| Title |
Line-Scanning Particle Image Velocimetry: An Optical Approach for Quantifying a Wide Range of Blood Flow Speeds in Live Animals
|
|---|---|
| Published in |
PLOS ONE, June 2012
|
| DOI | 10.1371/journal.pone.0038590 |
| Pubmed ID | |
| Authors |
Tyson N. Kim, Patrick W. Goodwill, Yeni Chen, Steven M. Conolly, Chris B. Schaffer, Dorian Liepmann, Rong A. Wang |
| Abstract |
The ability to measure blood velocities is critical for studying vascular development, physiology, and pathology. A key challenge is to quantify a wide range of blood velocities in vessels deep within living specimens with concurrent diffraction-limited resolution imaging of vascular cells. Two-photon laser scanning microscopy (TPLSM) has shown tremendous promise in analyzing blood velocities hundreds of micrometers deep in animals with cellular resolution. However, current analysis of TPLSM-based data is limited to the lower range of blood velocities and is not adequate to study faster velocities in many normal or disease conditions. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 1% |
| Israel | 1 | 1% |
| Finland | 1 | 1% |
| Canada | 1 | 1% |
| Korea, Republic of | 1 | 1% |
| Unknown | 92 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 28% |
| Researcher | 24 | 25% |
| Student > Master | 13 | 13% |
| Student > Doctoral Student | 5 | 5% |
| Student > Bachelor | 4 | 4% |
| Other | 9 | 9% |
| Unknown | 15 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 20 | 21% |
| Agricultural and Biological Sciences | 18 | 19% |
| Neuroscience | 17 | 18% |
| Physics and Astronomy | 7 | 7% |
| Medicine and Dentistry | 6 | 6% |
| Other | 9 | 9% |
| Unknown | 20 | 21% |