Biomedical subjects

Mark P Wachowiak

Publications and source records attributed to Mark P Wachowiak.

5 recordsLinked to original sources

PubMed2006

High-performance medical image registration using new optimization techniques.

Optimization of a similarity metric is an essential component in intensity-based medical image registration. The increasing availability of parallel computers makes parallelizing some registration tasks an attractive option to increase speed. In this paper, two new deterministic, derivative-free, and intrinsically parallel optimization methods are adapted for image registration. DIviding RECTangles (DIRECT) is a global technique for linearly bounded problems, and multidirectional search (MDS) is a recent local method. The performance of DIRECT, MDS, and hybrid methods using a parallel implementation of Powell's method for local refinement, are compared. Experimental results demonstrate that DIRECT and MDS are robust, accurate, and substantially reduce computation time in parallel implementations.

Algorithms↗

PubMed2005

Registration of two-dimensional cardiac images to preprocedural three-dimensional images for interventional applications.

PURPOSE: To evaluate the accuracy and efficiency of rigid-body registration of two-dimensional fast cine and real-time cardiac images to high-resolution and SNR three-dimensional preprocedural reference volumes for application during MRI-guided interventional procedures. MATERIALS AND METHODS: Mutual information (MI) and correlation ratio (CR) similarity measures were evaluated. The dependence of registration accuracy and efficiency on different resolution and SNR parameters, and also on cardiac-phase differences was evaluated in a porcine model. Two-dimensional images were initially misoriented at distances (d) of 2-10 mm, and rotations of +/-5 degrees about all axes. Registration error and computation time were evaluated, and performance was also assessed visually. RESULTS: The maximum registration error using MI (<2.7 mm and <3.6 degrees ) occurred for d = 10 mm, misrotation of +/-5 degrees , and relative SNR = 1. The computation time was 15 seconds for MI and 10 seconds for CR. CONCLUSION: Registration accuracy was not highly dependent on the relative timing, within the cycle, between the two-dimensional and three-dimensional images. Registration using CR was faster than that using MI, although accuracy was marginally higher with MI. J.

Algorithms↗

PubMed2005

A real time finite element based tissue simulation method incorporating nonlinear elastic behavior.

This paper presents a new finite element simulation approach for surgical simulators. Based on the solution of the algebraic equations derived from a nonlinear elastic model, we propose a real time simulation rule based on the implicit relation between the displacements of contacted and free nodes. This rule is an analytic expression in the linear case, and an approximation of the implicit relation in the non-linear case. We also remove some of the restrictions on flexibility exhibited by previous linear and nonlinear approaches. In the linear case, real time reconfiguration of the contacted nodes and the boundary constraints is realized using the simulation rule, while in the nonlinear case, a similar result is obtained by employing affine mapping. These methods allow nonlinear material properties to be applied to real time tissue simulation, with an efficiency comparable to that of the tensor matrix method for linear elastic models.

Biomechanical Phenomena↗

PubMed2005

Enhanced pre-computed finite element models for surgical simulation.

Soft tissue modeling is an important component in effective surgical simulation systems. A pre-computed finite element method based on elastic models is well suited to modeling soft tissue deformation. This paper addresses two principal issues: the flexibility of the pre-computed FE method and the approximation approach to non-linear elastic models. We describe a dynamic mechanism of the reconfiguration of the contacted nodes and the fixed boundary, without re-computing the inverse of the global stiffness matrix. The flexibility of the pre-computed models is described for both linear and non-linear elastic models.

Canada↗

PubMed2002

Estimation of K distribution parameters using neural networks.

The K distribution is an accurate model for ultrasonic backscatter. A neural approach is developed to estimate K distribution parameters. Accuracy and consistency of the estimates from simulated K and envelope data compare favorably with other techniques. Neural networks can potentially be used as a complementary technique for tissue characterization.

Computer Simulation↗