Improved sparse reconstruction for fluorescence molecular tomography with L_1/2 regularization

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

doi:10.1364/boe.6.001648

Cited by 45 publications

(21 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

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

show abstract

“…where N X R ∈ is the image to be reconstructed; M b R ∈ is the angiograms stored in a single vector; A is the system matrix which models the cone-beam imaging process of the angiograms; 1 X is the 1 l norm of the reconstructed image which is minimized to enforce the sparsity of the image [24]. A novel strategy of alternate reconstruction and segmentation was developed to suppress the non-vessel background of the angiograms which have great adverse effect on the reconstruction quality.…”

Section: Initial Reconstructionmentioning

confidence: 99%

Local motion-compensated method for high-quality 3D coronary artery reconstruction

Liu

Bai

Zhou

2016

Biomed. Opt. Express

View full text Add to dashboard Cite

Abstract:The 3D reconstruction of coronary artery from X-ray angiograms rotationally acquired on C-arm has great clinical value. While cardiac-gated reconstruction has shown promising results, it suffers from the problem of residual motion. This work proposed a new local motion-compensated reconstruction method to handle this issue. An initial image was firstly reconstructed using a regularized iterative reconstruction method. Then a 3D/2D registration method was proposed to estimate the residual vessel motion. Finally, the residual motion was compensated in the final reconstruction using the extended iterative reconstruction method. Through quantitative evaluation, it was found that high-quality 3D reconstruction could be obtained and the result was comparable to state-of-the-art method.

show abstract

“…Recently, nonconvex L p regularizer¯nd its place in°uorescence molecular tomography (FMT) for sparsity enhancement. [15][16][17][18][19][20][21] Chen et al introduced L p regularizer into BLT and proposed the weighted interior-point algorithm (WIPA) for solving the nonconvex optimization problem. 22 In this paper, we present a L 1=2 norm-based reconstruction algorithm for BLT.…”

Section: Introductionmentioning

confidence: 99%

Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018

J. Innov. Opt. Health Sci.

Self Cite

View full text Add to dashboard Cite

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L 1 -regularized methods have been investigated due to the sparsity-inducing properties of L 1 norm. In this paper, we present a reconstruction method based on L 1=2 regularization to enhance sparsity of BLT solution and solve the nonconvex L 1=2 norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under di®erent noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

show abstract

Improved sparse reconstruction for fluorescence molecular tomography with L_1/2 regularization

Cited by 45 publications

References 44 publications

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

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

Local motion-compensated method for high-quality 3D coronary artery reconstruction

Source reconstruction for bioluminescence tomography via L1∕2 regularization

Contact Info

Product

Resources

About