Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
Coregistration of multimodal diagnostic images is crucial for qualitative and quantitative multiparametric analysis. While\nretrospective coregistration is computationally intense and could be inaccurate, hybrid PET/MR scanners allow acquiring\nimplicitly coregistered images. Aim of this study is to assess the performance of state-of-the-art coregistration methods applied\nto PET and MR acquired as single modalities, comparing the results with the implicitly coregistration of a hybrid PET/MR, in\ncomplex anatomical regions such as head/neck (HN). A dataset consisting of PET/CT and PET/MR subsequently acquired in\ntwenty-three patients was considered: performance of rigid (RR) and deformable (DR) registration obtained by a commercial\nsoftware and an open-source registration package was evaluated. Registration accuracy was qualitatively assessed in terms of\nvisual alignment of anatomical structures and qualitatively measured by the Dice scores computed on segmented tumors in PET\nand MRI. The resulting scores highlighted that hybrid PET/MR showed higher registration accuracy than retrospectively\ncoregistered images, because of an overall misalignment after RR, unrealistic deformations and volume variations after DR.\nDR revealed superior performance compared to RR due to complex nonrigid movements of HN district. Moreover,\nsimultaneous PET/MR offers unique datasets serving as ground truth for the improvement and validation of coregistration\nalgorithms, if acquired with PET/CT....
We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency\nconditions thatmathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier\nrepresentation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallelbeam\nand fan-beam imaging geometry.The derivation result indicates that there is a zero energy region forming a double-wedge\nshape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature.\nThe major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform\n(FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the\nparameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be\nused to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data.\nResults show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method....
Background: The purpose of this study was to investigate the anisotropic features of fetal pig cerebral white matter\n(WM) development by magnetic resonance diffusion tensor imaging, and to evaluate the developmental status of\ncerebral WM in different anatomical sites at different times.\nMethods: Fetal pigs were divided into three groups according to gestational age: E69 (n = 8), E85 (n = 11), and E114\n(n = 6). All pigs were subjected to conventional magnetic resonance imaging (MRI) and diffusion tensor imaging using\na GE Signa 3.0 T MRI system (GE Healthcare, Sunnyvale, CA, USA). Fractional anisotropy (FA) was measured in deep WM\nstructures and peripheral WM regions. After the MRI scans,the animals were sacrificed and pathology sections\nwere prepared for hematoxylin & eosin (HE) staining and luxol fast blue (LFB) staining. Data were statistically\nanalyzed with SPSS version 16.0 (SPSS, Chicago, IL, USA). A P-value < 0.05 was considered statistically significant. Mean FA\nvalues for each subject region of interest (ROI), and deep and peripheral WM at different gestational ages were calculated,\nrespectively, and were plotted against gestational age with linear correlation statistical analyses. The differences of data\nwere analyzed with univariate ANOVA analyses.\nResults: There were no significant differences in FAs between the right and left hemispheres. Differences were observed\nbetween peripheral WM and deep WM in fetal brains. A significant FA growth with increased gestational age was found\nwhen comparing E85 group and E114 group. There was no difference in the FA value of deep WM between the E69\ngroup and E85 group. The HE staining and LFB staining of fetal cerebral WM showed that the development from the E69\ngroup to the E85 group, and the E85 group to the E114 group corresponded with myelin gliosis and myelination,\nrespectively.\nConclusions: FA values can be used to quantify anisotropy of the different cerebral WM areas. FA values did not change\nsignificantly between 1/2 way and 3/4 of the way through gestation but was then increased dramatically at term, which\ncould be explained by myelin gliosis and myelination ,respectively....
Purpose. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular\nproperties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled ...
Background: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2\nweighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic\nmetastases (HM) and hepatocellular carcinoma (HCC).\nMethods: The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively\nanalyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA\nwas performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient\nco-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-sizezone\nmatrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of\nsingle liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied\nparameters were derived using ROC curves. Four supervised classification algorithms were trained with the most\ninfluential textural features in the classification of tumor types. The test datasets validated the reliability of the models.\nResults: The texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be\ndifferentiated by 9, 16 and 10 feature parameters, respectively. The model�s misclassification rates were 11.7, 9.6\nand 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single\nliver lesions at the same time. The BP-ANN model had better predictive ability.\nConclusion: Texture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC)\nand may serve as an adjunct tool for accurate diagnosis of these diseases....
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