"The new algorithm by Yadava and his colleagues goes one step further. It uses a more realistic physics model of the x-ray source, the detectors, and the x-ray beam. Each of these three is assumed to have specific diameters instead of being considered a point or a line, Yadava says. Depending on the type of scan, the technique is better than ASIR at cutting image noise, and thus the x-rays can be even less intense. The researchers got high-quality abdomen scans of a human model using an eighth of the radiation dose of a conventional scan."

"In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity.…"

"In this paper, a parametric level set method for reconstruction of obstacles in general inverse problems is considered. General evolution equations for the reconstruction of unknown obstacles are derived in terms of the underlying level set parameters. We show that using the appropriate form of parameterizing the level set function results a significantly lower dimensional problem, which bypasses many difficulties with traditional level set methods, such as regularization, re-initialization and use of signed distance function.…"

"… The aim is to find efficient decompositions that simultaneously minimize the irradiation time, the cardinality of the decomposition and the setup-time to configure the multi-leaf collimator at each step of the decomposition. We propose for this NP-hard multiobjective combinatorial problem a heuristic, based on the adaptation of the two-phase Pareto local search. Experiments are carried out on different size instances and the results are reported."

"The beam-splitting method for fine-heterogeneity cor- rection will inevitably multiply beams to transport and thus will slow down dose calculation. With the GDS al- gorithm, the dose convolution is made only once after all the beams have been transported, which minimizes the impact of the beam multiplication on computing time. In fact, for the beams individually split into several tens, the calculation time increased only by several times with the GDS. This algorithmic framework will thus enable fast and accurate treatment planning of heavy charged particle ra- diotherapy in the presence of density heterogeneity finer than the size of intrinsic beam blurring."

"… The present work introduces new heuristic segmentation algorithms for the following optimization problem: Find a segmentation of an approximated matrix using only allowed fields and minimize the approximation error. Finally, the decomposition algorithms were implemented into an optimization programme in order to examine the assumptions of the algorithms for a clinical example. As a result, identical dose distributions with much fewer segments and a significantly smaller number of monitor units could be achieved using dosimetric constraints. Consequently, the dose delivery is more efficient and less time consuming."