Accurate and fast reconstruction for bioluminescence tomography based on adaptive Newton hard thresholding pursuit algorithm

Wang, Yuejie; Zhang, Heng; Guo, Hongbo; Li, Yanqiu; He, Xuelei; Yu, Jingjing; Yi, Huangjian; He, Xiaowei

doi:10.1364/josaa.449917

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Progress in the field of BLI has been made on the one hand at the technical level, notably through the development of tomographic procedures and the correlation of these with CT and MRI images [ 5 , 6 ]. On the other hand, reconstruction methods that aim to solve the inverse problem, either mathematically or, more recently, using artificial intelligence [ 7 , 8 ], represent another area of new developments. These methods, however, are based on assumptions regarding optical parameters and anticipated tissue behavior.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment on Optical Properties as a Basis for Bioluminescence Imaging: An Experimental and Numerical Approach to the Transport of Optical Photons in Phantom Materials

Karstens,

Staufer,

Buchin

et al. 2023

Sensors

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Bioluminescence imaging (BLI) is a widely used technique in preclinical scientific research, particularly in the development of mRNA-based medications and non-invasive tumor diagnostics. It has become an essential tool in current science. However, the current state of bioluminescence imaging is primarily qualitative, making it challenging to obtain quantitative measurements and to draw accurate conclusions. This fact is caused by the unique properties of optical photons and tissue interactions. In this paper, we propose an experimental setup and Geant4-simulations to gain a better understanding of the optical properties and processes involved in bioluminescence imaging. Our goal is to advance the field towards more quantitative measurements. We will discuss the details of our experimental setup, the data we collected, the outcomes of the Geant4-simulations, and additional information on the underlying physical processes.

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Section: Introductionmentioning

confidence: 99%

Quantitative Assessment on Optical Properties as a Basis for Bioluminescence Imaging: An Experimental and Numerical Approach to the Transport of Optical Photons in Phantom Materials

Karstens,

Staufer,

Buchin

et al. 2023

Sensors

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“…Recently, the iterative hard thresholding (IHT) algorithm has developed to solve the problems related to L 0 -norm regularization optimization [33] and has been successfully applied in many areas of biomedical imaging. For example, Wang et al have used an adaptive Newton IHT to improve the performance of bioluminescence tomography [34]. Yuan et al have applied the IHT algorithm to solve the inverse problem of Fluorescence molecular tomography [35].…”

Section: Introductionmentioning

confidence: 99%

Comparative Studies of Reconstruction Algorithms for Sparse-View Photoacoustic Tomography

Liu,

Dai,

Wang

et al. 2023

IEEE Photonics J.

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Inverse source reconstruction is one of the most challenging problems in photoacoustic tomography (PAT) due to its ill-posed nature. Despite the extensive work done by researchers on this problem, there is currently no universally accepted solution. Regularization methods assume a significant role in the sparse-view PAT inverse problem. This study compares six inverse source reconstruction methods based on Lp (0≤p≤2) regularization and investigates the effects of signal sampling quantity, measurement noise, and sparsity on the performance of the reconstruction algorithms through a series of numerical simulations. The experimental results indicate that the average peak signal-to-noise ratio of the iterative hard threshold (IHT) method is twice that of the Tikhonov method and 3dB higher than that of the L1magic method. The average running time of the IHT method is half that of the Tikhonov method and one-seventh of the L1magic method. To further assess the performance of these six reconstruction methods, we conducted agar phantom experiments to examine their ability to resolve details. The aim is to provide valuable guidance for the development and application of algorithms in relevant fields.

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“…Therefore, the geometry and material properties of the structures on a wafer are approximately known beforehand, but not exactly due to process variations. To extract these aspects of the produced structures precisely, the estimated spatial permittivity distribution is adjusted such that it minimizes the differences between the measurements and the output of the computational model [3][4][5][6]. This is a so-called inverse scattering problem.…”

Section: Introductionmentioning

confidence: 99%

Inverse scattering with a parametrized spatial spectral volume integral equation for finite scatterers

Eijsvogel,

Dilz,

van Beurden

2023

J. Opt. Soc. Am. A

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In wafer metrology, the knowledge of the photomask together with the deposition process only reveals the approximate geometry and material properties of the structures on a wafer as a priori information. With this prior information and a parametrized description of the scatterers, we demonstrate the performance of the Gauss–Newton method for the precise and noise-robust reconstruction of the actual structures, without further regularization of the inverse problem. The structures are modeled as 3D finite dielectric scatterers with a uniform polygonal cross-section along their height, embedded in a planarly layered medium. A continuous parametrization in terms of the homogeneous permittivity and the vertex coordinates of the polygons is employed. By combining the global Gabor frame in the spatial spectral Maxwell solver with the consistent parametrization of the structures, the underlying linear system of the Maxwell solver inherits all the continuity properties of the parametrization. Two synthetically generated test cases demonstrate the noise-robust reconstruction of the parameters by surpassing the reconstruction capabilities of traditional imaging methods at signal-to-noise ratios up to −3dB with geometrical errors below λ/7, where λ is the illumination wavelength. For signal-to-noise ratios of 10 dB, the geometrical parameters are reconstructed with errors of approximately λ/60, and the material properties are reconstructed with errors of around 0.03%. The continuity properties of the Maxwell solver and the use of prior information are key contributors to these results.

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Accurate and fast reconstruction for bioluminescence tomography based on adaptive Newton hard thresholding pursuit algorithm

Cited by 7 publications

References 29 publications

Quantitative Assessment on Optical Properties as a Basis for Bioluminescence Imaging: An Experimental and Numerical Approach to the Transport of Optical Photons in Phantom Materials

Quantitative Assessment on Optical Properties as a Basis for Bioluminescence Imaging: An Experimental and Numerical Approach to the Transport of Optical Photons in Phantom Materials

Comparative Studies of Reconstruction Algorithms for Sparse-View Photoacoustic Tomography

Inverse scattering with a parametrized spatial spectral volume integral equation for finite scatterers

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