Purpose This research examines template-based squared-difference registration for motion correction in dynamic contrast-enhanced (DCE) MRI studies of the carotid artery wall and compares the results of fixed-frame template-based registration with a previously proposed consecutive-frame registration method. each image in the series. The results were also compared with unregistered data and data after consecutive-frame squared-difference registration. Results An analysis of variance test of root mean-square error values between gold standard curve and curves from unregistered data and data registered with consecutive-frame and fixed-frame template-based methods was significant (< 0.005) with template-based squared-difference registration producing curves that most closely matched the gold standard. Conclusion A fixed-frame template-based squared-difference registration method was proposed and validated for alignment of DCE-MRI of carotid arteries. is the image to be aligned and x and are the position and displacement in the CGS 21680 hydrochloride x-y plane respectively. is the reference image to which the image is registered. The reference image is either the template in CGS 21680 hydrochloride template-based registration or the preceding image CGS 21680 hydrochloride in consecutive-frame registration. By varying and displacing was translated by this final displacement in the Fourier domain using the Fourier shift theorem. Template-Based Registration For template-based registration one image in each series was selected as a fixed-frame “template” image (16 17 and all images in the series were registered to this template. An image midway through the series was chosen as the template as an intermediate image would have intensities and motion comparable to the images acquired before and after it. Therefore before registration the 13th image of each series was examined visually to determine if it was an appropriate template using the following criteria: (i) The contours of the carotid artery wall were clearly visible in the image; (ii) No flow artifacts in the lumen of the vessel in the image; (iii) Good contrast-to-noise ratio between the vessel wall and the lumen. If the 13th frame met all the criteria it was designated as the template for the series. If not adjacent images (either the 12th or the 14th frame) were examined Rabbit polyclonal to RAB4A. and selected as the template instead. Figure 1b shows the 14th image from a 25-frame DCE-MRI sequence used as the registration template for that series. Figure 1c displays the 15th frame overlaid on the template from Figure 1b to the show the misalignment between sequential frames and the need for registration. Once the template was selected all the remaining frames in the series were registered to the fixed-frame template using the squared-difference measure. Consecutive-Frame Registration In consecutive-frame registration each image was registered to CGS 21680 hydrochloride the preceding frame in the series using the squared-difference in intensities as the metric for minimization. Thus each image served as the template for matching the following frame. Validation of Registration Various methods were used to validate the results of registration and quantify improvement after application of the registration techniques. The simplest validation method consisted in visual inspection of the average image of each DCE-MRI series before and after registration. For a more quantitative approach enhancement curves were generated from the DCEMRI series. A gold standard time-intensity curve (18 19 was created by drawing a ROI on CGS 21680 hydrochloride the carotid artery wall in each DCE-MRI frame from in CGS 21680 hydrochloride the original data to get the mean signal intensity at that time point. While this gold standard curve shows the actual tissue uptake of the contrast agent in practice this approach would increase time and effort needed to analyze a study. A more efficient method was to trace the artery wall on the average image of the registered series and to propagate this ROI throughout the whole series to generate enhancement curves. Curves produced by drawing ROIs on the average original and postregistration images were compared with the gold standard curves. All ROIs for this study were drawn by the first author (SR). Root mean squared-error (RMSE) values were computed between the gold standard curve the original unregistered data curve and curves generated after application.