An accuracy approach to robotic microsurgery in the ear

Concerns of rising healthcare costs and the ever increasing desire to improve surgical outcome have motivated the development of a new robotic assisted surgical procedure for the implantation of artificial hearing devices (AHDs). This paper describes our efforts to enable minimally invasive, cost effective surgery for the implantation of AHDs. We approach this problem with a fundamental goal to reduce errors from every component of the surgical workflow from imaging and trajectory planning to patient tracking and robot development. These efforts were successful in reducing overall system error to a previously unattained level.

Diagnostic performance and accuracy of 3-D spoiled gradient-dual-echo MRI with water- and fat-signal separation in liver-fat quantification: comparison to liver biopsy

To prospectively evaluate a 3-dimensional spoiled gradient-dual-echo (3D SPGR-DE) magnetic resonance imaging (MRI) sequence for the qualitative and quantitative analysis of liver fat content (LFC) in patients with the suspicion of fatty liver disease using histopathology as the standard of reference.

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.

Accuracy of diagnostic tests for clinically suspected upper extremity deep vein thrombosis: a systematic review

The best available test for the diagnosis of upper extremity deep venous thrombosis (UEDVT) is contrast venography. The aim of this systematic review was to assess whether the diagnostic accuracy of other tests for clinically suspected UEDVT is high enough to justify their use in clinical practise and to evaluate if any test can replace venography.

Influence of Smoothing on Voxel-Based Mesh Accuracy in Micro-Finite Element

The interest in automatic volume meshing for finite element analysis (FEA) has grown more since the appearance of microfocus CT (μCT), due to its high resolution, which allows for the assessment of mechanical behaviour at a high precision. Nevertheless, the basic meshing approach of generating one hexahedron per voxel produces jagged edges. To prevent this effect, smoothing algorithms have been introduced to enhance the topology of the mesh. However, whether smoothing also improves the accuracy of voxel-based meshes in clinical applications is still under question. There is a trade-off between smoothing and quality of elements in the mesh. Distorted elements may be produced by excessive smoothing and reduce accuracy of the mesh. In the present work, influence of smoothing on the accuracy of voxel-based meshes in micro-FE was assessed. An accurate 3D model of a trabecular structure with known apparent mechanical properties was used as a reference model. Virtual CT scans of this reference model (with resolutions of 16, 32 and 64 μm) were then created and used to build voxel-based meshes of the microarchitecture. Effects of smoothing on the apparent mechanical properties of the voxel-based meshes as compared to the reference model were evaluated. Apparent Young’s moduli of the smooth voxel-based mesh were significantly closer to those of the reference model for the 16 and 32 μm resolutions. Improvements were not significant for the 64 μm, due to loss of trabecular connectivity in the model. This study shows that smoothing offers a real benefit to voxel-based meshes used in micro-FE. It might also broaden voxel-based meshing to other biomechanical domains where it was not used previously due to lack of accuracy. As an example, this work will be used in the framework of the European project ContraCancrum, which aims at providing a patient-specific simulation of tumour development in brain and lungs for oncologists. For this type of clinical application, such a fast, automatic, and accurate generation of the mesh is of great benefit.

Prediction accuracy of young and middle-aged adults in memory for familiar and unfamiliar texts

This study investigated the influence of age, familiarity, and level of exposure on the metamemorial skill of prediction accuracy on a future test. Young (17 to 23 years old) and middle-aged adults (35 to 50 years old) were asked to predict their memory for text material. Participants made predictions on a familiar text and an unfamiliar text, at three different levels of exposure to each. The middle-aged adults were superior to the younger adults at predicting performance. This finding indicates that metamemory may increase from youth to middle age. Other findings include superior prediction accuracy for unfamiliar compared to familiar material, a result conflicting with previous findings, and an interaction between level of exposure and familiarity that appears to modify the main effects of those variables.