METHOD: We simulate the CT head examination using a water phantom with a standard protocol (120 kVp/180 mAs) and a low dose protocol (100 kVp/142 mAs). The table height was adjusted to simulate miscentering by 5 cm from the isocenter, where the height was miscentered superiorly (MCS) at 109, 114, 119, and 124 cm, and miscentered inferiorly (MCI) at 99, 94, 89, and 84 cm. Seven circular regions of interest were used, with one drawn at the center, four at the peripheral area of the phantom, and two at the background area of the image.

METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension.

RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively.

METHOD: Medical image data for five types of defects were selected, segmented, converted and decimated to 3D polygon models on a personal computer. The models were transferred to a computer aided design (CAD) software which aided in designing the prosthesis according to the virtual models. Two templates were designed for each defect, one by an OS (free) system and one by CS. The parameters for analyses were the virtual volume, Dice similarity coefficient (DSC) and Hausdorff's distance (HD) and were executed by the OS point cloud comparison tool.

RESULT: There was no significant difference (p > 0.05) between CS and OS when comparing the volume of the template outputs. While HD was within 0.05-4.33 mm, evaluation of the percentage similarity and spatial overlap following the DSC showed an average similarity of 67.7% between the two groups. The highest similarity was with orbito-facial prostheses (88.5%) and the lowest with facial plate prosthetics (28.7%).

METHODS: A Total of 51 patients with 105 carotid artery plaques were screened using 3D and 2D US probes attached to the same US scanner. Two independent observers characterized the plaques based on the morphological features namely echotexture, echogenicity and surface characteristics. The scores assigned to each morphological feature were used to determine intra- and inter-observer performance. The level of agreement was measured using Kappa coefficient.

RESULTS: The first observer with 2D US showed fair (k=0.4-0.59) and very strong (k>0.8) with 3D US intra-observer agreements using three morphological features. The second observer indicated moderate strong (k=0.6-0.79) with 2D US and very strong with 3D US (k>0.8) intra-observer performances. Moderate strong (k=0.6-0.79) and very strong (k>0.8) inter-observer agreements were reported with 2D US and 3D US respectively. The results with 2D and 3D US were correlated 62% using only echotexture and 56% using surface morphology coupled with echogenicity. 3D US gave a lower score than 2D 71% of the time (p=0.005) in disagreement cases.