Name
Abstract | ||
|---|---|---|
Cardiac gated SPECT imaging suffers from several degrading factors including limited data counts, depth-dependent blur, attenuation, scatter, and motion blur. In recent work we demonstrated that use of temporal processing could lead to the most improvement in 4D reconstruction. In this work we investigate how motion estimation models can affect the reconstruction in 4D, and in particular, we examine how much room is still left in optical flow estimation toward improving the image quality of reconstruction. In our experiments we conducted a thorough, quantitative evaluation using simulated imaging study with multiple noise realizations from the NCAT phantom. As an upper bound, the known motion from NCAT was used for reconstruction. The results show that 4D reconstruction with a periodic optical flow model can achieve results almost matching that with the known motion. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISBI.2012.6235731 | ISBI |
Keywords | Field | DocType |
motion compensation,degrading factors,depth dependent blur,motion blur,image attenuation,cardiac gated spect imaging,temporal processing,image quality improvement,4d reconstruction,motion estimation methods,ncat phantom,periodic optical flow model,4d gated cardiac spect reconstruction,motion estimation,spatiotemporal processing,image reconstruction,image sequences,motion estimation effects,single photon emission computed tomography,optical flow,gated cardiac spect,motion estimation models,limited data counts,phantoms,medical image processing,optical flow estimation,upper bound,computer vision,image quality,multiplicative noise,logic gate,optical imaging,logic gates | Iterative reconstruction,Computer vision,Computer science,Motion compensation,Imaging phantom,Image quality,Motion blur,Artificial intelligence,Motion estimation,Optical flow,Gated SPECT | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4577-1857-1 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenyuan Qi | 1 | 8 | 5.56 |
| Xiaofeng Niu | 2 | 8 | 7.48 |
| Yongyi Yang | 3 | 1409 | 140.74 |