Iterative and discrete reconstruction in the evaluation of the rabbit model of osteoarthritis

Juuso H Ketola,Simo Saarakkala,Rami K Korhonen,Miika T Nieminen,Mikko A J Finnilä,Sakari S Karhula,Walter Herzog,Samuli Siltanen

doi:10.1038/s41598-018-30334-8

Abstract

Micro-computed tomography (µCT) is a standard method for bone morphometric evaluation. However, the scan time can be long and the radiation dose during the scan may have adverse effects on test subjects, therefore both of them should be minimized. This could be achieved by applying iterative reconstruction (IR) on sparse projection data, as IR is capable of producing reconstructions of sufficient image quality with less projection data than the traditional algorithm requires. In this work, the performance of three IR algorithms was assessed for quantitative bone imaging from low-resolution data in the evaluation of the rabbit model of osteoarthritis. Subchondral bone images were reconstructed with a conjugate gradient least squares algorithm, a total variation regularization scheme, and a discrete algebraic reconstruction technique to obtain quantitative bone morphometry, and the results obtained in this manner were compared with those obtained from the reference reconstruction. Our approaches were sufficient to identify changes in bone structure in early osteoarthritis, and these changes were preserved even when minimal data were provided for the reconstruction. Thus, our results suggest that IR algorithms give reliable performance with sparse projection data, thereby recommending them for use in µCT studies where time and radiation exposure are preferably minimized.

Highlights

Micro-computed tomography has long been considered as the ‘gold standard’ method for structural bone analysis due to its ability of retrieving high-resolution volumetric data in a non-invasive manner, and providing optimal contrast between bone and soft tissue[1]
The inverse problems associated with sparse X-ray tomography data reconstructions often do not have stable and unique solutions and require a priori knowledge about the object to converge to a reliable solution[16,17]
In the reference FDK reconstruction, BV/total variation (TV), Tb.Th, Tb.N and ellipsoid factor (EF) were lower in the anterior cruciate ligament transection (ACLT) group compared to the control group, and Pl.Th and Tb.S were higher in the ACLT group compared to the control group (Table 2)

Summary

Introduction

Micro-computed tomography (μCT) has long been considered as the ‘gold standard’ method for structural bone analysis due to its ability of retrieving high-resolution volumetric data in a non-invasive manner, and providing optimal contrast between bone and soft tissue[1]. The inverse problems associated with sparse X-ray tomography data reconstructions often do not have stable and unique solutions and require a priori knowledge about the object to converge to a reliable solution[16,17] This so-called regularization consists of iterative algorithms aimed at solving an optimization problem (e.g. the minimization of the L2 norm) with a penalty term containing prior information. In the context of μCT imaging, IR or regularizing methods could enable the acquisition of a large number of high-resolution scans, thereby increasing reliability of many biologically relevant μCT studies while simultaneously reducing movement artefacts and harmful radiation exposure. Due to their iterative nature, these algorithms are associated with high computational demands.

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