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Our study was intended to demonstrate the different signal intensity (SI) pattern of the putamen seen on susceptibility-weighted imaging (SWI) between that of Parkinson’s disease (PD) and Parkinsonism-predominant multiple system atrophy (MSA-P), and to correlate it with 18 F-flurodeoxyglucose positron-emission tomography ( 18 F-FDG PET). Thirty patients with PD and 17 with MSA-P underwent SWI, and 18 F-FDG PET were included. The SI was measured on SWI in the anterior and posterior halves of the putamen using a region-of-interest (ROI) on both sides. The normalized regional glucose metabolism (standardized uptake value ratio, SUVR) was measured on co-registered 18 F-FDG PET images using the ROI obtained with SWI. Analysis included a group-level comparison of the SI values obtained on SWI, and these results were correlated with the SUVR on 18 F-FDG PET. The SIs of the bilateral posterior, dominant-side of the posterior, mean values of the bilateral anterior and posterior halves of the putamen on SWI, differed significantly between the two groups ( P < 0.001, respectively). The SUVR of the all locations also differed significantly between PD and MSA-P ( P < 0.001, respectively). There was a moderate degree of positive correlation between the SI and the SUVR of the left posterior half, and mean value of the bilateral posterior putamen in MSA-P ( r = 0.634, P = 0.006, r = 0.492, P = 0.045). In conclusion, the low SI seen on the posterior putamen may differentiate MSA-P from PD. Furthermore, low SI in the putamen correlated with hypometabolism on 18 F-FDG PET. Therefore, SWI could be a potential complementary diagnostic tool to 18 F-FDG PET for differentiating these conditions. [ABSTRACT FROM AUTHOR]

Abstract: Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method. [Copyright &y& Elsevier]