Characterization of the dose distribution in the halo region of a clinical proton pencil beam using emulsion film detectors

Proton therapy is a high precision technique in cancer radiation therapy which allows irradiating the tumor with minimal damage to the surrounding healthy tissues. Pencil beam scanning is the most advanced dose distribution technique and it is based on a variable energy beam of a few millimeters FWHM which is moved to cover the target volume. Due to spurious effects of the accelerator, of dose distribution system and to the unavoidable scattering inside the patient's body, the pencil beam is surrounded by a halo that produces a peripheral dose. To assess this issue, nuclear emulsion films interleaved with tissue equivalent material were used for the first time to characterize the beam in the halo region and to experimentally evaluate the corresponding dose. The high-precision tracking performance of the emulsion films allowed studying the angular distribution of the protons in the halo. Measurements with this technique were performed on the clinical beam of the Gantry1 at the Paul Scherrer Institute. Proton tracks were identified in the emulsion films and the track density was studied at several depths. The corresponding dose was assessed by Monte Carlo simulations and the dose profile was obtained as a function of the distance from the center of the beam spot.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.