Name
Abstract | ||
|---|---|---|
Dynamic structural and functional remodeling of the Central Nervous System occurs throughout the lifespan of the organism from the molecular to the systems level. MRI offers several advantages to observe this phenomenon: it is non-invasive and non-destructive, the contrast can be tuned to interrogate different tissue properties and imaging resolution can range from cortical columns to whole brain networks in the same session. To measure these changes reliably, functional maps generated over time with high resolution fMRI need to be registered accurately. This article presents a new method for registering automatically thin cortical MR volumes that are aligned with the functional maps. These acquisitions focus on the primary somato-sensory cortex, a region in the anterior parietal part of the brain, responsible for fine touch and proprioception. Currently, these slabs are acquired in approximately the same orientation from acquisition to acquisition and then registered by hand. Because they only cover a small portion of the cortex, their direct automatic registration is difficult. To address this issue, we propose a method relying on an intermediate image, acquired with a surface coil that covers a larger portion of the head to which the slabs can be registered. Because images acquired with surface coils suffer from severe intensity attenuation artifact, we also propose a method to register these. The results from data sets obtained with three squirrel monkeys show a registration accuracy of thirty micrometers. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.877024 | Proceedings of SPIE |
Keywords | Field | DocType |
Rigid registration,longitudinal studies,attenuation artifacts in MR,primates,functional studies | Computer vision,Data set,Functional magnetic resonance imaging,Artificial intelligence,Magnetic resonance imaging,Physics | Conference |
Volume | ISSN | Citations |
7962 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy Lecoeur | 1 | 22 | 2.63 |
| Feng Wang | 2 | 38 | 7.16 |
| Li Min Chen | 3 | 20 | 3.38 |
| Rui Li | 4 | 311 | 19.49 |
| malcom j avison | 5 | 0 | 0.68 |
| Benoit M. Dawant | 6 | 1388 | 223.11 |