Name
Abstract | ||
|---|---|---|
Segmentation of the body edge in tomographic images with low count, noisy edges is needed for both PET and SPECT respiratory motion correction and brain SPECT attenuation correction. To reduce noise we re-projected tomographic images and searched for edges in the projection count profiles or their spatial derivatives. Edge location versus projection angle was fitted with cosine basis functions after rejecting outliers and including information about edges of previous sections. We processed 10s duration FDG PET of the thorax, HMPAO brain, DAT brain and lung perfusion SPECT. Stable edges for all four types of scan were achieved but with different profiles. Edges were validated against edges of coregistered CT or MRI. The best root mean square (rms) accuracy was 8.2mm (PET) and 5.2mm (brain SPECT). Inter-scan variability (standard deviation) in the estimated-to-control edge distance for 17 PET scans was 0.4mm, for 25 ordered subset expectation maximisation (OSEM) reconstructed brain SPECT 1.0mm and for 18 filtered back-projection (FBP) reconstructed brain SPECT 1.4mm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.cmpb.2006.08.001 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
filtered back projection,root mean square,pet,standard deviation,attenuation correction | Nuclear medicine,Computer vision,Respiratory motion,Segmentation,Tomography,Artificial intelligence,Root mean square,Correction for attenuation,Standard deviation,Mathematics | Journal |
Volume | Issue | ISSN |
84 | 2-3 | 0169-2607 |
Citations | PageRank | References |
1 | 0.36 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leighton R. Barnden | 1 | 9 | 1.82 |
| John Dickson | 2 | 55 | 3.74 |
| Brian F. Hutton | 3 | 98 | 14.33 |