Microsurgical reconstruction of the head and neck - Current practice of maxillofacial units in Germany, Austria, and Switzerland

Refinement in microvascular reconstructive techniques over the last 30 years has enabled an increasing number of patients to be rehabilitated for both functional and aesthetic reasons. The purpose of this study was to evaluate different microsurgical practice, including perioperative management, in Germany, Austria, and Switzerland. The DÖSAK collaborative group for Microsurgical Reconstruction developed a detailed questionnaire which was circulated to units in the three countries. The current practice of the departments was evaluated. Thirty-eight questionnaires were completed resulting in a 47.5% response rate. A considerable variation in the number of microsurgical reconstructions per year was noted. In relation to the timing of bony reconstruction, 10 hospitals did reconstructions primarily (26.3%), 19 secondarily (50%) and 9 (23.7%) hospitals used both concepts. In the postoperative course, 15.8% of hospitals use inhibitors of platelet aggregation, most hospitals use low molecular heparin (52.6%) or other heparin products (44.7%). This survey shows variation in the performance, management, and care of microsurgical reconstructions of patients. This is due in part to the microvascular surgeons available in the unit but it is also due to different types of hospitals where various types of care can be performed in these patients needing special perioperative care.

Iterative Reconstruction Algorithm for Abdominal Multidetector CT at Different Tube Voltages: Assessment of Diagnostic Accuracy, Image Quality, and Radiation Dose in a Phantom Study

To assess the diagnostic accuracy, image quality, and radiation dose of an iterative reconstruction algorithm compared with a filtered back projection (FBP) algorithm for abdominal computed tomography (CT) at different tube voltages.

Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience

To investigate whether an adaptive statistical iterative reconstruction (ASIR) algorithm improves the image quality at low-tube-voltage (80-kVp), high-tube-current (675-mA) multidetector abdominal computed tomography (CT) during the late hepatic arterial phase.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.