Two major factors preventing the routine clinical use of finite-element analysis for image registration are: 1) the substantial labor required to construct a finite-element model for an individual patient's anatomy and 2) the difficulty of determining an appropriate set of finite-element boundary conditions. This paper addresses these issues by presenting algorithms that automatically generate a high quality hexahedral finite-element mesh and automatically calculate boundary conditions for an imaged patient. Medial shape models called m-reps are used to facilitate these tasks and reduce the effort required to apply finite-element analysis to image registration. Encouraging results are presented for the registration of CT image pairs which exhibit deformation caused by pressure from an endorectal imaging probe and deformation due to swelling.

A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representation's prior probability of local geometry by reflecting variabilities in the net's node and link parameter values, and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm for image analysis of deforming such a model to optimize a posteriori probability is described, and this paradigm is shown to be usable as a uniform approach for object definition, object-based registration between images of the same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object, compared with a normal anatomical object. Examples of applications of these methods in radiotherapy, surgery, and psychiatry are given.

This paper presents a new approach towards automating the identification and labeling of brachytherapy sources. Brachytherapy is a cancer treatment method in which radioactive sources (seeds) are implanted into and around cancerous tissues. A common method for determining the radiation dose distribution delivered to the patient requires analysis of two orthogonal radiographs that show the interstitial or intercavitary seed placement. First, the seeds must be identified on the radiographs, and then the corresponding seeds must be identified on both films. Once this labeling process is complete the spatial coordinates of the seeds can be determined and the 3 dimensional dose distribution can be computed. For the type of implant studied, typically the number of seeds is between 50 and 250, and the seed distributions are dense. The methodology for automatic seed labeling consists of three steps. First, Multiscale Geometric Statistical Pattern Recognition (MGSPR) is used to identify treatment relevant objects in the radiographs. Second, a priori information must be processed to verify the integrity of the object identification by MGSPR and to resolve any object identification ambiguities. Third, the identified objects are projected into 3 dimensions, completing the labeling task, and allowing computation of the dose distribution.>