Name
Title | ||
|---|---|---|
Joint parametric reconstruction and motion correction framework for dynamic PET data. |
Abstract | ||
|---|---|---|
In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18 F] Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_15 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Dynamic PET,direct parametric reconstruction,motion correction,optimisation transfer,kinetic analysis | Iterative reconstruction,Residual,Computer vision,Noisy data,Kinetic analysis,Pattern recognition,Computer science,Parametric statistics,Artificial intelligence,Motion correction | Conference |
Volume | Issue | ISSN |
8673 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieqing Jiao | 1 | 12 | 3.24 |
| Alexandre Bousse | 2 | 8 | 2.96 |
| Kris Thielemans | 3 | 3 | 3.43 |
| P. J. Markiewicz | 4 | 18 | 4.20 |
| Ninon Burgos | 5 | 94 | 11.64 |
| David Atkinson | 6 | 67 | 6.89 |
| Simon R Arridge | 7 | 532 | 74.17 |
| Brian F. Hutton | 8 | 98 | 14.33 |
| Sébastien Ourselin | 9 | 2499 | 237.61 |