Abstract
Recently new analytical sufficient conditions and inversion formulas have been found for exact reconstruction of a region of interest (ROI) from truncated projections. However, it remains unknown whether these results can be applied to iterative reconstruction methods which are based discrete-discrete imaging models. In this paper, we explore the behavior of iterative reconstruction methods for truncated data. We evaluate the maximum-likelihood (ML) expectation-maximization (EM) method under three data truncation cases, namely, the classical interior and exterior tomography problems, and a new type of peripheral ROIs which satisfy the data sufficiency condition for the two-step Hilbert transform method [1]. The simulation results show that the peripheral ROIs can be reconstructed by ML-EM method regardless of truncation, but the interior and exterior problem suffer from different degrees of artifacts. These results are consistent with existing analytical data sufficiency conditions. We also numerically calculate the singular value decomposition (SVD) of the truncated system matrix, which shows that when the analytical sufficient condition for an ROI is satisfied, the singular vectors associated with very small singular values have little intersection with the ROI.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Nuclear Science Symposium Conference Record |
| Pages | 2236-2241 |
| Number of pages | 6 |
| Volume | 4 |
| DOIs | |
| State | Published - 2007 |
| Event | 2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD - San Diego, CA, United States Duration: Oct 29 2006 → Nov 4 2006 |
Other
| Other | 2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 10/29/06 → 11/4/06 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Industrial and Manufacturing Engineering
Cite this
Evaluation of 2D ROI image reconstruction using ML-EM method from truncated projections. / Fu, Lin; Liao, Jinxiu; Qi, Jinyi.
IEEE Nuclear Science Symposium Conference Record. Vol. 4 2007. p. 2236-2241 4179473.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of 2D ROI image reconstruction using ML-EM method from truncated projections
AU - Fu, Lin
AU - Liao, Jinxiu
AU - Qi, Jinyi
PY - 2007
Y1 - 2007
N2 - Recently new analytical sufficient conditions and inversion formulas have been found for exact reconstruction of a region of interest (ROI) from truncated projections. However, it remains unknown whether these results can be applied to iterative reconstruction methods which are based discrete-discrete imaging models. In this paper, we explore the behavior of iterative reconstruction methods for truncated data. We evaluate the maximum-likelihood (ML) expectation-maximization (EM) method under three data truncation cases, namely, the classical interior and exterior tomography problems, and a new type of peripheral ROIs which satisfy the data sufficiency condition for the two-step Hilbert transform method [1]. The simulation results show that the peripheral ROIs can be reconstructed by ML-EM method regardless of truncation, but the interior and exterior problem suffer from different degrees of artifacts. These results are consistent with existing analytical data sufficiency conditions. We also numerically calculate the singular value decomposition (SVD) of the truncated system matrix, which shows that when the analytical sufficient condition for an ROI is satisfied, the singular vectors associated with very small singular values have little intersection with the ROI.
AB - Recently new analytical sufficient conditions and inversion formulas have been found for exact reconstruction of a region of interest (ROI) from truncated projections. However, it remains unknown whether these results can be applied to iterative reconstruction methods which are based discrete-discrete imaging models. In this paper, we explore the behavior of iterative reconstruction methods for truncated data. We evaluate the maximum-likelihood (ML) expectation-maximization (EM) method under three data truncation cases, namely, the classical interior and exterior tomography problems, and a new type of peripheral ROIs which satisfy the data sufficiency condition for the two-step Hilbert transform method [1]. The simulation results show that the peripheral ROIs can be reconstructed by ML-EM method regardless of truncation, but the interior and exterior problem suffer from different degrees of artifacts. These results are consistent with existing analytical data sufficiency conditions. We also numerically calculate the singular value decomposition (SVD) of the truncated system matrix, which shows that when the analytical sufficient condition for an ROI is satisfied, the singular vectors associated with very small singular values have little intersection with the ROI.
UR - http://www.scopus.com/inward/record.url?scp=38649131994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38649131994&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2006.354359
DO - 10.1109/NSSMIC.2006.354359
M3 - Conference contribution
AN - SCOPUS:38649131994
SN - 1424405610
SN - 9781424405619
VL - 4
SP - 2236
EP - 2241
BT - IEEE Nuclear Science Symposium Conference Record
ER -