A new scheme, called direct aperture deformation (DAD), for online correction of interfraction geometric uncertainties under volumetric imaging guidance is presented. Using deformable image registration, the three-dimensional geometric transformation matrix can be derived that associates the planning image set and the images acquired on the day of treatment. Rather than replanning or moving the patient, we use the deformation matrix to morph the treatment apertures as a potential online correction method. A proof-of-principle study using an intensity-modulated radiation therapy plan for a prostate cancer patient was conducted. The method, procedure, and algorithm of DAD are described. The dose-volume histograms from the original plan, reoptimized plan, and rigid-body translation plan are compared with the ones from the DAD plan. The study showed the feasibility of the DAD as a general method for both target dislocation and deformation. As compared with using couch translation to move the patient, DAD is capable of correcting both target dislocation and deformations. As compared with reoptimization, online correction using the DAD scheme could be completed within a few minutes rather than tens of minutes and the speed gain would be at a very small cost of plan quality.

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.

Mutual information-based image registration, shown to be effective in registering a range of medical images, is a computationally expensive process, with a typical execution time on the order of minutes on a modern single-processor computer. Accelerated execution of this process promises to enhance efficiency and therefore promote routine use of image registration clinically. This paper presents details of a hardware architecture for real-time three-dimensional (3-D) image registration. Real-time performance can be achieved by setting up a network of processing units, each with three independent memory buses: one each for the two image memories and one for the mutual histogram memory. Memory access parallelization and pipelining, by design, allow each processing unit to be 25 times faster than a processor with the same bus speed, when calculating mutual information using partial volume interpolation. Our architecture provides superior per-processor performance at a lower cost compared to a parallel supercomputer.

Real-time three-dimensional (3-D) echocardiography is a new imaging modality that presents the unique opportunity to visualize the complex 3-D shape and motion of the left ventricle (LV) in vivo and to measure the associated global and local function parameters. To take advantage of this opportunity in routine clinical practice, automatic segmentation of the LV in the 3-D echocardiographic data, usually hundreds of megabytes large, is essential. We report a new segmentation algorithm for this task. Our algorithm has two distinct stages, initialization of a deformable model and its refinement, which are connected by a dual "voxel + wiremesh" template. In the first stage, mutual-information-based registration of the voxel template with the image to be segmented helps initialize the wiremesh template. In the second stage, the wiremesh is refined iteratively under the influence of external and internal forces. The internal forces have been customized to preserve the nonsymmetric shape of the wiremesh template in the absence of external forces, defined using the gradient vector flow approach. The algorithm was validated against expert-defined segmentation and demonstrated acceptable accuracy. Our segmentation algorithm is fully automatic and has the potential to be used clinically together with real-time 3-D echocardiography for improved cardiovascular disease diagnosis.