![]() A standardized evaluation methodology was employed. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. Methods: Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction more » gradient-based (RGB) registration method, were compared. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. Purpose: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. ![]() We have demonstrated that the DEFGEL deformable dosimeter can be used to evaluate DIR performance and the accuracy of dose-warping results by direct measurement. Substantial differences can be seen between the results of different algorithms indicating that DIR performance should be scrutinized before application todose-warping. Conclusions: We have confirmed that, for a range of mass and density conserving deformations representative of those observable in anatomical targets, DIR-based dose-warping can yield accurate predictions of the dose distribution. Finally, a validation framework is proposed and applied to evaluate the quality of reconstruction using both real sections and a synthetic = 96.7%. The warping algorithm efficiently computes a restricted class of 2D local deformations that are characteristic between successive tissue sections. In particular, we present a novel image warping algorithm based on dynamic programming that extends Dynamic Time Warping in 1D speech recognition to compute pairwise warps between high-resolution 2D images. The proposed method can be used with any existing 2D image registration method for 3D reconstruction. ![]() The average warps deform each section to match its neighboring sections, thus creating a smooth volume where corresponding features on successive sections lie close to each other. Using a Gaussian filter, an average warp is computed for each section from the pairwise warps in a group of its neighboring sections. The method is based on pairwise elastic image warps between successive tissue sections, which can be computed by 2D image registration. Here we present an automatic method for producing a smooth 3D volume from distorted 2D sections in the absence of any undistorted references. ![]() If it is the mouse warp function, is there any way I can get a smooth relocation of the mouse? I've done the same thing in flash and it works flawlessly, I know that the loop isn't just taking so much time to execute that it's slowing things down because it only runs maybe 4 or 5 times.Sectioning tissues for optical microscopy often introduces upon the resulting sections distortions that make 3D reconstruction difficult. The mouse doesn't get moved cleanly/smoothly when running along the edges of the area, it instead jumps in a very choppy manner, I believe this might be due to CGWarpMouseCursorPosition causing a delay upon each "warp".Ĭan anyone tell if it's something in my code that is causing this delay, or if it is in fact the mouse warp function. I have code here that restricts the mouse to a region on the screen, it is working relatively well, with only one big problem.
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