Comparison of radiobiological effects induced by ultra-high and standard dose rate of x-rays on a radio-resistant cell line

Radiotherapy (RT) has recently evolved with the emergence of heavy ion radiations or new fractionation schemes of photon therapy, which modify the dose rate of treatment delivery. The aim of the present study was then to evaluate the in vitro influence of a ultra-high dose rate comparing them with standard dose rate. In this regard, a radioresistant SK-MEL-28 cell line were irradiated with x-ray in order to have a total dose of 2 and 4 Gy, at two different dose rate. The ultra-high dose rate is a specific property of the dense plasma focus (DPF) device, which has pulsed operation and thus gives short and highly energetic pulses of multiple types of rays and particles, in this case, we focused our study on the influence of X-rays. While a low dose rate is obtained with conventional X-ray tube. In this study it results that a ultra-high dose rate enhances radiosensitivity of melanoma cells while reducing the adhesion, proliferation and migration ability of cells.

Compressed sensing in digital tomosynthesis reconstruction

In this work we study a model for the breast image reconstruction in Digital Tomosynthesis, that is a non-invasive and non-destructive method for the three-dimensional visualization of the inner structures of an object, in which the data acquisition includes measuring a limited number of low-dose two-dimensional projections of an object by moving a detector and an X-ray tube around the object within a limited angular range. The problem of reconstructing 3D images from the projections provided in the Digital Tomosynthesis is an ill-posed inverse problem, that leads to a minimization problem with an object function that contains a data fitting term and a regularization term. The contribution of this thesis is to use the techniques of the compressed sensing, in particular replacing the standard least squares problem of data fitting with the problem of minimizing the 1-norm of the residuals, and using as regularization term the Total Variation (TV). We tested two different algorithms: a new alternating minimization algorithm (ADM), and a version of the more standard scaled projected gradient algorithm (SGP) that involves the 1-norm. We perform some experiments and analyse the performance of the two methods comparing relative errors, iterations number, times and the qualities of the reconstructed images. In conclusion we noticed that the use of the 1-norm and the Total Variation are valid tools in the formulation of the minimization problem for the image reconstruction resulting from Digital Tomosynthesis and the new algorithm ADM has reached a relative error comparable to a version of the classic algorithm SGP and proved best in speed and in the early appearance of the structures representing the masses.