Discrete tomography in an in vivo small animal bone study

Radiological characterization of gilthead seabream (Sparus aurata) by X-ray computed tomography

In recent years, the increasing use of fish as new animal models in scientific research and the growth of fish farming (mainly for human consumption) have highlighted the need for advanced technology to deepen our knowledge of fish biology. Hence, the present study was carried out to radiologically analyse the whole body of gilthead seabream (Sparus aurata) specimens using X-ray computed tomography (CT). Images were acquired in an Albira SPECT/PET/CT tri-modal preclinical-scanner. Segmentation, measurements and three-dimensional reconstruction were made using the Carestream Molecular imaging Albira CT system in conjunction with Pmod, AMIDE and Amira software packages. The results showed that the density values of gilthead seabream are in the range -700 to +2500 HU for the whole body. We also determined the density ranges that topographically coincide with the swim bladder, soft tissues, fat, skin and skeleton. This work describes, validates and demonstrates the application of a fully automated image analysis technique to study and quantify fish body composition, whether segmented or as a whole. In addition, the basis for applying this image technique in other in vivo studies is established.

poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT

The discrete algebraic reconstruction technique (DART) is a tomographic method to reconstruct images from X-ray projections in which prior knowledge on the number of object materials is exploited. In monochromatic X-ray CT (e.g., synchrotron), DART has been shown to lead to high-quality reconstructions, even with a low number of projections or a limited scanning view. However, most X-ray sources are polychromatic, leading to beam hardening effects, which significantly degrade the performance of DART. In this work, we propose a new discrete tomography algorithm, poly-DART, that exploits sparsity in the attenuation values using DART and simultaneously accounts for the polychromatic nature of the X-ray source. The results show that poly-DART leads to a vastly improved segmentation on polychromatic data obtained from Monte Carlo simulations as well as on experimental data, compared to DART.

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

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.