Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

Prakash, Jaya; Shaw, Calvin B.; Manjappa, Rakesh; Rajan, K.; Yalavarthy, Phaneendra K.

doi:10.1109/jstqe.2013.2278218

Cited by 53 publications

(76 citation statements)

References 32 publications

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“…However, their system matrix was much smaller in size and there is no non-negativity constraint, so their method can not be directly applied to our FMT model. Also in the field of DOT, it has been shown very recently that L q (0 < q ≤ 1) regularizations hold promise in improving the reconstructed image quality (Prakash et al 2014). Nonetheless, the DOT system is different and their approach is different than ours and can only handle nearly noise-free data.…”

Section: Introductionmentioning

confidence: 95%

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Zhu

Li²

2014

Phys. Med. Biol.

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Abstract. In vivo fluorescence imaging has been a popular functional imaging modality in preclinical imaging. Near infrared probes used in fluorescence molecular tomography (FMT) are designed to localize in the targeted tissues, hence sparse solution to the FMT image reconstruction problem is preferred. Nonconvex regularization methods are reported to enhance sparsity in the fields of statistical learning, compressed sensing etc. We investigated such regularization methods in FMT for small animal imaging with numerical simulations and phantom experiments. We adopted a majorization-minimization (MM) algorithm for the iterative reconstruction process and compared the reconstructed images using our proposed nonconvex regularizations with those using the well known L 1 regularization. We found that the proposed nonconvex methods outperform L 1 regularization in accurately recovering sparse targets in FMT.

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Section: Introductionmentioning

confidence: 95%

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Zhu

Li²

2014

Phys. Med. Biol.

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“…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

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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...

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“…A variety of algorithms have been proposed to improve the accuracy of the inverse problem of DOT [21][22][23][24][25][26][27][28][29][30][31][32][33]. As one of the most popular methods to solve nonlinear least square problems, Gauss-Newton algorithm was introduced to solve the DOT image reconstruction problem iteratively [32].…”

Section: Introductionmentioning

confidence: 99%

“…Model resolution-based basis pursuit deconvolution method and dimensionality reduction based optimization algorithm were implemented in DOT [29,27]. Sparse recovery methods had also been investigated by several groups with promising results for DOT image reconstruction [22,26]. Because of the strong optical scattering from deep targets, a depth compensation algorithm, based on the maximum singular values (MSVs) of layered measurement sensitivities, was used to reconstruct two 3-cm-deep absorbers with acceptable position errors [24].…”

Section: Introductionmentioning

confidence: 99%

Kernel-based anatomically-aided diffuse optical tomography reconstruction

Baikejiang¹,

Zhang²,

Zhu³

et al. 2017

Biomed. Phys. Eng. Express

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Image reconstruction in diffuse optical tomography (DOT) is a challenging task because its inverse problem is nonlinear, ill-posed and ill-conditioned. Anatomical guidance from imaging modalities with high spatial resolution can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we proposed a kernel method to introduce anatomical guidance into the DOT image reconstruction. In this kernel method, the optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT) images. Compared with Laplacian approaches that include structural priors, the proposed method does not require image segmentation. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. 5% Gaussian noise was added to both the numerical DOT measurements and the simulated CT image. The proposed method was also validated by an agar phantom experiment with the anatomical guidance from a cone beam CT scan. The effects of voxel size and number of nearest neighborhood size in the kernel method on the reconstructed DOT images were studied. The results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method comparing to the case without guidance. Furthermore, we demonstrated that the kernel method was able to utilize clinical breast CT images as anatomical guidance without segmentation. In addition, we found that the proposed kernel method was robust to the false positive guidance in the anatomical image.

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Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

Cited by 53 publications

References 32 publications

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

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

Kernel-based anatomically-aided diffuse optical tomography reconstruction

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