Name
Abstract | ||
|---|---|---|
In this paper, we proposed a local region structured noise reduction method for cortical optical imaging (OI). In our method, block-designed task paradigm was employed. Canonical correlation analysis (CCA) technique was used to extract the underlying structured sources voxel by voxel. The response signals were detected among structured sources by surrogate test based on the reduced autoregression model (ST-RARM) technique. The power of structured noise was eliminated from original time series and then the data were reconstructed. Monte-Carlo simulation was applied to demonstrate the validity of our method. The results showed that our method was more efficient in activated voxel detection compared to the generally used methods PCA, DCT. Further, by using our method the phase knowledge of response signals was well preserved in the reconstructed data and hence a more accurate estimate was obtained. The final activity mapping was generated by utilizing the knowledge of both response amplitude and phase. The vein artifacts were efficiently reduced. Six sets of true OI data collected from the hind-paw (HP) area of rat's cortex were processed and improved activity mappings were obtained. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neucom.2009.01.011 | Neurocomputing |
Keywords | Field | DocType |
true oi data,multi-taper method,canonical correlation analysis,response signal,optical imaging,structured noise,response amplitude,monte-carlo simulation,structured source,final activity mapping,noise reduction method,activated voxel detection,local region,cortical optical imaging,underlying structured sources voxel,reconstructed data,time series,block design,data collection,autoregressive model,monte carlo simulation,noise reduction | Noise reduction,Voxel,Autoregressive model,Monte Carlo method,Pattern recognition,Canonical correlation,Computer science,Discrete cosine transform,Response Amplitude,Artificial intelligence,Optical imaging | Journal |
Volume | Issue | ISSN |
72 | 10-12 | Neurocomputing |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yadong Liu | 1 | 105 | 14.04 |
| Dewen Hu | 2 | 1290 | 101.20 |
| Zongtan Zhou | 3 | 412 | 33.89 |
| Fayi Liu | 4 | 1 | 0.71 |