Calculation of the weighting functions for the reconstruction of absorbing inhomogeneities in tissue by time-resolved optical projections

Konovalov, Alexander; Vlasov, Vitaly V.

doi:10.1070/qe2014v044n08abeh015495

Cited by 4 publications

(4 citation statements)

References 43 publications

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“…However, in the last years the CS-like algorithms 10,22,[25][26][27][28][29][30][31][32][33][34] have begun to seriously compete with it. We have gained rich experience of successful ART application both in DOT 52,53,[64][65][66] and in limited-data X-ray computed tomography. 73,74 Our attempts to use ART in combination with L p regularization (0 ≤ p ≤ 1) also refer to the same tomography applications.…”

Section: Reconstruction Algorithmsmentioning

confidence: 99%

“…As a result, we can make the banana‐shaped zones of photon trajectory distributions ultimately narrow and thus minimize spatial resolution. A significant gain in resolution due to the use of EPs has recently been demonstrated for DOT 16,50,52,53 and FMT 11,20,21 …”

Section: Introductionmentioning

confidence: 99%

“…As a result, we can make the banana-shaped zones of photon trajectory distributions ultimately narrow and thus minimize spatial resolution. A significant gain in resolution due to the use of EPs has recently been demonstrated for DOT 16,50,52,53 and FMT. 11,20,21 The basic postulate of the recently developed theory of compressed sensing (CS), 54 as applied to image reconstruction, can be formulated as follows.…”

Section: Introductionmentioning

confidence: 99%

“…ART-TV results are at the top and ART-FIST results are at the bottom. Quantitative characteristics(52) and(53) calculated for images inFigures 4-6are summarized in…”

mentioning

confidence: 99%

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Early‐photon reflectance fluorescence molecular tomography for small animal imaging: Mathematical model and numerical experiment

Konovalov

Vlasov

Uglov

2020

Numer Methods Biomed Eng

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The paper presents an original approach to time‐domain reflectance fluorescence molecular tomography (FMT) of small animals. It is based on the use of early arriving photons and state‐of‐the‐art compressed‐sensing‐like reconstruction algorithms and aims to improve the spatial resolution of fluorescent images. We deduce the fundamental equation that models the imaging operator and derive analytical representations for the sensitivity functions which are responsible for the reconstruction of the fluorophore absorption coefficient. The idea of fluorescence lifetime tomography with our approach is also discussed. We conduct a numerical experiment on 3D reconstruction of box phantoms with spherical fluorescent inclusions of small diameters. For modeling measurement data and constructing the sensitivity matrix we assume a virtual fluorescence tomograph with a scanning fiber probe that illuminates and collects light in reflectance geometry. It provides for large source‐receiver separations which correspond to the macroscopic regime. Two compressed‐sensing‐like reconstruction algorithms are used to solve the inverse problem. These are the algebraic reconstruction technique with total variation regularization and our modification of the fast iterative shrinkage‐thresholding algorithm. Results of our numerical experiment show that our approach is capable of achieving as good spatial resolution as 0.2 mm and even better at depths to 9 mm inclusive.

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Section: Reconstruction Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…ART-TV results are at the top and ART-FIST results are at the bottom. Quantitative characteristics(52) and(53) calculated for images inFigures 4-6are summarized in…”

mentioning

confidence: 99%

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