Name
Abstract | ||
|---|---|---|
Inverse imaging (InI) supercharges the sampling rate of traditional functional MRI 10–100 fold at a cost of a moderate reduction in spatial resolution. The technique is inspired by similarities between multi-sensor magnetoencephalography (MEG) and highly parallel radio-frequency (RF) MRI detector arrays. Using presently available 32-channel head coils at 3T, InI can be sampled at 10Hz and provides about 5-mm cortical spatial resolution with whole-brain coverage. Here we discuss the present applications of InI, as well as potential future challenges and opportunities in further improving its spatiotemporal resolution and sensitivity. InI may become a helpful tool for clinicians and neuroscientists for revealing the complex dynamics of brain functions during task-related and resting states. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neuroimage.2012.01.072 | NeuroImage |
Keywords | Field | DocType |
InI,Fast imaging,Parallel imaging,Inverse problem | Brain mapping,Complex dynamics,Neuroscience,Computer science,Sampling (signal processing),Cognitive psychology,Artificial intelligence,Inverse problem,Detector,Pattern recognition,Image resolution,Magnetoencephalography,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
62 | 2 | 1053-8119 |
Citations | PageRank | References |
8 | 0.47 | 16 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fa-Hsuan Lin | 1 | 246 | 24.33 |
| Kevin Wen-Kai Tsai | 2 | 49 | 4.60 |
| Ying-Hua Chu | 3 | 33 | 2.99 |
| Thomas Witzel | 4 | 374 | 28.70 |
| Aapo Nummenmaa | 5 | 256 | 19.18 |
| Tommi Raij | 6 | 23 | 3.86 |
| Jyrki Ahveninen | 7 | 89 | 15.15 |
| Wen-Jui Kuo | 8 | 163 | 20.42 |
| John W Belliveau | 9 | 256 | 38.29 |