Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

Background: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible.

Accurate image reconstruction using real C-arm data from a Circle-plus-arc trajectory.

Objective: Developing an efficient tool for accurate three-dimensional imaging from projections measured with C-arm systems.

Material And Methods: A circle-plus-arc trajectory, which is complete and thus amenable to accurate reconstruction, is used. This trajectory is particularly attractive as its implementation does not require moving the patient.

Truncation correction for oblique filtering lines.

State-of-the-art filtered backprojection (FBP) algorithms often define the filtering operation to be performed along oblique filtering lines in the detector. A limited scan field of view leads to the truncation of those filtering lines, which causes artifacts in the final reconstructed volume. In contrast to the case where filtering is performed solely along the detector rows, no methods are available for the case of oblique filtering lines.

Geometric calibration of the circle-plus-arc trajectory.

In this paper, a novel geometric calibration method for C-arm cone-beam scanners is presented which allows the calibration of the circle-plus-arc trajectory. The main idea is the separation of the trajectory into two circular segments (circle segment and arc segment) which are calibrated independently. This separation makes it possible to reuse a calibration phantom which has been successfully applied in clinical environments to calibrate numerous routinely used C-arm systems.

A new scheme for view-dependent data differentiation in fan-beam and cone-beam computed tomography.

In computed tomography, analytical fan-beam (FB) and cone-beam (CB) image reconstruction often involves a view-dependent data differentiation. The implementation of this differentiation step is critical in terms of resolution and image quality. In this work, we present a new differentiation scheme that is robust to changes in the data acquisition geometry and to coarse view sampling.

Spectroscopy of the shear force interaction in scanning near-field optical microscopy.

Shear force detection is a common method of tip-sample distance control in scanning near-field optical microscopy. Shear force is the force acting on a laterally oscillating probe tip near a surface. Despite its frequent use, the nature of the interaction between tip and sample surface is a matter of debate.