Optical projection tomography(OPT) provides an approach to recreating three-dimensional images of small biological specimens. Light traverses through a straight line to achieve a homogeneous illumination of the specimen. As the specimens in the conventional OPT could not survive or the survival time was too short, this paper proposes a new type of sample fixation method for OPT imaging. The specimen was anaesthetized in a petri dish, and the dish was fixed under the rotational stage of our homemade OPT system for imaging. This method can reduce the damage to the specimen and be more conducive to the continuous observation for in vivo OPT. However, the sample fixation causes the problem of insufficient sampling. To obtain optical projection tomographic image with insufficient samples, this paper uses the iterative reconstruction algorithm combining with the prior information to solve the inverse reconstruction problem.
KEYWORDS: Reconstruction algorithms, Image quality, Tomography, Raman spectroscopy, 3D image processing, Head, 3D acquisition, Data modeling, Data acquisition, Sensors
As an emerging volumetric imaging technique, Stimulated Raman projection tomography (SRPT) can provide quantitative distribution of chemical components in a three-dimensional (3D) volume, with a label-free manner. Currently, the filtered back-projection (FBP) algorithm is used to reconstruct the 3D volume in SRPT. However, to obtain a satisfactory reconstruction result, the FBP algorithm requires a certain amount of projection data, usually, at least 180 projections in a half circle. This leads to a long data acquisition time and hence limits dynamic and longitudinal observation of living systems. Iterative reconstruction from sparsely sampled data may reduce the total data acquisition time by reducing the projections used in the reconstruction. In this work, two total variation regularization based iterative reconstruction algorithms were selected and used in SRPT, including the simultaneous algebra reconstruction technique (SART) and the two-step iterative shrinkage/thresholding algorithm (TwIST). The well-known distance-driven model was utilized as the forward and back-projectors. We evaluated these two algorithms with numerical simulations. Using the original image as the reference, we calculated the quality of the reconstructed images. Simulation results showed that both the SART and TwIST performed better than the FBP algorithm, with larger values of the structural similarity (SSIM). Furthermore, the number of projection images can be largely reduced when the iterative reconstruction algorithm was used. Especially when the SART was used, the projection number can be reduced to 15, providing a satisfactory reconstruction image (SSIM is larger than 0.9).
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