Edward L. Chaney

Segmentation, registration, and measurement of shape variation via image object shape

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 representations prior probability of local geometry by reflecting variabilities in the nets 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.

Deformable M-Reps for 3D Medical Image Segmentation

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures—each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure.A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects.The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.

Computation of digitally reconstructed radiographs for use in radiotherapy treatment design

The increasing use of 3-dimensional radiotherapy treatment design has created greater reliance on methods for computing images from CT data which correspond to the conventional simulation film. These images, known as computed or digitally reconstructed radiographs, serve as reference images for verification of computer-designed treatments. Used with software that registers graphic overlays of target and anatomic structures, digitally reconstructed radiographs are also valuable tools for designing portal shape. We have developed radiograph reconstruction software that takes full advantage of the contrast and spatial detail inherent in the original CT data. This goal has been achieved by using a ray casting algorithm which explicitly takes into account every intersected voxel, and a heuristic approach for approximating the images that would result from purely photoelectric or Compton interactions. The software also offers utilities to superimpose outlines of anatomic structures, field edges, beam crosshairs, and linear scales on digitally reconstructed radiographs. The pixel size of the computed image can be controlled, and several methods of interslice interpolation are offered. The software is written in modular format in the C language, and can stand alone or interface with other treatment planning software.

The portable virtual simulator

The Virtual Simulator is a software tool for support and management of the geometric component of 3-dimensional radiotherapy treatment design. The Virtual Simulator is a software implementation of a physical simulator with additional functionality not currently available on physical simulators. Treatment of a virtual patient, derived from CT or other source, is simulated using the Virtual Simulator in the same way a physical simulator would be used. The intent of this approach is to provide the user with a familiar working environment for radiotherapy treatment design. Key features include an effective and efficient user interface, and the use of computing techniques and software standards which enhance portability to a variety of computer workstations. The Virtual Simulator is implemented in the C programming language using the X Window System, and has been written with the generic UNIX workstation in mind. It has been demonstrated that it can be installed and run without modification on workstations from a number of vendors.

Virtual simulation in the clinical setting : some practical considerations

Virtual simulation departs from normal practice by replacing conventional treatment simulation with 3-dimensional image data and computer software. Implementation of virtual simulation requires the ability to transfer the planned treatment geometry from the computer to the treatment room in a way which is accurate, reproducible, and efficient enough for routine use. We have separated this process into: (a) immobilization of the patient; (b) establishment and alignment of a practical coordinate system for the patient/couch system; and (c) setup of the patient/couch been addressed by the use of hemi- or full-body foam casts, the second by use of an alignment jig on the treatment couch, and the third with the aid of a patient coordinate system referenced to easily located landmarks. Phantom studies and clinical practice have shown these techniques to be practical and effective within reasonable clinical bounds.

A method and software for segmentation of anatomic object ensembles by deformable m‐reps

Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic image segmentation. Published studies by others and our own research results strongly suggest that segmentation of a normal or near-normal object from 3D medical images will be most successful when the DSM approach uses (1) knowledge of the geometry of not only the target anatomic object but also the ensemble of objects providing context for the target object and (2) knowledge of the image intensities to be expected relative to the geometry of the target and contextual objects. The segmentation will be most efficient when the deformation operates at multiple object-related scales and uses deformations that include not just local translations but the biologically important transformations of bending and twisting, i.e., local rotation, and local magnification. In computer vision an important class of DSM methods uses explicit geometric models in a Bayesian statistical framework to provide a priori information used in posterior optimization to match the DSM against a target image. In this approach a DSM of the object to be segmented is placed in the target image data and undergoes a series of rigid and nonrigid transformations that deform the model to closely match the target object. The deformation process is driven by optimizing an objective function that has terms for the geometric typicality and model-to-image match for each instance of the deformed model. The success of this approach depends strongly on the object representation, i.e., the structural details and parameter set for the DSM, which in turn determines the analytic form of the objective function. This paper describes a form of DSM called m-reps that has or allows these properties, and a method of segmentation consisting of large to small scale posterior optimization of m-reps. Segmentation by deformable m-reps, together with the appropriate data representations, visualizations, and user interface, has been implemented in software that accomplishes 3D segmentations in a few minutes. Software for building and training models has also been developed. The methods underlying this software and its abilities are the subject of this paper.

Core-based portal image registration for automatic radiotherapy treatment verification

PURPOSE Portal imaging is the most important quality assurance procedure for monitoring the reproducibility of setup geometry in radiation therapy. The role of portal imaging has become even more critical in recent years due to the migration of three-dimensional (3D) treatment planning technology, including high-precision conformal therapy, from the research setting to routine clinical practice. Unfortunately, traditional methods for acquiring and interpreting portal images suffer from a number of deficiencies that contribute to the well-documented observation that many setup errors go undetected, and some persist for a clinically significant portion of the prescribed dose. Significant improvements in both accuracy and efficiency of detecting setup errors can, in principle, be achieved by using automatic image registration for on-line screening of images obtained from electronic portal imaging devices (EPIDs). METHODS AND MATERIALS This article presents recent developments in a method called core-based image analysis that shows great promise for achieving the desired improvements in error detection. Core-based image analysis is a fundamental computer vision method that is capable of exploiting the full power of EPIDs by providing for on-line detection of setup errors via automatic registration of user-selected anatomical structures. We describe a robust method for automatic portal image registration based on core analysis and demonstrate an approach for assessing both accuracy and precision of registration methods using realistic, digitally reconstructed portal radiographs (DRPRs) where truth is known. RESULTS Automatic core-based analysis of a set of 20 DRPRs containing known, random field positioning errors was performed for a patient undergoing treatment for prostate cancer. In all cases, the reported translation was within 1 mm of the actual translation with mean absolute errors of 0.3 mm and standard deviations of 0.3 mm. In all cases, the reported rotation was within 0.6 degree of the actual rotation with a mean absolute error of 0.18 degree and a standard deviation of 0.23 degree. CONCLUSION Our results, using digitally reconstructed portal radiographs that closely resemble clinical portal images, suggest that automatic core-based registration is suitable as an on-line screening tool for detecting and quantifying patient setup errors.

Volume rendering in radiation treatment planning

Successful treatment planning in radiation therapy depends in part on understanding the spatial relationship between patient anatomy and the distribution of radiation dose. Several visualizations based on volume rendering that offer potential solutions to this problem are presented. The visualizations use region boundary surfaces to display anatomy, polygonal meshes to display treatment beams, and isovalue contour surfaces to display dose. To improve perception of spatial relationships, metallic shading, surface and solid texturing, synthetic fog, shadows, and other artistic devices are used. Also outlined is a method based on 3-D mip maps for efficiently generating perspective volume renderings and beams-eye views.<<ETX>>

Automated Finite-Element Analysis for Deformable Registration of Prostate Images

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 patients 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.

Segmentation of Medical Image Objects Using Deformable Shape Loci

Robust segmentation of normal anatomical objects in medical images requires (1) methods for creating object models that adequately capture object shape and expected shape variation across a population, and (2) methods for combining such shape models with unclassified image data to extract modeled objects. Described in this paper is such an approach to model-based image segmentation, called deformable shape loci (DSL), that has been successfully applied to 2D MR slices of the brain ventricle and CT slices of abdominal organs. The method combines a model and image data by warping the model to optimize an objective function measuring both the conformation of the warped model to the image data and the preservation of local neighbor relationships in the model. Methods for forming the model and for optimizing the objective function are described.