No abstract

The concept of tissue-maximum ratios (TMR) as a basis of dose computations has posed problems when applied to a whole range of clinically used megavoltage beams. Another problem is the definition of scatter-maximum ratio (SMR) which assumes a value of zero at the depth of maximum dose. This paper describes new methods of measuring collimator and phantom scatter correction factors. The definitions of TMR and SMR are modified in order to compute phantom scatter at any depth, including depth of maximum dose. The revised concept is basic and general enough that it can be applied to x-ray beams of any energy fields of any shape and isocentric as well as non-isocentric modes of treatment.

For pt.I, see ibid., vol.11, no.1, p.53.61 (1992). Based on the statistical properties of X-ray CT imaging given in pt.I, an unsupervised stochastic model-based image segmentation technique for X-ray CT images is presented. This technique utilizes the finite normal mixture distribution and the underlying Gaussian random field (GRF) as the stochastic image model. The number of image classes in the observed image is detected by information theoretical criteria (AIC or MDL). The parameters of the model are estimated by expectation-maximization (EM) and classification-maximization (CM) algorithms. Image segmentation is performed by a Bayesian classifier. Results from the use of simulated and real X-ray computerized tomography (CT) image data are presented to demonstrate the promise and effectiveness of the proposed technique.

In electron beam therapy, alterations in dosimetry occur as a result of air space between the end-of-treatment cone and the skin surface. A large air gap may be introduced in order to obtain a field size larger than that available at the cone end. Needed dosimetry corrections related to these air space problems are discussed, along with a proposed method of measuring effective source-to-cone end distance. Data presented show the modifications of a dosimetric field which occur with an increase in the air space below the treatment cone.

A statistical description of X-ray CT (computerized tomography) imaging, from the projection data to the reconstructed image, is presented. The Gaussianity of the pixel image generated by the convolution (image reconstruction) algorithm is justified. The conditions for two pixel images to be statistically independent (for a given probability) and the conditions for a group of pixel images to be a spatial stationary random process and ergodic in mean and autocorrelations are derived. These properties provide the basis for establishing the stochastic image model and conducting the statistical image analysis of X-ray CT images.