Automatic extraction of the mid-facial plane for cranio-maxillofacial surgery planning

Recently developed computer applications provide tools for planning cranio-maxillofacial interventions based on 3-dimensional (3D) virtual models of the patient's skull obtained from computed-tomography (CT) scans. Precise knowledge of the location of the mid-facial plane is important for the assessment of deformities and for planning reconstructive procedures. In this work, a new method is presented to automatically compute the mid-facial plane on the basis of a surface model of the facial skeleton obtained from CT. The method matches homologous surface areas selected by the user on the left and right facial side using an iterative closest point optimization. The symmetry plane which best approximates this matching transformation is then computed. This new automatic method was evaluated in an experimental study. The study included experienced and inexperienced clinicians defining the symmetry plane by a selection of landmarks. This manual definition was systematically compared with the definition resulting from the new automatic method: Quality of the symmetry planes was evaluated by their ability to match homologous areas of the face. Results show that the new automatic method is reliable and leads to significantly higher accuracy than the manual method when performed by inexperienced clinicians. In addition, the method performs equally well in difficult trauma situations, where key landmarks are unreliable or absent.

Extended CT scale overcomes restoration caused streak artifacts for dental identification in CT--3D color encoded automatic discrimination of dental restorations

OBJECTIVE: Besides DNA, dental radiographs play a major role in the identification of victims in mass casualties or in corpses with major postmortem alterations. Computed tomography (CT) is increasingly applied in forensic investigations and is used to scan the dentition of deceased persons within minutes. We investigated different restoration materials concerning their radiopacity in CT for dental identification purposes. METHODS: Extracted teeth with different filling materials (composite, amalgam, ceramic, temporary fillings) were CT scanned. Radiopacities of the filling materials were analyzed in extended CT scale images. RESULTS: Radiopacity values ranged from 6000-8500HU (temporary fillings), 4500-17000HU (composite fillings) and >30710HU (Amalgam and Gold). The values were used to define presets for a 3D colored volume rendering software. CONCLUSIONS: The effects of filling material caused streak artifacts could be distinctively reduced for the assessment of the dental status and a postprocessing algorithm was introduced that allows for 3D color encoded visualization and discrimination of different dental restorations based on postmortem CT data.

Effect of patient size on radiation dose for abdominal MDCT with automatic tube current modulation: phantom study

OBJECTIVE: The purpose of this study was to evaluate in a phantom study the effect of patient size on radiation dose for abdominal MDCT with automatic tube current modulation. MATERIALS AND METHODS: One or two 4-cm-thick circumferential layers of fat-equivalent material were added to the abdomen of an anthropomorphic phantom to simulate patients of three sizes: small (cross-sectional dimensions, 18 x 22 cm), average size (26 x 30 cm), and oversize (34 x 38 cm). Imaging was performed with a 64-MDCT scanner with combined z-axis and xy-axis tube current modulation according to two protocols: protocol A had a noise index of 12.5 H, and protocol B, 15.0 H. Radiation doses to three abdominal organs and the skin were assessed. Image noise also was measured. RESULTS: Despite increasing patient size, the image noise measured was similar for protocol A (range, 11.7-12.2 H) and protocol B (range, 13.9-14.8 H) (p > 0.05). With the two protocols, in comparison with the dose of the small patient, the abdominal organ doses of the average-sized patient and the oversized patient increased 161.5-190.6%and 426.9-528.1%, respectively (p < 0.001). The skin dose increased as much as 268.6% for the average-sized patient and 816.3% for the oversized patient compared with the small patient (p < 0.001). CONCLUSION: Oversized patients undergoing abdominal MDCT with tube current modulation receive significantly higher doses than do small patients. The noise index needs to be adjusted to the body habitus to ensure dose efficiency.

Automatic Reduction of Artifacts in EEG-Signals

Electroencephalograms (EEG) are often contaminated with high amplitude artifacts limiting the usability of data. Methods that reduce these artifacts are often restricted to certain types of artifacts, require manual interaction or large training data sets. Within this paper we introduce a novel method, which is able to eliminate many different types of artifacts without manual intervention. The algorithm first decomposes the signal into different sub-band signals in order to isolate different types of artifacts into specific frequency bands. After signal decomposition with principal component analysis (PCA) an adaptive threshold is applied to eliminate components with high variance corresponding to the dominant artifact activity. Our results show that the algorithm is able to significantly reduce artifacts while preserving the EEG activity. Parameters for the algorithm do not have to be identified for every patient individually making the method a good candidate for preprocessing in automatic seizure detection and prediction algorithms.

Automatic extraction of femur contours from calibrated fluoroscopic images

Automatic identification and extraction of bone contours from X-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated X-ray images. The automatic initialization is solved by an estimation of Bayesian network algorithm to fit a multiple component geometrical model to the X-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the X-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Preliminary experiments on clinical data sets verified its validity

Semi-automatic segmentation of fetal cardiac cavities: progress towards an automated fetal echocardiogram

OBJECTIVE: To develop a novel application of a tool for semi-automatic volume segmentation and adapt it for analysis of fetal cardiac cavities and vessels from heart volume datasets. METHODS: We studied retrospectively virtual cardiac volume cycles obtained with spatiotemporal image correlation (STIC) from six fetuses with postnatally confirmed diagnoses: four with normal hearts between 19 and 29 completed gestational weeks, one with d-transposition of the great arteries and one with hypoplastic left heart syndrome. The volumes were analyzed offline using a commercially available segmentation algorithm designed for ovarian folliculometry. Using this software, individual 'cavities' in a static volume are selected and assigned individual colors in cross-sections and in 3D-rendered views, and their dimensions (diameters and volumes) can be calculated. RESULTS: Individual segments of fetal cardiac cavities could be separated, adjacent segments merged and the resulting electronic casts studied in their spatial context. Volume measurements could also be performed. Exemplary images and interactive videoclips showing the segmented digital casts were generated. CONCLUSION: The approach presented here is an important step towards an automated fetal volume echocardiogram. It has the potential both to help in obtaining a correct structural diagnosis, and to generate exemplary visual displays of cardiac anatomy in normal and structurally abnormal cases for consultation and teaching.