Name
Abstract | ||
|---|---|---|
A novel dynamic (4D) PET to PET image registration procedure is proposed and applied to multiple PET scans acquired with the high resolution research tomograph (HRRT), the highest resolution human brain PET scanner available in the world. By extending the recent diffeomorphic log-demons (DLD) method and applying it to multiple dynamic [C-11]raclopride scans from the HRRT, an important step towards construction of a PET atlas of unprecedented quality for [C-11]raclopride imaging of the human brain has been achieved. Accounting for the temporal dimension in PET data improves registration accuracy when compared to registration of 3D to 3D time-averaged PET images. The DLD approach was chosen for its ease in providing both an intensity and shape template, through iterative sequential pair-wise registrations with fast convergence. The proposed method is applicable to any PET radiotracer, providing 4D atlases with useful applications in high accuracy PET data simulations and automated PET image analysis. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40763-5_5 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Pattern recognition,Computer science,Tomography,Atlas (anatomy),Artificial intelligence,Scanner,Image registration | Conference | 8150 |
Issue | ISSN | Citations |
Pt 2 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marie Bieth | 1 | 3 | 2.10 |
| Herve Lombaert | 2 | 392 | 29.98 |
| Andrew J. Reader | 3 | 4 | 0.86 |
| Kaleem Siddiqi | 4 | 3259 | 242.07 |