Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation

He, Xiaowei; Dong, Feifei; Yu, Jingjing; Guo, Hongbo; Hou, Yi

doi:10.1364/josaa.32.001928

Cited by 14 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The qualities of reconstruction results are quantitatively evaluated in terms of the absolute location error (LE) [3], reconstructed fluorescent yield (Recon. FY) [3], normalized root mean square error (NRMSE) [16], the percentage of nonzero coefficient (PNZ) [16], and time cost.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…FY) [3], normalized root mean square error (NRMSE) [16], the percentage of nonzero coefficient (PNZ) [16], and time cost. The experiment codes were written in MATLAB and were performed on a desktop computer with 3.40 GHz Intel® Xeon® Processor E3-1231 and 12 G RAM.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, FMT reconstruction suffers from high ill-posedness due to the insufficiency of external measurements, which is caused by high absorption and scattering in photon propagation through biological tissues [3]. …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast and Robust Reconstruction for Fluorescence Molecular Tomography viaL1-2Regularization

Zhang

Geng

Wang

et al. 2016

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L 1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L 1 and L 2 norms. To solve the nonconvex L 1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L 1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L 1-2 minimization outperforms the representative algorithms for L 1, L 2, L 1/2, and L 0 when the system matrix is highly coherent.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fast and Robust Reconstruction for Fluorescence Molecular Tomography viaL1-2Regularization

Zhang

Geng

Wang

et al. 2016

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this case the functional (12) is written and minimised already for the signal in the space of the transform (for the image f ). Such approach, called compressive sensing or compressive sampling [255][256][257], is successfully applied in DOT [248,249,252,254], but is even more used in DFMT [258][259][260][261][262][263][264][265][266][267][268][269][270][271][272]. As seen from the presented references, the publication boom falls on the recent 3-4 years.…”

Section:  mentioning

confidence: 99%

Diffuse optical mammotomography: state-of-the-art and prospects

Konovalov

Genina

Bashkatov

2016

JBPE

View full text Add to dashboard Cite

Abstract:The principles of diffuse optical tomography (DOT) of tissues are presented. The DOT capabilities as a method of breast cancer diagnostics are analysed. The state-ofthe-art of the DOT instrumentation and methodological base in application to solving the mammography problems are described. The significant contribution of Russian scientists to the development of the DOT methodology is emphasised. Basing on the results of the analysis, the authors expect the possibility of soonest entry of diffuse optical mammotomographs to the market of medical imaging instrumentation, and the capability of Russian researchers to take part in the competition for this market. © 2016 Journal of Biomedical Photonics & Engineering.Key words: diffuse optical tomography, breast, optical and functional parameters, methods of determining optical parameters, methods of image reconstruction, perturbation model of reconstruction, temporal point spread function, spatial resolution, reconstruction time. University Press, Cambridge (2016). 7. S. R. Arridge, and M. Schweiger, "Inverse methods for optical tomography," in Information Processing in Medical Imaging, H.H. Barrett (ed.), Springer-Verlag, Flagstaff, 259-277 (1993). 8. J. C. Hebden, S. R. Arridge, and D. T. Delpy, "Optical imaging in medicine: I. Experimental techniques,"Phys. Med. Biol. 42, 825-840 (1997). 9. S. R. Arridge, and J. C. Hebden, "Optical imaging in medicine: II. Modelling and reconstruction," Phys. Med. Biol. 42, 841-853 (1997). 10. B. B. Das, F. Liu, and R. R. Alfano, "Time-resolved fluorescence and photon migration studies in biomedical and random media," Rep. Prog. Phys. 60, 227-292 (1997). 11. K. Michielsen, H. De'Raedt, J. Przeslawski, and N. Garcia, "Computer simulation of time-resolved optical imaging of objects hidden in turbid media," Phys. Rep. 304, 89-144 (1998). 12. S. R. Arridge, "Optical tomography in medical imaging," Inverse Problems 15, R41-R93 (1999). Darzi, "Diffuse optical imaging of the healthy and diseased breast: a systematic review," Breast Cancer Research and Treatment 108, 9-22 (2008). 19. S. L. Jacques, and B. W. Pogue, "Tutorial on diffuse light transport," J. Biomed. Opt. 13, 041302 (2008 Biol. 361, 171-179 (1994). 31. A. Yodh, and B. Chance, "Spectroscopy and imaging with diffusing light," Phys. Today 48, 34-40 (1995). 32. J. C. Hebden, S. R. Arridge, "Imaging through scattering media by the use of an analytical model of perturbation amplitudes in the time domain," Appl. Opt. 35, 6788-6796 (1996). 33. K. D. Paulsen, and H. Jiang, "Enhanced frequency-domain optical image reconstruction in tissues through total-variation minimization," Appl. Opt. 35, 3447-3458 (1996). 34. R. J. Grable, "Optical tomography improves mammography," Laser Focus World 32, 113-118 (1996). 35. V. V. Lyubimov, "The physical foundation of the strongly scattering media laser tomography," Proc. SPIE 2769SPIE , 107-110 (1996. 36. V. V. Lyubimov, "Optical tomography of highly scattering media by using the first transmitted photons of ultrashort pulses," Optics a...

show abstract

“…In [21, 22], the feasible region can be derived from the near-infrared measured boundary data. A region-shrinking strategy is utilized to make the feasible region gradually shrink from the whole imaging domain to a small region in [24]. In addition, feasible region can also be extracted from the previously computed procedure, and a mesh refinement scheme is further used in the feasible region [25–27].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy

Zhang

Peng

et al. 2016

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Limited-projection fluorescence molecular tomography (FMT) has short data acquisition time that allows fast resolving of the three-dimensional visualization of fluorophore within small animal in vivo. However, limited-projection FMT reconstruction suffers from severe ill-posedness because only limited projections are used for reconstruction. To alleviate the ill-posedness, a feasible region extraction strategy based on a double mesh is presented for limited-projection FMT. First, an initial result is rapidly recovered using a coarse discretization mesh. Then, the reconstructed fluorophore area in the initial result is selected as a feasible region to guide the reconstruction using a fine discretization mesh. Simulation experiments on a digital mouse and small animal experiment in vivo are performed to validate the proposed strategy. It demonstrates that the presented strategy provides a good distribution of fluorophore with limited projections of fluorescence measurements. Hence, it is suitable for reconstruction of limited-projection FMT.

show abstract

Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation

Cited by 14 publications

References 28 publications

Fast and Robust Reconstruction for Fluorescence Molecular Tomography viaL1-2Regularization

Fast and Robust Reconstruction for Fluorescence Molecular Tomography viaL1-2Regularization

Diffuse optical mammotomography: state-of-the-art and prospects

Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy

Contact Info

Product

Resources

About