A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography

Nadir, Zeeshan; Brown, Michael S.; Comer, Mary L.; Bouman, Charles A.

doi:10.1109/tci.2017.2690143

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…The complexity of reconstruction problems has evolved beyond the conventional 2D and 3D spatial representations to tackle more intricate 4D and even 5D challenges involving the dimensions of space and time, as well as specific aspects like heart or respiratory phases [94][95][96][97][98][99][100]. This heightened dimensionality in reconstruction offers valuable opportunities to enhance the quality of reconstruction by capitalizing on the inherent patterns within this multidimensional space.…”

Section: Multi-slice Fusion In Ct Reconstructionmentioning

confidence: 99%

Algorithms in Tomography and Related Inverse Problems—A Review

Tassiopoulou,

Koukiou,

Anastassopoulos

2024

Algorithms

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In the ever-evolving landscape of tomographic imaging algorithms, this literature review explores a diverse array of themes shaping the field’s progress. It encompasses foundational principles, special innovative approaches, tomographic implementation algorithms, and applications of tomography in medicine, natural sciences, remote sensing, and seismology. This choice is to show off the diversity of tomographic applications and simultaneously the new trends in tomography in recent years. Accordingly, the evaluation of backprojection methods for breast tomographic reconstruction is highlighted. After that, multi-slice fusion takes center stage, promising real-time insights into dynamic processes and advanced diagnosis. Computational efficiency, especially in methods for accelerating tomographic reconstruction algorithms on commodity PC graphics hardware, is also presented. In geophysics, a deep learning-based approach to ground-penetrating radar (GPR) data inversion propels us into the future of geological and environmental sciences. We venture into Earth sciences with global seismic tomography: the inverse problem and beyond, understanding the Earth’s subsurface through advanced inverse problem solutions and pushing boundaries. Lastly, optical coherence tomography is reviewed in basic applications for revealing tiny biological tissue structures. This review presents the main categories of applications of tomography, providing a deep insight into the methods and algorithms that have been developed so far so that the reader who wants to deal with the subject is fully informed.

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Section: Multi-slice Fusion In Ct Reconstructionmentioning

confidence: 99%

Algorithms in Tomography and Related Inverse Problems—A Review

Tassiopoulou,

Koukiou,

Anastassopoulos

2024

Algorithms

View full text Add to dashboard Cite

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“…The bilinear ratio method is used, which involves selecting two laser beams of specific wavelengths and using the integral absorbance ratio obtained from the projection data to reconstruct the temperature and concentration values within each pixel grid. This method offers a dependable way to extract spatial data on temperature and concentration distributions in the combustion field [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net

Shi,

Hao,

Huang

et al. 2024

Applied Sciences

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Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

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“…Tunable diode laser absorption tomography (TDLAT), based on absorption spectroscopy and computerized tomography (CT), is one of the most powerful tools for high-speed combustion flow diagnostics, owing to its noninvasive nature, quick response, and high sensitivity. − Thus, it is generally adapted to all complex and bad test combustion environments. On the other hand, TDLAT can realize the simultaneous measurement of temperature and gas concentration, which has made it the most popular and necessary technique for research on combustion diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

et al. 2022

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Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT) has proven to be a powerful combustion diagnosis method for efficient detection. However, restricted by the line-of-sight (LOS) measurement, the reconstruction resolution of TDLAT was dependent on the size of the detection data, which made it difficult to obtain sufficient data for extreme environmental measurements. This severely limits the development of TDLAT in combustion diagnosis. To overcome this limitation, we proposed a super-resolution reconstruction method based on the super-resolution residual U-Net (SRResUNet) to improve the reconstruction resolution using a software method that could take full advantage of residual networks and U-Net to extract the deep features from the limited data of TDLAT to reconstruct the temperature distribution efficiently. A simulation study was conducted to investigate how the parameters would affect the performance of the super-resolution model and to optimize the reconstruction. The results show that our SRResUNet model can effectively improve the accuracy of reconstruction with super-resolution, with good antinoise performance, with the errors of 2-, 4-, and 8-times super-resolution reconstructions of approximately 5.3, 7.4, and 9.7%, respectively. The successful demonstration of SRResUNet in this work indicates the possible applications of other deep learning methods, such as enhanced super-resolution generative adversarial networks (ESRGANs) for limited-data TDLAT.

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A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography

Cited by 11 publications

References 52 publications

Algorithms in Tomography and Related Inverse Problems—A Review

Algorithms in Tomography and Related Inverse Problems—A Review

Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

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