An Automatic Regularization Method: An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data

Abstract

X-ray tomography is a reliable tool for determining the inner structure of 3D object with penetrating X-rays. However, traditional reconstruction methods such as FDK require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3D reconstruction from such a sparsely sampled dataset is difficult because the measurement data is usually contaminated by errors and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the three-dimensional shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted 0 < Cpr ≤ 1. The number Cpr can be calibrated from a few dense-angle reconstructions and fixed. Human subchondral bone samples were tested and morphometric parameters of the bone reconstructions were then analyzed using standard metrics. The proposed method is shown to outperform the baseline algorithm (FDK) in the case of sparsely collected data. The number of X-ray projections can be reduced up to 10% of the total amount 300 projections over 180 degrees with uniform angular step while retaining the quality of the reconstruction images and of the morphometric parameters.

Probabilistic approach to limited-data computed tomography reconstruction

“…Investigating finer resolution images and statistical records would also be interesting future research to evaluate other image quality parameters. Moreover, the proposed method can be applied to multidetector CT imaging [46,47] as well as 3D CT problems using sparse data [48,49].…”

Sparse-view statistical image reconstruction with improved total variation regularization for X-ray micro-CT imaging

“…However, iterative reconstruction algorithms are not using routinely in micro-CT [12]. Since the long scanning time is one of the major issues in micro-CT [4,5,12,13], sparse-view iterative reconstruction…”

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Cholinergic dysfunction-induced insufficient activation of alpha7 nicotinic acetylcholine receptor drives the development of rheumatoid arthritis through promoting protein citrullination via the SP3/PAD4 pathway

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