augmenting intraoperative ultrasound with preoperative magnetic resonance planning models for percutaneous renal access
|
|---|---|
| Published in |
BioMedical Engineering OnLine, August 2012
|
| DOI | 10.1186/1475-925x-11-60 |
| Pubmed ID | |
| Authors |
Zhi-Cheng Li, Kai Li, Hai-Lun Zhan, Ken Chen, Jia Gu, Lei Wang |
| Abstract |
Ultrasound (US) is a commonly-used intraoperative imaging modality for guiding percutaneous renal access (PRA). However, the anatomy identification and target localization abilities of the US imaging are limited. This paper evaluates the feasibility and efficiency of a proposed image-guided PRA by augmenting the intraoperative US with preoperative magnetic resonance (MR) planning models. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 28 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 24% |
| Student > Doctoral Student | 4 | 14% |
| Other | 3 | 10% |
| Student > Postgraduate | 3 | 10% |
| Student > Bachelor | 2 | 7% |
| Other | 6 | 21% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 24% |
| Computer Science | 4 | 14% |
| Engineering | 3 | 10% |
| Business, Management and Accounting | 2 | 7% |
| Psychology | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 9 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 26 January 2022.
All research outputs
#7,355,930
of 25,373,627 outputs
Outputs from BioMedical Engineering OnLine
#186
of 867 outputs
Outputs of similar age
#53,177
of 186,735 outputs
Outputs of similar age from BioMedical Engineering OnLine
#5
of 29 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 77% 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 186,735 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.