Comparison of image reconstruction techniques for optical projection tomography

Trull, Anna K.; Horst, Jelle van der; Vliet, Lucas J. van; Kalkman, Jeroen

doi:10.1364/ao.57.001874

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Filtered back projection (FBP) algorithms are the most commonly used algorithm for OPT reconstruction . However, FBP algorithm cannot be fully applied to OPT reconstructions . It does not account for refraction, scattering and diffraction of the light .…”

Section: Introductionmentioning

confidence: 99%

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Dual‐modality optical projection tomography reconstruction method from fewer views

Dou

Liu

Yang

2018

Journal of Biophotonics

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In optical projection tomography (OPT), for longitudinal living model studies, multiple measurements of the same sample are required at different time points. It is important to decrease both the total acquisition time and the light dose to the sample. We improved the ordered subsets expectation maximization reconstruction algorithm for OPT, which reduces the acquisition time and number of projections greatly compared with filtered back projection (FBP), and obtained satisfactory reconstructed images. Using zebrafish, in transmission and fluorescence mode, we demonstrate the capability of the method to reconstruct image from downsampled projection subsets. The result shows that the reconstruction image quality of the proposed method using 30 projections is comparable to that of FBP using 720 projections. The total acquisition procedure can be finished in a few seconds. The method also provides OPT with the remarkable capability to resist noises and artifacts. Projection image and fused image of zebrafish.

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Section: Introductionmentioning

confidence: 99%

“…However, FBP algorithm cannot be fully applied to OPT reconstructions . It does not account for refraction, scattering and diffraction of the light . Moreover, to obtain high quality of reconstruction image, hundreds of projections are often required.…”

Section: Introductionmentioning

confidence: 99%

Dual‐modality optical projection tomography reconstruction method from fewer views

Dou

Liu

Yang

2018

Journal of Biophotonics

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“…A recent study compared several reconstruction techniques in fluorescence OPT 50 . They concluded that a reconstruction method based on point spread function analysis, which GBM and SGBM are, is good for nonsparse objects, such as the zebrafish embryo.…”

Section: Discussionmentioning

confidence: 99%

Gaussian Light Model in Brightfield Optical Projection Tomography

Koskela

Montonen

Belay

et al. 2019

Sci Rep

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This study focuses on improving the reconstruction process of the brightfield optical projection tomography (OPT). OPT is often described as the optical equivalent of X-ray computed tomography, but based on visible light. The detection optics used to collect light in OPT focus on a certain distance and induce blurring in those features out of focus. However, the conventionally used inverse Radon transform assumes an absolute focus throughout the propagation axis. In this study, we model the focusing properties of the detection by coupling Gaussian beam model (GBM) with the Radon transform. The GBM enables the construction of a projection operator that includes modeling of the blurring caused by the light beam. We also introduce the concept of a stretched GBM (SGBM) in which the Gaussian beam is scaled in order to avoid the modeling errors related to the determination of the focal plane. Furthermore, a thresholding approach is used to compress memory usage. We tested the GBM and SGBM approaches using simulated and experimental data in mono- and multifocal modes. When compared with the traditionally used filtered backprojection algorithm, the iteratively computed reconstructions, including the Gaussian models GBM and SGBM, provided smoother images with higher contrast.

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“…No aliasing sampling and quantization super-resolution reconstruction, these blurs are usually characterized by point spread functions [14]. (3) Sampling: the light emitted by the object is converted into an electric signal on the sensor.…”

Section: Continuous Scenementioning

confidence: 99%

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Guo

Woźniak

2020

Mobile Netw Appl

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The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.

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Comparison of image reconstruction techniques for optical projection tomography

Cited by 15 publications

References 18 publications

Dual‐modality optical projection tomography reconstruction method from fewer views

Dual‐modality optical projection tomography reconstruction method from fewer views

Gaussian Light Model in Brightfield Optical Projection Tomography

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

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