Hi, my name is Dr. YoungTaek Oh
Computer Scientist & Entrepreneur

To compare the accuracy and required time for image fusion of real-time ultrasound (US) with pre-procedural magnetic resonance (MR) images between positioning auto-registration and manual registration for percutaneous radiofrequency ablation or biopsy of hepatic lesions.

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3-16 s] vs. 32 s [range, 21-38 s]; P < 0.001] and complete (median, 34.0 s [range, 26-66 s] vs. 47.5 s [range, 32-90]; P=0.001] image fusion. Registration error of Positioning auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0-84.6 mm] vs. 18.2 mm [6.7-73.4 mm]; P=0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7-9.9 mm] vs. 5.8 mm [range, 2.0-13.0 mm]; P=0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2-3] vs. 1 [range, 1-2]; P=0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

The aim of this study was to compare the accuracy of and the time required for image fusion between real-time ultrasonography (US) and pre-procedural magnetic resonance (MR) images using automatic registration by a liver surface only method and automatic registration by a liver surface and vessel method. This study consisted of 20 patients referred for planning US to assess the feasibility of percutaneous radiofrequency ablation or biopsy for focal hepatic lesions. The first 10 consecutive patients were evaluated by an experienced radiologist using the automatic registration by liver surface and vessel method, whereas the remaining 10 patients were evaluated using the automatic registration by liver surface only method. For all 20 patients, image fusion was automatically executed after following the protocols and fused real-time US and MR images moved synchronously. The accuracy of each method was evaluated by measuring the registration error, and the time required for image fusion was assessed by evaluating the recorded data using in-house software. The results obtained using the two automatic registration methods were compared using the Mann-Whitney U-test. Image fusion was successful in all 20 patients, and the time required for image fusion was significantly shorter with the automatic registration by liver surface only method than with the automatic registration by liver surface and vessel method (median: 43.0 s, range: 29-74 s vs. median: 83.0 s, range: 46-101 s; p = 0.002). The registration error did not significantly differ between the two methods (median: 4.0 mm, range: 2.1-9.9 mm vs. median: 3.7 mm, range: 1.8-5.2 mm; p = 0.496). The automatic registration by liver surface only method offers faster image fusion between real-time US and pre-procedural MR images than does the automatic registration by liver surface and vessel method. However, the degree of accuracy was similar for the two methods.

We present an interactive-speed algorithm for computing the Hausdorff Distance (HD) between two freeform geometric models represented with NURBS surfaces. The algorithm is based on an effective technique for matching a surface patch from one model to the corresponding nearby surface patch on the other model. To facilitate the matching procedure, we employ a bounding volume hierarchy (BVH) for freeform NURBS surfaces, which provides a hierarchy of Coons patches and bilinear surfaces approximating the NURBS surfaces (Kim et al., 2011 [1]). Comparing the local HD upper bound against a global HD lower bound, we can eliminate the majority of redundant surface patches from further consideration. The resulting algorithm and the associated data structures are considerably simpler than the previous BVH-based HD algorithms. As a result, we can compute the HD of two freeform geometric models efficiently and robustly even when the two models are in close proximity. We demonstrate the effectiveness of our approach using several experimental results.

We present a compact representation for the bounding volume hierarchy (BVH) of freeform NURBS surfaces using Coons patches. Following the Coons construction, each subpatch can be bounded very efficiently using the bilinear surface determined by the four corners. The BVH of freeform surfaces is represented as a hierarchy of Coons patch approximation until the difference is reduced to within a given error bound. Each leaf node contains a single Coons patch, where a detailed BVH for the patch can be represented very compactly using two lists (containing curve approximation errors) of length proportional only to the height of the BVH. We demonstrate the effectiveness of our compact BVH representation using several experimental results from real-time applications in collision detection and minimum distance computation for freeform models.

We present an efficient algorithm for projecting a continuously moving query point to a family of planar freeform curves. The algorithm is based on the one-sided Hausdorff distance from the trajectory curve (of the query point) to the planar curves. Using a bounding volume hierarchy (BVH) of the planar curves, we estimate an upper bound h of the one-sided Hausdorff distance and eliminate redundant curve segments when they are more than distance h away from the trajectory curve. Recursively subdividing the trajectory curve and repeating the same elimination procedure to the BVH of the remaining curves, we can efficiently determine where to project the moving query point. The explicit continuous point projection is then interpreted as a curve reparameterization problem, for which we propose a few simple approximation techniques. Using several experimental results, we demonstrate the effectiveness of the proposed approach.

We present an efficient algorithm for projecting a given point to its closest point on a family of freeform curves and surfaces. The algorithm is based on an efficient culling technique that eliminates redundant curves and surfaces which obviously contain no projection from the given point. Based on this scheme, we can reduce the whole computation to considerably smaller subproblems, which are then solved using a numerical method. For monotone spiral planar curves with no inflection, we show that a few simple geometric tests are sufficient to guarantee the convergence of numerical methods to the closest point. In several experimental results, we demonstrate the effectiveness of the proposed approach.

We present a real-time algorithm for computing the precise Hausdorff Distance (HD) between two planar freeform curves. The algorithm is based on an effective technique that approximates each curve with a sequence of G1 biarcs within an arbitrary error bound. The distance map for the union of arcs is then given as the lower envelope of trimmed truncated circular cones, which can be rendered efficiently to the graphics hardware depth buffer. By sampling the distance map along the other curve, we can estimate a lower bound for the HD and eliminate many redundant curve segments using the lower bound. For the remaining curve segments, we read the distance map and detect the pixel(s) with the maximum distance. Checking a small neighborhood of the maximum-distance pixel, we can reduce the computation to considerably smaller subproblems, where we employ a multivariate equation solver for an accurate solution to the original problem. We demonstrate the effectiveness of the proposed approach using several experimental results.