Name
Abstract | ||
|---|---|---|
Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model that combines tissue information available immediately after onset, measured using fluid attenuated inversion recovery (FLAIR), with multimodal perfusion features (Tmax, MTT, and TTP) to infer the likely outcome of the tissue. A key component is the use of randomly extracted, overlapping, cuboids (i.e. rectangular volumes) whose size is automatically determined during learning. The prediction problem is formalized into a nonlinear spectral regression framework where the inputs are the local, multi-modal cuboids extracted from FLAIR and perfusion images at onset, and where the output is the local FLAIR intensity of the tissue 4 days after intervention. Experiments on 7 stroke patients demonstrate the effectiveness of our approach in predicting tissue fate and its superiority to linear models that are conventionally used. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17274-8_29 | ISVC |
Keywords | Field | DocType |
multi-modal cuboids,accurate prediction,tissue fate,multimodal perfusion feature,likely outcome,acute ischemic stroke,local flair intensity,tissue information,perfusion image,tissue outcome,cuboid model,tissue fate prediction,fluid attenuated inversion recovery,prediction model,linear model | Cerebral blood volume,Computer science,Artificial intelligence,Spectral regression,Likely outcome,Perfusion,Pattern recognition,Internal medicine,Linear model,Fluid-attenuated inversion recovery,Cardiology,Stroke,Cuboid | Conference |
Volume | ISSN | ISBN |
6454 | 0302-9743 | 3-642-17273-3 |
Citations | PageRank | References |
3 | 0.48 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabien Scalzo | 1 | 68 | 15.42 |
| Qing Hao | 2 | 4 | 0.91 |
| Jeffrey R. Alger | 3 | 5 | 0.86 |
| Xiao Hu | 4 | 72 | 13.64 |
| David S. Liebeskind | 5 | 12 | 3.93 |