A photographic technique for quick assessment of mechanical isocenter of a linear accelerator.

Demands for mechanical accuracy of medical linear accelerators are increased due to the stereotactic and modulated rotational treatments. Mechanical inaccuracies affect the size and shape of the mechanical and radiation isocenters. In practice, the mechanical isocenter is defined by the intersection of rotational axes. However, there are no simple tools to check the properties of the mechanical isocenter in 3D. We introduce a new photography-based method for quick and sub-millimeter accurate determination of the mechanical isocenter. The method is based on image-processing algorithm and modified front pointer. The results demonstrate the quick measurement and visualization of the mechanical isocenter.

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.

Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.

Computational calibration method for optical tomography.

We propose a computational calibration method for optical tomography. The model of the calibration scheme is based on the rotation symmetry of source and detector positions in the measurement setup. The relative amplitude losses and phase shifts at the optic fibers are modeled by complex-valued coupling coefficients. The coupling coefficients can be estimated when optical tomography data from a homogeneous and isotropic object are given. Once these coupling coefficients have been estimated, any data measured with the same measurement setup can be corrected for the relative variation in the data due to source and detector losses. The final calibration of the data for the source and detector losses and the source calibration between the data and the forward model are obtained as part of the initial estimation for reconstruction. The calibration method was tested with simulations and measurements. The results show that the coupling coefficients of the sources and detectors can be estimated with good accuracy. Furthermore, the results show that the method can significantly improve the quality of reconstructed images.