An adaptive Tikhonov regularization method for fluorescence molecular tomography.
The high degree of absorption and scattering of photons propagating through biological tissues makes fluorescence molecular tomography (FMT) reconstruction a severe ill-posed problem and the reconstructed result is susceptible to noise in the measurements. To obtain a reasonable solution, Tikhonov r...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 8; pp. 849 - 859 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104081473&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104081473 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2013 vid: 51 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104081473 NLM23504309 2012188593 10.1007/s11517-013-1054-5 NLM23504309 104081473 ppf: 849 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An adaptive Tikhonov regularization method for fluorescence molecular tomography. aug: au: Cao, Xu Zhang, Bin Wang, Xin Liu, Fei Liu, Ke Luo, Jianwen Bai, Jing affil: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China. sug: subj: Image Processing, Computer Assisted Methods Diagnostic Imaging Methods Tomography Methods Phantoms, Imaging Regression ab: The high degree of absorption and scattering of photons propagating through biological tissues makes fluorescence molecular tomography (FMT) reconstruction a severe ill-posed problem and the reconstructed result is susceptible to noise in the measurements. To obtain a reasonable solution, Tikhonov regularization (TR) is generally employed to solve the inverse problem of FMT. However, with a fixed regularization parameter, the Tikhonov solutions suffer from low resolution. In this work, an adaptive Tikhonov regularization (ATR) method is presented. Considering that large regularization parameters can smoothen the solution with low spatial resolution, while small regularization parameters can sharpen the solution with high level of noise, the ATR method adaptively updates the spatially varying regularization parameters during the iteration process and uses them to penalize the solutions. The ATR method can adequately sharpen the feasible region with fluorescent probes and smoothen the region without fluorescent probes resorting to no complementary priori information. Phantom experiments are performed to verify the feasibility of the proposed method. The results demonstrate that the proposed method can improve the spatial resolution and reduce the noise of FMT reconstruction at the same time. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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