Vladimir Zagrodsky

Mutual information-based rigid and nonrigid registration of ultrasound volumes

We investigated the registration of ultrasound volumes based on the mutual information measure, a technique originally applied to multimodality registration of brain images. A prerequisite for successful registration is a smooth, quasi-convex mutual information surface with an unambiguous maximum. We discuss the necessary preprocessing to create such a surface for ultrasound volumes. Abdominal and thoracic organs imaged with ultrasound typically move relative to the exterior of the body and are deformable. Consequently, four specific instances of image registration involving progressively generalized transformations were studied: rigid-body, rigid-body + uniform scaling, rigid-body + nonuniform scaling, and affine. Registration was applied to clinically acquired volumetric images. The accuracy was comparable with the voxel dimension for all transformation modes, although it degraded as the transformation grew more complex. Likewise, the capture range became narrower with the complexity of transformation. As the use of real-time three-dimensional ultrasound becomes more prevalent, the method we present should work well for a variety of applications examining serial anatomic and physiologic changes. Developers of these clinical applications would match the deformation model of their problem to one of the four transformation models presented here.

Cine MPR: interactive multiplanar reformatting of four-dimensional cardiac data using hardware-accelerated texture mapping

Four-dimensional (4-D) imaging to capture the three-dimensional (3-D) structure and motion of the heart in real time is an emerging trend. We present here our method of interactive multiplanar reformatting (MPR), i.e., the ability to visualize any chosen anatomical cross section of 4-D cardiac images and to change its orientation smoothly while maintaining the original heart motion. Continuous animation to show the time-varying 3-D geometry of the heart and smooth dynamic manipulation of the reformatted planes, as well as large image size (100-300 MB), make MPR challenging. Our solution exploits the hardware acceleration of 3-D texture mapping capability of high-end commercial PC graphics boards. Customization of volume subdivision and caching concepts to periodic cardiac data allows us to use this hardware effectively and efficiently. We are able to visualize and smoothly interact with real-time 3-D ultrasound cardiac images at the desired frame rate (25 Hz). The developed methods are applicable to MPR of one or more 3-D and 4-D medical images, including 4-D cardiac images collected in a gated fashion.

Multifunction extension of simplex optimization method for mutual information-based registration of ultrasound volumes

Mutual information has been demonstrated to be an accurate and reliable criterion function to perform registration of medical data. Due to speckle noise, ultrasound volumes do not provide a smooth mutual information function. Consequently the optimization technique used must be robust enough to avoid local maxima and converge on the desired global maximum eventually. While the well-known downhill simplex optimization uses a single criterion function, our extension to multi-function optimization uses three criterion functions, namely mutual information computed at three levels of intensity quantization and hence three degrees of noise suppression. Registration was performed with rigid as well as simple non-rigid transformation modes for real-time 3D ultrasound datasets of the left ventricle. Pairs of frames corresponding to the most stationary end- diastolic cardiac phase were chosen, and an initial misalignment was artificially introduced between them. The multi-function simplex optimization reduced the failure rate by a factor of two in comparison to the standard simplex optimization, while the average accuracy for the successful cases was unchanged. A more robust registration resulted form the parallel use of criterion functions. The additional computational cost was negligible, as each of the three implementations of the mutual information used the same joint histogram and required no extra spatial transformation.

Mutual information-based registration of cardiac ultrasound volumes

Real-time volume ultrasound imaging of the heart is a new trend, and the registration of acquired volume framesets is clinically important. This registration may be accomplished by processing a selected pair of volume frames having identical cardiac phase (preferably end diastolic) from two framesets. The registration solves for the optimal rigid transformation between selected volumes through maximization of mutual information, a voxel similarity measure. The accuracy of registration was estimated through retrieval of an artificially introduced misalignment. Two volume frames, belonging to the same frameset and separated in time by 250 ms, were selected. The secondary volume was translated by seven voxels along each axis and rotated by seven degrees about each axis relative to the primary prior to registration. The translational mismatch upon registration was within one voxel and the rotational mismatch less than two degrees. Reduction of the speckle noise by spatio-temporal averaging followed by intensity binning was a key step in successful application of mutual information approach to ultrasound imaging. The application of our method to nine framesets arising from four different patients demonstrates the feasibility of using of mutual information for automatic registration of cardiac ultrasound data.

Nonlinear tissue characterization with intravascular ultrasound harmonic imaging

Current intravascular ultrasound (IVUS) techniques of tissue characterization are morphology based, a characterization based on surface echogenicity and spectral parameters from the fundamental imaging alone. Complex harmonic signals develop as a result of heterogeneous composition of nonlinear tissue such as calcified lipids, not easily characterized in the fundamental mode. Tissue harmonic imaging (THI) offers substantial advantages such as nonlinear information, better lateral resolution, higher contrast resolution, low near field spatial variation and decreased sidelobes. The objective is to enhance tissue characterization with the extent of tissue nonlinearity for a better diagnosis.