Abstract

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds.

Highlights

  • Optical projection tomography (OPT) is the optical analogue of X-ray computed tomography (CT) [1]

  • The images obtained from the down-sampled data set consisting of 50 projections using different parameters are shown in S2 Fig. and are visually very similar

  • If image reconstruction speed is preferred over image quality, we can use a small number of TV minimisation iterations (TVit) for a faster convergence of the algorithm, without noticeably reducing image quality

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Summary

Introduction

Optical projection tomography (OPT) is the optical analogue of X-ray computed tomography (CT) [1]. OPT can provide high (μm) resolution three-dimensional (3-D) images of the optical attenuation (anatomy) and/or fluorescence intensity distribution within transparent samples. The technique entails wide-field imaging of the sample from different angles, typically using a scientific camera to sequentially capture the projection image at each angle. The sample should be illuminated along the optical axis of the projection, but for fluorescence OPT the direction of the illumination is immaterial and only reasonably uniform illumination throughout the sample is required. The sequential acquisition of each row of pixels of the camera provides a series of 1-D images as a function of the rotation angle to form a sinogram. Each sinogram can be reconstructed using the filtered back projection (FBP) algorithm [2], which is based on the inverse Radon transform, to obtain a 2-D slice through the sample. Reconstruction of all sinograms (slices) yields the whole 3-D sample volume

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