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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. Index Terms--Deformable models, image registration, image segmentation, real-time three-dimensional echocardiography, three-dimensional ultrasound.

Real-time, elastic and fully automated 3D image registration is critical to the efficiency and effectiveness of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. True, real-time performance will make many 3D image registration-based techniques clinically viable. Hierarchical volume subdivision-based image registration techniques are inherently faster than most elastic registration techniques, e.g. free-form deformation (FFD)-based techniques, and are more amenable for achieving real-time performance through hardware acceleration. Our group has previously reported an FPGA-based architecture for accelerating FFD-based image registration. In this article we show how our existing architecture can be adapted to support hierarchical volume subdivision-based image registration. A proof-of-concept implementation of the architecture achieved speedups of 100 for elastic registration against an optimized software implementation on a 3.2 GHz Pentium III Xeon workstation. Due to inherent parallel nature of the hierarchical volume subdivision-based image registration techniques further speedup can be achieved by using several computing modules in parallel.

Combined PET/CT scanners provide the ability to produce matching metabolic (from PET) and anatomic (from CT) information in a single examination. However, misalignments continue to exist in tumor localization in PET and CT images acquired using these scanners, due to their inability to compensate for nonrigid misalignment resulting from patient breathing and involuntary movement. We demonstrate that our automatic image subdivision-based elastic registration algorithm can correct this misalignment. In a quantitative validation involving 13 expert-identified tumor nodules in six PET-CT image pairs, the algorithm demonstrated statistically significant improvement over the scanner-defined localization. The accuracy of algorithm-determined localization was evaluated to be comparable to average manually defined localization. The results indicate the potential of using our registration algorithm for applications like radiotherapy treatment planning and treatment-monitoring involving combined PET/CT scanners.