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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Se Young Chun Fessler, J.A. Dewaraja, Y.K. |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA (Se Young Chun; Fessler, J.A.) || Dept. of Radiol., Univ. of Michigan, Ann Arbor, MI, USA (Dewaraja, Y.K.) |
| Abstract | Recently, non-local means (NLM) methods for both image denoising and inverse problems have shown promising results in image processing and medical imaging. Moreover, some researchers have also shown that using additional information with low noise and/or high resolution for these problems can improve the image quality further. We investigated several NLM methods including NLM filters and NLM regularizers with and without CT side information for 1-131 SPECT image reconstruction. We compared two different ways to incorporate CT side information in the NLM filtering methods. We also propose a new way of incorporating side information in the NLM reconstruction method. XCAT simulation results with two uniform tumors (72 cc, 11 cc) show that the NLM filtering method with CT side information decreased the root mean square error (RMSE) up to 18.6% as compared to unregularized method. Our proposed NLM-based regularizers in iterative image reconstructions with CT side information also yielded up to 29.2% better RMSE and up to 18.1 % better recovery coefficient than unregularized reconstruction. |
| Sponsorship | IEEE Nucl. Plasma Sci. Soc |
| Starting Page | 3362 |
| Ending Page | 3366 |
| File Size | 1076941 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467320283 |
| ISSN | 10823654 |
| e-ISBN | 9781467320306 |
| DOI | 10.1109/NSSMIC.2012.6551766 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Content Type | Text |
| Resource Type | Article |
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