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This study aimed at assessing the feasibility of a discrete algebraic reconstruction technique (DART) to be used in in vivo small animal bone studies. The advantage of discrete tomography is the possibility to reduce the amount of X-ray projection images, which makes scans faster and implies also a significant reduction of radiation dose, without compromising the reconstruction results. Bone studies are ideal for being performed with discrete tomography, due to the relatively small number of attenuation coefficients contained in the image [namely three: background (air), soft tissue and bone]. In this paper, a validation is made by comparing trabecular bone morphometric parameters calculated from images obtained by using DART and the commonly used standard filtered back-projection (FBP). Female rats were divided into an ovariectomized (OVX) and a sham-operated group. In vivo micro-CT scanning of the tibia was done at baseline and at 2, 4, 8 and 12 weeks after surgery. The cross-section images were reconstructed using first the full set of projection images and afterwards reducing them in number to a quarter and one-sixth (248, 62, 42 projection images, respectively). For both reconstruction methods, similar changes in morphometric parameters were observed over time: bone loss for OVX and bone growth for sham-operated rats, although for DART the actual values were systematically higher (bone volume fraction) or lower (structure model index) compared to FBP, depending on the morphometric parameter. The DART algorithm was, however, more robust when using fewer projection images, where the standard FBP reconstruction was more prone to noise, showing a significantly bigger deviation from the morphometric parameters obtained using all projection images. This study supports the use of DART as a potential alternative method to FBP in X-ray micro-CT animal studies, in particular, when the number of projections has to be drastically minimized, which directly reduces scanning time and dose. [ABSTRACT FROM AUTHOR]

Recently, the concept of X-ray diffraction contrast tomography (DCT) has been extended to the case of more widely available laboratory source CT systems. Using well known concepts from geometrical ray optics, an exact formulation is derived for the forward and backward projection geometry encountered under polychromatic cone beam illumination, and it is shown how this projection model can be efficiently implemented in practice. The new projection model is subsequently used for iterative tomographic reconstruction of the three-dimensional shape of a grain from a set of experimentally observed cone beam projections and shows a clear improvement compared to the simplified projection model used previously. [ABSTRACT FROM AUTHOR]

A new superresolution algorithmis proposed to reconstruct a high-resolution license plate image from a set of low-resolution camera images. The reconstruction methodology is based on the discrete algebraic reconstruction technique (DART), a recently developed reconstruction method. While DART has already been successfully applied in tomographic imaging, it has not yet been transferred to the field of camera imaging. DART is introduced for camera imaging through a demonstration of how prior knowledge of the colors of the license plate can be directly exploited during the reconstruction of a high-resolution image from a set of low-resolution images. Simulation experiments show that DART can reconstruct images with superior quality compared to conventional reconstruction methods. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is a technique for noninvasive imaging of physical objects. In the discrete algebraic reconstruction technique (DART), prior knowledge about the material's densities is exploited to obtain high quality reconstructed images from a limited number of its projections. In practice, this prior knowledge is typically not readily available. Here, a fully automatic method, called projection distance minimization DART (PDM-DART), is proposed in which the optimal grey level parameters are adaptively estimated during the reconstruction process. To apply PDM-DART, only the number of different grey levels should be known in advance. Simulation as well as real \muCT experiments show that PDM-DART is capable of computing reconstructed images of which the quality is similar to reconstructions computed by conventional DART based on exact prior knowledge, thereby eliminating the need for tedious and error-prone user interaction. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to segment dense, homogeneous objects in a tomographic reconstruction (or tomogram). A popular method to extract such objects from a tomogram is global thresholding, in which the threshold value is determined from the image histogram. However, accurate threshold selection is not straightforward, since, due to noise or artefacts in the reconstruction, the histogram does not always contain a clear, separate peak for the dense object. We propose a new threshold estimation approach, segmentation inconsistency minimization, that exploits the available projection data to determine the optimal global threshold. The proposed algorithm was tested on simulation data and on experimental \muCT data. The results show that this method results in more accurate segmentations, compared to alternative threshold selection methods. [ABSTRACT FROM AUTHOR]